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| author | SoloDShelby | 2024-07-19 14:41:40 +0300 |
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| committer | SoloDShelby | 2024-07-19 14:41:40 +0300 |
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| tree | 34d581648b0e0d3fc8dbe6577752a4fd433a3258 /gnqa/paper1_eval/src/data/responses/aging | |
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Evaluation code for paper 1
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diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/01.json b/gnqa/paper1_eval/src/data/responses/aging/experts/01.json new file mode 100644 index 0000000..9b654c3 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/01.json @@ -0,0 +1,396 @@ +{ + "created_at": "2024-05-31T12:56:07.845098Z", + "data": { + "amplify": false, + "answer": "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "context": { + "0c6c0977-2cf9-4bbf-bc31-fe025f008089": [ + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n24 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10006, 10006\ngenenetwork.org/\nshow_trait?trait_id=\n10006&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10010, 10010\ngenenetwork.org/\nshow_trait?trait_id=\n10010&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10011, 10011\ngenenetwork.org/\nshow_trait?trait_id=\n10011&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10021, 10021\ngenenetwork.org/\nshow_trait?trait_id=\n10021&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10022, 10022\ngenenetwork.org/\nshow_trait?trait_id=\n10022&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10025, 10025\ngenenetwork.org/\nshow_trait?trait_id=\n10025&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10066, 10066\ngenenetwork.org/\nshow_trait?trait_id=\n10066&dataset=BXD-\nLongevityPublish\n\nReferences\nAlbertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P,\nCarlson M. 1994." + } + ], + "2464a084-1a11-44eb-8bce-4b344de049ff": [ + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n24 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10006, 10006\ngenenetwork.org/\nshow_trait?trait_id=\n10006&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10010, 10010\ngenenetwork.org/\nshow_trait?trait_id=\n10010&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10011, 10011\ngenenetwork.org/\nshow_trait?trait_id=\n10011&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10021, 10021\ngenenetwork.org/\nshow_trait?trait_id=\n10021&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10022, 10022\ngenenetwork.org/\nshow_trait?trait_id=\n10022&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10025, 10025\ngenenetwork.org/\nshow_trait?trait_id=\n10025&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10066, 10066\ngenenetwork.org/\nshow_trait?trait_id=\n10066&dataset=BXD-\nLongevityPublish\n\nReferences\nAlbertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P,\nCarlson M. 1994." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Leduc MS, Hageman RS, Meng Q et al (2010) Identification of\ngenetic determinants of IGF-1 levels and longevity among mouse\ninbred strains. Aging Cell 9(5):823–836. doi:10.1111/j.14749726.2010.00612.x\n10. Lang DH, Gerhard GS, Griffith JW et al (2010) Quantitative trait\nloci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin Exp Res 22(1):8–19\n11. Gelman R, Watson A, Bronson R et al (1988) Murine chromosomal\nregions\ncorrelated\nwith\nlongevity. Genetics\n118(4):693–704\n12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci\nassociated with life span exhibit sex-specific and epistatic effects." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text":"Conclusions These results suggest a novel locus influencing survival in the B6/D2 genetic background, perhaps\nvia a metabolic disorder that emerges by 200 days of age in\nmale animals. Keywords\nPathology\n\nLongevity Lifespan Mouse Linkage \n\nIntroduction\nLongevity, the quintessential complex trait, likely reflects\nall aspects of an organism’s life history. In humans, the\nestimated heritability of age at death is estimated at\n25–33 % [1]. Genetic contributions to mortality rates are\nthus of great interest and may aid in the understanding of\ndisease etiology and the process of aging itself [2]." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Here, we have extended this analysis to search for\ngenotypes related to survival to the age of 800 days in a\npopulation of a reciprocal F2 cross between (B6) and (D2)\nmice. Since QTL for longevity in mice have shown strong\nsex specificity [10, 12], we conducted sex-specific analyses. In addition, we also determined whether there were\nany change in pathology changes associated with the loci\nthat showed frequency distortions with aging. To confirm\nthe associations of the loci of interest with longevity and\npathology, we performed replication analyses on a panel of\nBXD recombinant inbred strains." + } + ], + "64886b4e-8599-4f61-84e6-9add7663a1b3": [ + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": "352(6291): p. aad0189. Liao, C.Y. , et al. , Genetic variation in the murine lifespan response to dietary restriction: from life extension to life\nshortening. Aging Cell, 2010. 9(1): p. 92-5. Johnson, M., Laboratory Mice and Rats. Mater. Methods, 2012. 2: p. 113. Fontaine, D.A. and D.B. Davis, Attention to Background Strain Is Essential for Metabolic Research: C57BL/6 and\nthe International Knockout Mouse Consortium. Diabetes, 2016. 65(1): p. 25-33. Simon, M.M. , et al. , A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains. Genome Biol, 2013. 14(7): p. R82. Lilue, J., et al." + } + ], + "8dad24f7-b658-44fa-af65-6f33db69c15a": [ + { + "document_id": "8dad24f7-b658-44fa-af65-6f33db69c15a", + "text":"Mamm Genome 2001;12: 930–2. 21 Gelman R, Watson A, Bronson R, Yunis E. Murine chromosomal\nregions correlated with longevity. Genetics 1988;118:693–704. 22 Peirce JL, Lu L, Gu J, Silver LM, Williams RW. A new set of BXD\nrecombinant inbred lines from advanced intercross populations in\nmice. BMC Genet 2004;5:7. 23 Rahman ZS, Tin SK, Buenaventura PN et al. A novel susceptibility\nlocus on chromosome 2 in the (New Zealand Black New Zealand\nWhite) F1 hybrid mouse model of systemic lupus erythematosus. J Immunol 2002;168:3042–9. 24 Kono DH, Burlingame RW, Owens DG et al." + } + ], + "958b37c9-9bd5-4e84-939d-8f12dccf1055": [ + { + "document_id": "958b37c9-9bd5-4e84-939d-8f12dccf1055", + "text": "Conversely, the BXD strain with the shortest life span\n(BXD14) has the lowest responsiveness to the stimulatory effect of\nTGF-2 when old (48). The region on chromosome 2 where a\nsuggestive QTL regulating the responsiveness to TGF-2 in old\nmice is located also contains two QTL for longevity (32). Finally,\nthe strongest support for this hypothesis is the correlation between\nlongevity and the age-related increase in the serum-dependent effect of TGF-2 on LSK cells, the extent of which may determine\nstem cell function in aged mice." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nFIGURE 8-5 Genetic regulation of longevity in mice stratified by cause of death.Female mice that inherit the C3H allele at D2Mit58 plus the BALB allele at D16Mit182 (light gray bars) have significantly higher longevity than their sisters (dark gray bars) with the C57BL/6 plus DBA/2 allele combination (\"all causes\" of death combined).Subsets of mice that died either of cancer or of a nonneoplastic (\"benign\") illness both show the association between genotype and longevity.Among the mice dying of neoplasia, subsets dying of lymphoma or of fibrosarcoma show equivalent, and significant, genotypic effects.Bars indicate means plus standard error of the mean.SOURCE:Miller et al. (unpublished results)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nThe available dataset also provides examples in which genetic variants seem to influence the risk of specific late-life diseases.Figure 8-6, for example, shows longevity results for mice stratified by their inheritance at the 12th chromosome locus D12Mit167.This is a locus associated with differential longevity in both male and female mice, with the strongest effect (adjusted p < 0.01) seen in those mice living more than 657 days (Jackson et al., unpublished results).The longest-lived mice are those that inherit both the C57BL/6 allele from their mother and the C3H allele from their father; on average, they survive 93 days longer than siblings with the BALB plus C3H combination.Figure 8-6 shows that the D12Mit167, like the pair of loci illustrated in Figure 8-5, has significant and similar effects in mice dying of cancer (85 days) and in mice dying of non-neoplastic diseases (126 days).A more detailed analysis of the cancers, however, suggests that while lymphoma and hepatoma victims are equally protected by the favorable alleles (effect sizes of 93 and 167 days, respec- mice of two subgroups: those dying of the urinary syndrome MUS, and those dying of all other causes.The genetic analysis contrasts mice with both the C57BL/6 allele at D4Mit84 and the C3H allele at D9Mit110 to mice with any of the three other allele combinations.In the males dying of causes other than MUS, this allele pair is associated with a 170-day increment in longevity (post-hoc p < 0.00003).But for males that do die of MUS, the same allele combination is associated with a 187-day decline in mean life span (post-hoc p < 0.03).This effect is thus pleiotropic, in that these alleles accelerate death in mice susceptible to MUS, while postponing death for all other males in the population.Although these loci are associated with differential longevity in mice that do develop MUS, they do not have a significant effect on the chances that MUS will indeed occur (not shown).The risk of developing MUS seems to be under control of a separate locus on chromosome 6.As shown in the bottom panel of Figure 8-7, males that inherit the C3H allele at D6Mit268 are far more likely to develop MUS (28 percent risk) than are their brothers who receive the DBA/2 allele at this locus (7 percent risk; p = 0.012 by two-tailed Fisher's exact test)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nHigh levels of CD8M cells are associated with diminished longevity in mated females (left panel; p < 0.001), but not in virgin females (center panel).Among virgin males, those dying of diseases other than the urinary syndrome MUS show no association between CD8M and longevity (open circles, upper line), but those dying because of MUS show a nonsignificant trend (filled circles, lower line, R = -0.27,p = 0.13) similar to the relationship observed in mated females.SOURCE : Miller et al. (unpublished results).Male or female mice that inherit the C57BL/6 (maternal) and C3H (paternal) alleles at D12Mit167 (light gray bars) are longer lived than their siblings that inherit the BALB plus C3H combination.The \"effect size\" shown at the right represents that difference in mean longevity between mice in the two genetically different groups, with (**) = p < 0.01 and (*) = p < 0.05 by t-test.Similar effect sizes are seen for mice dying of cancer or of non-neoplastic illnesses (\"benign\"), and among the cancer deaths the genetic effect is similar for deaths due to lymphoma and hepatoma.The genetic effect on longevity seems to be minimal, however, for mice dying of fibrosarcoma.Bars show means plus standard errors.SOURCE : Miller et al. (unpublished results)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nOur own work has taken a different tack: we have attempted to determine whether mutations with differential effects on aging may be present within the many available populations of laboratory-adopted inbred mice.The goal is not so much to clone these genes-if indeed they existbecause positional cloning strategies of this kind require many thousands of animals and would be extremely expensive using an assay, age at death, that is itself so costly.Instead, the goal has been to use gene mapping methods to test hypotheses about aging and to develop new animal models that will be useful for testing well-specified hypotheses about the molecular basis for age-dependent changes.In the absence of a validated battery of biomarkers of aging, we (like most others) have reluctantly decided to use mouse life span as a crude surrogate for aging itself, reasoning that genetic alleles that extend life span well beyond the median for the tested population may be operating via an influence on aging itself.Work conducted using recombinant inbred mouse stocks (Gelman et al., 1988;de Haan and Van Zant, 1999) has suggested that life-span differences between pairs of inbred mouse lines might reflect the influence of as few as 4-7 polymorphic loci, providing some basis for hope that some of these would have an effect large enough to be detected by a genome scan experiment involving 300-1,200 mice." + } + ], + "9ac0b7e7-6294-4cfb-97e3-e5a4546af324": [ + { + "document_id": "9ac0b7e7-6294-4cfb-97e3-e5a4546af324", + "text": ", Vogler, G.P. , Vandenbergh,\nD.J. , Blizard, D.A. , Stout, J.T. & McClearn, G.E. Quantitative Trait\nLocus (QTL) Analysis of Longevity in C57BL/6J byDBA/2J (BXD)\nRecombinant Inbred Mice. Aging Clin Exp Res (in press). Lionikas, A., Blizard, D.A. , Vandenbergh, D.J. , Glover, M.G. ,\nStout, J.T. , Vogler, G.P. , McClearn, G.E. & Larsson, L. (2003)\nGenetic architecture of fast- and slow-twitch skeletal muscle\nweight in 200-day-old mice of the C57BL/6J and DBA/2J lineage. Physiol Genomics 16, 141–152. Lionikas A., Blizard D.A. , Gerhard G.S. , Vandenbergh D.J. , Stout J.T. ,\nVogler G.P. , McClearn G.E." + } + ], + "cb3f9967-9762-4a9b-96cb-0acccdc316d2": [ + { + "document_id": "cb3f9967-9762-4a9b-96cb-0acccdc316d2", + "text": "Deficiency mapping of quantitative trait loci affecting longevity\nin Drosophila melanogaster. Genetics 2000;156:1129–1146. [PubMed: 11063689]\n33. Ma RZ, et al. Identification of Bphs, an autoimmune disease locus, as histamine receptor H1. Science\n2002;297:620–623. [PubMed: 12142541]\n\nNat Rev Genet. Author manuscript; available in PMC 2007 November 5. Page 12\n\nNIH-PA Author Manuscript\n\n34. Vivian JL, Chen Y, Yee D, Schneider E, Magnuson T. An allelic series of mutations in Smad2 and\nSmad4 identified in a genotype-based screen of N-ethyl-N-nitrosourea-mutagenized mouse\nembryonic stem cells. Proc. Natl Acad. Sci. USA 2002;99:15542–15547. [PubMed: 12432092]\n35. Vogel G. Scientists dream of 1001 complex mice." + } + ], + "ce2c68bf-878d-460c-8d9b-d45ce3034ef7": [ + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "text": "34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with\nlongevity. Genetics 118, 693–704 (1988). [PubMed: 3163317]\n35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011). 36. Houtkooper RHet al.Mitonuclear protein imbalance as a conserved longevity mechanism. Nature497, 451–457 (2013). [PubMed: 23698443]\n37. Williams EGet al.An Evolutionarily conserved role for the aryl hydrocarbon receptor in the\nregulation of movement. PLOS Genet. 10, e1004673 (2014). [PubMed: 25255223]\n38. Lang DHet al.Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin. Exp. Res. 22, 8–19 (2010)." + } + ], + "db0459f8-6602-48d7-be9b-14863a88bbe1": [ + { + "document_id": "db0459f8-6602-48d7-be9b-14863a88bbe1", + "text": "In addition,\nthe B6 mouse strain is one of the longest-lived mouse strains with a mean lifespan of 3\nyears versus other mouse strains with mean lifespan from 1.5-2 years. Therefore, it is\nevident that the genetic background of a particular mouse strain can have a profound\neffect on the biology of the HSC population as well as organismal longevity. Indeed, it is\nfor this reason that it is difficult to compare findings from various laboratories where\ndifferent mouse strains are used." + } + ], + "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748": [ + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "NIH-PA Author Manuscript\n\nThis study indicated a large amount of genetic variation for mouse longevity; heritability\nwas 34% for AL and 36% for DR (60% of AL food intake). There was no significant\ncorrelation between mean longevity under these two conditions, although maximum\nlifespans of the AL and DR mice were significantly correlated. Similar observations were\nmade at the UTHSCSA on the ILSXISS RI mice (Liao et al. , 2010a, b; Mattson 2010),\nwhere they also observed similar heritability (28% AL males, 36% AL females, 55% DR\nmales, 53% DR females)." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "For females, hairs of the congenic mice grew 31% faster, also highly significant (P =\n0.0006, 1-tailed). These results validated the presence of a gene in the differential region\naffecting FE. Discussion\nWe report the outcomes of a quantitative genetic study on aging and longevity in the mouse. We studied an extant series of recombinant inbred strains (ILSXISS) that have been used\nboth in DR aging studies as well as to study alcohol sensitivity (Williams et al. , 2004)." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "(2007) is a separate issue from the analyses conducted in this\nstudy (the AL efficiency model will be tested in future studies). Exp Gerontol. Author manuscript; available in PMC 2011 September 1. Rikke et al. Page 8\n\nNIH-PA Author Manuscript\n\nOther studies have also reported that individual mice that maintained the highest BW were\nlikely to be the longest-lived individuals among cohorts of genetically identical mice\n(Weindruch et al. , 1986; Harper et al. , 2006)." + } + ], + "f116ee1c-b275-4239-98e9-c2032b8f05c5": [ + { + "document_id": "f116ee1c-b275-4239-98e9-c2032b8f05c5", + "text": "Age-associated changes are conserved between mouse strains\n\nLife span and aging vary between mouse strains.For example, C57BL/6 mice are long-lived compared to the short-lived DBA/2 mice (Turturro et al. 1999).To test the generality of our observations, we also examined LT-HSCs, ST-HSC and MPPs in young and old mice from the DBA/2 strain, which originates from a distinct breeding lineage (Fox 1997)." + } + ] + }, + "data_source": [ + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "section_type": "main", + "text": "34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with\nlongevity. Genetics 118, 693–704 (1988). [PubMed: 3163317]\n35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011).\n 36. Houtkooper RHet al.Mitonuclear protein imbalance as a conserved longevity mechanism.\n Nature497, 451–457 (2013). [PubMed: 23698443]\n37. Williams EGet al.An Evolutionarily conserved role for the aryl hydrocarbon receptor in the\nregulation of movement. PLOS Genet. 10, e1004673 (2014). [PubMed: 25255223]\n38. Lang DHet al.Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin. Exp. Res. 22, 8–19 (2010)." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "Leduc MS, Hageman RS, Meng Q et al (2010) Identification of\ngenetic determinants of IGF-1 levels and longevity among mouse\ninbred strains. Aging Cell 9(5):823–836. doi:10.1111/j.14749726.2010.00612.x\n10. Lang DH, Gerhard GS, Griffith JW et al (2010) Quantitative trait\nloci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin Exp Res 22(1):8–19\n11. Gelman R, Watson A, Bronson R et al (1988) Murine chromosomal\nregions\ncorrelated\nwith\nlongevity.\n Genetics\n118(4):693–704\n12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci\nassociated with life span exhibit sex-specific and epistatic effects." + }, + { + "document_id": "8dad24f7-b658-44fa-af65-6f33db69c15a", + "section_type": "main", + "text":"Mamm Genome 2001;12: 930–2.\n 21 Gelman R, Watson A, Bronson R, Yunis E. Murine chromosomal\nregions correlated with longevity. Genetics 1988;118:693–704.\n 22 Peirce JL, Lu L, Gu J, Silver LM, Williams RW. A new set of BXD\nrecombinant inbred lines from advanced intercross populations in\nmice. BMC Genet 2004;5:7.\n 23 Rahman ZS, Tin SK, Buenaventura PN et al. A novel susceptibility\nlocus on chromosome 2 in the (New Zealand Black New Zealand\nWhite) F1 hybrid mouse model of systemic lupus erythematosus.\n J Immunol 2002;168:3042–9.\n 24 Kono DH, Burlingame RW, Owens DG et al." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "\n\nThe available dataset also provides examples in which genetic variants seem to influence the risk of specific late-life diseases.Figure 8-6, for example, shows longevity results for mice stratified by their inheritance at the 12th chromosome locus D12Mit167.This is a locus associated with differential longevity in both male and female mice, with the strongest effect (adjusted p < 0.01) seen in those mice living more than 657 days (Jackson et al., unpublished results).The longest-lived mice are those that inherit both the C57BL/6 allele from their mother and the C3H allele from their father; on average, they survive 93 days longer than siblings with the BALB plus C3H combination.Figure 8-6 shows that the D12Mit167, like the pair of loci illustrated in Figure 8-5, has significant and similar effects in mice dying of cancer (85 days) and in mice dying of non-neoplastic diseases (126 days).A more detailed analysis of the cancers, however, suggests that while lymphoma and hepatoma victims are equally protected by the favorable alleles (effect sizes of 93 and 167 days, respec- mice of two subgroups: those dying of the urinary syndrome MUS, and those dying of all other causes.The genetic analysis contrasts mice with both the C57BL/6 allele at D4Mit84 and the C3H allele at D9Mit110 to mice with any of the three other allele combinations.In the males dying of causes other than MUS, this allele pair is associated with a 170-day increment in longevity (post-hoc p < 0.00003).But for males that do die of MUS, the same allele combination is associated with a 187-day decline in mean life span (post-hoc p < 0.03).This effect is thus pleiotropic, in that these alleles accelerate death in mice susceptible to MUS, while postponing death for all other males in the population.Although these loci are associated with differential longevity in mice that do develop MUS, they do not have a significant effect on the chances that MUS will indeed occur (not shown).The risk of developing MUS seems to be under control of a separate locus on chromosome 6.As shown in the bottom panel of Figure 8-7, males that inherit the C3H allele at D6Mit268 are far more likely to develop MUS (28 percent risk) than are their brothers who receive the DBA/2 allele at this locus (7 percent risk; p = 0.012 by two-tailed Fisher's exact test)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "\n\nFIGURE 8-5 Genetic regulation of longevity in mice stratified by cause of death.Female mice that inherit the C3H allele at D2Mit58 plus the BALB allele at D16Mit182 (light gray bars) have significantly higher longevity than their sisters (dark gray bars) with the C57BL/6 plus DBA/2 allele combination (\"all causes\" of death combined).Subsets of mice that died either of cancer or of a nonneoplastic (\"benign\") illness both show the association between genotype and longevity.Among the mice dying of neoplasia, subsets dying of lymphoma or of fibrosarcoma show equivalent, and significant, genotypic effects.Bars indicate means plus standard error of the mean.SOURCE:Miller et al. (unpublished results)." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text":"Conclusions These results suggest a novel locus influencing survival in the B6/D2 genetic background, perhaps\nvia a metabolic disorder that emerges by 200 days of age in\nmale animals.\n Keywords\nPathology\n\nLongevity Lifespan Mouse Linkage \n\nIntroduction\nLongevity, the quintessential complex trait, likely reflects\nall aspects of an organism’s life history. In humans, the\nestimated heritability of age at death is estimated at\n25–33 % [1]. Genetic contributions to mortality rates are\nthus of great interest and may aid in the understanding of\ndisease etiology and the process of aging itself [2]." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "\n\nHigh levels of CD8M cells are associated with diminished longevity in mated females (left panel; p < 0.001), but not in virgin females (center panel).Among virgin males, those dying of diseases other than the urinary syndrome MUS show no association between CD8M and longevity (open circles, upper line), but those dying because of MUS show a nonsignificant trend (filled circles, lower line, R = -0.27,p = 0.13) similar to the relationship observed in mated females.SOURCE : Miller et al. (unpublished results).Male or female mice that inherit the C57BL/6 (maternal) and C3H (paternal) alleles at D12Mit167 (light gray bars) are longer lived than their siblings that inherit the BALB plus C3H combination.The \"effect size\" shown at the right represents that difference in mean longevity between mice in the two genetically different groups, with (**) = p < 0.01 and (*) = p < 0.05 by t-test.Similar effect sizes are seen for mice dying of cancer or of non-neoplastic illnesses (\"benign\"), and among the cancer deaths the genetic effect is similar for deaths due to lymphoma and hepatoma.The genetic effect on longevity seems to be minimal, however, for mice dying of fibrosarcoma.Bars show means plus standard errors.SOURCE : Miller et al. (unpublished results)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "\n\nOur own work has taken a different tack: we have attempted to determine whether mutations with differential effects on aging may be present within the many available populations of laboratory-adopted inbred mice.The goal is not so much to clone these genes-if indeed they existbecause positional cloning strategies of this kind require many thousands of animals and would be extremely expensive using an assay, age at death, that is itself so costly.Instead, the goal has been to use gene mapping methods to test hypotheses about aging and to develop new animal models that will be useful for testing well-specified hypotheses about the molecular basis for age-dependent changes.In the absence of a validated battery of biomarkers of aging, we (like most others) have reluctantly decided to use mouse life span as a crude surrogate for aging itself, reasoning that genetic alleles that extend life span well beyond the median for the tested population may be operating via an influence on aging itself.Work conducted using recombinant inbred mouse stocks (Gelman et al., 1988;de Haan and Van Zant, 1999) has suggested that life-span differences between pairs of inbred mouse lines might reflect the influence of as few as 4-7 polymorphic loci, providing some basis for hope that some of these would have an effect large enough to be detected by a genome scan experiment involving 300-1,200 mice." + }, + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "section_type": "main", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n24 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10006, 10006\ngenenetwork.org/\nshow_trait?trait_id=\n10006&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10010, 10010\ngenenetwork.org/\nshow_trait?trait_id=\n10010&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10011, 10011\ngenenetwork.org/\nshow_trait?trait_id=\n10011&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10021, 10021\ngenenetwork.org/\nshow_trait?trait_id=\n10021&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10022, 10022\ngenenetwork.org/\nshow_trait?trait_id=\n10022&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10025, 10025\ngenenetwork.org/\nshow_trait?trait_id=\n10025&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10066, 10066\ngenenetwork.org/\nshow_trait?trait_id=\n10066&dataset=BXD-\nLongevityPublish\n\nReferences\nAlbertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P,\nCarlson M. 1994." + }, + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "section_type": "main", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n24 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10006, 10006\ngenenetwork.org/\nshow_trait?trait_id=\n10006&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10010, 10010\ngenenetwork.org/\nshow_trait?trait_id=\n10010&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10011, 10011\ngenenetwork.org/\nshow_trait?trait_id=\n10011&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10021, 10021\ngenenetwork.org/\nshow_trait?trait_id=\n10021&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10022, 10022\ngenenetwork.org/\nshow_trait?trait_id=\n10022&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10025, 10025\ngenenetwork.org/\nshow_trait?trait_id=\n10025&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10066, 10066\ngenenetwork.org/\nshow_trait?trait_id=\n10066&dataset=BXD-\nLongevityPublish\n\nReferences\nAlbertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P,\nCarlson M. 1994." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "section_type": "main", + "text": "NIH-PA Author Manuscript\n\nThis study indicated a large amount of genetic variation for mouse longevity; heritability\nwas 34% for AL and 36% for DR (60% of AL food intake). There was no significant\ncorrelation between mean longevity under these two conditions, although maximum\nlifespans of the AL and DR mice were significantly correlated. Similar observations were\nmade at the UTHSCSA on the ILSXISS RI mice (Liao et al. , 2010a, b; Mattson 2010),\nwhere they also observed similar heritability (28% AL males, 36% AL females, 55% DR\nmales, 53% DR females)." + }, + { + "document_id": "958b37c9-9bd5-4e84-939d-8f12dccf1055", + "section_type": "main", + "text": "Conversely, the BXD strain with the shortest life span\n(BXD14) has the lowest responsiveness to the stimulatory effect of\nTGF-2 when old (48). The region on chromosome 2 where a\nsuggestive QTL regulating the responsiveness to TGF-2 in old\nmice is located also contains two QTL for longevity (32). Finally,\nthe strongest support for this hypothesis is the correlation between\nlongevity and the age-related increase in the serum-dependent effect of TGF-2 on LSK cells, the extent of which may determine\nstem cell function in aged mice." + }, + { + "document_id": "cb3f9967-9762-4a9b-96cb-0acccdc316d2", + "section_type": "main", + "text": "Deficiency mapping of quantitative trait loci affecting longevity\nin Drosophila melanogaster. Genetics 2000;156:1129–1146. [PubMed: 11063689]\n33. Ma RZ, et al. Identification of Bphs, an autoimmune disease locus, as histamine receptor H1. Science\n2002;297:620–623. [PubMed: 12142541]\n\nNat Rev Genet. Author manuscript; available in PMC 2007 November 5.\n Page 12\n\nNIH-PA Author Manuscript\n\n34. Vivian JL, Chen Y, Yee D, Schneider E, Magnuson T. An allelic series of mutations in Smad2 and\nSmad4 identified in a genotype-based screen of N-ethyl-N-nitrosourea-mutagenized mouse\nembryonic stem cells. Proc. Natl Acad. Sci. USA 2002;99:15542–15547. [PubMed: 12432092]\n35. Vogel G. Scientists dream of 1001 complex mice." + }, + { + "document_id": "9ac0b7e7-6294-4cfb-97e3-e5a4546af324", + "section_type": "main", + "text": ", Vogler, G.P. , Vandenbergh,\nD.J. , Blizard, D.A. , Stout, J.T. & McClearn, G.E. Quantitative Trait\nLocus (QTL) Analysis of Longevity in C57BL/6J byDBA/2J (BXD)\nRecombinant Inbred Mice. Aging Clin Exp Res (in press).\n Lionikas, A., Blizard, D.A. , Vandenbergh, D.J. , Glover, M.G. ,\nStout, J.T. , Vogler, G.P. , McClearn, G.E. & Larsson, L. (2003)\nGenetic architecture of fast- and slow-twitch skeletal muscle\nweight in 200-day-old mice of the C57BL/6J and DBA/2J lineage.\n Physiol Genomics 16, 141–152.\n Lionikas A., Blizard D.A. , Gerhard G.S. , Vandenbergh D.J. , Stout J.T. ,\nVogler G.P. , McClearn G.E." + }, + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "section_type": "main", + "text": "352(6291): p. aad0189.\n Liao, C.Y. , et al. , Genetic variation in the murine lifespan response to dietary restriction: from life extension to life\nshortening. Aging Cell, 2010. 9(1): p. 92-5.\n Johnson, M., Laboratory Mice and Rats. Mater. Methods, 2012. 2: p. 113.\n Fontaine, D.A. and D.B. Davis, Attention to Background Strain Is Essential for Metabolic Research: C57BL/6 and\nthe International Knockout Mouse Consortium. Diabetes, 2016. 65(1): p. 25-33.\n Simon, M.M. , et al. , A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains.\n Genome Biol, 2013. 14(7): p. R82.\n Lilue, J., et al." + }, + { + "document_id": "db0459f8-6602-48d7-be9b-14863a88bbe1", + "section_type": "main", + "text": "In addition,\nthe B6 mouse strain is one of the longest-lived mouse strains with a mean lifespan of 3\nyears versus other mouse strains with mean lifespan from 1.5-2 years. Therefore, it is\nevident that the genetic background of a particular mouse strain can have a profound\neffect on the biology of the HSC population as well as organismal longevity. Indeed, it is\nfor this reason that it is difficult to compare findings from various laboratories where\ndifferent mouse strains are used." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "Here, we have extended this analysis to search for\ngenotypes related to survival to the age of 800 days in a\npopulation of a reciprocal F2 cross between (B6) and (D2)\nmice. Since QTL for longevity in mice have shown strong\nsex specificity [10, 12], we conducted sex-specific analyses. In addition, we also determined whether there were\nany change in pathology changes associated with the loci\nthat showed frequency distortions with aging. To confirm\nthe associations of the loci of interest with longevity and\npathology, we performed replication analyses on a panel of\nBXD recombinant inbred strains." + }, + { + "document_id": "f116ee1c-b275-4239-98e9-c2032b8f05c5", + "section_type": "main", + "text": "Age-associated changes are conserved between mouse strains\n\nLife span and aging vary between mouse strains.For example, C57BL/6 mice are long-lived compared to the short-lived DBA/2 mice (Turturro et al. 1999).To test the generality of our observations, we also examined LT-HSCs, ST-HSC and MPPs in young and old mice from the DBA/2 strain, which originates from a distinct breeding lineage (Fox 1997)." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "section_type": "main", + "text": "For females, hairs of the congenic mice grew 31% faster, also highly significant (P =\n0.0006, 1-tailed). These results validated the presence of a gene in the differential region\naffecting FE.\n\n Discussion\nWe report the outcomes of a quantitative genetic study on aging and longevity in the mouse.\n We studied an extant series of recombinant inbred strains (ILSXISS) that have been used\nboth in DR aging studies as well as to study alcohol sensitivity (Williams et al. , 2004)." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "section_type": "main", + "text": "(2007) is a separate issue from the analyses conducted in this\nstudy (the AL efficiency model will be tested in future studies).\n\n Exp Gerontol. Author manuscript; available in PMC 2011 September 1.\n Rikke et al.\n\n Page 8\n\nNIH-PA Author Manuscript\n\nOther studies have also reported that individual mice that maintained the highest BW were\nlikely to be the longest-lived individuals among cohorts of genetically identical mice\n(Weindruch et al. , 1986; Harper et al. , 2006)." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "section_type": "main", + "text": "These strains of mice are now available from\nthe Jackson Laboratory.\n\n NIH-PA Author Manuscript\n\nPrevious studies have identified several physiological responses to DR, such as lower body\ntemperature and reduced body weight (BW), that exhibit genetic variation in the ILSXISS;\nheritability was 35% for body temperature and 42% for BW (Rikke et al. , 2003; Rikke et al. ,\n2004; Rikke et al. , 2006; Rikke and Johnson, 2007). Here we suggest a role for metabolic\nefficiency in specifying longevity and other anti-aging actions of DR. This is consistent with\nobservations of Weindruch et al." + }, + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "section_type": "main", + "text": "Liao C-Y, Rikke BA, Johnson TE, Diaz V & Nelson JF Genetic variation in the murine lifespan\nresponse to dietary restriction: from life extension to life shortening. Aging Cell 9, 92–95 (2010).\n [PubMed: 19878144]\n\nNat Metab. Author manuscript; available in PMC 2022 March 22.\n Roy et al.\n\n Page 19\n\nAuthor Manuscript\nAuthor Manuscript\nAuthor Manuscript\nAuthor Manuscript\n\n18. Mitchell SJet al.Effects of sex, strain, and energy intake on hallmarks of aging in mice. Cell Metab.\n 23, 1093–1112 (2016). [PubMed: 27304509]\n19." + }, + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "section_type": "main", + "text": "Rikke BA, Liao C-Y, McQueen MB, Nelson JF & Johnson TE Genetic dissection of dietary\nrestriction in mice supports the metabolic efficiency model of life extension. Exp. Gerontol. 45,\n691–701 (2010). [PubMed: 20452416]\n20. Azzu V & Valencak TG Energy metabolism and ageing in the mouse: A mini-review. Gerontology\n63, 327–336 (2017). [PubMed: 28118636]\n21. Pennacchio LA & Rubin EM Comparative genomic tools and databases: providing insights into the\nhuman genome. J. Clin. Invest. 111, 1099–1106 (2003). [PubMed: 12697725]\n22. Miller RAet al.An Aging Interventions Testing Program: study design and interim report. Aging\nCell6, 565–575 (2007). [PubMed: 17578509]\n23." + }, + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "section_type": "main", + "text": "Strong Ret al.Evaluation of resveratrol, green tea extract, curcumin, oxaloacetic acid, and medium\nchain triglyceride oil on life span of genetically heterogeneous mice. J. Gerontol. A. Biol. Sci.\n Med. Sci. 68, 6–16 (2013). [PubMed: 22451473]\n24. Yuan R, Peters LL & Paigen B Mice as a mammalian model for research on the genetics of aging.\n ILAR J. Natl. Res. Counc. Inst. Lab. Anim. Resour. 52, 4–15 (2011).\n 25. Saul MC, Philip VM, Reinholdt LG & Chesler EJ High-diversity mouse populations for complex\ntraits. Trends Genet. 35, 501–514 (2019). [PubMed: 31133439]\n26." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "\n\nFIGURE 8-1 Correlation of mouse longevity with the percentage of CD4M cells measured at 18 months of age.The filled circles and darker line represent female mice, and the open circles and lighter line represent males.There is a significant correlation between CD4M levels and longevity; R 2 = 0.18, p = 0.0003 after adjustment for gender effects.SOURCE: Miller et al. (1997)." + }, + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "section_type": "main", + "text": "Longevity data\nwas obtained from a parallel cohort of BXD mice housed in the same UTHSC colony, and members\nof this ‘longevity cohort’ were allowed to age until natural death (more detail on the longevity cohort\ncan be found in Roy et al. , 2021). Males were excluded and strain-by-diet lifespan summary statistics\nwere derived. Only strain-by-diet groups with five or more observations for lifespan were included in\nthe correlational analyses with the epigenetic predictors.\n\n Multivariable EWAS\nSite-by-site differential methylation analysis (EWAS) was performed on the 27,966 CpGs using a\nmultivariable regression model." + }, + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "section_type": "main", + "text": "Longevity data\nwas obtained from a parallel cohort of BXD mice housed in the same UTHSC colony, and members\nof this ‘longevity cohort’ were allowed to age until natural death (more detail on the longevity cohort\ncan be found in Roy et al. , 2021). Males were excluded and strain-by-diet lifespan summary statistics\nwere derived. Only strain-by-diet groups with five or more observations for lifespan were included in\nthe correlational analyses with the epigenetic predictors.\n\n Multivariable EWAS\nSite-by-site differential methylation analysis (EWAS) was performed on the 27,966 CpGs using a\nmultivariable regression model." + }, + { + "document_id": "5e47c149-228e-41fb-b93b-3ea5bef15d6c", + "section_type": "main", + "text": "Using a large panel of BXD\nrecombinant inbred (RI) strains of mice generated by crossing strains\n\nB6 and D2, we defined a QTL on chromosome 11 called stem cell\nproliferation-2 (Scp2) that modulates the percentage of cells in\nS phase6. The same locus was associated with the difference in mean\nmouse lifespan between these two strains6, suggesting that increased\nstem cell turnover is one of the factors that underlie the aging process.\n The relevance of this 10-cM region in isolation was confirmed in an\nextensive analysis of backcrossed mice and, ultimately, in a congenic\nmouse model9." + }, + { + "document_id": "969427e9-5901-402d-9d30-216c3c2f528c", + "section_type": "main", + "text": "Using a large panel of BXD\nrecombinant inbred (RI) strains of mice generated by crossing strains\n\nB6 and D2, we defined a QTL on chromosome 11 called stem cell\nproliferation-2 (Scp2) that modulates the percentage of cells in\nS phase6. The same locus was associated with the difference in mean\nmouse lifespan between these two strains6, suggesting that increased\nstem cell turnover is one of the factors that underlie the aging process.\n The relevance of this 10-cM region in isolation was confirmed in an\nextensive analysis of backcrossed mice and, ultimately, in a congenic\nmouse model9." + }, + { + "document_id": "6b2dba7c-0249-448e-9e84-92de7088109b", + "section_type": "main", + "text": "[PubMed: 29945935]\nWilliams EG, Roy S, Statzer C, Ingels J, Bohl C, Hasan M, Cuklina J, Lu L, Ewald CY, Williams RW,\net al. (2020). The Molecular Landscape of the Aging Mouse Liver. BioRxiv Syst Biol\n2020.08.20.222968.\n Williams RW, Strom RC, and Goldowitz D (1998). Natural variation in neuron number in mice is\nlinked to a major quantitative trait locus on Chr 11. J Neurosci 18, 138–146. [PubMed: 9412494]\nWilliams RW, Gu J, Qi S, and Lu L (2001). The genetic structure of recombinant inbred mice: highresolution consensus maps for complex trait analysis. Genome Biol 2, RESEARCH0046." + }, + { + "document_id": "75813bc2-f0b5-400c-92d7-0958df97a04f", + "section_type": "main", + "text": "Accessing data resources in the mouse\nphenome database for genetic analysis of murine life span and health span. J.\nGerontol. A Biol. Sci. Med. Sci. 71 (2), 170–177.\n Brown, R.E. , Stanford, L., Schellinck, H.M., 2000. Developing standardized behavioral\ntests for knockout and mutant mice. ILAR J. 41 (3), 163–174.\n Bubier, J.A. , Jay, J.J., Baker, C.L. , Bergeson, S.E. , Ohno, H., Metten, P., Crabbe, J.C.,\nChesler, E.J. , 2014. Identification of a QTL in Mus musculus for alcohol preference,\nwithdrawal, and Ap3m2 expression using integrative functional genomics and precision genetics. Genetics 197 (4), 1377–1393.\n Burn, C.C. , 2008." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "\n\nThe strongest associations in these initial studies had involved T-cell subsets measured on 18-month-old mice, i.e., mice that had already completed 70 percent of the median life span (approximately 26 months) of the population, but correlations of longevity and T-cells subsets tested in (Tuffery, 1966), which is seen only in nondominant males housed with more aggressive males.This lesion, thought to be secondary to adjustments in dominance hierarchy, typically causes death at relatively early ages, and therefore mice dying of MUS are treated as a separate subgroup.None of the T-cell subsets tested at 8 months of age was able to predict subsequent longevity in the virgin males or virgin females, but there was a significant inverse correlation between CD8M cells and longevity in the mated females.Figure 8-4 shows the scatterplots for all four sets of mice.The correlation for mated females (R = -0.22,p < 0.001) is in the predicted direction, that is, with high levels of memory cells associated with lower life expectancy.There is no correlation in virgin females or in the virgin males dying of causes other than MUS.Males dying of MUS, similar to mated females, show an inverse correlation (R = -0.27,p = 0.13), which, however, is not statistically significant.These data thus support the idea that tests of age-sensitive traits, measured at ages as early as the first third of the life span, may be able to predict subsequent longevity, but raise the concern that the associations may vary with gender and either hormonal exposure or reproductive history.Levels of CD4M and CD8M cells are strongly and positively correlated at all ages (R = 0.70, 0.65, and 0.40 at 8, 14, and 20 months, respectively, all p < 0.005) (Miller, 1997b), and there is no a priori reason to expect that the former subset would be associated with longevity only in virgin animals and the latter only in mated females.We have now initiated a number of collaborations to see if these subsets correlate in expected directions with indices of age-sensitive change in cells and tissues outside the immune system, as well as with life span and protective immune function in these heterogeneous mice." + }, + { + "document_id": "75e0ffe8-7675-4e11-be3e-880bfeb3dabd", + "section_type": "main", + "text": "Bogue MA, Peters LL, Paigen B, Korstanje R, Yuan R, Ackert-Bicknell C, et al. Accessing Data\nResources in the Mouse Phenome Database for Genetic Analysis of Murine Life Span and Health\nSpan. J Gerontol A Biol Sci Med Sci. 2016; 71: 170–177. https://doi.org/10.1093/gerona/glu223 PMID:\n25533306\n\n48.\n\n Ackert-Bicknell CL, Shockley KR, Horton LG, Lecka-Czernik B, Churchill GA, Rosen CJ. Strain-specific\neffects of rosiglitazone on bone mass, body composition, and serum insulin-like growth factor-I. Endocrinology. 2009; 150: 1330–1340. https://doi.org/10.1210/en.2008-0936 PMID: 18948404\n\n49.\n\n Yang H, Ding Y, Hutchins LN, Szatkiewicz J, Bell TA, Paigen BJ, et al." + }, + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "section_type": "main", + "text": "Although genes clustered by treatment,\nconsiderable overlap among treatments was nevertheless observed, suggesting a connection among starvation, dessication, and longevity phenotypes previously noted by\nHoffman and Harshman 1999 and others.\n Expression profiling has also been carried out on mice selected in the laboratory for\nincreased voluntary wheel running (Bronikowski et al. 2004). Gene expression profiles\nwere obtained on hippocampus tissue, as that brain region had previously been shown\nto undergo marked physiological changes in response to wheel running." + }, + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "section_type": "main", + "text": "Although genes clustered by treatment,\nconsiderable overlap among treatments was nevertheless observed, suggesting a connection among starvation, dessication, and longevity phenotypes previously noted by\nHoffman and Harshman 1999 and others.\n Expression profiling has also been carried out on mice selected in the laboratory for\nincreased voluntary wheel running (Bronikowski et al. 2004). Gene expression profiles\nwere obtained on hippocampus tissue, as that brain region had previously been shown\nto undergo marked physiological changes in response to wheel running." + }, + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "section_type": "main", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n23 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10072, 10072\ngenenetwork.org/\nshow_trait?trait_id=\n10072&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10073, 10073\ngenenetwork.org/\nshow_trait?trait_id=\n10073&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10074, 10074\ngenenetwork.org/\nshow_trait?trait_id=\n10074&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10075, 10075\ngenenetwork.org/\nshow_trait?trait_id=\n10075&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10076, 10076\ngenenetwork.org/\nshow_trait?trait_id=\n10076&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2022\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10093, 10093\ngenenetwork.org/\nshow_trait?trait_id=\n10093&dataset=BXD-\nLongevityPublish\n\nThe following previously published datasets were used:\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10001, 10001\ngenenetwork.org/\nshow_trait?trait_id=\n10001&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10002, 10002\ngenenetwork.org/\nshow_trait?trait_id=\n10002&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10003, 10003\ngenenetwork.org/\nshow_trait?trait_id=\n10003&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10004, 10004\ngenenetwork.org/\nshow_trait?trait_id=\n10004&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10005, 10005\ngenenetwork.org/\nshow_trait?trait_id=\n10005&dataset=BXD-\nLongevityPublish\n\nContinued on next page\n\nMozhui et al." + }, + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "section_type": "main", + "text": "Burger, J. M. S., K. Munjong, J. Pont, and T. Kawecki. 2008. Learning ability and longevity:\nA symmetrical evolutionary trade-off. Evolution 62:1294–1304.\n Carlson, K. A., and L. G. Harshman. 1999a. Extended longevity lines of Drosophila\nmelanogaster: Abundance of yolk protein gene mRNA in fat body and ovary. Experimental\nGerontology 34:173–184.\n ———. 1999b. Extended longevity lines of Drosophila melanogaster: Characterization of\noocyte stages and ovariole numbers as a function of age and diet. Journal of Gerontology,\nBiological Sciences 54A:B432–B440.\n Carlson, K. A., T. J. Nusbaum, M. R. Rose, and L. G. Harshman. 1998." + }, + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "section_type": "main", + "text": "Burger, J. M. S., K. Munjong, J. Pont, and T. Kawecki. 2008. Learning ability and longevity:\nA symmetrical evolutionary trade-off. Evolution 62:1294–1304.\n Carlson, K. A., and L. G. Harshman. 1999a. Extended longevity lines of Drosophila\nmelanogaster: Abundance of yolk protein gene mRNA in fat body and ovary. Experimental\nGerontology 34:173–184.\n ———. 1999b. Extended longevity lines of Drosophila melanogaster: Characterization of\noocyte stages and ovariole numbers as a function of age and diet. Journal of Gerontology,\nBiological Sciences 54A:B432–B440.\n Carlson, K. A., T. J. Nusbaum, M. R. Rose, and L. G. Harshman. 1998." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "section_type": "main", + "text": "Because most of the mice in our lifespan study were\ncannibalized before they were found, we did not conduct pathology studies, nor did we have\nsufficient funds to perform detailed autopsies.\n\n NIH-PA Author Manuscript\n\nIt’s also important to note that our lifespan data correlated significantly with female fertility,\npost DR (R = 0.44, P = 0.006, N = 33 strains). This observation suggests genetic segregation\nof a common anti-aging component, which we called Aging Measure 1. Several previous\nstudies of female reproductive capabilities under DR (Weindruch and Walford, 1988; Merry\nand Holehan, 1991; Johnston et al." + }, + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "section_type": "main", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n23 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10072, 10072\ngenenetwork.org/\nshow_trait?trait_id=\n10072&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10073, 10073\ngenenetwork.org/\nshow_trait?trait_id=\n10073&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10074, 10074\ngenenetwork.org/\nshow_trait?trait_id=\n10074&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10075, 10075\ngenenetwork.org/\nshow_trait?trait_id=\n10075&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10076, 10076\ngenenetwork.org/\nshow_trait?trait_id=\n10076&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2022\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10093, 10093\ngenenetwork.org/\nshow_trait?trait_id=\n10093&dataset=BXD-\nLongevityPublish\n\nThe following previously published datasets were used:\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10001, 10001\ngenenetwork.org/\nshow_trait?trait_id=\n10001&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10002, 10002\ngenenetwork.org/\nshow_trait?trait_id=\n10002&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10003, 10003\ngenenetwork.org/\nshow_trait?trait_id=\n10003&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10004, 10004\ngenenetwork.org/\nshow_trait?trait_id=\n10004&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10005, 10005\ngenenetwork.org/\nshow_trait?trait_id=\n10005&dataset=BXD-\nLongevityPublish\n\nContinued on next page\n\nMozhui et al." + } + ], + "document_id": "2D2D12594F1A6AC91E150695D70A4FFA", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "C57BL/6&allele", + "C3H&allele", + "BALB&allele", + "D2Mit58", + "D16Mit182", + "longevity", + "mouse", + "genetic", + "D12Mit167", + "IGF-1" + ], + "metadata": [ + { + "object": "using in vitro prolactin induced lactogenic differentiation in an HC11 mouse cell model and an in vivo conditional knockout mouse model we showed that mouse Zfhx3 is essential for mouse mammary epithelial cell differentiation and mouse mammary gland development at the lactation stage through regulation of prolactin receptor expression and the downstream Jak2-Stat5 signaling pathway.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab989160" + }, + { + "object": "Genetic variants of mA3 are associated with the restriction factor Rfv3 recovery from Friend leukemia virus and with resistance to mouse mammary tumor virus. We sequenced mA3 from laboratory strains and wild mouse species to examine its evolution. We discovered that the mA3 allele in virus resistant mice such as C57BL/6J but not DBA/2J is disrupted by insertion of the regulatory sequences of a mouse leukemia virus, and this insertion is associated with enhanced mA3 expression. C Kozak", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab2087" + }, + { + "object": "Enhancing IGF-1 expression by astrocytes provided hippocampal neuroprotection and improved memory and motor function after traumatic brain injury. Delivering IGF-1 through reactive astrocytes targeted IGF-1 overexpression to the damaged hippocampus, producing a progressive increase in IGF-1 over 72 h which led to activation of the Akt pro-survival pathway and reduced hippocampal neuron loss in multiple regions.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab259579" + }, + { + "object": "Study found that IL-6, GP130, IGF-1 and IGF-1R were highly expressed in non-small cell lung cancer NSCLC and there was the correlation between GP130, IGF-1, and IGF-1R. Co-stimulation of IL-6 and IGF-1 resulted in significantly enhanced cell proliferation, invasion, and apoptosis of NSCLC cells. This experiment revealed that IL-6 and IGF-1 can synergistically promote the progression of NSCLC.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab741940" + }, + { + "object": "Strong cis eQTL LRS of 60, LRS 22, high B in mouse BXD data sets EPFL/LISP BXD HFD Muscle Affy Mouse Gene 1.0 ST Nov12 RMA Exon Level and in EPFL/LISP BXD CD+HFD and Liver Affy Mouse Gene 1.0 ST Apr13 RMA. Close to Numts and linked to longevity.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab5403" + }, + { + "object": "The rasH2 mouse is a hemizygous transgenic mouse carrying the c-Ha-ras oncogene and that gene's promoter/enhancer within the genetic background of a BALB/cByJ x C57BL/6F1 mouse.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab854885" + }, + { + "object": "review on novel mouse genetic studies that manipulate mHtt to answer questions related to spatio-temporal requirement for mHtt expression in eliciting Huntington's disease-like phenotypes in mouse models and on novel mouse models that aim to address the impact of huntingtin cis-domains or post-translational modifications on disease pathogenesis", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab677590" + }, + { + "object": "IGF-1 has been associated with the pathogenesis of diabetic retinopathy\\r\\nA paracrine effect of IGF-1 in the retina initiated vascular alterations that progressed from nonproliferative to proliferative retinopathy and retinal detachment.\\r\\nIncreased IGF-1 induction of VEGF expression in retinal glial cells\\r\\nThese findings suggest a role of IGF-1 in the development of ocular complications in long-term diabetes.\\r\\nMK, Yates Lab Summer 2015", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab2596" + }, + { + "object": "the effect of genetic inactivation of K-Cl cotransporters KCC1 and KCC3 in a mouse model of beta-thalassemia intermedia; conclude that genetic inactivation of K-Cl cotransport can reverse red cell dehydration and partially attenuate the hematologic phenotype in a mouse model of beta-thalassemia.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab804082" + }, + { + "object": "IGF-1 and IGF-1 receptor may be involved in the pathogenesis of Graves' disease; IGF-1 and IGF-1 receptor act by different mechanisms paracrine vs. autocrine as suggested by their differential expression in epithelial and stromal cells.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab376152" + } + ], + "question": "Which mouse genes have been associated with longevity?", + "subquestions": null, + "task_id": "2D2D12594F1A6AC91E150695D70A4FFA", + "usage": { + "chatgpt": 9852, + "gpt-4": 6314, + "gpt-4-turbo-preview": 4895 + }, + "user_id": 2 + }, + "document_id": "2D2D12594F1A6AC91E150695D70A4FFA", + "task_id": "2D2D12594F1A6AC91E150695D70A4FFA" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/02.json b/gnqa/paper1_eval/src/data/responses/aging/experts/02.json new file mode 100644 index 0000000..44da449 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/02.json @@ -0,0 +1,398 @@ +{ + "created_at": "2024-05-31T13:01:42.014610Z", + "data": { + "amplify": false, + "answer": "Lifespan is partially determined by genetics, with studies estimating that genetics contribute to about 25-30% of the variation in human lifespan. However, it is also significantly influenced by non-heritable factors such as environment and lifestyle.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nOur results show that longevity is partly determined by the predisposition to common diseases and, to an even greater extent, by modifiable risk factors.The genetic architecture of lifespan appears complex and diverse and there appears to be no single genetic elixir of long life." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nL ongevity is of interest to us all, and philosophers have long speculated on the extent to which it is pre-determined by fate.Here we focus on a narrower question-the extent and nature of its genetic basis and how this inter-relates with that of health and disease traits.In what follows, we shall use longevity as an umbrella term.We shall also more specifically refer to lifespan (the duration of life) and long-livedness (living to extreme old age, usually defined by a threshold, such as 90 years).Up to 25% of the variability in human lifespan has been estimated to be genetic 1 , but genetic variation at only three loci (near APOE, FOXO3A and CHRNA3/5) [2][3][4][5] have so far been demonstrated to be robustly associated with lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "GENETICS OF LIFE SPAN IN HUMANS\n\nMost studies of human twins agree that the heritability of life span is less than 50% (45,68).Of particular interest is an ongoing study of aging in Swedish twins that includes a large group of adopted twins who were reared separately.Ljungquist et al. (68) concluded that \"a maximum of one-third the variance in integrated mortality risk is attributable to genetic factors and that almost all of the remaining variance is due to nonshared, individually unique environmental factors. \"Moreover, this heritability declined with age and was negligible after the age of 85 in men and 90 in women." + } + ], + "1ccb0d11-1c88-4b08-b40d-4039a954745f": [ + { + "document_id": "1ccb0d11-1c88-4b08-b40d-4039a954745f", + "text": "\n\nHow can lifespan be controlled by a single gene?Two possibilities are, first, that the mutations that extend lifespan are in genes whose products regulate the activity of many other genes and, second, that these genes do not in fact control the rate of ageing." + } + ], + "4ca8d070-8b58-4bd5-86be-127089b70324": [ + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "text": "\n\nSince that time, observations across species have shown that life span can be extended by genetic factors.One of the first demonstrations of this entailed the study of recombinant inbred populations of the nematode worm Caenorhabditis elegans by Thomas E. Johnson.Then a postdoc in William (Bill) Wood's lab at the University of Colorado Boulder, Tom and Bill demonstrated that crosses of C. elegans strains did not display the heterosis effect that interfered with many other studies, \"As predicted, we found significant genetic effects on life span as well as other life history traits. \"This finding established a method for evaluating genetic factors that influenced life-span variation.In fact, their measurements of life span of the recombinant inbred strains demonstrated the heritability of life span to be 19%-51% (1).Consistent with theories of the 1970s and 1980s, it was concluded that these genetic factors were a collection of small influences across many genes.This finding was one of the first steps in demonstrating that genetic factors influence aging.As genetic analysis was making great progress in understanding other biological processes, such as developmental programming, the realization that aging could be investigated using the same tools was highly significant." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nAlthough it is known that health and lifespan are heavily influenced by genetics [14], variations in the lifespan of different individuals within the same species seem to be more the result of the accumulation over time of molecular damage that compromises the function of the cells [15].These molecular alterations can occur both at the genetic and epigenetic levels and depend on genetic, environmental, and stochastic factors [16].This complex multifactorial mix determined characteristics, such as longevity and a healthy lifespan, which are central concerns of human existence (Fig. 13.1).This chapter describes different types of tools in genomics used in ageing research and their different applications in clinical scenarios." + } + ], + "593b752f-f448-47be-8b83-13bc5e9eb0d4": [ + { + "document_id": "593b752f-f448-47be-8b83-13bc5e9eb0d4", + "text": "\n\nAge at death in adulthood has a moderate genetic component overall, with a heritability of approximately 25% (Murabito et al., 2012).Heritability of longevity increases with age, with a negligible genetic contribution to survival up to approximately 60 years of age, after which an increasing genetic component to survival is observed (Brooks-Wilson, 2013;Christensen et al., 2006).Most genetic studies of aging have focused on long-lived individuals, typically defined as centenarians 100 years or older, who may have had exceptional survival due to medical interventions (Murabito et al., 2012).A number of genetic associations with exceptional longevity have been made (Atzmon et al., 2006;Bojesen and Nordestgaard, 2008;Hurme et al., 2005;Kuningas et al., 2007;Melzer et al., 2007;Pawlikowska et al., 2009;Sanders et al., 2010;Suh et al., 2008;Willcox et al., 2008), with only markers at APOE and FOXO3A being well replicated (Murabito et al., 2012).Overall, the results of genetic and epidemiological longevity studies suggest aging is a complex trait and that achievement of exceptional longevity may not best capture the genetics of resistance to or delay of age-associated disease (Christensen et al., 2006)." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "Introduction\n\nWorldwide human populations have shown an increase in mean life expectancy in the past two centuries (Oeppen & Vaupel, 2002).This is mainly because of environmental factors such as improved hygiene, nutrition, and health care.The large variation in healthy lifespan among the elderly has prompted research into the determinants of aging and lifespan regulation.The genetic contribution to human lifespan variation was estimated at 25-30% in twin studies (Gudmundsson et al., 2000;Skytthe et al., 2003;Hjelmborg et al., 2006).The most prominent genetic influence is observed in families in which the capacity to attain a long lifespan clusters (Perls et al., 2000;Schoenmaker et al., 2006).Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al., 2008;Terry et al., 2008), raising the question whether protective mechanisms against disease exist in long-lived subjects." + } + ], + "78a43a45-84b0-4d73-9396-95b99cfd3983": [ + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "text": "Introduction\n\nHuman lifespan is a highly complex trait, the product of myriad factors involving health, lifestyle, genetics, environment, and chance.The extent of the role of genetic variation in human lifespan has been widely debated (van den Berg et al., 2017), with estimates of broad sense heritability ranging from around 25% based on twin studies (Ljungquist et al., 1998;Herskind et al., 1996;McGue et al., 1993) (perhaps over-estimated [Young et al., 2018]) to around 16.1%, (narrow sense 12.2%) based on large-scale population data (Kaplanis et al., 2018).One very recent study suggests it is much lower still (<7%) (Ruby et al., 2018), pointing to assortative mating as the source of resemblance amongst kin." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "text": "\n\nMany factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone.Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases.If such genes exist, their effects were too small to be detected in this study.The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "Life Span\n\nDuring the last decade a variety of twin studies have shown that approximately 25 percent of the variation in life span is caused by genetic differences.This seems to be a rather consistent finding in various Nordic countries in different time periods and even so among other species not living in the wild (Herskind et al., 1996;Iachine et al., 1999;Finch and Tanzi, 1997).their relative magnitude and pattern depend on sex and on the socioeconomic environment experienced by successive birth cohorts.Genetic effects were most pronounced in periods with consciously controlled fertility, suggesting that the genetic disposition primarily affects fertility behavior and motivation for having children.Analyses of fertility motivation in some of the more recent twin cohorts, measured by age at first attempt to have children, supported this interpretation." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "The Height-Life Span Nexus\n\nSeveral observations and lines of experimentation have raised the issue of whether interindividual differences in aging rate are influenced by genes that modulate body size and early-life growth patterns.These include (a) the association between small stature and exceptional longevity in calorically restricted rodents (Yu et al., 1985), methionine-restricted rats (Orentreich et al., 1993), and mutant dwarf mice (Brown-Borg et al., 1996;Miller, 1999); and (b) the association between small body size and longer life span in natural populations of mice (Falconer et al., 1978), flies (Hillesheim and Stearns, 1992), dogs (Li et al., 1996), and, possibly, people (Samaras andStorms, 1992).The correlation in dogs is particularly striking: selective breeding for dogs of different body size has produced breeds varying in size from Chihuahua to Irish wolfhound.These breeds also vary greatly in mean longevity, from approximately 7 to 10.5 years, and the correlation between breed longevity and breed body weight (Miller, 1999) is a remarkable R 2 = 0.56.These differences are genetic and affect stature rather than obesity: no amount of overeating will convert a West Highland white terrier to a St. Bernard.The selective pressures applied were designed to create dogs of specific sizes and temperaments and were not intended to influence aging rate or life span.The clear implication is that the effects on longevity are pleiotropic, i.e., that genes selected for their effect on body size and conformation influenced life span as a side effect.It is of interest to note that the few analyses (Eigenmann et al., 1984(Eigenmann et al., , 1988) ) of the hormonal basis for interbreed differences in body size have shown that the genes in question influence levels of IGF-1, the most likely mediator of the life-span effects in the long-lived df/df and dw/dw mouse mutants.Could it be mere coincidence that long-lived mutant nematode worms (Kimura et al., 1997) also show mutations in genes related to insulin and IGF-1 receptors?" + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nAltogether, the twin and genealogical studies have shown that human lifespan is heritable, but is significantly influenced by non-heritable factors, which may explain why genetic studies of lifespan have proven to be challenging." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nTwin studies have shown that the heritability of lifespan ranges between 0.01 and 0.27 in various European populations (Ljungquist et al., 1998;van den Berg et al., 2017).Large genealogical studies are more powered to address questions FIGURE 1 | Relationship between aging and lifespan variation versus species defining lifespan. (A) Lifespan comparisons within species, measured as mean (50%) or portion of a population living till extended limits of lifespan (90-95%).Differences between populations (orange and green) can identify specific genetic or environmental changes associating with long life.These factors promote viability and often associate with increasing healthspan.Mutant analysis within a particular model organism often encompasses these types of changes as it relates to lifespan. (B) Maximum lifespans recorded for different species (A-E).While lifespan variation within a species is capped to a certain extent, variation between species can range dramatically.Changes to maximum lifespan often are associated with protective mechanisms for genomic and genetic fidelity as well as life history changes as they relate to maturation and reproduction." + } + ], + "c7361625-831a-44a2-b04d-157a49d00c6a": [ + { + "document_id": "c7361625-831a-44a2-b04d-157a49d00c6a", + "text": "\n\nThe genetic component of human lifespan based on twin studies has been estimated to be around 20-30 percent in the normal population [7], but higher in long-lived families [8][9][10].Furthermore, siblings, parents, and offspring of centenarians also live well beyond average [11,12].Lifestyle choices in terms of smoking, alcohol consumption, exercise, or diet does not appear to differ between centenarians and controls [13].Taken together, these findings provide ample evidence that extreme longevity has a genetic component ." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "Introduction\n\nHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006)." + } + ], + "dbf4c446-7c25-470a-9532-a564b8683eef": [ + { + "document_id": "dbf4c446-7c25-470a-9532-a564b8683eef", + "text": "\n\nUnraveling the heritability of human longevity was one of the first problems faced by geneticists.Just over a century ago, Mary Beeton and Karl Pearson [1] described a resemblance among relatives for the duration of life.A short time later, Yule [2] and Fisher [3] proved that the correlation is to be expected if lifespan is influenced by what had recently been termed 'genes' [4].Indeed, a century of correlation studies have established that something on the order of 30-50% of the total variation in human life span is attributable to genetic variation [5].Despite the wealth of diversity, specific genes contributing to this variation have proven notoriously difficult to identify.Sample size and issues of shared environment limit family-based methods such as linkage analysis, where rough genomic positions of important genetic variants are identified by comparing a small number of exceptionally long-lived people in defined pedigrees." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nStudies of mono-and dizygous twins have revealed that the genetic contribution to the variation in human lifespan is about 25-30% [12,13], and is most prominent in families clustered for longevity [14,15].This genetic contribution is mainly apparent after the age of 60 years and seems to increase with age [13,16].Furthermore, human lifespan is a complex trait which is assumed to be determined by many genes with small individual effects [17], although the polygenic architecture still needs to be characterized [18,19].The diverse health features of long-lived families illustrate that different age-related diseases have common determinants and implicate that pathways can be identified that attenuate aging and delay age-related disease.From a genomic perspective, individuals from long-lived families are assumed to be characterized by a decreased prevalence of disease-promoting variants (referred to as disease-susceptibility alleles) and an increased prevalence of variants conferring maintenance of health and protection from disease, when compared to population controls.In the last 5 years, many diseasesusceptibility alleles have been identified (National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) Catalog; http://www.genome.gov/gwastudies/)[20].A first comparison between long-lived individuals, selected from both long-lived families (LLS) and the general population (Leiden 85-plus study), and young controls showed no difference in the distribution or frequency of disease-susceptibility alleles identified in cancer, coronary artery disease and type 2 diabetes [21].The search for lifespan regulating loci -contributing to longevity and population mortality -must therefore extend beyond a focus on disease-susceptibility alleles.We will first discuss the efforts to identify longevity loci by genetics approaches." + } + ] + }, + "data_source": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nAltogether, the twin and genealogical studies have shown that human lifespan is heritable, but is significantly influenced by non-heritable factors, which may explain why genetic studies of lifespan have proven to be challenging." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nAlthough it is known that health and lifespan are heavily influenced by genetics [14], variations in the lifespan of different individuals within the same species seem to be more the result of the accumulation over time of molecular damage that compromises the function of the cells [15].These molecular alterations can occur both at the genetic and epigenetic levels and depend on genetic, environmental, and stochastic factors [16].This complex multifactorial mix determined characteristics, such as longevity and a healthy lifespan, which are central concerns of human existence (Fig. 13.1).This chapter describes different types of tools in genomics used in ageing research and their different applications in clinical scenarios." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "main", + "text": "\n\nOur results show that longevity is partly determined by the predisposition to common diseases and, to an even greater extent, by modifiable risk factors.The genetic architecture of lifespan appears complex and diverse and there appears to be no single genetic elixir of long life." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "section_type": "main", + "text": "Introduction\n\nHuman lifespan is a highly complex trait, the product of myriad factors involving health, lifestyle, genetics, environment, and chance.The extent of the role of genetic variation in human lifespan has been widely debated (van den Berg et al., 2017), with estimates of broad sense heritability ranging from around 25% based on twin studies (Ljungquist et al., 1998;Herskind et al., 1996;McGue et al., 1993) (perhaps over-estimated [Young et al., 2018]) to around 16.1%, (narrow sense 12.2%) based on large-scale population data (Kaplanis et al., 2018).One very recent study suggests it is much lower still (<7%) (Ruby et al., 2018), pointing to assortative mating as the source of resemblance amongst kin." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "section_type": "main", + "text": "\n\nMany factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone.Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases.If such genes exist, their effects were too small to be detected in this study.The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "main", + "text": "\n\nL ongevity is of interest to us all, and philosophers have long speculated on the extent to which it is pre-determined by fate.Here we focus on a narrower question-the extent and nature of its genetic basis and how this inter-relates with that of health and disease traits.In what follows, we shall use longevity as an umbrella term.We shall also more specifically refer to lifespan (the duration of life) and long-livedness (living to extreme old age, usually defined by a threshold, such as 90 years).Up to 25% of the variability in human lifespan has been estimated to be genetic 1 , but genetic variation at only three loci (near APOE, FOXO3A and CHRNA3/5) [2][3][4][5] have so far been demonstrated to be robustly associated with lifespan." + }, + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "section_type": "main", + "text": "GENETICS OF LIFE SPAN IN HUMANS\n\nMost studies of human twins agree that the heritability of life span is less than 50% (45,68).Of particular interest is an ongoing study of aging in Swedish twins that includes a large group of adopted twins who were reared separately.Ljungquist et al. (68) concluded that \"a maximum of one-third the variance in integrated mortality risk is attributable to genetic factors and that almost all of the remaining variance is due to nonshared, individually unique environmental factors. \"Moreover, this heritability declined with age and was negligible after the age of 85 in men and 90 in women." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "The Height-Life Span Nexus\n\nSeveral observations and lines of experimentation have raised the issue of whether interindividual differences in aging rate are influenced by genes that modulate body size and early-life growth patterns.These include (a) the association between small stature and exceptional longevity in calorically restricted rodents (Yu et al., 1985), methionine-restricted rats (Orentreich et al., 1993), and mutant dwarf mice (Brown-Borg et al., 1996;Miller, 1999); and (b) the association between small body size and longer life span in natural populations of mice (Falconer et al., 1978), flies (Hillesheim and Stearns, 1992), dogs (Li et al., 1996), and, possibly, people (Samaras andStorms, 1992).The correlation in dogs is particularly striking: selective breeding for dogs of different body size has produced breeds varying in size from Chihuahua to Irish wolfhound.These breeds also vary greatly in mean longevity, from approximately 7 to 10.5 years, and the correlation between breed longevity and breed body weight (Miller, 1999) is a remarkable R 2 = 0.56.These differences are genetic and affect stature rather than obesity: no amount of overeating will convert a West Highland white terrier to a St. Bernard.The selective pressures applied were designed to create dogs of specific sizes and temperaments and were not intended to influence aging rate or life span.The clear implication is that the effects on longevity are pleiotropic, i.e., that genes selected for their effect on body size and conformation influenced life span as a side effect.It is of interest to note that the few analyses (Eigenmann et al., 1984(Eigenmann et al., , 1988) ) of the hormonal basis for interbreed differences in body size have shown that the genes in question influence levels of IGF-1, the most likely mediator of the life-span effects in the long-lived df/df and dw/dw mouse mutants.Could it be mere coincidence that long-lived mutant nematode worms (Kimura et al., 1997) also show mutations in genes related to insulin and IGF-1 receptors?" + }, + { + "document_id": "1ccb0d11-1c88-4b08-b40d-4039a954745f", + "section_type": "main", + "text": "\n\nHow can lifespan be controlled by a single gene?Two possibilities are, first, that the mutations that extend lifespan are in genes whose products regulate the activity of many other genes and, second, that these genes do not in fact control the rate of ageing." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "dbf4c446-7c25-470a-9532-a564b8683eef", + "section_type": "main", + "text": "\n\nUnraveling the heritability of human longevity was one of the first problems faced by geneticists.Just over a century ago, Mary Beeton and Karl Pearson [1] described a resemblance among relatives for the duration of life.A short time later, Yule [2] and Fisher [3] proved that the correlation is to be expected if lifespan is influenced by what had recently been termed 'genes' [4].Indeed, a century of correlation studies have established that something on the order of 30-50% of the total variation in human life span is attributable to genetic variation [5].Despite the wealth of diversity, specific genes contributing to this variation have proven notoriously difficult to identify.Sample size and issues of shared environment limit family-based methods such as linkage analysis, where rough genomic positions of important genetic variants are identified by comparing a small number of exceptionally long-lived people in defined pedigrees." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "Life Span\n\nDuring the last decade a variety of twin studies have shown that approximately 25 percent of the variation in life span is caused by genetic differences.This seems to be a rather consistent finding in various Nordic countries in different time periods and even so among other species not living in the wild (Herskind et al., 1996;Iachine et al., 1999;Finch and Tanzi, 1997).their relative magnitude and pattern depend on sex and on the socioeconomic environment experienced by successive birth cohorts.Genetic effects were most pronounced in periods with consciously controlled fertility, suggesting that the genetic disposition primarily affects fertility behavior and motivation for having children.Analyses of fertility motivation in some of the more recent twin cohorts, measured by age at first attempt to have children, supported this interpretation." + }, + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "section_type": "main", + "text": "\n\nSince that time, observations across species have shown that life span can be extended by genetic factors.One of the first demonstrations of this entailed the study of recombinant inbred populations of the nematode worm Caenorhabditis elegans by Thomas E. Johnson.Then a postdoc in William (Bill) Wood's lab at the University of Colorado Boulder, Tom and Bill demonstrated that crosses of C. elegans strains did not display the heterosis effect that interfered with many other studies, \"As predicted, we found significant genetic effects on life span as well as other life history traits. \"This finding established a method for evaluating genetic factors that influenced life-span variation.In fact, their measurements of life span of the recombinant inbred strains demonstrated the heritability of life span to be 19%-51% (1).Consistent with theories of the 1970s and 1980s, it was concluded that these genetic factors were a collection of small influences across many genes.This finding was one of the first steps in demonstrating that genetic factors influence aging.As genetic analysis was making great progress in understanding other biological processes, such as developmental programming, the realization that aging could be investigated using the same tools was highly significant." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "abstract", + "text": "\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nTwin studies have shown that the heritability of lifespan ranges between 0.01 and 0.27 in various European populations (Ljungquist et al., 1998;van den Berg et al., 2017).Large genealogical studies are more powered to address questions FIGURE 1 | Relationship between aging and lifespan variation versus species defining lifespan. (A) Lifespan comparisons within species, measured as mean (50%) or portion of a population living till extended limits of lifespan (90-95%).Differences between populations (orange and green) can identify specific genetic or environmental changes associating with long life.These factors promote viability and often associate with increasing healthspan.Mutant analysis within a particular model organism often encompasses these types of changes as it relates to lifespan. (B) Maximum lifespans recorded for different species (A-E).While lifespan variation within a species is capped to a certain extent, variation between species can range dramatically.Changes to maximum lifespan often are associated with protective mechanisms for genomic and genetic fidelity as well as life history changes as they relate to maturation and reproduction." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nStudies of mono-and dizygous twins have revealed that the genetic contribution to the variation in human lifespan is about 25-30% [12,13], and is most prominent in families clustered for longevity [14,15].This genetic contribution is mainly apparent after the age of 60 years and seems to increase with age [13,16].Furthermore, human lifespan is a complex trait which is assumed to be determined by many genes with small individual effects [17], although the polygenic architecture still needs to be characterized [18,19].The diverse health features of long-lived families illustrate that different age-related diseases have common determinants and implicate that pathways can be identified that attenuate aging and delay age-related disease.From a genomic perspective, individuals from long-lived families are assumed to be characterized by a decreased prevalence of disease-promoting variants (referred to as disease-susceptibility alleles) and an increased prevalence of variants conferring maintenance of health and protection from disease, when compared to population controls.In the last 5 years, many diseasesusceptibility alleles have been identified (National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) Catalog; http://www.genome.gov/gwastudies/)[20].A first comparison between long-lived individuals, selected from both long-lived families (LLS) and the general population (Leiden 85-plus study), and young controls showed no difference in the distribution or frequency of disease-susceptibility alleles identified in cancer, coronary artery disease and type 2 diabetes [21].The search for lifespan regulating loci -contributing to longevity and population mortality -must therefore extend beyond a focus on disease-susceptibility alleles.We will first discuss the efforts to identify longevity loci by genetics approaches." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "section_type": "main", + "text": "Introduction\n\nHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006)." + }, + { + "document_id": "c7361625-831a-44a2-b04d-157a49d00c6a", + "section_type": "main", + "text": "\n\nThe genetic component of human lifespan based on twin studies has been estimated to be around 20-30 percent in the normal population [7], but higher in long-lived families [8][9][10].Furthermore, siblings, parents, and offspring of centenarians also live well beyond average [11,12].Lifestyle choices in terms of smoking, alcohol consumption, exercise, or diet does not appear to differ between centenarians and controls [13].Taken together, these findings provide ample evidence that extreme longevity has a genetic component ." + }, + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "section_type": "main", + "text": "Introduction\n\nWorldwide human populations have shown an increase in mean life expectancy in the past two centuries (Oeppen & Vaupel, 2002).This is mainly because of environmental factors such as improved hygiene, nutrition, and health care.The large variation in healthy lifespan among the elderly has prompted research into the determinants of aging and lifespan regulation.The genetic contribution to human lifespan variation was estimated at 25-30% in twin studies (Gudmundsson et al., 2000;Skytthe et al., 2003;Hjelmborg et al., 2006).The most prominent genetic influence is observed in families in which the capacity to attain a long lifespan clusters (Perls et al., 2000;Schoenmaker et al., 2006).Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al., 2008;Terry et al., 2008), raising the question whether protective mechanisms against disease exist in long-lived subjects." + }, + { + "document_id": "593b752f-f448-47be-8b83-13bc5e9eb0d4", + "section_type": "main", + "text": "\n\nAge at death in adulthood has a moderate genetic component overall, with a heritability of approximately 25% (Murabito et al., 2012).Heritability of longevity increases with age, with a negligible genetic contribution to survival up to approximately 60 years of age, after which an increasing genetic component to survival is observed (Brooks-Wilson, 2013;Christensen et al., 2006).Most genetic studies of aging have focused on long-lived individuals, typically defined as centenarians 100 years or older, who may have had exceptional survival due to medical interventions (Murabito et al., 2012).A number of genetic associations with exceptional longevity have been made (Atzmon et al., 2006;Bojesen and Nordestgaard, 2008;Hurme et al., 2005;Kuningas et al., 2007;Melzer et al., 2007;Pawlikowska et al., 2009;Sanders et al., 2010;Suh et al., 2008;Willcox et al., 2008), with only markers at APOE and FOXO3A being well replicated (Murabito et al., 2012).Overall, the results of genetic and epidemiological longevity studies suggest aging is a complex trait and that achievement of exceptional longevity may not best capture the genetics of resistance to or delay of age-associated disease (Christensen et al., 2006)." + }, + { + "document_id": "c8fbb24d-0a72-4a45-a552-6cd98a4a25a2", + "section_type": "main", + "text": "Translational\n\nA LTHOUGH there is much debate about the processes driving human aging, there is little doubt that genetic influences play a significant role (1).Humans clearly live very much longer than the currently favored laboratory models of aging, and such interspecies differences in reproductively 'fit' life span must have an inherited genetic foundation.Within human populations, environmental and behavioral exposures are important but at least a quarter of life expectancy variation in twin or family studies is attributable to inherited genetic or epigenetic factors (2).Age-related conditions such as type 2 diabetes, myocardial infarction, common cancers, and Alzheimer's disease (AD) typically have onsets after the fourth decade of life; \"successful\" agers delay these onsets until relatively late in life (3).Many aging traits and diseases show moderate heritability, including cardiovascular disease (CVD) (4) and impaired physical functioning (5), independent of known environmental risk factors." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY\n\nHeritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE ε2), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nThe recent emergence of the UK Biobank has significantly enhanced research on the genetics of lifespan.The most recent effort using parental lifespan data from this databank, as well as several additional studies in the LifeGen initiative, has resulted in the identification of 12 loci that passed threshold for genomewide significance (5 * 10 −8 ).Many of the loci have previously been associated with age-related diseases, including cardiometabolic, autoimmune and neuropsychiatric diseases -all underlying major death causes -which likely explains their association with lifespan in this study (Timmers et al., 2019)." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Influence of Genetic Factors in Ageing and Lifespan\n\nAgeing is defined as the decline of physiological functions in several tissues and organs inducing an increasing probability of death [17].The understanding of genetic factors involved in ageing has been limited due to the complexity of this process and the heterogeneity among individuals and even among tissues [18][19][20].Tissue cells adopt a senescent phenotype as a consequence of multiple intrinsic, extrinsic, and stochastic factors [21].The combination of these genetic factors is related to longevity and healthy ageing [22].Although this decline is somewhat predictable, some individuals show a much slower decline and get to live past the age of 100.Studies in these individuals showed polymorphisms in some genes which are associated with long life, such as APOE and FOXO3.However, these associations have not been consistent across different populations, suggesting that ageing is rather polygenic [23]." + }, + { + "document_id": "da4a9500-831f-48ab-acea-5ec7097276ed", + "section_type": "main", + "text": "\n\nStudies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways." + }, + { + "document_id": "e4773b3b-814d-4306-8250-59dc03f09bc2", + "section_type": "main", + "text": "\n\nLarge differences in species maximum lifespan potential [MLSP] must ultimately be genetically encoded; however, if a specific ''lifespan program'' existed, one might expect that genetic revertants of such a program could be identified to enable immortality.To date, no such observation has been made.So while it is highly unlikely that age of death is programmed, genetic regulation of the many pathways that contribute to survival of the individual (e.g., resistance to stress, damage eradication, and/or somatic repair), as well as genetic regulation of the metabolic pathways that inflict age-related damage, is likely to be directly involved in organismal longevity (Gems and Partridge 2013)." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "section_type": "main", + "text": "\n\nThe DNA of over 500,000 people was read to reveal the specific 'genetic fingerprints' of each participant.Then, after asking each of the participants how long both of their parents had lived, Timmers et al. pinpointed 12 DNA regions that affect lifespan.Five of these regions were new and had not been linked to lifespan before.Across the twelve as a whole several were known to be involved in Alzheimer's disease, smoking-related cancer or heart disease.Looking at the entire genome, Timmers et al. could then predict a lifespan score for each individual, and when they sorted participants into ten groups based on these scores they found that top group lived five years longer than the bottom, on average." + }, + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "section_type": "main", + "text": "\n\nT he average human life expectancy has been increasing for centuries 1 .Based on twin studies, the heritability of human lifespan has been estimated to be ~25%, although this estimate differs among studies 2 .On the other hand, the heritability of lifespan based on the correlation of the mid-parent (i.e., the average of the father and mother) and offspring difference between age at death and expected lifespan was estimated to be 12% 3 .A recent study has indicated that the different heritability estimates may be inflated due to assortative mating, leaving a true heritability that is below 10% 4 .The heritability of lifespan, estimated using the sibling relative risk, increases with age 5 and is assumed to be enriched in long-lived families, particularly when belonging to the 10% longest-lived of their generation 6 .To identify genetic associations with human lifespan, several genome-wide association (GWA) studies have been performed [7][8][9][10][11][12][13][14][15][16][17][18][19][20] .These studies have used a discrete (i.e., older cases versus younger controls) or a continuous phenotype (such as age at death of individuals or their parents).The selection of cases for the studies using a discrete longevity phenotype has been based on the survival to ages above 90 or 100 years or belonging to the top 10% or 1% of survivors in a population.Studies defining cases using a discrete longevity phenotype often need to rely on controls from more contemporary birth cohorts, because all others from the case birth cohorts have died before sample collection.Previous GWA studies have identified several genetic variants, but the only locus that has shown genome-wide significance (P ≤ 5 × 10 −8 ) in multiple independent meta-analyses of GWA studies is apolipoprotein E (APOE) 21 , where the ApoE ε4 variant is associated with lower odds of being a long-lived case." + }, + { + "document_id": "e4773b3b-814d-4306-8250-59dc03f09bc2", + "section_type": "main", + "text": "\n\nAging and longevity research has relied extensively on a battery of commonly used and relatively short-lived eukaryote model organisms, namely yeast, worms, flies, and fish, as well as mice and rats, to explore both genetic and environmental determinants of lifespan.While these short-lived models have each yielded a number of fascinating findings and insights into hypotheses surrounding extended lifespan and healthspan, they may also have constrained this complex, multifactorial field to areas in which they are best suited, most notably short-term intervention studies and genetic manipulations.Studies based upon these organisms revealed that changes in even a single gene (e.g., age-1, phosphatidylinositol 3 kinase) can extend lifespan of Caenorhabditis elegans (Friedman and Johnson 1988).Similar lifespan extension effects are evident in flies and mice when the insulin/IGF, gastric hormone, and the Nrf2/skn-1 detoxification/xenobiotic pathways are genetically manipulated (Kenyon et al. 1993;Brown-Borg et al. 1996;Morris et al. 1996;Clancy et al. 2001;An and Blackwell 2003;Sykiotis and Bohmann 2008;Selman and Withers 2011;Ziv and Hu 2011).Furthermore, various types of dietary restrictions, whether limiting access to calories or amino acids, generally have a conserved effect of enhancing longevity across model systems (McCay et al. 1935;Klass 1977;Weindruch and Walford 1982;Jiang 2000;Selman and Withers 2011;McIsaac et al. 2016), although exceptions do exist (Liao et al. 2010).Collectively, these data support the premise that longevity can be modulated, likely through the regulation of nutrient signaling and stress response, which in turn impacts development, growth, reproduction, and survival.Strikingly, monozygotic human twins, as well as genetically identical individuals of these animal models (e.g., C57BL/6 mice), even when housed in the same environment and fed the same diet do not all have the same lifespans, suggesting that stochastic factors and epigenetic drift influence the hazard rate (i.e., the risk of death as it changes over a lifespan) and subsequent mortality (Finch and Kirkwood 2000;Herndon et al. 2002;Fraga et al. 2005)." + }, + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "section_type": "main", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "main", + "text": "\n\nGenes do not drive the aging process but by governing the levels of excess physiological capacity, repair, and turnover they indirectly determine potential longevity.There are no genes that specifically drive longevity but there are genes that govern biological processes that increase the likelihood of survival to reproductive maturity.The variations in excess physiological capacity, repair, and turnover accounts for the variations found in longevity both within and between species." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "section_type": "main", + "text": "\n\nAgeing is complex and takes a long time to study -a lifetime in fact.This makes it difficult to discern its causes, among the countless possibilities based on an individual's genes, behaviour or environment.While thousands of regions in an individual's genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle.Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nsuch as to what extent non-additive genetic variance contributes to the heritability of lifespan.Thus, in more than 3 million pairs of relatives, Kaplanis et al. (2018) found that the additive component of lifespan's heritability was 0.16 (comparable to twin studies), while there was only a mild effect of the non-additive component of heritability (∼0.04).Ruby et al. (2018) using an impressive dataset consisting of hundreds of millions of historical individuals showed a similar heritability of lifespan.The study on the heritability of \"longevity\" performed in twins by Ljungquist et al. (1998) found that the heritability of longevity was higher in women and increased with advancing age.Some of the most interesting individuals that may shed reveal secrets of longevity originate from multigenerational, longevity-enriched families, since such families have propensity to be long-lived, but also seem to evade age-related morbidity.Several genealogical studies of long-lived families evidenced that parental longevity could be considered a proxy for lifespan.Long-lived parents have a high probability to beget long-lived offspring, which gives an indication that longevity is indeed heritable (van den Berg et al., 2017).Notably, members of longlived families have an interesting phenotype beyond extended lifespan, as they seem to be escaping or delaying age-related disease and show a compression of late life morbidity (extended healthspan).Unraveling the genetics of these individuals might help identifying novel mechanisms involved in healthy aging that can subsequently be targeted by therapeutic interventions.An important drawback of longevity research is the arbitrary age thresholds that often were used to signify an extreme age (Baghdadi et al., 2020).In the pre-GWAS era, the age-thresholds used to define longevity were relatively low (i.e., reaching an age above 80 or 85 years) and the sample size was limited.van den Berg et al. (2019) used two independent multi-generational genealogical datasets to determine the most optimal definition of longevity.They found that the strongest heritable component of longevity is present in individuals belonging to the top 10% survivors of their birth cohort with equally long-lived family members (reviewed in Baghdadi et al., 2020)." + }, + { + "document_id": "3c78c2be-0bd2-4954-bb47-8b48f6125ed7", + "section_type": "main", + "text": "\n\nNotably, numerous novel determinants of chronological life span were identified in all three competitive-survival screens (Fabrizio et al. 2010;Gresham et al. 2011;Matecic et al. 2010) as well as the candidate gene approach reported by Burtner et al. (2011).This suggests that many genes involved in chronological aging have yet to be identified.The screen of each individual strain from the deletion collection for increased chronological life span that is currently underway is anticipated to identify many of these unknown genes." + }, + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "section_type": "main", + "text": "\n\nIt is also likely that environmental factors and possibly the genetic ancestry may influence the likelihood of an individual to live long ages directly or by interacting with the genetic background.The NECS has shown that the chance of male and female siblings of centenarians to live past 100 can be 8 and 17 times higher than the risk in the general population (Perls et al., 2002).Consistent with this observation, our data suggest that the genetic contribution increases with older and older ages as the limit of lifespan is approached (Sebastiani et al., 2012).The male supercentenarian included in this study had strong longevity in his family.Although we do not have information about the family history of the female supercentenarian, she has living offspring who are approaching their nineties in good health and are currently enrolled in the NECS.The heterogeneity of the results herein suggest that sequencing additional exceptionally old individuals of different genetic ancestry and possibly their family members will provide the critical information to understand roles of common and rare genetic determinants of exceptional longevity and healthspan." + }, + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "section_type": "main", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "LONGEVITY AND AGING -SEPARATE METRICS OF EXTENT AND QUALITY\n\nThe drive to understand why we have a limited license in life has permeated scientific and artistic thought for millennia.Although lifespan has obvious heritable components, the effect of environmental factors and extrinsic mortality factors shape a complex scenario for which clear answers of the regulation of longevity have been difficult to distill.With the discovery of genetic factors underlying aging in experimental laboratory models, forays into the genetic regulation of these properties have rapidly expanded, uncovering conserved mechanisms across diverse metazoa that influence expression of aging phenotypes and lifespan.Yet, the story gets muddled in that these factors are often quite pleiotropic, having broad roles in normal development and physiology of organisms.To date there has not been a singular defining mechanism or factor specifying how and why we age." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "Longevity Genes-A Special Case\n\nDemographers are fascinated by the possibility that one or more genes might determine the rate of decline in multiple organ systems.Several such genes have been identified in other species (Vaupel et al., 1998).These genes are sometimes called gerontogenes or longevity genes.The discovery of one or more genes that act as aging \"clocks\" in humans would be a major breakthrough for genetics.However, the mere existence of such genes would not have a major effect on demographic research.For example, a mutation in a longevity gene that was present in 0.1 percent of the population would still be rare (probably less than 1 percent) among centenarians. 19Such a genotype would not explain much about survival to the oldest ages.Therefore, in order to be important for demographic research, there would have to be common polymorphisms associated with large differences in survival.Vaupel has estimated that there could be hundreds of genotypes with frequencies of 5-10 percent that lower death rates by 5-10 percent (Vaupel, personal communication)." + }, + { + "document_id": "1ccb0d11-1c88-4b08-b40d-4039a954745f", + "section_type": "main", + "text": "\n\nAnother major challenge is to uncover the genes and processes that determine the differences in lifespan among animal species.Animal lifespans vary to a remarkable degree, and can evolve rapidly.For example, the common ancestors of Homo sapiens and chimpanzees walked the Earth only some 5.4 million years ago, yet our maximum lifespan is twice that of our closest living relative (w110 years versus w59 years).Do the genes and processes that have been the focus of model organism work (e.g.IIS and cellular detoxification) also specify species differences in ageing?Do they also control the remarkable phenotypic plasticity of lifespan seen in, for instance, social insects?Answering these questions will require an approach analogous to that used in understanding the evolution of differences in development that lead to differences in anatomy (i.e.evolutionary developmental biology, or evodevo).One might naturally refer to such an approach as evolutionary gerontology (or evo-gero) (Box 3)." + } + ], + "document_id": "5AE03C65B85643330DE58348F4946E8A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "genetics", + "lifespan", + "heritability", + "environmental&factors", + "twin&studies", + "genealogical&studies", + "longevity", + "genomic&research", + "biomarker&research", + "aging" + ], + "metadata": [ + { + "object": "AGE are an important factor for cardiac aging and fibrosis, whereas the receptor for AGE and TGF-beta/Smad signaling pathway might be involved in the AGE-induced cardiac aging process.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab49862" + }, + { + "object": "Both normal-expression and over-expression of the CG9940 resulted in positive influences on the adaptation of cardiac functions, mobility, and lifespan to exercise in aging Drosophila. Exercise slowed age-related decline of cardiac function, mobility and extent of lifespan in flies, while lower expression of CG9940 led to negative impacts on the adaptation of mobility and lifespan to exercise in Drosophila.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab107731" + }, + { + "object": "Expression of HDAC4 in hippocampus Affy probe set 10356653, UTHSC BXD Aged Hippocampus Affy Mouse Gene 1.0 ST Jun15 Exon Level RMA has a strong negative correlation with age of animal BXD. Like many other age-linked traits, genetic variance of expression maps to Chr 7 at about 87 Mb also see Smc3, top positive age-associated exon probe set in hippocampus. Rupert Overall, Gerd Kempermann, Lu Lu, and Rob Williams Aug 2019 note by RWW", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1771" + }, + { + "object": "Based on a cumulative risk of 0.55% to age 35 for BRCA1 mutation carriers and of 0.56% to age 45 for BRCA2 mutation carriers, we recommend bilateral salpingo-oophorectomy before age 40, but by age 35, for women with a BRCA1 mutation and by age 45 for those with a BRCA2 mutation to maximize prevention and to minimize adverse effects.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab95128" + }, + { + "object": "Study detected age-related differences in the therapeutic effect of calcium-channel blockers, in association with a commonly occurring genetic variant in the COMT gene; proposed a relevant role of estrogen and catecholamines in the age-specific pathogenesis of hypertension and underline the need for individualized therapy approaches taking age into account.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab740177" + }, + { + "object": "Study of genetic risk of prevalent hrHPV infections in Nigerian women found significant associations with SNPs on ribosomal protein gene S19 RPS19 and Thymidylate Synthase gene TYMS, in an allelic model. This risk remained significant, after adjusting for age, body mass index, smoking, age at menarche, age at sexual debut, lifetime total number of sexual partners and the total number of pregnancies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab745428" + }, + { + "object": "4E-BP determines lifespan in the context of temperature changes, revealing a genetic mechanism for cold-induced longevity in this model organism. Our results suggest that the 4E-BP pathway, chiefly thought of as a nutrient sensor, may represent a master metabolic switch responding to diverse environmental factors", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab10515" + }, + { + "object": "Results showed that median age, menarche age, childbearing age, number of children, menopause age, and body-mass indexes were similar in both HER-2 pos and neg groups. # of involved lymph nodes and HER-2 status found to be prog. factors for survival.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab665374" + }, + { + "object": "Women with PAPP-A </=10th percentile in the first trimester are more likely to have an small-for gestational age infant at all gestational ages. PAPP-A >/=90th percentile is protective against small for gestational age, and is associated with an increased risk of large for gestational age for infants born after 32 weeks gestation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab1019190" + }, + { + "object": "Top exon level covariate of age in the hippocampus of the BXD family r of 0.59, n = 229 using \tUTHSC BXD Aged Hippocampus Affy Mouse Gene 1.0 ST Jun15 Exon Level RMA and record ID Record ID 10463979. Strongly bimodal expression of this exon probe set genetic effect with nearly +100 day shift in age of those with high D allele expression that maps to Chr 7 at the lncRNA gene Gm32647 lethality associated and ODZ4 no cis effect at all. Show to Rupert Overall and Gerd Kempermann.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab6050" + } + ], + "question": "Is lifespan determined by genetics?", + "subquestions": null, + "task_id": "5AE03C65B85643330DE58348F4946E8A", + "usage": { + "chatgpt": 8198, + "gpt-4": 4730, + "gpt-4-turbo-preview": 3782 + }, + "user_id": 2 + }, + "document_id": "5AE03C65B85643330DE58348F4946E8A", + "task_id": "5AE03C65B85643330DE58348F4946E8A" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/03.json b/gnqa/paper1_eval/src/data/responses/aging/experts/03.json new file mode 100644 index 0000000..09f95d2 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/03.json @@ -0,0 +1,390 @@ +{ + "created_at": "2024-05-31T20:36:11.762580Z", + "data": { + "amplify": false, + "answer": "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "context": { + "29e232a4-a580-411d-83a3-7ff6a4e8f0ad": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "\n\nOur result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes." + } + ], + "6e570a0b-a876-4263-b32f-cee85088756d": [ + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "text": "\n\nThere are two major factors that underlie these alarming projections.The first is T2D is associated with age, and Western populations are aging rapidly.The second major explanation is our lifestyles have changed dramatically in recent years.Epidemiological studies have identified strong T2D risk relationships for obesity, sedentary behavior [2][3][4], and diets rich in energy [5], processed carbohydrates [6], and animal fats [7].Collectively, these lifestyle factors impede the actions of insulin and raise hepatic glucose production, which can result in the diminution of endogenous insulin production and T2D.The strongest evidence for a causal relationship between adverse lifestyle behaviors and T2D comes from randomized controlled trials that show intensive lifestyle interventions involving structured exercise regimes which promote habitual physical activity (PA) and have a major beneficial impact on diabetes incidence in high-risk individuals [8,9]." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "text": "\n\nEpidemiological studies examining the associations between lifestyle behaviors and diabetes risk have reached similar conclusions as the clinical trials described above.For example, the 14-year follow-up University of Pennsylvania Alumni Health Study [52] (n = 5,990 men aged 39-68 years) showed PA (leisure time physical activity [LTPA] expressed in kcal expended per week through walking, stair climbing, and sports) was inversely associated with the incidence of T2D.Incidence rates declined as energy expenditure rose from 500 through 3,500 kcal/week.The age-adjusted relative risk ratio (RR) of T2D was reduced by about 6% for each 500 kcal increment increase in PA energy expenditure." + } + ], + "71172700-7bcc-42f5-9354-d8e9290e8743": [ + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "\n\nOverall, results were similar in analyses restricted to diabetes mellitus identified at baseline only, although the confidence interval included 1.These results suggest that diabetes mellitus is related to risk of AD in old age.These findings are consistent with the results of 2 large longitudinal cohort studies. 5,6In one study, 5 diabetes mellitus doubled the risk of AD during 2 years of follow-up in a sample of more than 6000 older persons from a defined cohort.The other study, 6 using data from about 2500 Japanese American men, found a similar result: diabetes mellitus approximately doubled the risk of AD.In contrast, 2 other longitudinal studies 7,8 did not demonstrate a significant association between diabetes mellitus and incident AD, but in both, the results were in the direction of increased risk.Some, [9][10][11] but not all, 12 previous studies found that diabetes mellitus was related to change in cognitive function.One factor that may contribute to variability from study to study is that diabetes mellitus may be related to decline in some cognitive systems but not others.4][15] Although diabetes mellitus was related to level of global cognition and multiple cognitive domains at baseline, we found that diabetes mellitus was only related to decline in perceptual speed.The one study 12 that did not find a relation between diabetes mellitus and cognitive decline did not include a measure of perceptual speed." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "COMMENT\n\nIn a cohort of more than 800 older persons, we found that diabetes mellitus sometime in the study was associated with an increased risk of developing AD during a mean of 5.5 years of observation.The risk of incident AD was 65% higher in those with diabetes mellitus than in those without it." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "\n\nIn summary, these findings suggest that diabetes mellitus is associated with AD and decline in cognitive function in older persons.December 12, 2003." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "DIABETES MELLITUS AND RISK OF AD\n\nDuring the follow-up evaluations, 151 persons developed AD, of whom 31 had diabetes mellitus.In a proportional hazards model adjusted for age, sex, and educational level, there was a 65% increase in the risk of developing AD in those with diabetes mellitus compared with those without diabetes mellitus (hazard ratio, 1.65; 95% confidence interval, 1.10-2.47).The cumulative hazard of AD over time, adjusted for age, sex, and educational level, is shown graphically in Figure 1 for typical participants with and without diabetes mellitus.Similar results were found in analyses with diabetes mellitus identified at baseline only (hazard ratio, 1.53; 95% confidence interval, 0.96-2.45)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAge. Age is another factor that has a considerable effect on outcomes in obesity and T2DM research.In humans, body weight increases with age and peaks at ~55 years in both men and women.Ageing per se is associated with a redistribution of both the fat-free mass and the fat mass, with the latter increase starting at ~30 years of age 129 .Intramuscular and intrahepatic fat are particularly increased in older persons, and this increase has been linked to insulin resistance 130 .Partially on the basis of these changes, ageing has been proposed to be an independent determinant of glucose tolerance, which progressively worsens with age 131,132 ." + } + ], + "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a": [ + { + "document_id": "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a", + "text": "\n\nAge also plays a vital role in the onset of diabetes (Cowie & Eberhardt, 1995).In south-east Asia almost 97% diabetic patients are 40 years old or more (IDF Atlas, 2017).In Bangladesh, the reported age of diabetes is ≥40 years in 71% urban and 85% rural female, while in the case of male the proportion is 85.5% urban and 86.5% in rural population (IDF Atlas, 2017).The current study also pinpointed an exponential increase in the risk of onset of T2DM with the increase of age when 40 years was chosen as the reference (Table S4)." + }, + { + "document_id": "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a", + "text": "\n\nWhether age and stress variables are risk factors for type 2 diabetes incidence was assessed by multivariate logistic regression (Table S4).Subjects in the age groups of (40-60) and >60 years had 1.78× (p = .005)and 3.19× (p = .006)greater risk for type 2 diabetes respectively than group of <40 years.Overall, patients under stressful condition are more likely to develop T2DM than that of nonstressed respondent (p = .000).Moreover, when stress is divided into two groups-low stress and high stress, we found that both males (p = .000)and females (p = .000)with high stress were at high risk of diabetes mellitus, whereas the association between low stress and T2DM incidence was significant only among males (Male: p = .002;Female: p = .115).The distribution and association of the genotypes, age, and stress with T2DM have been summarized in Table 3 and Figure 3.There was no difference in T2DM incidence between CT (p = .030)and TT/CC (p = .034)genotype containing people who were in age group of 40-60 years (Table 3).In contrast, people who were more than 60 years old with CT genotype (OR = 4.636, p = .029)were more prone to T2DM than that of TT/CC genotype (OR = 3.714, p = .007)subjects (Table 3)." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Research Gaps\n\nThere is a clear correlation of environmental influences to diabetes risk.Yet, the assembled experts agreed that hypothesis-driven research is needed to define direct causal relationships between specific environmental factors and pathophysiologies leading to diabetes.Research efforts need to address environmental etiologies of type 1 diabetes and determine their relative contribution to onset of autoimmunity and progression to symptomatic disease.Whether there is a direct causal role of the intestinal microbiota in pathogenesis of type 1 and type 2 diabetes and response to therapies needs to be determined.Public health interventions that successfully reduce the levels of consumption of energy-dense foods and/or reduce sedentary time and increase time spent in physical activity need to be evaluated to determine whether they can reduce type 2 diabetes incidence at a population level." + } + ], + "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6": [ + { + "document_id": "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6", + "text": "\n\nIn sum, it is clear that multiple risk factors are involved in diabetes-associated cognitive decrements as well as in dementia in relation to diabetes 38 .On the basis of our assessment of the literature, it is also clear that there are still substantial knowledge gaps on how the risk factors interconnect, how the risk factors translate to potentially modifiable mechanisms and which genetic factors are involved." + } + ], + "b21bbbce-b53f-416b-8378-b635f4270ace": [ + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\n\nThe aim of this study was to investigate the association between age at natural menopause and risk of developing type 2 diabetes, and to assess whether this association is independent of potential intermediate risk factors for type 2 diabetes.Furthermore, we examined the role of endogenous sex hormone levels in the association between age at natural menopause and type 2 diabetes." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\n\nAims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\nAims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens.Results During a median follow-up of 9.2 years, we identified 348 individuals with incident type 2 diabetes.After adjustment for confounders, HRs for type 2 diabetes were 3.7 (95% CI 1.8, 7.5), 2.4 (95% CI 1.3, 4.3) and 1.60 (95% CI 1.0, 2.8) for women with premature, early and normal menopause, respectively, relative to those with late menopause (ptrend <0.001).The HR for type 2 diabetes per 1 year older at menopause was 0.96 (95% CI 0.94, 0.98).Further adjustment for BMI, glycaemic traits, metabolic risk factors, C-reactive protein, endogenous sex hormone levels or shared genetic factors did not affect this association.Conclusions/interpretation Early onset of natural menopause is an independent marker for type 2 diabetes in postmenopausal women." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\n\nassociation and explore whether the timing of natural menopause can add value to diabetes prediction and prevention." + } + ], + "d1449eee-d4ec-4886-87d1-835fb54a5f56": [ + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\n\nAlthough drawing of definitive conclusions is difficult from these observational studies, their results suggest that young-onset type 2 diabetes is associated with a much more frequent occurrence of adverse macrovascular and microvascular outcomes and a more rapidly progressing severity of complications than is seen in type 1 diabetes or later-onset type 2 diabetes." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\n\nIn a study of the age-specific incidence of type 2 diabetes in the UK (a retrospective cohort study of patients with newly diagnosed type 2 diabetes between 1990 and 2010), the investigators reported a substantial increase in the proportion of people aged 40 years or younger at diagnosis" + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\nThe prevalence of type 2 diabetes in adolescents and young adults is dramatically increasing.Similar to older-onset type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle.Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications.Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease.Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype, leading to premature development of complications, with adverse effects on quality of life and unfavourable effects on long-term outcomes, raising the possibility of a future public health catastrophe.In this Review, we describe the epidemiology and existing knowledge regarding pathophysiology, risk factors, complications, and management of type 2 diabetes in adolescents and young adults." + } + ], + "f53cd4d6-2d42-47e1-b58f-5bf8f2d65ef5": [ + { + "document_id": "f53cd4d6-2d42-47e1-b58f-5bf8f2d65ef5", + "text": "\n\nThe biological processes linking aging and disease risk are poorly understood.Still, aging is considered to date as one of the main factors responsible for several complex diseases including cancer, cardiovascular diseases, and diabetes." + } + ] + }, + "data_source": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "section_type": "main", + "text": "\n\nOur result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes." + }, + { + "document_id": "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a", + "section_type": "main", + "text": "\n\nAge also plays a vital role in the onset of diabetes (Cowie & Eberhardt, 1995).In south-east Asia almost 97% diabetic patients are 40 years old or more (IDF Atlas, 2017).In Bangladesh, the reported age of diabetes is ≥40 years in 71% urban and 85% rural female, while in the case of male the proportion is 85.5% urban and 86.5% in rural population (IDF Atlas, 2017).The current study also pinpointed an exponential increase in the risk of onset of T2DM with the increase of age when 40 years was chosen as the reference (Table S4)." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "section_type": "main", + "text": "\n\nOverall, results were similar in analyses restricted to diabetes mellitus identified at baseline only, although the confidence interval included 1.These results suggest that diabetes mellitus is related to risk of AD in old age.These findings are consistent with the results of 2 large longitudinal cohort studies. 5,6In one study, 5 diabetes mellitus doubled the risk of AD during 2 years of follow-up in a sample of more than 6000 older persons from a defined cohort.The other study, 6 using data from about 2500 Japanese American men, found a similar result: diabetes mellitus approximately doubled the risk of AD.In contrast, 2 other longitudinal studies 7,8 did not demonstrate a significant association between diabetes mellitus and incident AD, but in both, the results were in the direction of increased risk.Some, [9][10][11] but not all, 12 previous studies found that diabetes mellitus was related to change in cognitive function.One factor that may contribute to variability from study to study is that diabetes mellitus may be related to decline in some cognitive systems but not others.4][15] Although diabetes mellitus was related to level of global cognition and multiple cognitive domains at baseline, we found that diabetes mellitus was only related to decline in perceptual speed.The one study 12 that did not find a relation between diabetes mellitus and cognitive decline did not include a measure of perceptual speed." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "section_type": "main", + "text": "COMMENT\n\nIn a cohort of more than 800 older persons, we found that diabetes mellitus sometime in the study was associated with an increased risk of developing AD during a mean of 5.5 years of observation.The risk of incident AD was 65% higher in those with diabetes mellitus than in those without it." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "section_type": "main", + "text": "\n\nIn summary, these findings suggest that diabetes mellitus is associated with AD and decline in cognitive function in older persons.December 12, 2003." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "section_type": "main", + "text": "\n\nThe aim of this study was to investigate the association between age at natural menopause and risk of developing type 2 diabetes, and to assess whether this association is independent of potential intermediate risk factors for type 2 diabetes.Furthermore, we examined the role of endogenous sex hormone levels in the association between age at natural menopause and type 2 diabetes." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "section_type": "main", + "text": "\n\nThere are two major factors that underlie these alarming projections.The first is T2D is associated with age, and Western populations are aging rapidly.The second major explanation is our lifestyles have changed dramatically in recent years.Epidemiological studies have identified strong T2D risk relationships for obesity, sedentary behavior [2][3][4], and diets rich in energy [5], processed carbohydrates [6], and animal fats [7].Collectively, these lifestyle factors impede the actions of insulin and raise hepatic glucose production, which can result in the diminution of endogenous insulin production and T2D.The strongest evidence for a causal relationship between adverse lifestyle behaviors and T2D comes from randomized controlled trials that show intensive lifestyle interventions involving structured exercise regimes which promote habitual physical activity (PA) and have a major beneficial impact on diabetes incidence in high-risk individuals [8,9]." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "Research Gaps\n\nThere is a clear correlation of environmental influences to diabetes risk.Yet, the assembled experts agreed that hypothesis-driven research is needed to define direct causal relationships between specific environmental factors and pathophysiologies leading to diabetes.Research efforts need to address environmental etiologies of type 1 diabetes and determine their relative contribution to onset of autoimmunity and progression to symptomatic disease.Whether there is a direct causal role of the intestinal microbiota in pathogenesis of type 1 and type 2 diabetes and response to therapies needs to be determined.Public health interventions that successfully reduce the levels of consumption of energy-dense foods and/or reduce sedentary time and increase time spent in physical activity need to be evaluated to determine whether they can reduce type 2 diabetes incidence at a population level." + }, + { + "document_id": "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a", + "section_type": "main", + "text": "\n\nWhether age and stress variables are risk factors for type 2 diabetes incidence was assessed by multivariate logistic regression (Table S4).Subjects in the age groups of (40-60) and >60 years had 1.78× (p = .005)and 3.19× (p = .006)greater risk for type 2 diabetes respectively than group of <40 years.Overall, patients under stressful condition are more likely to develop T2DM than that of nonstressed respondent (p = .000).Moreover, when stress is divided into two groups-low stress and high stress, we found that both males (p = .000)and females (p = .000)with high stress were at high risk of diabetes mellitus, whereas the association between low stress and T2DM incidence was significant only among males (Male: p = .002;Female: p = .115).The distribution and association of the genotypes, age, and stress with T2DM have been summarized in Table 3 and Figure 3.There was no difference in T2DM incidence between CT (p = .030)and TT/CC (p = .034)genotype containing people who were in age group of 40-60 years (Table 3).In contrast, people who were more than 60 years old with CT genotype (OR = 4.636, p = .029)were more prone to T2DM than that of TT/CC genotype (OR = 3.714, p = .007)subjects (Table 3)." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "section_type": "main", + "text": "\n\nAims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "section_type": "main", + "text": "\n\nAlthough drawing of definitive conclusions is difficult from these observational studies, their results suggest that young-onset type 2 diabetes is associated with a much more frequent occurrence of adverse macrovascular and microvascular outcomes and a more rapidly progressing severity of complications than is seen in type 1 diabetes or later-onset type 2 diabetes." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "section_type": "abstract", + "text": "\nAims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens.Results During a median follow-up of 9.2 years, we identified 348 individuals with incident type 2 diabetes.After adjustment for confounders, HRs for type 2 diabetes were 3.7 (95% CI 1.8, 7.5), 2.4 (95% CI 1.3, 4.3) and 1.60 (95% CI 1.0, 2.8) for women with premature, early and normal menopause, respectively, relative to those with late menopause (ptrend <0.001).The HR for type 2 diabetes per 1 year older at menopause was 0.96 (95% CI 0.94, 0.98).Further adjustment for BMI, glycaemic traits, metabolic risk factors, C-reactive protein, endogenous sex hormone levels or shared genetic factors did not affect this association.Conclusions/interpretation Early onset of natural menopause is an independent marker for type 2 diabetes in postmenopausal women." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "section_type": "main", + "text": "\n\nEpidemiological studies examining the associations between lifestyle behaviors and diabetes risk have reached similar conclusions as the clinical trials described above.For example, the 14-year follow-up University of Pennsylvania Alumni Health Study [52] (n = 5,990 men aged 39-68 years) showed PA (leisure time physical activity [LTPA] expressed in kcal expended per week through walking, stair climbing, and sports) was inversely associated with the incidence of T2D.Incidence rates declined as energy expenditure rose from 500 through 3,500 kcal/week.The age-adjusted relative risk ratio (RR) of T2D was reduced by about 6% for each 500 kcal increment increase in PA energy expenditure." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "section_type": "main", + "text": "\n\nIn a study of the age-specific incidence of type 2 diabetes in the UK (a retrospective cohort study of patients with newly diagnosed type 2 diabetes between 1990 and 2010), the investigators reported a substantial increase in the proportion of people aged 40 years or younger at diagnosis" + }, + { + "document_id": "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6", + "section_type": "main", + "text": "\n\nIn sum, it is clear that multiple risk factors are involved in diabetes-associated cognitive decrements as well as in dementia in relation to diabetes 38 .On the basis of our assessment of the literature, it is also clear that there are still substantial knowledge gaps on how the risk factors interconnect, how the risk factors translate to potentially modifiable mechanisms and which genetic factors are involved." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "section_type": "main", + "text": "DIABETES MELLITUS AND RISK OF AD\n\nDuring the follow-up evaluations, 151 persons developed AD, of whom 31 had diabetes mellitus.In a proportional hazards model adjusted for age, sex, and educational level, there was a 65% increase in the risk of developing AD in those with diabetes mellitus compared with those without diabetes mellitus (hazard ratio, 1.65; 95% confidence interval, 1.10-2.47).The cumulative hazard of AD over time, adjusted for age, sex, and educational level, is shown graphically in Figure 1 for typical participants with and without diabetes mellitus.Similar results were found in analyses with diabetes mellitus identified at baseline only (hazard ratio, 1.53; 95% confidence interval, 0.96-2.45)." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "section_type": "abstract", + "text": "\nThe prevalence of type 2 diabetes in adolescents and young adults is dramatically increasing.Similar to older-onset type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle.Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications.Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease.Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype, leading to premature development of complications, with adverse effects on quality of life and unfavourable effects on long-term outcomes, raising the possibility of a future public health catastrophe.In this Review, we describe the epidemiology and existing knowledge regarding pathophysiology, risk factors, complications, and management of type 2 diabetes in adolescents and young adults." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "section_type": "main", + "text": "\n\nassociation and explore whether the timing of natural menopause can add value to diabetes prediction and prevention." + }, + { + "document_id": "80500e0d-0e39-4e46-bb60-8721f4f512c0", + "section_type": "main", + "text": "Clinical Factors Predicting Incidence of Diabetes\n\nIn both the MPP and Botnia studies, a family history of diabetes, an increased BMI, and increased levels of blood pressure and serum levels of triglycerides, apolipoprotein A-I, and liver enzymes were independent predictors of future type 2 diabetes (Table 1).In the MPP study, current smoking was also associated with a marked increase in the risk of diabetes.Impaired insulin secretion and action, particularly insulin secretion adjusted for insulin resistance (disposition index), were strong predictors of future diabetes.The presence of a first-degree family history of diabetes doubled the risk of the disease that was seen with an increased BMI (Fig. 2A) and a low disposition index (Fig. 2B)." + }, + { + "document_id": "92004cb7-4f79-4dde-a8e7-d1e93a253dc3", + "section_type": "main", + "text": "\n\nWe identified 164 (78%, >3:4) participants with evidence of age-related chronic disease or risk factors.One hundred eighteen study participants (56%) had evidence of diabetes or risk for diabetes: 15 (7%) had type 2 diabetes, 80 (38%) had prediabetes, and 23 (11%) had insulin resistance suggesting prediabetes risk (based on Quantose IR).Only 19 (9%) reported a history of type 2 diabetes or prediabetes.One hundred twentyfour participants (59%) had evidence of atherosclerotic disease or risk.Thirty-three (16%) had evidence of metabolic syndrome.Twenty-eight participants (13%) met a screening definition for NAFLD, and one had suspected NASH.Many participants had multiple overlapping conditions, including 29 with prediabetes and atherosclerotic disease or risk; 19 with prediabetes, atherosclerotic disease or risk, and metabolic syndrome; and 13 with insulin resistance and atherosclerotic disease or risk.When diabetes, prediabetes, and insulin resistance were considered as a group of diseases and conditions, 28 (11%) had all four of the common diseases and conditions (diabetes and diabetes risk, atherosclerosis or atherosclerosis risk, metabolic syndrome, and NAFLD).As expected, there was a strong effect of age on the prevalence of these conditions, with exception of NAFLD (Fig. 2)." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "section_type": "main", + "text": "\n\nType 2 diabetes is a major risk factor for CVD, and it is unclear whether age at menopause is associated with risk of type 2 diabetes [3,4].Data from cross-sectional studies examining the association between age at menopause and type 2 diabetes are contradictory, with a few studies reporting no association and some other reporting higher odds of having type 2 diabetes with early onset of menopause [5][6][7].Recently, a nested case-cohort study reported that an increased risk of type 2 diabetes is associated with early onset of menopause, but it did not adjust for potential intermediate risk factors such as glucose metabolism, insulin or shared genetic factors [8].Menopause transition is associated with weight gain, an increase in visceral fat and impairment of glucose homeostasis, all of which are important risk factors for type 2 diabetes [9][10][11].However, no study has examined the role of postmenopausal hormone levels in the association between age of menopause and risk of type 2 diabetes.Although the available evidence is not persuasive and the mechanisms remain unclear, age of menopause might be associated with levels of endogenous sex hormones, which might affect the risk of type 2 diabetes in postmenopausal women [12][13][14][15][16][17].Therefore, it is not clear whether the observed association between early onset of menopause and risk of type 2 diabetes can be explained by differences in sex hormones levels in women who experience early vs late menopause." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "section_type": "main", + "text": "Summary and future research directions\n\nAlthough it is tempting to extrapolate the disease course of type 2 diabetes in young people as just an earlier and more rapid form of type 2 diabetes in older adults, distinctive differences are evident.The young-onset phenotype has a stronger family history, a greater association with obesity, early loss of both first and second phases of insulin secretion alongside often severe insulin resistance, early onset and rapid progression of microvascular and macrovascular complications, and poor sustainability of responsiveness to oral glucose-lowering therapies, frequently neces sitating early introduction of insulin." + }, + { + "document_id": "756b902b-cbc7-40e8-84a5-9372221d83a4", + "section_type": "abstract", + "text": "\nBackground: Type 2 diabetes mellitus is an important risk factor for Alzheimer disease and is more prevalent in elderly minority persons compared with non-Hispanic white persons.Objective: To determine whether diabetes is related to a higher risk of mild cognitive impairment (MCI), a transitional stage between normal cognition and Alzheimer disease, in a multiethnic cohort with a high prevalence of diabetes." + }, + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "section_type": "main", + "text": "\n\nAge. Age is another factor that has a considerable effect on outcomes in obesity and T2DM research.In humans, body weight increases with age and peaks at ~55 years in both men and women.Ageing per se is associated with a redistribution of both the fat-free mass and the fat mass, with the latter increase starting at ~30 years of age 129 .Intramuscular and intrahepatic fat are particularly increased in older persons, and this increase has been linked to insulin resistance 130 .Partially on the basis of these changes, ageing has been proposed to be an independent determinant of glucose tolerance, which progressively worsens with age 131,132 ." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "section_type": "main", + "text": "\n\nThe prevalence of type 2 diabetes in adolescents and young adults is dramatically increasing.Similar to older-onset type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle.Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications.Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease.Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype, leading to premature development of complications, with adverse effects on quality of life and unfavourable effects on long-term outcomes, raising the possibility of a future public health catastrophe.In this Review, we describe the epidemiology and existing knowledge regarding pathophysiology, risk factors, complications, and management of type 2 diabetes in adolescents and young adults." + }, + { + "document_id": "756b902b-cbc7-40e8-84a5-9372221d83a4", + "section_type": "main", + "text": "\n\nObjective: To determine whether diabetes is related to a higher risk of mild cognitive impairment (MCI), a transitional stage between normal cognition and Alzheimer disease, in a multiethnic cohort with a high prevalence of diabetes." + }, + { + "document_id": "756b902b-cbc7-40e8-84a5-9372221d83a4", + "section_type": "main", + "text": "\n\nOur results provide further support to the potentially important independent role of diabetes in the pathogenesis of AD.Diabetes may also be a risk factor for nonamnestic forms of MCI and cognitive impairment, but our analyses need to be repeated in a larger sample." + }, + { + "document_id": "756b902b-cbc7-40e8-84a5-9372221d83a4", + "section_type": "main", + "text": "\n\nBackground: Type 2 diabetes mellitus is an important risk factor for Alzheimer disease and is more prevalent in elderly minority persons compared with non-Hispanic white persons." + }, + { + "document_id": "ceab3d6d-62ca-459a-9a97-02a16d4dd193", + "section_type": "main", + "text": "Aetiological factors\n\nProspective studies suggest that the main pathophysiological defects leading to type 2 diabetes are insulin resistance and a relative insulin secretory defect.The main aetiological risk factors are age, obesity, family history, and physical inactivity.Dietary risk factors have recently emerged: risk is increased by high consumption of red and processed meat 13 and sugar-sweetened beverages, 14 and reduced by intake of fruit and vegetables, 15 some types of dairy products, 16 and some overall dietary patterns. 17Novel strategies to use quantifiable nutritional biomarkers are paving the way for more detailed understanding of the association between diet and diabetes.Although the heritability of type 2 diabetes is high (30e70%) and more than 60 genetic variants related with diabetes risk have now been identified, 18 even when combined into a genetic score, known genes contribute little to the prediction of diabetes.Phenotype-based risk models provide greater discrimination for diabetes, and the addition of genotypic information adds no more than 5e10% improvement in prediction.The current conclusion is that genetic variants provide insights into biological pathways and pathogenesis of diabetes, but not its prediction.It is likely that interactions between the environment/lifestyle and genetic factors provide the explanation for the risk of type 2 diabetes, but demonstrating such interaction is challenging.Encouraging research findings have recently shown higher absolute risk of diabetes associated with obesity at any level of genetic risk. 19evention and screening" + }, + { + "document_id": "195cace4-f298-4910-8b7c-c4e6f208cd35", + "section_type": "main", + "text": "Does a shared pathogenesis underlie both obesity and type 2 diabetes? Although the link between obesity and type 2 diabetes is widely held to involve two discrete lesions-obesityinduced insulin resistance and -cell failure-both disorders may share an underlying defect.This \"unified field theory\" raises questions about whether defects favoring progressive weight gain and metabolic impairment also contribute to -cell decompensation." + }, + { + "document_id": "893e83e6-05f4-4917-9dee-6ec2cb847def", + "section_type": "abstract", + "text": "\nThe worldwide explosion of the rates of diabetes and other metabolic diseases in the last few decades cannot be fully explained only by changes in the prevalence of classical lifestyle-related risk factors, such as physical inactivity and poor diet.For this reason, it has been recently proposed that other \"nontraditional\" risk factors could contribute to the diabetes epidemics.In particular, an increasing number of reports indicate that chronic exposure to and accumulation of a low concentration of environmental pollutants (especially the so-called persistent organic pollutants (POPs)) within the body might be associated with diabetogenesis.In this review, the epidemiological evidence suggesting a relationship between dioxin and other POPs exposure and diabetes incidence will be summarized, and some recent developments on the possible underlying mechanisms, with particular reference to dioxin, will be presented and discussed." + }, + { + "document_id": "92eb0c69-5e98-41aa-9084-506e7f223b1a", + "section_type": "main", + "text": "\n\nAlthough Alzheimer's disease is a chronic neurodegenerative disease, seemingly not related to DM, several studies support the fact DM and AD have a strong causal relationship [86].Alzheimer's disease is often referred to as \"type 3\" diabetes.In [87], authors delved into the relationship between DM and AD via semantic data mining.Following extensive analysis of several paper abstracts, they managed to identify genes related to both diseases.Efforts were also made to construct an interaction network in order to identify existing links (genes and molecules) in the network." + }, + { + "document_id": "516de7be-3cef-47ee-8338-199fb922bc6f", + "section_type": "main", + "text": "\n\nWhat these predisposing factors share is an ability to negatively impact the glucose homeostasis system through worsening of insulin resistance or to impair b-cell function.Superimposing these factors onto a genetically compromised glucose homeostasis system raises the risk of progressing to hyperglycemia.It is the rapid emergence of these disadvantageous environmental factors that is causing the worldwide diabetes epidemic.This concept of environmental changes promoting diabetes was highlighted many years ago by populations that rarely experienced type 2 diabetes, but then moved from a nomadic or farm existence to urban environments followed by an explosion of diabetes, typically with profound obesity: Pima Indians in the Southwest U.S., Saharan nomadic tribes, Australian Aborigines, and many others.Particularly dramatic were studies that showed reversal of the diabetes when they returned to their prior way of life (15).A recent example of this is the rapidly rising incidence of type 2 diabetes in China and India as people move from the country to cities-there is a 0.1-0.2%incidence of diabetes for rural farmers in China as opposed to well more than 5% for city dwellers.Perhaps the scariest example of this is children in the U.S. where the obesity statistics worsen yearly.As many as 20% of U.S. children are now obese, and they are developing all of the elements of the metabolic syndrome-insulin resistance, hypertension, hyperlipidemia, and glucose intolerance (16)." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "section_type": "main", + "text": "Discussion\n\nIn this large population-based study of postmenopausal women free of type 2 diabetes at baseline, we showed that early onset of natural menopause is associated with an increased risk of type 2 diabetes, independent of potential intermediate risk factors for type 2 diabetes (including BMI, glucose and insulin levels) and of levels of endogenous sex hormones and SHBG.We also showed that shared genetic factors could not explain the association between age at natural menopause and risk of type 2 diabetes." + }, + { + "document_id": "29d09d03-fd2f-48b3-a020-ea574d583dc4", + "section_type": "main", + "text": "Diet, Nutrition, and Type 2 Diabetes\n\nObesity is pathophysiologically associated with the development of type II diabetes [199,200].Oxidative stress and inflammation, metabolic impairment and accelerated aging on both the micro-and macrocellular level contribute to the pathogenesis of metabolic diseases [201,202]." + }, + { + "document_id": "f53cd4d6-2d42-47e1-b58f-5bf8f2d65ef5", + "section_type": "main", + "text": "\n\nThe biological processes linking aging and disease risk are poorly understood.Still, aging is considered to date as one of the main factors responsible for several complex diseases including cancer, cardiovascular diseases, and diabetes." + }, + { + "document_id": "893e83e6-05f4-4917-9dee-6ec2cb847def", + "section_type": "main", + "text": "\n\nThe worldwide explosion of the rates of diabetes and other metabolic diseases in the last few decades cannot be fully explained only by changes in the prevalence of classical lifestyle-related risk factors, such as physical inactivity and poor diet.For this reason, it has been recently proposed that other \"nontraditional\" risk factors could contribute to the diabetes epidemics.In particular, an increasing number of reports indicate that chronic exposure to and accumulation of a low concentration of environmental pollutants (especially the so-called persistent organic pollutants (POPs)) within the body might be associated with diabetogenesis.In this review, the epidemiological evidence suggesting a relationship between dioxin and other POPs exposure and diabetes incidence will be summarized, and some recent developments on the possible underlying mechanisms, with particular reference to dioxin, will be presented and discussed." + }, + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "section_type": "main", + "text": "\n\nIndependent of geography, the risk of developing type 2 diabetes is associated with low socioeconomic status.Low educational level increases risk by 41%, low occupation level by 31%, and low income level by 40% (16)." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "section_type": "main", + "text": "\n\nBackground: Few prospective studies have assessed diabetes mellitus as a risk factor for incident Alzheimer disease (AD) and decline in cognitive function." + }, + { + "document_id": "f53cd4d6-2d42-47e1-b58f-5bf8f2d65ef5", + "section_type": "main", + "text": "Discussion\n\nBased on available data, it is still unclear if a faster rate of telomere attrition and the consequent premature cell senescence can be a cause or a consequence of type 2 diabetes 8 .Although telomere length in different cell types may better reflect specific diseases, tissue-specific aging, or cell-specific adaptations, several studies have shown not only a significant association between LTL shortening and T2D 10 , but also a correlation with time of onset, duration of disease and increasing number of diabetes related complications 6,[21][22][23] .Indeed, the attrition of this chromosome region seems to be attenuated in patients with well-controlled diabetes 24 .Therefore, telomere shortening in leukocytes may correspond to a similar shortening of telomeres in organs and tissues such as islet β-cells, which lead to premature senescence and subsequent impaired insulin secretion and glucose tolerance 25,26 .On the other hand, many studies show that physical activity seems to confer a beneficial effect on LTL maintenance in healthy and diseased elderly people 19,[27][28][29][30][31] ." + } + ], + "document_id": "B627313B69829B6ACD923E8E6588A622", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "aging", + "diabetes", + "T2DM", + "age", + "susceptibility", + "onset", + "risk&factors", + "incidence", + "type&2&diabetes", + "age-related&diseases" + ], + "metadata": [ + { + "object": "The NeuroD1-Ala45Thr variation may itself have an important role in susceptibility to or be in disequilibrium with early-onset T2DM in Chinese. The Ala45Thr may affect the onset pattern of T2DM, i.e., early-onset but not late-onset T2DM in Chinese.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab839109" + }, + { + "object": "Data suggest that subjects with point mutation 3243A>G in mtRNA-LeuUUR develop MIDD maternally inherited diabetes and deafness; as compared to patients with T1DM type 1 diabetes mellitus or early-onset T2DM type 2 diabetes mellitus matched for sex, age, duration of diabetes, such MIDD patients have highest rate of osteoporosis.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab211558" + }, + { + "object": "The SORBS1 GG genotype of rs2281939 was associated with a higher risk of diabetes at baseline, an earlier onset of diabetes, and higher steady-state plasma glucose levels in the modified insulin suppression test. The minor allele T of rs2296966 was associated with higher prevalence and incidence of diabetes, an earlier onset of diabetes, and higher 2-h glucose during oral glucose tolerance test in Chinese patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab872946" + }, + { + "object": "The present study shows that elevated plasma levels of RBP4 were associated with diabetic retinopathy and vision-threatening diabetic retinopathy in Chinese patients with type 2 diabetes, suggesting a possible role of RBP4 in the pathogenesis of diabetic retinopathy complications. Lowering RBP4 could be a new strategy for treating type 2 diabetes with diabetic retinopathy .", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab851311" + }, + { + "object": "The mean age of Parkinsonism onset among LRRK2 G2385R carriers was 42.7 years old for early-onset compared to 74.3 for late-onset patients. LRRK2 G2385R mutation appears to be as prevalent among early-onset as late-onset patients.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab833283" + }, + { + "object": "Study shows nucleotide substitutions in CD101, the human homolog of a diabetes susceptibility gene in non-obese diabetic mouse, in patients with type 1 diabetes. The results raise the possibility that CD101 is a susceptibility gene for type 1 diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab750084" + }, + { + "object": "This study investigates the involvement of a 14-bp deletion polymorphism rs371194629 at the 3' untranslated region of HLA-G in the context of T1DM and age of onset.the deletion/deletion DEL/DEL genotype was found to be associated with an early age of onset P = 0.001, while the presence of the insertion allele INS was associated to a later age of onset of type I diabetes mellitus", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab944007" + }, + { + "object": "Data confirm the association between the FTO first intron polymorphism and the presence of type 2 diabetes mellitus in the Slavonic Czech population. The same variant is likely to be associated with development of chronic complications of diabetes mellitus, especially with diabetic neuropathy and diabetic kidney disease in either T2DM or both T1DM and T2DM.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab173943" + }, + { + "object": "Blockade of IL-27 significantly delayed the onset of diabetic splenocyte-transferred diabetes, while IL-27-treated diabetic splenocytes promoted the onset of autoimmune diabetes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab103352" + }, + { + "object": "Polymorphism of eNOS G894T is not a risk factor for diabetic foot ulcer formation. T allele is a risk factor for diabetes, but T allele is not a risk factor for diabetic foot ulcer formation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab604531" + } + ], + "question": "Is there a direct association between aging and susceptibility to having diabetes?", + "subquestions": null, + "task_id": "B627313B69829B6ACD923E8E6588A622", + "usage": { + "chatgpt": 5686, + "gpt-4": 3837, + "gpt-4-turbo-preview": 2918 + }, + "user_id": 2 + }, + "document_id": "B627313B69829B6ACD923E8E6588A622", + "task_id": "B627313B69829B6ACD923E8E6588A622" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/04.json b/gnqa/paper1_eval/src/data/responses/aging/experts/04.json new file mode 100644 index 0000000..fd6430e --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/04.json @@ -0,0 +1,396 @@ +{ + "created_at": "2024-05-31T20:43:30.117900Z", + "data": { + "amplify": false, + "answer": "The genes associated with aging in humans are APOE and FOXO3A.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Genetic\nlinkage studies of long-lived human families identified a\nlongevity locus while candidate gene approaches have been\nused to identify and confirm the association between\nspecific variants in the FOXO3A gene and human\nlongevity [3–7]. Genome-wide association studies have\nalso been used to identify the association of APOE with life\n\n123\nAging Clin Exp Res\n\nspan and have yielded insights into potential biological\npathways and processes related to aging. Despite these\nsuccesses, several problems are inherent in human\nlongevity studies including potentially high degrees of\nenvironmental heterogeneity, genetic diversity, and lack of\nbirth matched controls, among others [8]." + } + ], + "4a27da1c-b184-47e8-bef2-de6435d7c3f5": [ + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + } + ], + "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4": [ + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "text": "\n\nIn conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies." + } + ], + "5e6ad994-9cad-4b8b-903d-2d5c350e25dc": [ + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\n\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nIn addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha ˜es et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + } + ], + "f3610ccc-2831-42f6-a3d3-1a0feeba4902": [ + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\n\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + } + ] + }, + "data_source": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "section_type": "main", + "text": "\n\nIn addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha ˜es et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "section_type": "main", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "section_type": "main", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nGenes/loci identified by genome-wide association studies of longevity and lifespan traits." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "\n\nAlthough the models data set comprises all genes (to our knowledge) shown by the time of the latest update to statistically increase longevity or alter the aging process in a noticeable way, in the human data set we try to evaluate whether a given intervention is affecting the aging process itself or not.For example, many mutations may increase longevity by decreasing the incidence of specific diseases, rather than by altering the basic process of aging (de Magalhães et al ., 2005a(de Magalhães et al ., , 2005b)).Therefore, the human data set is not merely an extension of the work conducted in model organisms and of its bibliography, but a manually selected list of the most pertinent human aging candidate genes, each presented with a higher annotation level.We cite studies on whether the functions of aging-associated genes in model organisms are conserved in their human orthologues.Likewise, we cite flaws in previous studies based on new published observations, although we have a neutral stance on conflicting findings from different research groups.Our policy is to cite all conflicting reports and let visitors make their own decisions on how to interpret them.By contrast, each entry in GenAge model organisms has only one reference: the first publication reporting an association of the gene with longevity or aging.Moreover, one of the latest enhancements in the human data set was the inclusion of Gene Ontology annotation.Gene Ontology terms and annotation files were obtained from the Gene Ontology Consortium website (http://www.geneontology.org/ ) and provide an additional layer of description for the gene products in a cellular context (Ashburner et al ., 2000)." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "section_type": "main", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "section_type": "main", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "Candidate gene studies identified APOE and FOXO3A as human longevity genes\n\nThe first genetic longevity studies mainly focused on lifespan regulating loci that emerged from animal models [22].Lifespan Prospects & Overviews .... extension in animal models was obtained by applying caloric restriction or by modifying gene functions (mutagenesis) using RNA interference, knock-out or overexpression of single genes (GenAge; http://genomics.senescence.info/genes/)[23].The most interesting pathways identified using these models are the growth hormone (GH)/insulin/insulin-like growth factor 1 (IGF-1) signaling and mammalian target of rapamycin (mTOR) signaling pathways [24].Thus far, lifespan has been the main phenotype investigated in animal models.In order to make these models more translatable to human studies research should focus on defining the parameters that reflect the physiology and pathology of aging in both animals and humans [25,26]." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "abstract", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "\n\nGenAge consists of several searchable data sets.Considering the extraordinary discoveries in the genetics of aging in model organisms, GenAge includes a data set of genes associated with longevity and/or aging in model organisms.We consider a given gene for inclusion in GenAge if genetic manipulations of the gene result in noticeable changes in the aging phenotype and/or longevity.Most genes in GenAge are from the four typical model organisms: mice, worms, fruit flies and yeast (Table 1).Strikingly, homologues of many genes -such as insulin receptors and sirtuins -have been shown to regulate aging in model organisms separated by large evolutionary distances (Kenyon, 2005;Liu et al ., 2005;Smith et al ., 2008).Moreover, we have shown that genes associated with aging and/or longevity in model organisms are evolutionary conserved in terms of having more homologues than predicted by chance (Budovsky et al ., 2007(Budovsky et al ., , 2008) ) and exhibiting slower molecular evolution rates (de Magalhães & Church, 2007).Therefore, it is now clear that at least some genes identified in model organisms may be relevant to human aging." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "main", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "abstract", + "text": "\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010)." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "abstract", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "section_type": "main", + "text": "[PubMed: 18208581]\n3. de Magalhães JP, Wuttke D, Wood SH, Plank M & Vora C Genome-environment interactions that\nmodulate aging: Powerful targets for drug discovery. Pharmacol. Rev. 64, 88–101 (2012). [PubMed:\n22090473]\n4. McDaid AFet al.Bayesian association scan reveals loci associated with human lifespan and linked\nbiomarkers. Nat. Commun. 8, 15842 (2017). [PubMed: 28748955]\n5. Fontana L & Partridge L Promoting health and longevity through diet: From model organisms to\nhumans. Cell 161, 106–118 (2015). [PubMed: 25815989]\n6." + }, + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "section_type": "main", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "Genetic\nlinkage studies of long-lived human families identified a\nlongevity locus while candidate gene approaches have been\nused to identify and confirm the association between\nspecific variants in the FOXO3A gene and human\nlongevity [3–7]. Genome-wide association studies have\nalso been used to identify the association of APOE with life\n\n123\nAging Clin Exp Res\n\nspan and have yielded insights into potential biological\npathways and processes related to aging. Despite these\nsuccesses, several problems are inherent in human\nlongevity studies including potentially high degrees of\nenvironmental heterogeneity, genetic diversity, and lack of\nbirth matched controls, among others [8]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "main", + "text": "\n\nStudies revealed from 300 to 750 genes related to longevity that are critically involved in a variety of life activities, such as growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition [4].These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [5,6].Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability, and the endocrine-related pathway [7][8][9].In addition, the candidates for longevity encompass genes related to drug metabolism, the ones involved in protein folding, stabilization, and degradation, as well those related to coagulation and regulation of circulation [10], etc.In most cases, these genes or their polymorphic sites were examined in multiple population replication studies, which discovered certain longevity-associated genes or pathways [4][5][6][7][8][9][10]." + }, + { + "document_id": "690a2ae6-962a-438c-91ca-60425a0c8d02", + "section_type": "abstract", + "text": "\nClear evidence exists for heritability of human longevity, and much interest is focused on identifying genes associated with longer lives.To identify such longevity alleles, we performed the largest genomewide linkage scan thus far reported.Linkage analyses included 2118 nonagenarian Caucasian sibling pairs that have been enrolled in fifteen study centers of eleven European countries as part of the Genetics of Healthy Ageing (GEHA) project.In the joint linkage analyses we observed four regions that" + }, + { + "document_id": "0fc75a0d-3aa3-481a-8c0f-689bd7ae6104", + "section_type": "abstract", + "text": "\nAging is a complex process affecting different species and individuals in different ways.Comparing genetic variation across species with their aging phenotypes will help understanding the molecular basis of aging and longevity.Although most studies on aging have so far focused on short-lived model organisms, recent comparisons of genomic, transcriptomic, and metabolomic data across lineages with different lifespans are unveiling molecular signatures associated with longevity.Here, we examine the relationship between genomic variation and maximum lifespan across primate species.We used two different approaches.First, we searched for parallel amino-acid mutations that co-occur with increases in longevity across the primate linage.Twenty-five such amino-acid variants were identified, several of which have been previously reported by studies with different experimental setups and in different model organisms.The genes harboring these mutations are mainly enriched in functional categories such as wound healing, blood coagulation, and cardiovascular disorders.We demonstrate that these pathways are highly enriched for pleiotropic effects, as predicted by the antagonistic pleiotropy theory of aging.A second approach was focused on changes in rates of protein evolution across the primate phylogeny.Using the phylogenetic generalized least squares, we show that some genes exhibit strong correlations between their evolutionary rates and longevity-associated traits.These include genes in the Sphingosine 1-phosphate pathway, PI3K signaling, and the Thrombin/protease-activated receptor pathway, among other cardiovascular processes.Together, these results shed light into human senescence patterns and underscore the power of comparative genomics to identify pathways related to aging and longevity." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "Murabito JM, Yuan R, Lunetta KL (2012) The search for\nlongevity and healthy aging genes: insights from epidemiological\nstudies and samples of long-lived individuals. J Gerontol A Biol\nSci Med Sci 67(5):470–479. doi:10.1093/gerona/gls089\n20. Nuzhdin SV, Pasyukova EG, Dilda CL et al (1997) Sex-specific\nquantitative trait loci affecting longevity in Drosophila melanogaster. Proc Natl Acad Sci USA 94(18):9734–9739\n21. Gems D, Riddle DL (2000) Genetic, behavioral and environmental determinants of male longevity in Caenorhabditis elegans.\n Genetics 154(4):1597–1610\n\n123\n\n22." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "Discussion\n\nIn our analyses of over 25,000 individuals of 55 years and older followed for an average of 11 years, we did not identify genome-wide significant associations for all-cause mortality and survival free of major diseases.However, both traits highlighted loci with suggestive significance that were in the neighborhood of genes related to neural regulation.In addition, our pathway and network analyses identified an enrichment of genes associated with cellular and neural development and function, and cell communication that may contribute to variation in human aging.Brain development might be responsible for the creation of redundancy in brain circuitry, which is associated with functional reserve and resiliency.Brain function regulates most of the compensatory strategy supporting maintenance of homeostatic equilibrium.Both of these processes are essential to healthy aging and longevity." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nInvolvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "section_type": "abstract", + "text": "\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "section_type": "abstract", + "text": "\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "520b36a2-4c9c-4894-a818-9917bd357982", + "section_type": "abstract", + "text": "\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "section_type": "main", + "text": "\n\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "section_type": "main", + "text": "\n\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "section_type": "main", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "section_type": "main", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + }, + { + "document_id": "99a35e24-bbd2-495b-82dc-53d7e2075191", + "section_type": "main", + "text": "\n\nThus, substantially more work is needed in this area to establish whether longevity is driven by nuclear genomic stability.Diverse and unexpected bits of evidence support a relationship.For example, a disproportionate number of genes identified in unbiased and targeted genome-wide association studies (GWASs) as associated with longevity are involved in genome maintenance (75).One study involved age of natural menopause in ∼70,000 women and led to the identification of 44 genetic variants associated with early or late menopause, a strong biomarker of healthy TIFs (telomere dysfunction-induced foci): co-localization of multiple DNA damage response factors and repair proteins on uncapped telomeric DNA aging (76).Approximately two-thirds of these are associated with genome maintenance genes.Seven of ten significantly associated pathways are involved in DNA repair.The highly significant overrepresentation of DNA repair pathways indicates an intimate connection between genome maintenance and aging phenotypes.From unrelated studies, we know that reduced expression of the repair endonuclease ERCC1-XPF causes accelerated aging (3), whereas ERCC1 is one of the top genes under positive selective pressure in the longest-lived mammalian species, the bowhead whale (77).Intriguingly, hepatocytes from old rats have impaired NER, whereas caloric restriction, which extends longevity, restored the NER capacity of old rats to that of youthful levels (42).In a human interventional study, brief caloric restriction increased NER capacity in PBMCs of individuals who had low NER prior to dietary intervention (78).Therefore, increased DNA repair capacity could promote longevity and may even prove amenable to improvement." + }, + { + "document_id": "ae9d5a74-24c1-43f1-b514-5e3f10c91284", + "section_type": "abstract", + "text": "\nIn animal models, single-gene mutations in genes involved in insulin/IGF and target of rapamycin signalling pathways extend lifespan to a considerable extent.The genetic, genomic and epigenetic influences on human longevity are expected to be much more complex.Strikingly however, beneficial metabolic and cellular features of long-lived families resemble those in animals for whom the lifespan is extended by applying genetic manipulation and, especially, dietary restriction.Candidate gene studies in humans support the notion that human orthologues from longevity genes identified in lower species do contribute to longevity but that the influence of the genetic variants involved is small.Here we discuss how an integration of novel study designs, labour-intensive biobanking, deep phenotyping and genomic research may provide insights into the mechanisms that drive human longevity and healthy ageing, beyond the associations usually provided by molecular and genetic epidemiology.Although prospective studies of humans from the cradle to the grave have never been performed, it is feasible to extract life histories from different cohorts jointly covering the molecular changes that occur with age from early development all the way up to the age at death.By the integration of research in different study cohorts, and with research in animal models, biological research into human longevity is thus making considerable progress." + }, + { + "document_id": "520b36a2-4c9c-4894-a818-9917bd357982", + "section_type": "main", + "text": "\n\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases." + }, + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "section_type": "main", + "text": "\n\nIn conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies." + } + ], + "document_id": "9AA0126F9464E89A7B057D231376A79A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "longevity", + "human", + "aging", + "genes", + "GWAS", + "SNP", + "centenarians", + "genetic&variants" + ], + "metadata": [ + { + "object": "Transient overexpression of WRKY79 in protoplasts results in up-regulation of Gene:542165, Gene:541974, Gene:100274033, Gene:542688, Gene:542150, Gene:542151, Gene:100273457, Gene:100285509, Gene:103626248, Gene:103646045, Gene:100217270, Gene:100279981, Gene:100281950, Gene:542476, Gene:542369, Gene:100281950, and Gene:542260.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab969966" + }, + { + "object": "Uniform Mu insertion results in up-regulation of cytokinin synthesis genes and down-regulation of cytokinin degradation genes. The protein binds to Gene:103632693, Gene:100502174, Gene:100283866, Gene:542044, and Gene:100037786.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab983367" + }, + { + "object": "Data suggest that modulation of ARHGEF3 gene expression in humans with a promoter-localized SNP plays a role in human megakaryocytes and human platelet function-a finding resulting from the biological follow-up of human genetic studies. Arhgef3 KO mice partially recapitulate the human phenotype.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab209151" + }, + { + "object": "NO evident relationship was found between gene polymorphism of Eco RI loci of Apo B gene, Xba I loci of Apo B gene or 3'-VNTR of Apo B gene and ANFH.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab529998" + }, + { + "object": "Data suggest that the redox status of serum apoE might be related to the synthesis of HDL; the cysteine-thiol residue of reduced-apoE is in a naive state, while that of non-reduced-apoE is in a reversibly or irreversibly oxidized state. Data suggest that apoE homodimer and apoE-AII complex are typical reversibly oxidized forms of apoE. apoE-AII complex = a complex of apolipoprotein E and apolipoprotein A-II", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab212832" + }, + { + "object": "Data suggest that modulation of ARHGEF3 gene expression in humans with a promoter-localized SNP plays a role in human megakaryocytes and human platelet function-a finding resulting from the biological follow-up of human genetic studies. Arhgef3 KO mice partially recapitulate the human phenotype.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab209152" + }, + { + "object": "Genome-wide associations P < 5 x 10-8 were found at the PCSK9 gene, the APOB gene, theLPL gene, the APOA1-APOA5 locus, the LIPC gene, the CETP gene, the LDLR gene, and the APOE locus.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab320109" + }, + { + "object": "APOE genotype status moderated the age-related declines in episodic memory: APOE-epsilon4+ middle-aged adults exhibited impairments relative to both APOE-epsilon4- middle-aged participants, and APOE-epsilon4+ younger adults.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab77520" + }, + { + "object": "Very high gene-level linkage in Bayesian GWAS using Kutalik/Timmers data and MAGMA gene-level reanalysis to human longevity LOD 14.5; RW Williams July 2019. Linkage to longevity is second only to the TOMM40-APOE-APOC1-PVRL2-APOC4 region on Chr 19.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab6647" + }, + { + "object": "Studied six SNP loci: rs2279115 of BCL2 gene, rs804270 of NEIL2 gene, rs909253 of LTA gene, rs2294008 of PSCA gene, rs3765524 and rs10509670 of PLCE1 gene to evaluate gastric cancer risk using magnetic nanoparticles and universal tagged arrays.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab732925" + } + ], + "question": "Which genes are associated with aging in humans?", + "subquestions": null, + "task_id": "9AA0126F9464E89A7B057D231376A79A", + "usage": { + "chatgpt": 7766, + "gpt-4": 5012, + "gpt-4-turbo-preview": 4076 + }, + "user_id": 2 + }, + "document_id": "9AA0126F9464E89A7B057D231376A79A", + "task_id": "9AA0126F9464E89A7B057D231376A79A" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/05.json b/gnqa/paper1_eval/src/data/responses/aging/experts/05.json new file mode 100644 index 0000000..3f16805 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/05.json @@ -0,0 +1,398 @@ +{ + "created_at": "2024-05-31T20:40:14.163893Z", + "data": { + "amplify": false, + "answer": "Several genetic factors influence aging in humans. These include the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling pathway, and the exonuclease 1 (EXO1) gene. Other genes associated with aging are those involved in pathways such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response. Additionally, genes related to immune response, energy metabolism, signal transduction pathways, and cellular senescence also play a role in aging.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "Introduction\n\nWith the development of human genomics research, a large number of studies of the genetics of longevity have been conducted.Scientists from various countries have proposed many different theories concerning the mechanisms of aging from different perspectives, involving oxidative stress, energy metabolism, signal transduction pathways, immune response, etc. [1,2].These mechanisms interact with each other and are influenced by heredity to some degree [2,3].The identification of longevity-related biological markers is critical to an indepth understanding of the mechanisms of carrier protection against common disease and/or of the retardation of the process of aging." + } + ], + "1386c8ad-297d-48b1-aa34-41659a9f6544": [ + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Influence of Genetic Factors in Ageing and Lifespan\n\nAgeing is defined as the decline of physiological functions in several tissues and organs inducing an increasing probability of death [17].The understanding of genetic factors involved in ageing has been limited due to the complexity of this process and the heterogeneity among individuals and even among tissues [18][19][20].Tissue cells adopt a senescent phenotype as a consequence of multiple intrinsic, extrinsic, and stochastic factors [21].The combination of these genetic factors is related to longevity and healthy ageing [22].Although this decline is somewhat predictable, some individuals show a much slower decline and get to live past the age of 100.Studies in these individuals showed polymorphisms in some genes which are associated with long life, such as APOE and FOXO3.However, these associations have not been consistent across different populations, suggesting that ageing is rather polygenic [23]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + } + ], + "7291ceb2-482a-4f9b-a116-2b68ff24854f": [ + { + "document_id": "7291ceb2-482a-4f9b-a116-2b68ff24854f", + "text": "\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10)." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + } + ], + "c8fbb24d-0a72-4a45-a552-6cd98a4a25a2": [ + { + "document_id": "c8fbb24d-0a72-4a45-a552-6cd98a4a25a2", + "text": "Translational\n\nA LTHOUGH there is much debate about the processes driving human aging, there is little doubt that genetic influences play a significant role (1).Humans clearly live very much longer than the currently favored laboratory models of aging, and such interspecies differences in reproductively 'fit' life span must have an inherited genetic foundation.Within human populations, environmental and behavioral exposures are important but at least a quarter of life expectancy variation in twin or family studies is attributable to inherited genetic or epigenetic factors (2).Age-related conditions such as type 2 diabetes, myocardial infarction, common cancers, and Alzheimer's disease (AD) typically have onsets after the fourth decade of life; \"successful\" agers delay these onsets until relatively late in life (3).Many aging traits and diseases show moderate heritability, including cardiovascular disease (CVD) (4) and impaired physical functioning (5), independent of known environmental risk factors." + } + ], + "ca76f85d-9f72-4e15-8ba9-3bf94308c449": [ + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "Introduction\n\nHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006)." + } + ], + "db90a971-e55a-4ab0-a3b1-05908d6771a4": [ + { + "document_id": "db90a971-e55a-4ab0-a3b1-05908d6771a4", + "text": "Introduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go ¨gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha ¨chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + } + ], + "f4e2fa75-559b-4fa9-b722-bdac03f7715a": [ + { + "document_id": "f4e2fa75-559b-4fa9-b722-bdac03f7715a", + "text": "\n\nI NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + } + ] + }, + "data_source": [ + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "section_type": "main", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + }, + { + "document_id": "c8fbb24d-0a72-4a45-a552-6cd98a4a25a2", + "section_type": "main", + "text": "Translational\n\nA LTHOUGH there is much debate about the processes driving human aging, there is little doubt that genetic influences play a significant role (1).Humans clearly live very much longer than the currently favored laboratory models of aging, and such interspecies differences in reproductively 'fit' life span must have an inherited genetic foundation.Within human populations, environmental and behavioral exposures are important but at least a quarter of life expectancy variation in twin or family studies is attributable to inherited genetic or epigenetic factors (2).Age-related conditions such as type 2 diabetes, myocardial infarction, common cancers, and Alzheimer's disease (AD) typically have onsets after the fourth decade of life; \"successful\" agers delay these onsets until relatively late in life (3).Many aging traits and diseases show moderate heritability, including cardiovascular disease (CVD) (4) and impaired physical functioning (5), independent of known environmental risk factors." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "section_type": "main", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "main", + "text": "Introduction\n\nWith the development of human genomics research, a large number of studies of the genetics of longevity have been conducted.Scientists from various countries have proposed many different theories concerning the mechanisms of aging from different perspectives, involving oxidative stress, energy metabolism, signal transduction pathways, immune response, etc. [1,2].These mechanisms interact with each other and are influenced by heredity to some degree [2,3].The identification of longevity-related biological markers is critical to an indepth understanding of the mechanisms of carrier protection against common disease and/or of the retardation of the process of aging." + }, + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "section_type": "main", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "abstract", + "text": "\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010)." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "Conclusions and prospects\n\nOver the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nInvolvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nAging is an extremely complex process associated with interplay of genetic, biochemical, and metabolic factors in an organism in a given environment.Although genetic studies of various animal models suggest that even a single-gene mutation can remarkably extend lifespan (Kenyon 2005;Johnson 2006) and, thus, modulate aging, no such genes are revealed in humans so far.Given that a human organism is a much more complex system than a model organism (Christensen et al. 2006), it is evident that genetic effects on the aging process should be mediated via coordinate action of a large number of inter-related processes (Kirkwood 2011).Coordinated function is rather relevant to complex biological (Soltow et al. 2010;Slagboom et al. 2011) and genetic (Bloss et al. 2011) networks than to individual genes." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "f4e2fa75-559b-4fa9-b722-bdac03f7715a", + "section_type": "main", + "text": "\n\nI NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "main", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Conclusions and Perspectives\n\nThe advent of new technologies has allowed the identification of conserved pathways involved in the aging process, as well as the association of genomic variants with human longevity.Nevertheless, heritability of human longevity has been estimated from 20% to 30%, reinforcing the fact that external factors such as diet, environment, and physical activity play a critical role in the human life span." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "abstract", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "section_type": "main", + "text": "Introduction\n\nHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006)." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "section_type": "main", + "text": "\n\nMany factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone.Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases.If such genes exist, their effects were too small to be detected in this study.The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "abstract", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "7291ceb2-482a-4f9b-a116-2b68ff24854f", + "section_type": "main", + "text": "\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10)." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nOn the other hand, the same evolutionary-motivated strategy suggesting to focus on more heterogeneous phenotypes (as opposite to more homogenous) can be highly beneficial for unraveling genetic predisposition to fundamental mechanisms of intrinsic biological aging and, consequently, to geriatric diseases.Indeed, aging is associated with systemic remodeling of an organism's functioning which increases chances of virtually all geriatric disorders (Franco et al. 2009;Franceschi et al. 2000;Martin et al. 2007;Cutler and Mattson 2006).Experiments with laboratory animals (Johnson 2006) and heritability estimates in humans (Christensen et al. 2006;Iachine et al. 1998) show that aging can be genetically regulated (Finch and Tanzi 1997;Martin et al. 2007;Vaupel 2010).Accordingly, yielding insights in genetic predisposition to aging-related processes in an organism could be a major breakthrough in preventing and/or ameliorating not one geriatric trait, but perhaps a major subset of such traits (Martin et al. 2007) that can greatly advance progress in solving the problem of extending healthy lifespan in humans." + }, + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "section_type": "main", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + }, + { + "document_id": "a6bc2efd-61a7-4e07-ad5c-49234aa89431", + "section_type": "main", + "text": "\n\nIn 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13]." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + }, + { + "document_id": "db90a971-e55a-4ab0-a3b1-05908d6771a4", + "section_type": "main", + "text": "Introduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go ¨gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha ¨chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Influence of Genetic Factors in Ageing and Lifespan\n\nAgeing is defined as the decline of physiological functions in several tissues and organs inducing an increasing probability of death [17].The understanding of genetic factors involved in ageing has been limited due to the complexity of this process and the heterogeneity among individuals and even among tissues [18][19][20].Tissue cells adopt a senescent phenotype as a consequence of multiple intrinsic, extrinsic, and stochastic factors [21].The combination of these genetic factors is related to longevity and healthy ageing [22].Although this decline is somewhat predictable, some individuals show a much slower decline and get to live past the age of 100.Studies in these individuals showed polymorphisms in some genes which are associated with long life, such as APOE and FOXO3.However, these associations have not been consistent across different populations, suggesting that ageing is rather polygenic [23]." + }, + { + "document_id": "ea036684-619d-4b82-9242-c0b220f2d8df", + "section_type": "main", + "text": "The mechanisms that underlie healthy aging—particularly, the cognitive as-\n\npects—remain poorly understood. Research suggests that genetics play a significant role in determining an individual’s\nsusceptibility or resilience to cognitive decline and dementia\n(Harris and Deary 2011; Ridge et al. , 2013). Identification of precise genetic factors involved would provide insight into\n\nCell Reports 32, 108091, September 1, 2020 ª 2020 The Author(s). 1\nThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).\n ll\nOPEN ACCESS\n\nReport\n\nFigure 1." + }, + { + "document_id": "18e216d9-ea5c-4dfe-a30d-632163fcf39e", + "section_type": "main", + "text": "Current progress and problems of genetic studies of aging and longevity\n\nIn spite of aging being a risk factor for many diseases, a phenotype of aging to date is still tabula rasa.Yet, the choice of a phenotype is critical for the study of a complex genetic process, such as aging (Melzer et al. 2007).Furthermore, proposed treatments to delay or alleviate aging require that validated outcomes exist, which can be measurable earlier rather than later in the life (thus, longevity per se is impractical).To date, however, most of the twin and family studies focused on broad survival measures, primarily on age at death or survival to some arbitrary advanced age (Nicholas et al. 1994).Thus, it has been demonstrated that longevity has moderate heritability ðh 2 ¼ 0:20 À À0:30Þ (McGue et al. 1993;Herskind et al. 1996;Gillespie et al. 1998).There are several challenges in using longevity as a phenotype (reviewed in Karasik et al. 2005 and below).A better strategy would be to investigate a broader outcome such as \"successful\" or \"healthy\" aging (Mulsant et al. 1994;Seeman et al. 2004).However, there is no consensus definition for the latter categories, especially for a genetic study.Similarly, at present, there is no consensus about how to measure aging starting in midlife despite a plethora of publications on the biomarkers and risk factors of aging (Newman et al. 2008).Yet, researchers (Nilsson et al. 2003;Crabtree et al. 2002;Vaillant and Mukamal 2001) have argued that studies of aging genetics should be initiated earlier in life, when there are life expectations permissive of longitudinal studies as well as information on environmental exposures traceable to the outcomes." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nStudies of mono-and dizygous twins have revealed that the genetic contribution to the variation in human lifespan is about 25-30% [12,13], and is most prominent in families clustered for longevity [14,15].This genetic contribution is mainly apparent after the age of 60 years and seems to increase with age [13,16].Furthermore, human lifespan is a complex trait which is assumed to be determined by many genes with small individual effects [17], although the polygenic architecture still needs to be characterized [18,19].The diverse health features of long-lived families illustrate that different age-related diseases have common determinants and implicate that pathways can be identified that attenuate aging and delay age-related disease.From a genomic perspective, individuals from long-lived families are assumed to be characterized by a decreased prevalence of disease-promoting variants (referred to as disease-susceptibility alleles) and an increased prevalence of variants conferring maintenance of health and protection from disease, when compared to population controls.In the last 5 years, many diseasesusceptibility alleles have been identified (National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) Catalog; http://www.genome.gov/gwastudies/)[20].A first comparison between long-lived individuals, selected from both long-lived families (LLS) and the general population (Leiden 85-plus study), and young controls showed no difference in the distribution or frequency of disease-susceptibility alleles identified in cancer, coronary artery disease and type 2 diabetes [21].The search for lifespan regulating loci -contributing to longevity and population mortality -must therefore extend beyond a focus on disease-susceptibility alleles.We will first discuss the efforts to identify longevity loci by genetics approaches." + }, + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "section_type": "main", + "text": "\n\nIn this review, we give an overview of the major environmental factors that modulate aging in animals, in particular those with underlying gene-environment interactions with potential for improving human health and drug discovery.Moreover, we provide a snapshot of the relevance of these to human biology and to antiaging applications in diet, industry, pharmacy, and healthcare." + }, + { + "document_id": "aff67cef-4bf7-42dc-826b-2a259722008d", + "section_type": "abstract", + "text": "\nAs our society is growing older, the consequences of aging have begun to gain particular attention.Improvement of quality of life at old age and prevention of age-associated diseases have become the main focus of the aging research.The process of aging in humans is complex and underlies multiple influences, with the probable involvement of heritable and various environmental factors.In particular, hormones are decisively involved in the generation of aging.Over time, important circulating hormones decline due to a reduced secretion of the pituitary, the adrenal glands and the gonads or due to an intercurrent disease.Among them, serum levels of growth factors and sexual steroids show significant aging-associated changes.Within the scope of the Explorative Project 'Genetic aetiology of human longevity' supported by the German National Genome Research Network 2 (NGFN-2) an in vitro model of human hormonal aging has been developed.Human SZ95 sebocytes were maintained under a hormone-substituted environment consisting of growth factors and sexual steroids in concentrations corresponding to those circulating in 20-and in 60-year-old women.Eight hundred and ninety-nine genes showed a differential expression in SZ95 sebocytes maintained under the 20-and 60-year-old hormone mixture, respectively.Among them genes were regulated which are involved in biological processes which are all hallmarks of aging.The most significantly altered signaling pathway identified was that of the transforming growth factor-b (TGF-b).A disturbed function of this cascade has been associated with tumorigenesis, i.e. in pancreatic, prostate, intestine, breast, and uterine cancer.Interestingly, genes expressed in signaling pathways operative in age-associated diseases such as Huntington's disease (HD), dentatorubral-pallidoluysian atrophy (DRPLA), and amyotrophic lateral sclerosis (ALS) were also identified.These data demonstrate that skin and its appendages may represent an adequate model for aging research.Hormones interact in a complex fashion, and aging may be partly attributed to the changes in their circulating blood levels.Furthermore, a disturbed hormone status may partially act towards the manifestation of neurodegenerative diseases.Thus, these results could be a basis for an integrated and interdisciplinary approach to the analysis of the aging process." + }, + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "section_type": "main", + "text": "[PubMed: 18208581]\n3. de Magalhães JP, Wuttke D, Wood SH, Plank M & Vora C Genome-environment interactions that\nmodulate aging: Powerful targets for drug discovery. Pharmacol. Rev. 64, 88–101 (2012). [PubMed:\n22090473]\n4. McDaid AFet al.Bayesian association scan reveals loci associated with human lifespan and linked\nbiomarkers. Nat. Commun. 8, 15842 (2017). [PubMed: 28748955]\n5. Fontana L & Partridge L Promoting health and longevity through diet: From model organisms to\nhumans. Cell 161, 106–118 (2015). [PubMed: 25815989]\n6." + }, + { + "document_id": "0fc75a0d-3aa3-481a-8c0f-689bd7ae6104", + "section_type": "abstract", + "text": "\nAging is a complex process affecting different species and individuals in different ways.Comparing genetic variation across species with their aging phenotypes will help understanding the molecular basis of aging and longevity.Although most studies on aging have so far focused on short-lived model organisms, recent comparisons of genomic, transcriptomic, and metabolomic data across lineages with different lifespans are unveiling molecular signatures associated with longevity.Here, we examine the relationship between genomic variation and maximum lifespan across primate species.We used two different approaches.First, we searched for parallel amino-acid mutations that co-occur with increases in longevity across the primate linage.Twenty-five such amino-acid variants were identified, several of which have been previously reported by studies with different experimental setups and in different model organisms.The genes harboring these mutations are mainly enriched in functional categories such as wound healing, blood coagulation, and cardiovascular disorders.We demonstrate that these pathways are highly enriched for pleiotropic effects, as predicted by the antagonistic pleiotropy theory of aging.A second approach was focused on changes in rates of protein evolution across the primate phylogeny.Using the phylogenetic generalized least squares, we show that some genes exhibit strong correlations between their evolutionary rates and longevity-associated traits.These include genes in the Sphingosine 1-phosphate pathway, PI3K signaling, and the Thrombin/protease-activated receptor pathway, among other cardiovascular processes.Together, these results shed light into human senescence patterns and underscore the power of comparative genomics to identify pathways related to aging and longevity." + }, + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "section_type": "main", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + }, + { + "document_id": "da4a9500-831f-48ab-acea-5ec7097276ed", + "section_type": "main", + "text": "\n\nStudies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "aff67cef-4bf7-42dc-826b-2a259722008d", + "section_type": "main", + "text": "\n\nAs our society is growing older, the consequences of aging have begun to gain particular attention.Improvement of quality of life at old age and prevention of age-associated diseases have become the main focus of the aging research.The process of aging in humans is complex and underlies multiple influences, with the probable involvement of heritable and various environmental factors.In particular, hormones are decisively involved in the generation of aging.Over time, important circulating hormones decline due to a reduced secretion of the pituitary, the adrenal glands and the gonads or due to an intercurrent disease.Among them, serum levels of growth factors and sexual steroids show significant aging-associated changes.Within the scope of the Explorative Project 'Genetic aetiology of human longevity' supported by the German National Genome Research Network 2 (NGFN-2) an in vitro model of human hormonal aging has been developed.Human SZ95 sebocytes were maintained under a hormone-substituted environment consisting of growth factors and sexual steroids in concentrations corresponding to those circulating in 20-and in 60-year-old women.Eight hundred and ninety-nine genes showed a differential expression in SZ95 sebocytes maintained under the 20-and 60-year-old hormone mixture, respectively.Among them genes were regulated which are involved in biological processes which are all hallmarks of aging.The most significantly altered signaling pathway identified was that of the transforming growth factor-b (TGF-b).A disturbed function of this cascade has been associated with tumorigenesis, i.e. in pancreatic, prostate, intestine, breast, and uterine cancer.Interestingly, genes expressed in signaling pathways operative in age-associated diseases such as Huntington's disease (HD), dentatorubral-pallidoluysian atrophy (DRPLA), and amyotrophic lateral sclerosis (ALS) were also identified.These data demonstrate that skin and its appendages may represent an adequate model for aging research.Hormones interact in a complex fashion, and aging may be partly attributed to the changes in their circulating blood levels.Furthermore, a disturbed hormone status may partially act towards the manifestation of neurodegenerative diseases.Thus, these results could be a basis for an integrated and interdisciplinary approach to the analysis of the aging process." + } + ], + "document_id": "7530EBCCAFF1750013433CA62E07A82F", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "IGF", + "insulin", + "GH", + "LPA", + "HLA-DQA1/DRB1", + "CHRNA3/5", + "CDKN2A/B", + "SH2B3" + ], + "metadata": [ + { + "object": "We conclude that 1 GH signaling is normal in obesity, 2 in the obese state, the preservation of IGF-I with fasting and the augmented GH-induced central insulin resistance indicate increased hepatic GH sensitivity, 3 blunted GH levels in obesity may protect against insulin resistance without compromising IGF-I status.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab999203" + }, + { + "object": "insulin and IGF-I activate their cognate receptors and IGF-I also activates naturally occuring IGF-I/insulin hybrid receptors HR IGF-II activates insulin receptor, IGF-I receptor and HR", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab419763" + }, + { + "object": "Data suggest that the redox status of serum apoE might be related to the synthesis of HDL; the cysteine-thiol residue of reduced-apoE is in a naive state, while that of non-reduced-apoE is in a reversibly or irreversibly oxidized state. Data suggest that apoE homodimer and apoE-AII complex are typical reversibly oxidized forms of apoE. apoE-AII complex = a complex of apolipoprotein E and apolipoprotein A-II", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab212832" + }, + { + "object": "By depressing association of IGFs with soluble IGFBPs, Zn2+ is shown to repartition either [125I]-IGF-I or [125I]-IGF-II from soluble IGFBP-5 onto cell surface IGF receptors at physiological doses depressing IGF binding to IGFBP-5 and IGF-2R", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab112518" + }, + { + "object": "Study found that IL-6, GP130, IGF-1 and IGF-1R were highly expressed in non-small cell lung cancer NSCLC and there was the correlation between GP130, IGF-1, and IGF-1R. Co-stimulation of IL-6 and IGF-1 resulted in significantly enhanced cell proliferation, invasion, and apoptosis of NSCLC cells. This experiment revealed that IL-6 and IGF-1 can synergistically promote the progression of NSCLC.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab741940" + }, + { + "object": "Circulating IGF-I appears to be growth hormone GH-independent in GH deficiency GHD patients with a low IGF-I, but remains partially GH-dependent in GHD patients with a normal IGF-I.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab141796" + }, + { + "object": "Prospective associations of insulin, IGF-I, IGF-II and IGFBP-3 with physical performance in Caerphilly Prospective Study and cross-sectional insulin, IGF-I, IGF-II, IGFBP-2 and IGFBP-3 in the Boyd Orr cohort, were examined.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab618236" + }, + { + "object": "Confirmation of the impairment of GH-IGF-1 release in hyperphagic MC4R KO mice suggests a role for insulin in regulating both the release of GH, but also in mediating growth during periods of physiologically suppressed GH-IGF-1 levels", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab154279" + }, + { + "object": "Low apoE and mir-650 plasma concentrations were risk factors for developing Alzheimer's disease AD and were particularly pronounced in severe dementia. APOE E4 allele in both AD patients and controls led to a reduction in apoE, while APOE E3/E3 genotype was associated with an increased apoE concentration and level of miR-107 in AD, which inversely correlated with the number of APOE E4 alleles.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab459467" + }, + { + "object": "Insulin receptor substrates 1 and 2 IRS-1 and IRS-2 were targeted and compared as central distributors of the insulin signal, the insulin receptor, the insulin-like growth factor 1 receptor, and the insulin receptor-related receptor.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab419969" + } + ], + "question": "What genetic factors influence aging in humans?", + "subquestions": null, + "task_id": "7530EBCCAFF1750013433CA62E07A82F", + "usage": { + "chatgpt": 9172, + "gpt-4": 5514, + "gpt-4-turbo-preview": 4604 + }, + "user_id": 2 + }, + "document_id": "7530EBCCAFF1750013433CA62E07A82F", + "task_id": "7530EBCCAFF1750013433CA62E07A82F" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/06.json b/gnqa/paper1_eval/src/data/responses/aging/experts/06.json new file mode 100644 index 0000000..a3204b6 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/06.json @@ -0,0 +1,402 @@ +{ + "created_at": "2024-05-31T20:53:55.182883Z", + "data": { + "amplify": false, + "answer": "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "context": { + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "0d6942b4-e75f-4f44-98ac-b8378ef1d3c7": [ + { + "document_id": "0d6942b4-e75f-4f44-98ac-b8378ef1d3c7", + "text": "FUTURE DIRECTIONS: HIGHER RESOLUTION DATA VIA HIGHER THROUGHPUT ASSAYS\n\nOne inescapable conclusion of the aggregate results of genome-wide studies of aging to date (see summary Table 1) is that we have not come close to saturating the number of potentially lifespan-altering genes in any organism.This is in no small part because directly generating survival curves is a relatively time-consuming process in most model organisms using current methods.There are several possible ways to address this.One way that has been tried is by attempting to find surrogate phenotypes [72,73,126] that can be screened more rapidly, or even scored under selection.Another is mining candidates from the many whole-genome expression profiles.Results to date with these have been very fruitful, but have not suggested that these methods alone will rapidly saturate our search for lifespan-and healthspan-altering genes in tractable model organisms." + } + ], + "113cb521-b79d-4b44-8250-dc1013ea2cb3": [ + { + "document_id": "113cb521-b79d-4b44-8250-dc1013ea2cb3", + "text": "\n\nChromosome mapping of genes that were differentially expressed in mice of different ages and/or in response to CR revealed a wide distribution of genes with some physical clustering of responsive genes within the genome.The latter findings are consistent with the concept that aging is a complex process and that evolutionary adaptations to aging, if they exist, may or may not involve geographic clustering of functionally related genes." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Genetic\nlinkage studies of long-lived human families identified a\nlongevity locus while candidate gene approaches have been\nused to identify and confirm the association between\nspecific variants in the FOXO3A gene and human\nlongevity [3–7]. Genome-wide association studies have\nalso been used to identify the association of APOE with life\n\n123\nAging Clin Exp Res\n\nspan and have yielded insights into potential biological\npathways and processes related to aging. Despite these\nsuccesses, several problems are inherent in human\nlongevity studies including potentially high degrees of\nenvironmental heterogeneity, genetic diversity, and lack of\nbirth matched controls, among others [8]." + } + ], + "4ca8d070-8b58-4bd5-86be-127089b70324": [ + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "text": "\n\nThe aging process most certainly is under highly polygenic controls… This should not discourage us from pursuing a search for those loci which may be of profound importance to human aging as it ordinarily occurs in most human beings." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "606c59c5-5ae4-47e9-b3eb-58afa55669d1": [ + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + } + ], + "690a2ae6-962a-438c-91ca-60425a0c8d02": [ + { + "document_id": "690a2ae6-962a-438c-91ca-60425a0c8d02", + "text": "Accepted Article\n\n© 2013 The Authors Aging Cell © 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland over 90 years and 1,955 controls between 55 and 80 years did not reveal genome-wide significant loci (Newman et al., 2010) and neither did the analyses of all-cause mortality and survival free of major disease in this cohort (Walter et al., 2011).A smaller Dutch study of 403 nonagenarians and 1,670 controls younger than 65 years identified the APOE gene as a mortality locus (Deelen et al., 2011), which was confirmed in a German study of 763 long-lived individuals and 1,085 younger controls (Nebel et al., 2011) and a longitudinal study of 1,606 Danes showed that the effect size of this association increases at the highest ages (Jacobsen et al., 2010).Apparently, the influence of the common genetic variation on longevity is small which requires large meta-GWA studies for identification.Alternatively, rare genetic variants may play a more important role in longevity.Since the previous linkage studies showed contradictory results potentially due to heterogeneity in the longevity phenotype, it is expected that longevity is influenced by many private rare variants." + } + ], + "78a43a45-84b0-4d73-9396-95b99cfd3983": [ + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "text": "\n\nAgeing is complex and takes a long time to study -a lifetime in fact.This makes it difficult to discern its causes, among the countless possibilities based on an individual's genes, behaviour or environment.While thousands of regions in an individual's genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle.Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nSeveral explanations are possible for the lack of genomewide significant findings.First, mortality is arguably 1 of the most complex phenotypes, and several trajectories toward extreme old age have been identified (Evert et al., 2003).Multiple genes could mediate the aging process but would have their effects through numerous different patho-physiological processes and diseases that act as intermediate factors on the pathway to death (de Magalhaes et al., 2010).Therefore, any common variation in genes associated with aging probably has a small effect." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nSecond, the largely negative findings of this and other studies contrast with the intriguing animal studies of longevity.Very large effects of single genes on lifespan have indeed been observed in laboratory animals, but humans often have several homologues of these genes which might significantly differ in function or compensate for mutated genes through redundant mechanisms (Kuningas et al., 2008).This could explain why our top findings did not include genes in these pathways found in animal models.Animal models also represent genetically homogenous populations and are exposed to controlled environmental influences.The lack of replication of animal model findings in humans suggests that the use of knockout animals may not provide the optimal approach to understanding the variation in survival in humans as interactions with environmental factors may obscure the associations and prevent the identification of loci in humans." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + } + ], + "97290894-086d-438a-bbd2-907dd4cea2ab": [ + { + "document_id": "97290894-086d-438a-bbd2-907dd4cea2ab", + "text": "\n\nIn addition to timing differences, a small proportion of genes (10%-15%) exhibit opposite trends of expression changes with age in humans and macaques (Supplemental Fig. S13).Interestingly, such differences are ;1.5 times more common in aging than in development, an observation consistent with the lower strength of purifying selection on the gene regulation at old age (discussed below).These differences could also reflect extreme shifts in developmental timing between species, as well as technical artifacts.Future studies, using additional species and alternative methodology, are needed to address this issue." + } + ], + "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed": [ + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nThe remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha ˜es et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design." + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "TRANSLATION OF LONGEVITY MODEL ORGANISMS AND CORE AGING PATHWAYS\n\nGenetic studies on lifespan have proven to be challenging.While longevity is a defining trait for a given species, the lifespan of individuals is of limited heritability, making analyses more difficult.Exceptional human life span, although a rare phenotype, is likely multifactorial; refined analyses are required to obtain statistically robust genomic signatures of longevity (Zhang et al., 2020) and these have proven elusive.Unlike laboratory models, the effect of environmental variance cannot be controlled in human studies, potentially masking purely biological aging mechanisms.Even laboratory models cannot replicate the complex \"environment\" of humans; it includes psychosocial, economic, and cultural factors, rather than strictly biological.These human-specific confounders are difficult or impossible to target in traditional model organisms.Despite these limitations, experimentally tractable model organisms have proven invaluable in deciphering the purely genetic contribution to lifespan, including genes and pathways conserved across the tree of life." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY\n\nHeritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE ε2), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans." + } + ], + "efd5747f-9e8b-45e8-9e04-bb31131d44fa": [ + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "text": "\n\nWith modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "Conclusions and prospects\n\nOver the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges." + } + ] + }, + "data_source": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nSeveral explanations are possible for the lack of genomewide significant findings.First, mortality is arguably 1 of the most complex phenotypes, and several trajectories toward extreme old age have been identified (Evert et al., 2003).Multiple genes could mediate the aging process but would have their effects through numerous different patho-physiological processes and diseases that act as intermediate factors on the pathway to death (de Magalhaes et al., 2010).Therefore, any common variation in genes associated with aging probably has a small effect." + }, + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "section_type": "main", + "text": "\n\nWith modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms." + }, + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "section_type": "main", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists.\n However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + }, + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "section_type": "main", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists.\n However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + }, + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "section_type": "main", + "text": "\n\nThe aging process most certainly is under highly polygenic controls… This should not discourage us from pursuing a search for those loci which may be of profound importance to human aging as it ordinarily occurs in most human beings." + }, + { + "document_id": "113cb521-b79d-4b44-8250-dc1013ea2cb3", + "section_type": "main", + "text": "\n\nChromosome mapping of genes that were differentially expressed in mice of different ages and/or in response to CR revealed a wide distribution of genes with some physical clustering of responsive genes within the genome.The latter findings are consistent with the concept that aging is a complex process and that evolutionary adaptations to aging, if they exist, may or may not involve geographic clustering of functionally related genes." + }, + { + "document_id": "0d6942b4-e75f-4f44-98ac-b8378ef1d3c7", + "section_type": "main", + "text": "FUTURE DIRECTIONS: HIGHER RESOLUTION DATA VIA HIGHER THROUGHPUT ASSAYS\n\nOne inescapable conclusion of the aggregate results of genome-wide studies of aging to date (see summary Table 1) is that we have not come close to saturating the number of potentially lifespan-altering genes in any organism.This is in no small part because directly generating survival curves is a relatively time-consuming process in most model organisms using current methods.There are several possible ways to address this.One way that has been tried is by attempting to find surrogate phenotypes [72,73,126] that can be screened more rapidly, or even scored under selection.Another is mining candidates from the many whole-genome expression profiles.Results to date with these have been very fruitful, but have not suggested that these methods alone will rapidly saturate our search for lifespan-and healthspan-altering genes in tractable model organisms." + }, + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "section_type": "main", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + }, + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "section_type": "main", + "text": "\n\nThe remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha ˜es et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "Conclusions and prospects\n\nOver the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges." + }, + { + "document_id": "97290894-086d-438a-bbd2-907dd4cea2ab", + "section_type": "main", + "text": "\n\nIn addition to timing differences, a small proportion of genes (10%-15%) exhibit opposite trends of expression changes with age in humans and macaques (Supplemental Fig. S13).Interestingly, such differences are ;1.5 times more common in aging than in development, an observation consistent with the lower strength of purifying selection on the gene regulation at old age (discussed below).These differences could also reflect extreme shifts in developmental timing between species, as well as technical artifacts.Future studies, using additional species and alternative methodology, are needed to address this issue." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "\n\nAlthough the models data set comprises all genes (to our knowledge) shown by the time of the latest update to statistically increase longevity or alter the aging process in a noticeable way, in the human data set we try to evaluate whether a given intervention is affecting the aging process itself or not.For example, many mutations may increase longevity by decreasing the incidence of specific diseases, rather than by altering the basic process of aging (de Magalhães et al ., 2005a(de Magalhães et al ., , 2005b)).Therefore, the human data set is not merely an extension of the work conducted in model organisms and of its bibliography, but a manually selected list of the most pertinent human aging candidate genes, each presented with a higher annotation level.We cite studies on whether the functions of aging-associated genes in model organisms are conserved in their human orthologues.Likewise, we cite flaws in previous studies based on new published observations, although we have a neutral stance on conflicting findings from different research groups.Our policy is to cite all conflicting reports and let visitors make their own decisions on how to interpret them.By contrast, each entry in GenAge model organisms has only one reference: the first publication reporting an association of the gene with longevity or aging.Moreover, one of the latest enhancements in the human data set was the inclusion of Gene Ontology annotation.Gene Ontology terms and annotation files were obtained from the Gene Ontology Consortium website (http://www.geneontology.org/ ) and provide an additional layer of description for the gene products in a cellular context (Ashburner et al ., 2000)." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010)." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nResults from mutational analysis across eukaryote model organisms have shown unexpected conservation of genes and processes regulating aging.While unique properties exist within particular organisms that modulate these foundational networks, the conservation provides a tool to refine human genetic studies.As noted, GWAS for human longevity metrics suffer from large sample size requirements to obtain statistical resolution due to multiple hypothesis testing across the genome.Assuming that evolutionary genesets for longevity could be generated with confidence, an intersection of them with human variation data would increase the sensitivity of association studies.This would serve as a selective filter to refine the number of loci investigated for association in human populations.Similarly, such evolutionary filters could refine analysis of rare, unique variation within genome sequence data from extremely long-lived cohorts.A similar approach to refine human longevity GWAS used an intersection with age-related disease datasets.This 'disease-informed' GWAS helped refine candidates (iGWAS, Fortney et al., 2015), though, it should be noted that this particular strategy would further blur the distinction between aging and longevity as discussed above.The definition of gene sets from evolutionary experiments in longevity, across clades, would similarly empower detection of networks previously hidden under GWAS in human population analyses (Figure 3)." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "Genetic\nlinkage studies of long-lived human families identified a\nlongevity locus while candidate gene approaches have been\nused to identify and confirm the association between\nspecific variants in the FOXO3A gene and human\nlongevity [3–7]. Genome-wide association studies have\nalso been used to identify the association of APOE with life\n\n123\nAging Clin Exp Res\n\nspan and have yielded insights into potential biological\npathways and processes related to aging. Despite these\nsuccesses, several problems are inherent in human\nlongevity studies including potentially high degrees of\nenvironmental heterogeneity, genetic diversity, and lack of\nbirth matched controls, among others [8]." + }, + { + "document_id": "18e216d9-ea5c-4dfe-a30d-632163fcf39e", + "section_type": "main", + "text": "Conclusions\n\nIn the absence of a consensus phenotype for aging, genetic research is impeded (Melzer et al. 2007).At present, it is difficult to determine whether preventative and therapeutic strategies (such as calorie restriction) have beneficial effects in humans because there are no validated biomarkers that can serve as surrogate markers of aging (Matkovic et al. 1990).To have the \"phenome of aging\" (Xue et al. 2007) much better defined, we propose using the musculoskeletal aging phenotypes as an example and starting point." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + }, + { + "document_id": "04405b2b-901a-423c-9f08-418f5514c535", + "section_type": "main", + "text": "\n\nThese considerations suggest an intriguing question: why did \"Mother Nature\" conserve a common pathway of regulation between two genes involved in a process that is believed to have come out of natural selection?It has been recently proposed that a programmed and altruistic aging may occur in higher eukaryotes [5].Our findings are in line with this idea, although the deep evolutionary force that has driven such an architecture along evolution needs to be explored.The markers used for haplotype analysis are the following (in order): A21631G for PSMD13, G477T and 1-6 VNTR intron5 for SIRT3.Haplotype relative frequencies (RF) and standard errors (SE) are ×100.The p values refer to the null hypothesis of no difference between the transcription activity of the entire 788-bp promoter and the transcription activity of the deletion construct (ANOVA and LSD post hoc tests)." + }, + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "section_type": "main", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nSecond, the largely negative findings of this and other studies contrast with the intriguing animal studies of longevity.Very large effects of single genes on lifespan have indeed been observed in laboratory animals, but humans often have several homologues of these genes which might significantly differ in function or compensate for mutated genes through redundant mechanisms (Kuningas et al., 2008).This could explain why our top findings did not include genes in these pathways found in animal models.Animal models also represent genetically homogenous populations and are exposed to controlled environmental influences.The lack of replication of animal model findings in humans suggests that the use of knockout animals may not provide the optimal approach to understanding the variation in survival in humans as interactions with environmental factors may obscure the associations and prevent the identification of loci in humans." + }, + { + "document_id": "690a2ae6-962a-438c-91ca-60425a0c8d02", + "section_type": "main", + "text": "Accepted Article\n\n© 2013 The Authors Aging Cell © 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland over 90 years and 1,955 controls between 55 and 80 years did not reveal genome-wide significant loci (Newman et al., 2010) and neither did the analyses of all-cause mortality and survival free of major disease in this cohort (Walter et al., 2011).A smaller Dutch study of 403 nonagenarians and 1,670 controls younger than 65 years identified the APOE gene as a mortality locus (Deelen et al., 2011), which was confirmed in a German study of 763 long-lived individuals and 1,085 younger controls (Nebel et al., 2011) and a longitudinal study of 1,606 Danes showed that the effect size of this association increases at the highest ages (Jacobsen et al., 2010).Apparently, the influence of the common genetic variation on longevity is small which requires large meta-GWA studies for identification.Alternatively, rare genetic variants may play a more important role in longevity.Since the previous linkage studies showed contradictory results potentially due to heterogeneity in the longevity phenotype, it is expected that longevity is influenced by many private rare variants." + }, + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "section_type": "main", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + }, + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "section_type": "main", + "text": "These examples serve to illustrate the general point that the more complex designs of\nexperiments that manipulate the level of imposed mortality rates, unlike the simpler\nprocedure of altering the first age of reproduction in a laboratory population, may in turn\nmake these experiments systematically more difficult to interpret. Futuyma and Bennett\n(this volume) also discuss the merits of simple experimental manipulations.\n THE NUMBER OF GENES AFFECTING AGING\n\nEarly evolutionary discussions of aging, such as those by Williams (1957) and Maynard\nSmith (1966), characteristically concluded that a large number of loci are likely to affect\naging." + }, + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "section_type": "main", + "text": "These examples serve to illustrate the general point that the more complex designs of\nexperiments that manipulate the level of imposed mortality rates, unlike the simpler\nprocedure of altering the first age of reproduction in a laboratory population, may in turn\nmake these experiments systematically more difficult to interpret. Futuyma and Bennett\n(this volume) also discuss the merits of simple experimental manipulations.\n THE NUMBER OF GENES AFFECTING AGING\n\nEarly evolutionary discussions of aging, such as those by Williams (1957) and Maynard\nSmith (1966), characteristically concluded that a large number of loci are likely to affect\naging." + }, + { + "document_id": "9fed8fd1-fce5-4fc1-9911-05d312f88521", + "section_type": "main", + "text": "\n\nThe antagonistic pleiotropy and hyperfunction theories of ageing predict the presence of genetic variants important for growth and development in early life with deleterious effects towards the end of the reproductive window 19,20 .While we are unable to directly capture the genetic effects on individuals before age 40 due to the study design of our datasets, we found that the life-extending variant near FOXO3 is associated with a delay in the age at menarche and a decrease in intracranial volume and cognitive abilities.It thus appears that there are loci exhibiting antagonistic effects, although we are unable to discern whether this is due to true pleiotropy or due to linkage of causal variants within a region Genes which showed a significant effect (FDR < 5%) of gene expression on ageing traits are displayed here.Gene names are annotated with the direction of effect, where + andindicate whether the life-extending association of the locus is linked with higher or lower gene expression, respectively.Locus: nearest gene to lead variant in the multivariate analysis, Chr: chromosome, Position: base-pair position of lead variant (GRCh37), Cis-genes: genes in physical proximity (<500 kb) to the lead variant of the locus which colocalise with the multivariate signal, Trans-genes: genes located more than 500 kb from the lead variant of the locus." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "abstract", + "text": "\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "main", + "text": "\n\nWhy then are we not devoting significantly greater resources to understanding more about the greatest risk factor for every age-associated pathology by attempting to answer this fundamental question: \"What changes occur in biomolecules that lead to the manifestations of aging at higher orders of complexity and then increase vulnerability to all age-associated pathology?\"" + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "TRANSLATION OF LONGEVITY MODEL ORGANISMS AND CORE AGING PATHWAYS\n\nGenetic studies on lifespan have proven to be challenging.While longevity is a defining trait for a given species, the lifespan of individuals is of limited heritability, making analyses more difficult.Exceptional human life span, although a rare phenotype, is likely multifactorial; refined analyses are required to obtain statistically robust genomic signatures of longevity (Zhang et al., 2020) and these have proven elusive.Unlike laboratory models, the effect of environmental variance cannot be controlled in human studies, potentially masking purely biological aging mechanisms.Even laboratory models cannot replicate the complex \"environment\" of humans; it includes psychosocial, economic, and cultural factors, rather than strictly biological.These human-specific confounders are difficult or impossible to target in traditional model organisms.Despite these limitations, experimentally tractable model organisms have proven invaluable in deciphering the purely genetic contribution to lifespan, including genes and pathways conserved across the tree of life." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "996e02bf-91b2-4e81-89ba-1f661dfc662a", + "section_type": "main", + "text": "Concluding Remarks\n\nRather than expect differences in defensive or protective genes to regulate the pace of aging, which have never been found ( 13), it appears that the genetic factors that drive development may also regulate aging rates.Looking at aging as the unintended outcome of a programmed, well-orchestrated development explains why adult life span is proportional to developmental time among mammals.This perspective is also consistent with the antagonistic pleiotropy theory (53): alleles that favor early reproduction and a faster development may entail deleterious late-life effects and thus cause a faster senescence.Besides, mammals feature a robust set of developmental strategies, particularly compared with amphibians, and therefore it is not surprising that aging in different species of mammals appears to be the same process only timed at radically different rates." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "abstract", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nOn the other hand, the same evolutionary-motivated strategy suggesting to focus on more heterogeneous phenotypes (as opposite to more homogenous) can be highly beneficial for unraveling genetic predisposition to fundamental mechanisms of intrinsic biological aging and, consequently, to geriatric diseases.Indeed, aging is associated with systemic remodeling of an organism's functioning which increases chances of virtually all geriatric disorders (Franco et al. 2009;Franceschi et al. 2000;Martin et al. 2007;Cutler and Mattson 2006).Experiments with laboratory animals (Johnson 2006) and heritability estimates in humans (Christensen et al. 2006;Iachine et al. 1998) show that aging can be genetically regulated (Finch and Tanzi 1997;Martin et al. 2007;Vaupel 2010).Accordingly, yielding insights in genetic predisposition to aging-related processes in an organism could be a major breakthrough in preventing and/or ameliorating not one geriatric trait, but perhaps a major subset of such traits (Martin et al. 2007) that can greatly advance progress in solving the problem of extending healthy lifespan in humans." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nOne way to overcome (part of) this problem is by using a family-based study design (Box 1 and Fig. 1), in which the offspring of long-lived individuals -representing ''healthy agers'' -are compared to similar-aged controls from the general population.The differential gene expression profiles identified using this design may represent markers of healthy aging and familial longevity.This approach has been applied in the LLS to explore the transcriptome in whole blood for association with human familial longevity.Genes belonging to the mTOR pathway, as well as ASF1A and IL7R, were differentially expressed between offspring and controls [59,60].In addition, the expression of mTOR genes in blood associated to prevalent diabetes and serum glucose.However, the association with familial longevity was not dependent on this.Thus, gene expression profiles in blood mark human longevity in middle age and potentially provide information on the pathways that contribute to healthy aging and longevity." + }, + { + "document_id": "fe32b103-5dba-4cf0-b8af-762a71a5f5e6", + "section_type": "main", + "text": "\n\nAlthough many theories have tried to explain aging, only few experimental advances were made prior to the last two decades.Since then rapid progress in the genetics of aging has been made in invertebrate models such as C. elegans and D. melanogaster, demonstrating the existence of regulatory pathways that control the rate of aging in these organisms [1][2][3][4][5][6][7][8][9][10][11][12][13][14].They include the insulin-like pathway, the Jun kinase pathway and the Sir2 deacetylase pathway.Moreover, it was rapidly shown that some of these pathways are conserved from yeast to humans." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "section_type": "main", + "text": "\n\nAgeing is complex and takes a long time to study -a lifetime in fact.This makes it difficult to discern its causes, among the countless possibilities based on an individual's genes, behaviour or environment.While thousands of regions in an individual's genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle.Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY\n\nHeritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE ε2), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "\n\nGenAge consists of several searchable data sets.Considering the extraordinary discoveries in the genetics of aging in model organisms, GenAge includes a data set of genes associated with longevity and/or aging in model organisms.We consider a given gene for inclusion in GenAge if genetic manipulations of the gene result in noticeable changes in the aging phenotype and/or longevity.Most genes in GenAge are from the four typical model organisms: mice, worms, fruit flies and yeast (Table 1).Strikingly, homologues of many genes -such as insulin receptors and sirtuins -have been shown to regulate aging in model organisms separated by large evolutionary distances (Kenyon, 2005;Liu et al ., 2005;Smith et al ., 2008).Moreover, we have shown that genes associated with aging and/or longevity in model organisms are evolutionary conserved in terms of having more homologues than predicted by chance (Budovsky et al ., 2007(Budovsky et al ., , 2008) ) and exhibiting slower molecular evolution rates (de Magalhães & Church, 2007).Therefore, it is now clear that at least some genes identified in model organisms may be relevant to human aging." + }, + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "section_type": "main", + "text": "IV. Genome-Environment Interactions as Targets for Dietary Interventions and Drug Discovery\n\n\"…[It's] possible that we could change a human gene and double our life span. \"-CynthiaKenyon (Duncan, 2004) According to the GenAge database of aging-related genes (http://genomics.senescence.info/genes/),more than 700 genes have been identified that regulate lifespan in model organisms (de Magalha ˜es et al., 2009a).Many of these genes and their associated pathways-such as the insulin/IGF1/GH pathway-have been shown to affect longevity across different model organisms (Kenyon, 2010).Therefore, at least some mechanisms of aging are evolutionarily conserved and may have potential therapeutic applications (Baur et al., 2006).For example, evidence suggests the use of lowered IGF signaling (e.g., by targeting IGF receptors) to treat certain age-related diseases such as cancer (Pollak et al., 2004), Alzheimer's disease (Cohen et al., 2009), and autoimmune diseases (Smith, 2010).Moreover, a number of genes and pathways associated with longevity and CR are part of nutrient-sensing pathways that also regulate growth and development, including the insulin/IGF1/GH pathway (Narasimhan et al., 2009;Stanfel et al., 2009).Many of these genes modulate the response to environmental signals, such as food availability, and act in signaling pathways that if understood can be targeted (Fig. 1).The genetic regulation of aging is therefore an emerging field with multiple applications in the human nutrition, cosmetic, and pharmaceutical industries." + } + ], + "document_id": "E1F24400EE215327FE987A4DDC0768C8", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "GWAS", + "longevity", + "aging", + "human", + "gene", + "lifespan", + "genetic", + "environment" + ], + "metadata": [ + { + "object": "Transient overexpression of WRKY79 in protoplasts results in up-regulation of Gene:542165, Gene:541974, Gene:100274033, Gene:542688, Gene:542150, Gene:542151, Gene:100273457, Gene:100285509, Gene:103626248, Gene:103646045, Gene:100217270, Gene:100279981, Gene:100281950, Gene:542476, Gene:542369, Gene:100281950, and Gene:542260.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab969966" + }, + { + "object": "Data suggest that the redox status of serum apoE might be related to the synthesis of HDL; the cysteine-thiol residue of reduced-apoE is in a naive state, while that of non-reduced-apoE is in a reversibly or irreversibly oxidized state. Data suggest that apoE homodimer and apoE-AII complex are typical reversibly oxidized forms of apoE. apoE-AII complex = a complex of apolipoprotein E and apolipoprotein A-II", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab212832" + }, + { + "object": "APOE genotype status moderated the age-related declines in episodic memory: APOE-epsilon4+ middle-aged adults exhibited impairments relative to both APOE-epsilon4- middle-aged participants, and APOE-epsilon4+ younger adults.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab77520" + }, + { + "object": "In an Amish population, using expression profiling of genes within regions identified by a meta-analysis GWAS of survival to age 90, we localized PAPSS2 as a candidate gene for extended life span. These results provide novel evidence for genetic loci implicated in longevity and incorporate gene expression results from a unique population to locate positional candidates.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab389107" + }, + { + "object": "Genome-wide associations P < 5 x 10-8 were found at the PCSK9 gene, the APOB gene, theLPL gene, the APOA1-APOA5 locus, the LIPC gene, the CETP gene, the LDLR gene, and the APOE locus.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab320109" + }, + { + "object": "There was no association between the extended psychosis phenotype and BDNF rs6265/COMT rs4680 polymorphisms. The lack of an association between different expression levels of the extended psychosis phenotype and the BDNF rs6265/ COMT rs4680 polymorphism might be related to sample characteristics, underlying gene-gene, gene-environment and gene-environment-gene interactions.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab888369" + }, + { + "object": "Data suggest that modulation of ARHGEF3 gene expression in humans with a promoter-localized SNP plays a role in human megakaryocytes and human platelet function-a finding resulting from the biological follow-up of human genetic studies. Arhgef3 KO mice partially recapitulate the human phenotype.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab209151" + }, + { + "object": "Very high gene-level linkage in Bayesian GWAS using Kutalik/Timmers data and MAGMA gene-level reanalysis to human longevity LOD 14.5; RW Williams July 2019. Linkage to longevity is second only to the TOMM40-APOE-APOC1-PVRL2-APOC4 region on Chr 19.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab6647" + }, + { + "object": "NO evident relationship was found between gene polymorphism of Eco RI loci of Apo B gene, Xba I loci of Apo B gene or 3'-VNTR of Apo B gene and ANFH.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab529998" + }, + { + "object": "Data suggest that modulation of ARHGEF3 gene expression in humans with a promoter-localized SNP plays a role in human megakaryocytes and human platelet function-a finding resulting from the biological follow-up of human genetic studies. Arhgef3 KO mice partially recapitulate the human phenotype.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab209152" + } + ], + "question": "Why is it so diffuclut to map gene loci that control aging in humans?", + "subquestions": null, + "task_id": "E1F24400EE215327FE987A4DDC0768C8", + "usage": { + "chatgpt": 7866, + "gpt-4": 4669, + "gpt-4-turbo-preview": 3717 + }, + "user_id": 2 + }, + "document_id": "E1F24400EE215327FE987A4DDC0768C8", + "task_id": "E1F24400EE215327FE987A4DDC0768C8" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/07.json b/gnqa/paper1_eval/src/data/responses/aging/experts/07.json new file mode 100644 index 0000000..2ff5d59 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/07.json @@ -0,0 +1,402 @@ +{ + "created_at": "2024-05-31T20:55:47.964141Z", + "data": { + "amplify": false, + "answer": "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "context": { + "18e216d9-ea5c-4dfe-a30d-632163fcf39e": [ + { + "document_id": "18e216d9-ea5c-4dfe-a30d-632163fcf39e", + "text": "\n\nThere are multiple definitions of the aging process.Aging may be perceived as the random, systemic loss of molecular fidelity that, after reproductive maturity, accumulates to levels that eventually exceed tissue repair, turnover, or maintenance capacity (Hayflick 2004).The underlying molecular mechanisms of aging remain a subject of debates (de Magalhaes et al. 2009): tissue deterioration might not be programmed, being just a function of increase in entropy (Hayflick 2004).No genes are necessary to drive a stochastic process; however, there are genes that act to prevent an organism from destruction and disorganization.It may be due to the absence of specific disease-causing alleles or due to the presence of favorable alleles (Halaschek-Wiener et al. 2009).These genes may inhibit entropy, regulate inflammation, maintain DNA repair (such as telomere maintenance factors), or provide antioxidant functions (e.g., antagonists of reactive oxygen species).As healthy cells adapt to degeneration, differential expression of genes with age may indicate a transcriptional response to aging rather than a deleterious mechanism of aging per se (de Magalhaes et al. 2009).It might be postulated that there exist alleles that confer a pleiotropic effect on structure and function during aging (Lunetta et al. 2007).These alleles should regulate the ability of an organism to withstand challenging endogenous and exogenous influences." + } + ], + "1ccb0d11-1c88-4b08-b40d-4039a954745f": [ + { + "document_id": "1ccb0d11-1c88-4b08-b40d-4039a954745f", + "text": "Why does ageing evolve? The intrinsic decline in function that occurs during ageing appears to be caused by the accumulation of damage, particularly at the molecular level.As far as we know, no genes have evolved specifically because they cause damage to accumulate, and the evolution of ageing can therefore be understood only as a side-effect of other causes of evolutionary change.The mechanisms by which ageing can evolve were first elucidated by J.B.S. Haldane [14], P.B. Medawar [15] and G.C. Williams [16].Extrinsic hazards from disease, predation and accidents mean that even potentially immortal organisms will die.Genetic effects that become apparent only later in life encounter a reduced force of natural selection, because not all their bearers will survive to express them.Haldane pointed out that late-onset genetic diseases in humans, such as Huntington's disease, encounter only weak selection, because most reproduction is complete by the age of onset [14].Ageing could therefore result from the accumulation under mutation pressure of age-specific, deleterious mutations.In addition, if some mutations have pleiotropic effects, with beneficial effects in youth, such as high fecundity, but also with a higher subsequent rate of ageing, then they could be incorporated into the population by natural selection, which will act more strongly on the early, beneficial effect.Thus, variation in the rate of ageing would result from the readjustment of a tradeoff between youthful benefits and the subsequent rate of ageing.Both processes imply that faster ageing will evolve where the extrinsic hazard to adults is greatest, a hypothesis in general supported by the data [1,2,17]." + } + ], + "4f010a74-a9b4-4538-94f7-ae8f35c8b96e": [ + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "text": "A. Theories\n\nIn looking back at the development of aging studies, we can see that it did not follow a straight or logical course.On the contrary, it can be compared with the flow of several convergent streams winding in their course.To date, numerous proposals have been made for the paradigm of aging.These include Hayflick's contributions (153) on programmed cellular incapacitation derived from flbroblast studies, a decrease in immunologic response, deleterious endocrinological changes, nuclear somatic gene mutation, mitochondrial somatic gene mutation, oxygen free radical damage to proteins and nucleic acids, molecular instabilities, molecular cross-linking, glycation reactions, and so on.There is little doubt that many of these factors contribute to the overall aging, but what are primary causes, and what are secondary outcomes?" + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Ageing Is Adjusted by Genetic, Environmental, and Stochastic Processes\n\nEnough evidence suggests that ageing is the result of different events such as molecular damage, mutations, incomplete repair, genetic programs, and continued development, among others [16].These events, in turn, are caused by genetic factors, environmental conditions, and even stochastic factors, which are mentioned below in this chapter." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nDifferent stochastic theories of ageing focus on specific mechanisms that may lead to ageing.The catastrophic error theory poses that the accumulation of errors in protein synthesis causes damage in cell function.The theory of cross-linking holds this process between proteins and other macromolecules responsible for ageing, while the theory of free radicals suggests that ageing is the result of inadequate protection against cell and tissue damage by free radicals and oxidative stress throughout life.Finally, the wear-and-tear theory poses that the cumulative damage that eventually leads to ageing and death is, in fact, the result of the continuous functioning of vital processes, during which stochastic errors gradually arise." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Introduction\n\nAging is a natural and irreversible process characterized by a progressive decay in physiological, biochemical, and structural functions of individuals.Aging is a multifactorial process that can be affected by two main factors: environmental and genetic.Environmental factors are nutrition, pathologies, pollution exposure, physical activity, and microbiota, while genetic factors are issues that have been associated with antioxidant and DNA damage responses, the fidelity of genetic information transfer, the efficiency of protein degradation, the extent of cellular responsiveness to stress, the mechanisms of epigenetic regulation, and the ability to elongate telomeres.All of them can determine how fast we age.Traditionally, aging studies had used several model organisms, from yeast to mammals, especially rodents (rats and mice).Most of the studies are made under controlled conditions, where only a few variables are observed, and the subjects are members of the same strain with the same genetic backgrounds or the same mutations.The information that so far has been obtained about aging has helped us to describe different factors that influence this process and that are the fundamental concepts of the various theories of aging.However, these theories do not fully explain the aging process in the different models of aging study.This is the case of the study of aging in humans, where it is very difficult to control the environmental and genetic variables.That is why issues haven't been solved such as the following: How does time influence aging?When do we start to age?How do we know we are old?Is it possible to delay aging?Those and more questions are the cornerstones for aging studies.Biological aging has been associated with the decrease in the repair and regeneration capacity of tissues and organs; it is a time-dependent process.This reduction can be observed by an increase in the acquisition of diseases and functional and reproductive disability, which eventually lead to death.On the other hand, it has been observed that in humans, people with the same chronological age exhibit different trajectories in the decrease of physiological functions associated with biological aging and what complicates the understanding of the molecular and physiological phenomena that drive the complex and multifactorial processes that underlie biological aging in humans." + } + ], + "5030cbc8-e02c-4e3a-8cbc-0156ce123c99": [ + { + "document_id": "5030cbc8-e02c-4e3a-8cbc-0156ce123c99", + "text": "\nThe underlying cause of aging remains one of the central mysteries of biology.Recent studies in several different systems suggest that not only may the rate of aging be modified by environmental and genetic factors, but also that the aging clock can be reversed, restoring characteristics of youthfulness to aged cells and tissues.This Review focuses on the emerging biology of rejuvenation through the lens of epigenetic reprogramming.By defining youthfulness and senescence as epigenetic states, a framework for asking new questions about the aging process emerges." + } + ], + "5e157c2e-91b8-466d-a9fd-f91f8f432f0c": [ + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "text": "\n\nAging does not happen in a vacuum.Aging must be the result of changes that occur in molecules that have existed at one time with no age changes.It is the state of these pre-existing molecules that governs longevity determination.The pre-existing state is, as I have already described, maintained by repair and turnover systems that themselves eventually succumb to irreparable age changes.Longevity determination is the state of all molecules prior to succumbing to irreparable loss of molecular structure." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "text": "\n\nBiological aging is more than simply the occurrence of random changes in molecules.It also includes the role of the many repair systems found within cells.Thus, a more complete, but less concise, explanation of the first causes of aging in biological systems is the following:" + } + ], + "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c": [ + { + "document_id": "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c", + "text": "U\n\nnderstanding the deleterious processes that cause aging has been a human endeavor ever since we figured out that we grew old and that we didn't like it.Many hypotheses have been proposed to explain the root cause of aging (1).One broad-based hypothesis is that generalized homeostatic failure leads to age-related decline.Although notions of time-and use-related deterioration may be applicable to mechanical objects, they fall short as analogies to biological systems because energy input should theoretically maintain living systems indefinitely.Yet, despite the regenerative potential of biological organisms, progressive deterioration accompanies postmaturational aging.That the organism's repair capabilities cannot keep up with wear and tear is, according to evolutionary theory, explained by the inevitable declining force of natural selection with age.According to this reasoning, there is no selective advantage to maintaining somatic cells in perfect order much beyond reproductive maturation (1).Hence, a long life depends on the timing of maturation and the quality of somatic cell maintenance." + }, + { + "document_id": "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c", + "text": "\n\nWear and tear on the DNA often has been touted as a possible basis for our progressive age-related decline.Supporting this notion is the work of de Boer et al. (2) reported on page 1276 of this week's issue.They reveal important evidence for imperfect genome maintenance of DNA damage as a possible causal factor in aging.Harman, with his \"free radical theory of aging\" (3), was the first to propose that metabolic by-products called reactive oxygen species (ROS) continually damage cellular macromolecules, including DNA.Incomplete repair of such damage would lead to its accumulation over time and eventually result in age-related deterioration.A number of observations support the free radical theory, including the discovery that dietary restriction delays aging and extends life-span in a wide range of rodents and other species, possibly by reducing free radical damage.The notion that genomic DNA could be a major target of continual free radical attack over time is supported by the recent observation that genetic lesions accumulate with age and that dietary restriction reduces this accumulation in rodents (4).In addition, deletion of p66 shc , a signaling protein that maintains oxidant levels, increases resistance to oxidative damage and extends the life-span of mice (5)." + } + ], + "606c59c5-5ae4-47e9-b3eb-58afa55669d1": [ + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "text": "Instead, aging is expected to\nbe a pervasive failure of adaptation across most, if not all, of the physiological mechanisms\nthat sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute “the cause of\naging” to any one physiological mechanism or gene for aging or programmed death. Although common genetic pathways might be identified that contribute to aging among a\nvariety of organisms (cf." + } + ], + "846ae0a9-165f-4b25-8bcb-310c7da5eb44": [ + { + "document_id": "846ae0a9-165f-4b25-8bcb-310c7da5eb44", + "text": "Background\n\nAging is a complex process characterized by the progressive degeneration of a healthy phenotype and correlated with a decline in the ability to withstand cellular stress and damage.The subject of investigation for decades, the underlying molecular genetic causes of and responses to aging remain an area of active study.Research from model systems has characterized a range of physiological and molecular phenotypes associated with aging.These include genomic instability caused by accumulation of DNA damage, dysregulation of repair mechanisms, and telomere attrition; epigenetic alterations; dysregulation of transcription; loss of proteostasis; cellular senescence; and deregulated nutrient sensing, metabolic pathways, and energy use (reviewed in [1]).Separating causation from correlation between these phenotypes and aging remains a challenge, however." + } + ], + "870798fd-2c26-4819-9403-fe52836770eb": [ + { + "document_id": "870798fd-2c26-4819-9403-fe52836770eb", + "text": "Introduction\n\nUnderstanding what actually causes ageing remains admittedly a fundamental and fascinating problem in biology [1].Experimental data accumulated in the last three decades have led to the identification of various environmental and genetic factors, as well as chemical substances that influence lifespan in divergent eukaryotic species [1,2].Organisms normally age faster and hence live shorter under stress conditions that can lead to the generation of DNA mutations and, often as a consequence of mutations, damaged cytoplasmic constituents (including injured proteins, lipids, carbohydrates and organelles).Such types of damage can interfere with cellular functioning; thereby, they should be eliminated by effective repair and self-cleaning mechanisms to maintain cellular homeostasis.These mechanisms include DNA repair pathways, molecular chaperons, as well as the proteasome-ubiquitin system and lysosome-mediated autophagy, the main forms of cellular self-degradation [3].This has led to the attractive model that the gradual, lifelong accumulation of unrepaired cellular damage drives the ageing process and determines the incidence of age-related fatal diseases [4,5]." + } + ], + "996e02bf-91b2-4e81-89ba-1f661dfc662a": [ + { + "document_id": "996e02bf-91b2-4e81-89ba-1f661dfc662a", + "text": "\n\nIn conclusion, aging may not be primarily due to damage accumulating from the basic biochemical reactions that make up life but rather the result of the developmental program or of changes brought about by it.Our hypothesis is that the timing of development regulates the rate of aging among mammals, with a subset of developmental mechanisms determining the pace and causing most agerelated changes.Maybe people change as they grow old due to the same mechanisms that drive changes throughout the earlier stages in life." + } + ], + "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed": [ + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "text": "Instead, aging is expected to\nbe a pervasive failure of adaptation across most, if not all, of the physiological mechanisms\nthat sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute “the cause of\naging” to any one physiological mechanism or gene for aging or programmed death. Although common genetic pathways might be identified that contribute to aging among a\nvariety of organisms (cf." + } + ], + "a6bc2efd-61a7-4e07-ad5c-49234aa89431": [ + { + "document_id": "a6bc2efd-61a7-4e07-ad5c-49234aa89431", + "text": "\n\nIn 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13]." + } + ], + "ab6a47ba-2131-4fc5-be5e-b81dd80d2a65": [ + { + "document_id": "ab6a47ba-2131-4fc5-be5e-b81dd80d2a65", + "text": "Introduction\n\nThe fundamental manifestation of the aging process is a progressive decline in the functional maintenance of tissue homeostasis and an increasing propensity to degenerative diseases and death [1].It has attracted significant interest to study the underlying mechanisms of aging, and many theories have been put forward to explain the phenomenon of aging.There is an emerging consensus that aging is a multifactorial process, which is genetically determined and influenced epigenetically by environment [2].Most aging theories postulate a single physiological cause of aging, and likely these theories are correct to a certain degree and in certain aspects of aging." + } + ], + "ca76f85d-9f72-4e15-8ba9-3bf94308c449": [ + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + } + ], + "f4dd6a1d-062b-42bc-8e22-83fcb3135578": [ + { + "document_id": "f4dd6a1d-062b-42bc-8e22-83fcb3135578", + "text": "\n\nTrying to explain aging in terms of a singular process would be in conflict with evolutionary theory.Even if loss of genome sequence integrity was the most conserved cause of aging, already active in the first replicators (Vijg, 2007), natural selection would allow a multitude of mutations with late adverse effects to accumulate in the germline, many of which would be positively selected for because of their beneficial effects early in life (Williams, 1957), In this respect, somatic mutation accumulation could be a conserved, inevitable cause of aging but superposed on multiple other processes that usually cause the earlier demise of an individual." + } + ] + }, + "data_source": [ + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "section_type": "main", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + }, + { + "document_id": "870798fd-2c26-4819-9403-fe52836770eb", + "section_type": "main", + "text": "Introduction\n\nUnderstanding what actually causes ageing remains admittedly a fundamental and fascinating problem in biology [1].Experimental data accumulated in the last three decades have led to the identification of various environmental and genetic factors, as well as chemical substances that influence lifespan in divergent eukaryotic species [1,2].Organisms normally age faster and hence live shorter under stress conditions that can lead to the generation of DNA mutations and, often as a consequence of mutations, damaged cytoplasmic constituents (including injured proteins, lipids, carbohydrates and organelles).Such types of damage can interfere with cellular functioning; thereby, they should be eliminated by effective repair and self-cleaning mechanisms to maintain cellular homeostasis.These mechanisms include DNA repair pathways, molecular chaperons, as well as the proteasome-ubiquitin system and lysosome-mediated autophagy, the main forms of cellular self-degradation [3].This has led to the attractive model that the gradual, lifelong accumulation of unrepaired cellular damage drives the ageing process and determines the incidence of age-related fatal diseases [4,5]." + }, + { + "document_id": "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c", + "section_type": "main", + "text": "U\n\nnderstanding the deleterious processes that cause aging has been a human endeavor ever since we figured out that we grew old and that we didn't like it.Many hypotheses have been proposed to explain the root cause of aging (1).One broad-based hypothesis is that generalized homeostatic failure leads to age-related decline.Although notions of time-and use-related deterioration may be applicable to mechanical objects, they fall short as analogies to biological systems because energy input should theoretically maintain living systems indefinitely.Yet, despite the regenerative potential of biological organisms, progressive deterioration accompanies postmaturational aging.That the organism's repair capabilities cannot keep up with wear and tear is, according to evolutionary theory, explained by the inevitable declining force of natural selection with age.According to this reasoning, there is no selective advantage to maintaining somatic cells in perfect order much beyond reproductive maturation (1).Hence, a long life depends on the timing of maturation and the quality of somatic cell maintenance." + }, + { + "document_id": "846ae0a9-165f-4b25-8bcb-310c7da5eb44", + "section_type": "main", + "text": "Background\n\nAging is a complex process characterized by the progressive degeneration of a healthy phenotype and correlated with a decline in the ability to withstand cellular stress and damage.The subject of investigation for decades, the underlying molecular genetic causes of and responses to aging remain an area of active study.Research from model systems has characterized a range of physiological and molecular phenotypes associated with aging.These include genomic instability caused by accumulation of DNA damage, dysregulation of repair mechanisms, and telomere attrition; epigenetic alterations; dysregulation of transcription; loss of proteostasis; cellular senescence; and deregulated nutrient sensing, metabolic pathways, and energy use (reviewed in [1]).Separating causation from correlation between these phenotypes and aging remains a challenge, however." + }, + { + "document_id": "996e02bf-91b2-4e81-89ba-1f661dfc662a", + "section_type": "main", + "text": "\n\nIn conclusion, aging may not be primarily due to damage accumulating from the basic biochemical reactions that make up life but rather the result of the developmental program or of changes brought about by it.Our hypothesis is that the timing of development regulates the rate of aging among mammals, with a subset of developmental mechanisms determining the pace and causing most agerelated changes.Maybe people change as they grow old due to the same mechanisms that drive changes throughout the earlier stages in life." + }, + { + "document_id": "5030cbc8-e02c-4e3a-8cbc-0156ce123c99", + "section_type": "abstract", + "text": "\nThe underlying cause of aging remains one of the central mysteries of biology.Recent studies in several different systems suggest that not only may the rate of aging be modified by environmental and genetic factors, but also that the aging clock can be reversed, restoring characteristics of youthfulness to aged cells and tissues.This Review focuses on the emerging biology of rejuvenation through the lens of epigenetic reprogramming.By defining youthfulness and senescence as epigenetic states, a framework for asking new questions about the aging process emerges." + }, + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "section_type": "main", + "text": "Instead, aging is expected to\nbe a pervasive failure of adaptation across most, if not all, of the physiological mechanisms\nthat sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute “the cause of\naging” to any one physiological mechanism or gene for aging or programmed death.\n Although common genetic pathways might be identified that contribute to aging among a\nvariety of organisms (cf." + }, + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "section_type": "main", + "text": "Instead, aging is expected to\nbe a pervasive failure of adaptation across most, if not all, of the physiological mechanisms\nthat sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute “the cause of\naging” to any one physiological mechanism or gene for aging or programmed death.\n Although common genetic pathways might be identified that contribute to aging among a\nvariety of organisms (cf." + }, + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "section_type": "main", + "text": "A. Theories\n\nIn looking back at the development of aging studies, we can see that it did not follow a straight or logical course.On the contrary, it can be compared with the flow of several convergent streams winding in their course.To date, numerous proposals have been made for the paradigm of aging.These include Hayflick's contributions (153) on programmed cellular incapacitation derived from flbroblast studies, a decrease in immunologic response, deleterious endocrinological changes, nuclear somatic gene mutation, mitochondrial somatic gene mutation, oxygen free radical damage to proteins and nucleic acids, molecular instabilities, molecular cross-linking, glycation reactions, and so on.There is little doubt that many of these factors contribute to the overall aging, but what are primary causes, and what are secondary outcomes?" + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Ageing Is Adjusted by Genetic, Environmental, and Stochastic Processes\n\nEnough evidence suggests that ageing is the result of different events such as molecular damage, mutations, incomplete repair, genetic programs, and continued development, among others [16].These events, in turn, are caused by genetic factors, environmental conditions, and even stochastic factors, which are mentioned below in this chapter." + }, + { + "document_id": "a6bc2efd-61a7-4e07-ad5c-49234aa89431", + "section_type": "main", + "text": "\n\nIn 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Introduction\n\nAging is a natural and irreversible process characterized by a progressive decay in physiological, biochemical, and structural functions of individuals.Aging is a multifactorial process that can be affected by two main factors: environmental and genetic.Environmental factors are nutrition, pathologies, pollution exposure, physical activity, and microbiota, while genetic factors are issues that have been associated with antioxidant and DNA damage responses, the fidelity of genetic information transfer, the efficiency of protein degradation, the extent of cellular responsiveness to stress, the mechanisms of epigenetic regulation, and the ability to elongate telomeres.All of them can determine how fast we age.Traditionally, aging studies had used several model organisms, from yeast to mammals, especially rodents (rats and mice).Most of the studies are made under controlled conditions, where only a few variables are observed, and the subjects are members of the same strain with the same genetic backgrounds or the same mutations.The information that so far has been obtained about aging has helped us to describe different factors that influence this process and that are the fundamental concepts of the various theories of aging.However, these theories do not fully explain the aging process in the different models of aging study.This is the case of the study of aging in humans, where it is very difficult to control the environmental and genetic variables.That is why issues haven't been solved such as the following: How does time influence aging?When do we start to age?How do we know we are old?Is it possible to delay aging?Those and more questions are the cornerstones for aging studies.Biological aging has been associated with the decrease in the repair and regeneration capacity of tissues and organs; it is a time-dependent process.This reduction can be observed by an increase in the acquisition of diseases and functional and reproductive disability, which eventually lead to death.On the other hand, it has been observed that in humans, people with the same chronological age exhibit different trajectories in the decrease of physiological functions associated with biological aging and what complicates the understanding of the molecular and physiological phenomena that drive the complex and multifactorial processes that underlie biological aging in humans." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "main", + "text": "\n\nBiological aging is more than simply the occurrence of random changes in molecules.It also includes the role of the many repair systems found within cells.Thus, a more complete, but less concise, explanation of the first causes of aging in biological systems is the following:" + }, + { + "document_id": "18e216d9-ea5c-4dfe-a30d-632163fcf39e", + "section_type": "main", + "text": "\n\nThere are multiple definitions of the aging process.Aging may be perceived as the random, systemic loss of molecular fidelity that, after reproductive maturity, accumulates to levels that eventually exceed tissue repair, turnover, or maintenance capacity (Hayflick 2004).The underlying molecular mechanisms of aging remain a subject of debates (de Magalhaes et al. 2009): tissue deterioration might not be programmed, being just a function of increase in entropy (Hayflick 2004).No genes are necessary to drive a stochastic process; however, there are genes that act to prevent an organism from destruction and disorganization.It may be due to the absence of specific disease-causing alleles or due to the presence of favorable alleles (Halaschek-Wiener et al. 2009).These genes may inhibit entropy, regulate inflammation, maintain DNA repair (such as telomere maintenance factors), or provide antioxidant functions (e.g., antagonists of reactive oxygen species).As healthy cells adapt to degeneration, differential expression of genes with age may indicate a transcriptional response to aging rather than a deleterious mechanism of aging per se (de Magalhaes et al. 2009).It might be postulated that there exist alleles that confer a pleiotropic effect on structure and function during aging (Lunetta et al. 2007).These alleles should regulate the ability of an organism to withstand challenging endogenous and exogenous influences." + }, + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "section_type": "main", + "text": "\n\nThe dominant theory at the time was that aging was caused by the accumulation of molecular damage generated by oxygen radicals, particularly originating from the mitochondria.Independently, Pamela Larsen and Jacques Vanfleteren exposed wild-type and age-1 mutants to oxidants (hydrogen peroxide and paraquat, respectively) (26,27).The assays were conducted in young animals over days.The long-lived mutants were resistant to oxidative stress.Moreover, age-1 mutant worms had elevated levels of the antioxidant enzymes, superoxide dismutase, and catalase activities which could be sufficient to confer oxidative stress resistance and was consistent with the oxygen radical theory of aging." + }, + { + "document_id": "42cbc297-d57c-4c1f-8d3f-f9e52748b823", + "section_type": "main", + "text": "Conclusions\n\nSkin follows the pathway of aging, whereas in addition to the internal factors, several environmental ones contribute to this process and sometimes accelerate the onset of aging in the skin.Skin functions deteriorate, and this results in the development of a palette of diseases that sometimes jeopardize life quality or even life itself.Awareness of the pathophysiology of age-associated skin diseases as well as of preventive measurements to avoid skin damage is the first step for successful, healthy aging.Genomic technologies, such as gene chips, have identified gene expression signatures associated with skin aging and have become a fundamental basis in helping to develop new skin repair products.Proteomics and metabolomics can complete the increasing knowledge in this field.Research to understand a natural phenomenon such as aging should not only be considered as a privilege of modern Western society but also as the best prevention of age-associated diseases, including cancer." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "abstract", + "text": "\nThe belief that aging is still an unsolved problem in biology is no longer true.Of the two major classes of theories, the one class that is tenable is derivative of a single common denominator that results in only one fundamental theory of aging.In order to address this complex subject, it is necessary to first define the four phenomena that characterize the finitude of life.These phenomena are aging, the determinants of longevity, age-associated diseases, and death.There are only two fundamental ways in which age changes can occur.Aging occurs either as the result of a purposeful program driven by genes or by events that are not guided by a program but are stochastic or random, accidental events.The weight of evidence indicates that genes do not drive the aging process but the general loss of molecular fidelity does.Potential longevity is determined by the energetics of all molecules present at and after the time of reproductive maturation.Thus, every molecule, including those that compose the machinery involved in turnover, replacement, and repair, becomes the substrate that experiences the thermodynamic instability characteristic of the aging process.However, the determinants of the fidelity of all molecules produced before and after reproductive maturity are the determinants of longevity.This process is governed by the genome.Aging does not happen in a vacuum.Aging must be the result of changes that occur in molecules that have existed at one time with no age changes.It is the state of these pre-existing molecules that governs longevity determination.The distinction between the aging process and age-associated disease is not only based on the molecular definition of aging described above but it is also rooted in several practical observations.Unlike any disease, age changes (a) occur in every multicellular animal that reaches a fixed size at reproductive maturity, (b) cross virtually all species barriers, (c) occur in all members of a species only after the age of reproductive maturation, (d) occur in all animals removed from the wild and protected by humans even when that species probably has not experienced aging for thousands or even millions of years, (e) occur in virtually all animate and inanimate matter, and (f ) have the same universal molecular etiology, that is, thermodynamic instability.Unlike aging, there is no disease or pathology that shares these six qualities.Because this critical distinction is poorly understood, there" + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nDifferent stochastic theories of ageing focus on specific mechanisms that may lead to ageing.The catastrophic error theory poses that the accumulation of errors in protein synthesis causes damage in cell function.The theory of cross-linking holds this process between proteins and other macromolecules responsible for ageing, while the theory of free radicals suggests that ageing is the result of inadequate protection against cell and tissue damage by free radicals and oxidative stress throughout life.Finally, the wear-and-tear theory poses that the cumulative damage that eventually leads to ageing and death is, in fact, the result of the continuous functioning of vital processes, during which stochastic errors gradually arise." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "main", + "text": "\n\nAging then is a catabolic process that is chance driven.Longevity determination is an anabolic process that, indirectly, is genome driven." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "main", + "text": "\n\nThe evidence for the belief that aging is a stochastic process is, first, that everything in the universe changes or ages in space-time without being driven by a purposeful program.Second, there is no direct evidence that proves that age changes are governed by a genetic program.Finally, there is a huge body of knowledge indicating that age changes are characterized by the loss of molecular fidelity." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "main", + "text": "\n\nAging does not happen in a vacuum.Aging must be the result of changes that occur in molecules that have existed at one time with no age changes.It is the state of these pre-existing molecules that governs longevity determination.The pre-existing state is, as I have already described, maintained by repair and turnover systems that themselves eventually succumb to irreparable age changes.Longevity determination is the state of all molecules prior to succumbing to irreparable loss of molecular structure." + }, + { + "document_id": "ab6a47ba-2131-4fc5-be5e-b81dd80d2a65", + "section_type": "main", + "text": "Introduction\n\nThe fundamental manifestation of the aging process is a progressive decline in the functional maintenance of tissue homeostasis and an increasing propensity to degenerative diseases and death [1].It has attracted significant interest to study the underlying mechanisms of aging, and many theories have been put forward to explain the phenomenon of aging.There is an emerging consensus that aging is a multifactorial process, which is genetically determined and influenced epigenetically by environment [2].Most aging theories postulate a single physiological cause of aging, and likely these theories are correct to a certain degree and in certain aspects of aging." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "section_type": "main", + "text": "\n\nThe belief that aging is still an unsolved problem in biology is no longer true.Of the two major classes of theories, the one class that is tenable is derivative of a single common denominator that results in only one fundamental theory of aging.In order to address this complex subject, it is necessary to first define the four phenomena that characterize the finitude of life.These phenomena are aging, the determinants of longevity, age-associated diseases, and death.There are only two fundamental ways in which age changes can occur.Aging occurs either as the result of a purposeful program driven by genes or by events that are not guided by a program but are stochastic or random, accidental events.The weight of evidence indicates that genes do not drive the aging process but the general loss of molecular fidelity does.Potential longevity is determined by the energetics of all molecules present at and after the time of reproductive maturation.Thus, every molecule, including those that compose the machinery involved in turnover, replacement, and repair, becomes the substrate that experiences the thermodynamic instability characteristic of the aging process.However, the determinants of the fidelity of all molecules produced before and after reproductive maturity are the determinants of longevity.This process is governed by the genome.Aging does not happen in a vacuum.Aging must be the result of changes that occur in molecules that have existed at one time with no age changes.It is the state of these pre-existing molecules that governs longevity determination.The distinction between the aging process and age-associated disease is not only based on the molecular definition of aging described above but it is also rooted in several practical observations.Unlike any disease, age changes (a) occur in every multicellular animal that reaches a fixed size at reproductive maturity, (b) cross virtually all species barriers, (c) occur in all members of a species only after the age of reproductive maturation, (d) occur in all animals removed from the wild and protected by humans even when that species probably has not experienced aging for thousands or even millions of years, (e) occur in virtually all animate and inanimate matter, and (f ) have the same universal molecular etiology, that is, thermodynamic instability.Unlike aging, there is no disease or pathology that shares these six qualities.Because this critical distinction is poorly understood, there" + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nThus, ageing and age-related diseases are probably not mediated by a single factor or primary mechanism, but rather their result of multiple mechanisms, some of which may be genetically determined, and others may be the result of environmental exposures or stochastic.However, not all these processes are currently accounted for, and their precise contribution to ageing remains unclear.It is, therefore, necessary to further aim research efforts at identifying these connections; this may eventually lead to the development of better treatments for age-related diseases and maybe even anti-ageing strategies." + }, + { + "document_id": "489539fd-f7c5-44eb-bb58-5fc19d50a7cf", + "section_type": "main", + "text": "A common theme among many of these\ntheories is to take a reductionist approach and focus attention at the molecular level in\nhopes of understanding the aging of organisms through the aging of their components. In\nour quest to understand the aging process, we must face reality and succumb to the notion\nthat aging is a multifactorial process; therefore it’s likely that all of the aforementioned\nprocesses factor into this phenomenon.\n An important theme emerging in the field of aging research is the role of\nepigenetic alterations in aging mammalian tissues." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "Introduction\n\nDespite recent progress, human aging is a largely controversial process.Many age-related changes have been described, yet there are multiple and conflicting theories regarding what mechanism(s) drive such changes (de Magalhães, 2005).Moreover, we do not know why different species age at different paces, and there is still no proven intervention capable of delaying or postponing the human aging process (Olshansky et al ., 2002).As such, it is clear that aging is a complex, challenging phenomenon that requires extensive research using multiple, interdisciplinary approaches to unravel its puzzles." + }, + { + "document_id": "f4dd6a1d-062b-42bc-8e22-83fcb3135578", + "section_type": "main", + "text": "\n\nTrying to explain aging in terms of a singular process would be in conflict with evolutionary theory.Even if loss of genome sequence integrity was the most conserved cause of aging, already active in the first replicators (Vijg, 2007), natural selection would allow a multitude of mutations with late adverse effects to accumulate in the germline, many of which would be positively selected for because of their beneficial effects early in life (Williams, 1957), In this respect, somatic mutation accumulation could be a conserved, inevitable cause of aging but superposed on multiple other processes that usually cause the earlier demise of an individual." + }, + { + "document_id": "996e02bf-91b2-4e81-89ba-1f661dfc662a", + "section_type": "main", + "text": "\n\nThe developmental theory of aging states that the genetic mechanisms regulating the pace of aging are located in the latter; that is, they are part of the developmental program (FIGURE 1).This concept is supported by observations in a number of animals.In organisms such as the salmon or marsupials of the genus Antechinus, the neuroendocrine system-triggered by reproduction-directly causes the death of organisms (19).Other authors have argued that a morphogenetic program originates aging in response to reproductive impulses (30,38).It is dubious, however, that similar mechanisms occur in animals that rear their offspring, such as most mammals and birds.Besides, not only reproduction but a number of developmental processes have the potential to disrupt homeostasis and cause degeneration (see below).Nonetheless, Antechinus and, particularly, the remarkable physiological degeneration of the salmon after spawning demonstrate how a developmental program optimized for reproduction can trigger senescence (19)." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Stochastic Factors\n\nAgeing is no longer regarded as a programmed process, but rather the result of damage accumulation, which results from stochastic (i.e.random) events or exposures [40].The variables that affect the ageing of an organism are the result of chance and must be studied from a probabilistic approach.According to the stochastic theories of ageing, random factors may induce ageing directly (by nonspecified mechanisms) and increase the probability of developing age-related diseases." + }, + { + "document_id": "a733a920-9896-4ca4-910d-d6f0184a0777", + "section_type": "main", + "text": "Introduction\n\nThe basic similarity of biological processes in living systems pleads for a general mechanism underlying the aging process.Although there is no agreement on the nature of such a unifying mechanism of aging, changes in informational biomolecules are considered to play an important role in the etiology of age-related deteriorative processes.Conceptually, molecular biological theories of aging should first be assigned to the two fundamentally different schools of aging theories, according to which aging is regarded either as a species-specific genetically determined.program or as a series of stochastic events (Schneider 1987)." + }, + { + "document_id": "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c", + "section_type": "main", + "text": "\n\nWear and tear on the DNA often has been touted as a possible basis for our progressive age-related decline.Supporting this notion is the work of de Boer et al. (2) reported on page 1276 of this week's issue.They reveal important evidence for imperfect genome maintenance of DNA damage as a possible causal factor in aging.Harman, with his \"free radical theory of aging\" (3), was the first to propose that metabolic by-products called reactive oxygen species (ROS) continually damage cellular macromolecules, including DNA.Incomplete repair of such damage would lead to its accumulation over time and eventually result in age-related deterioration.A number of observations support the free radical theory, including the discovery that dietary restriction delays aging and extends life-span in a wide range of rodents and other species, possibly by reducing free radical damage.The notion that genomic DNA could be a major target of continual free radical attack over time is supported by the recent observation that genetic lesions accumulate with age and that dietary restriction reduces this accumulation in rodents (4).In addition, deletion of p66 shc , a signaling protein that maintains oxidant levels, increases resistance to oxidative damage and extends the life-span of mice (5)." + }, + { + "document_id": "aff67cef-4bf7-42dc-826b-2a259722008d", + "section_type": "abstract", + "text": "\nAs our society is growing older, the consequences of aging have begun to gain particular attention.Improvement of quality of life at old age and prevention of age-associated diseases have become the main focus of the aging research.The process of aging in humans is complex and underlies multiple influences, with the probable involvement of heritable and various environmental factors.In particular, hormones are decisively involved in the generation of aging.Over time, important circulating hormones decline due to a reduced secretion of the pituitary, the adrenal glands and the gonads or due to an intercurrent disease.Among them, serum levels of growth factors and sexual steroids show significant aging-associated changes.Within the scope of the Explorative Project 'Genetic aetiology of human longevity' supported by the German National Genome Research Network 2 (NGFN-2) an in vitro model of human hormonal aging has been developed.Human SZ95 sebocytes were maintained under a hormone-substituted environment consisting of growth factors and sexual steroids in concentrations corresponding to those circulating in 20-and in 60-year-old women.Eight hundred and ninety-nine genes showed a differential expression in SZ95 sebocytes maintained under the 20-and 60-year-old hormone mixture, respectively.Among them genes were regulated which are involved in biological processes which are all hallmarks of aging.The most significantly altered signaling pathway identified was that of the transforming growth factor-b (TGF-b).A disturbed function of this cascade has been associated with tumorigenesis, i.e. in pancreatic, prostate, intestine, breast, and uterine cancer.Interestingly, genes expressed in signaling pathways operative in age-associated diseases such as Huntington's disease (HD), dentatorubral-pallidoluysian atrophy (DRPLA), and amyotrophic lateral sclerosis (ALS) were also identified.These data demonstrate that skin and its appendages may represent an adequate model for aging research.Hormones interact in a complex fashion, and aging may be partly attributed to the changes in their circulating blood levels.Furthermore, a disturbed hormone status may partially act towards the manifestation of neurodegenerative diseases.Thus, these results could be a basis for an integrated and interdisciplinary approach to the analysis of the aging process." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nAging is an extremely complex process associated with interplay of genetic, biochemical, and metabolic factors in an organism in a given environment.Although genetic studies of various animal models suggest that even a single-gene mutation can remarkably extend lifespan (Kenyon 2005;Johnson 2006) and, thus, modulate aging, no such genes are revealed in humans so far.Given that a human organism is a much more complex system than a model organism (Christensen et al. 2006), it is evident that genetic effects on the aging process should be mediated via coordinate action of a large number of inter-related processes (Kirkwood 2011).Coordinated function is rather relevant to complex biological (Soltow et al. 2010;Slagboom et al. 2011) and genetic (Bloss et al. 2011) networks than to individual genes." + }, + { + "document_id": "a733a920-9896-4ca4-910d-d6f0184a0777", + "section_type": "main", + "text": "\n\nThe fundamental mechanisms involved in the physiological deterioration observed with age in mammalian organisms have not yet been elucidated.It appears that random alterations in informational biomolecules and in their synthesis could be the basis of such physiological changes.There is, however, a lack of knowledge with respect to the frequency and characteristics of changes introduced in the cellular molecular machinery.Moreover, the driving force initiating the generation of such alterations and the order of events in which they occur are unknown at present.In this article, data concerning the hypothesis that the aging process is associated with widespread genetic instability are reviewed in the context of the complex interactions between the three major informational biomolecules, DNA, RNA, and protein.We conclude that the results obtained to date do not rule out the possibility that genetic instability in a wide sense is a major causal factor in a number of age-related phenomena.However, it appears that new strategies based on a new technology are ultimately necessary to elucidate the alterations in the intricately interwoven patterns of molecular control that could underlie the various aspects of the aging process.A first attempt is made to formulate the problems in this field and to provide some solutions." + }, + { + "document_id": "a733a920-9896-4ca4-910d-d6f0184a0777", + "section_type": "abstract", + "text": "\nThe fundamental mechanisms involved in the physiological deterioration observed with age in mammalian organisms have not yet been elucidated.It appears that random alterations in informational biomolecules and in their synthesis could be the basis of such physiological changes.There is, however, a lack of knowledge with respect to the frequency and characteristics of changes introduced in the cellular molecular machinery.Moreover, the driving force initiating the generation of such alterations and the order of events in which they occur are unknown at present.In this article, data concerning the hypothesis that the aging process is associated with widespread genetic instability are reviewed in the context of the complex interactions between the three major informational biomolecules, DNA, RNA, and protein.We conclude that the results obtained to date do not rule out the possibility that genetic instability in a wide sense is a major causal factor in a number of age-related phenomena.However, it appears that new strategies based on a new technology are ultimately necessary to elucidate the alterations in the intricately interwoven patterns of molecular control that could underlie the various aspects of the aging process.A first attempt is made to formulate the problems in this field and to provide some solutions." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "section_type": "main", + "text": "\n\nThere are several reasons for the contention that distinguishing between biological aging and disease processes may be problematic.There is little agreement on a precise definition of aging, although many have offered general characteristics; this is usefully discussed by Arking (1998).Most scientific papers on the study of aging, basic or applied, do not offer definitions of aging as an explicit biological process separate from disease and dysfunction.Survivorship and longevity, among the most widely studied attributes of aging across species, are insufficient outcomes for the study of complex animal processes, particularly in humans or other mammals; nearly all humans die of one or more discrete, identifiable medical conditions.Further, most if not all hypothesized biological mechanisms of aging encompass concepts that have also been applied to disease causation and progression.For example, age-related shortening of chromosomal telomeres has been related both to aging processes and to carcinogenesis (Shay, 1997), as have cumulative somatic mutations (Vijg, 2000;Hernandez-Boussard et al., 1999) and age-related, progressively inefficient DNA repair processes (de Boer and Hoeijmakers, 2000).Even an environmental factor that experimentally has been shown to dramatically prolong mammalian survivorship as well as decrease the occurrence of age-related physiological change and disease, caloric restriction, has been shown to alter the rate of change in age-related gene function (Lee et al., 1999)." + }, + { + "document_id": "aff67cef-4bf7-42dc-826b-2a259722008d", + "section_type": "main", + "text": "\n\nAs our society is growing older, the consequences of aging have begun to gain particular attention.Improvement of quality of life at old age and prevention of age-associated diseases have become the main focus of the aging research.The process of aging in humans is complex and underlies multiple influences, with the probable involvement of heritable and various environmental factors.In particular, hormones are decisively involved in the generation of aging.Over time, important circulating hormones decline due to a reduced secretion of the pituitary, the adrenal glands and the gonads or due to an intercurrent disease.Among them, serum levels of growth factors and sexual steroids show significant aging-associated changes.Within the scope of the Explorative Project 'Genetic aetiology of human longevity' supported by the German National Genome Research Network 2 (NGFN-2) an in vitro model of human hormonal aging has been developed.Human SZ95 sebocytes were maintained under a hormone-substituted environment consisting of growth factors and sexual steroids in concentrations corresponding to those circulating in 20-and in 60-year-old women.Eight hundred and ninety-nine genes showed a differential expression in SZ95 sebocytes maintained under the 20-and 60-year-old hormone mixture, respectively.Among them genes were regulated which are involved in biological processes which are all hallmarks of aging.The most significantly altered signaling pathway identified was that of the transforming growth factor-b (TGF-b).A disturbed function of this cascade has been associated with tumorigenesis, i.e. in pancreatic, prostate, intestine, breast, and uterine cancer.Interestingly, genes expressed in signaling pathways operative in age-associated diseases such as Huntington's disease (HD), dentatorubral-pallidoluysian atrophy (DRPLA), and amyotrophic lateral sclerosis (ALS) were also identified.These data demonstrate that skin and its appendages may represent an adequate model for aging research.Hormones interact in a complex fashion, and aging may be partly attributed to the changes in their circulating blood levels.Furthermore, a disturbed hormone status may partially act towards the manifestation of neurodegenerative diseases.Thus, these results could be a basis for an integrated and interdisciplinary approach to the analysis of the aging process." + }, + { + "document_id": "489539fd-f7c5-44eb-bb58-5fc19d50a7cf", + "section_type": "main", + "text": "Poorly repaired\ndamage of chromosomal DNA, stress-related aberrations in structural enzymes or protein\nturnover, and/or deletions in mitochondrial DNA, for example, may compromise organ\nfunction and in turn limit longevity. Given the extremely complex phenotype of aging,\n\n2\nnumerous other theories such as the free radial theory of aging (Harman, 1956) and\nprotein damage accumulation theory (Levine, 2002) have been postulated in an attempt to\nexplain what aging is and why it happens." + }, + { + "document_id": "1e2d93e8-a0a4-4f4a-a470-2dfdd26fa846", + "section_type": "abstract", + "text": "\nLoss of genome maintenance may causally contribute to ageing, as exemplified by the premature appearance of multiple symptoms of ageing in a growing family of human syndromes and in mice with genetic defects in genome maintenance pathways.Recent evidence revealed a similarity between such prematurely ageing mutants and long-lived mice harbouring mutations in growth signalling pathways.At first sight this seems paradoxical as they represent both extremes of ageing yet show a similar 'survival' response that is capable of delaying age-related pathology and extending lifespan.Understanding the mechanistic basis of this response and its connection with genome maintenance would open exciting possibilities for counteracting cancer or agerelated diseases, and for promoting longevity.In Greek mythology, Klotho, Lakhesis and Atropos, the three fates, spun, wove and snipped the thread of life, an unalterable process to which both gods and humans had to submit themselves.Human efforts over recent centuries have succeeded in substantially lengthening the thread, allowing ageing to become a common feature of society.However, despite intense research, the molecular basis of the processes that cause loss of bodily functions, and degeneration of cells and tissues is still unresolved.It is widely accepted that ageing is the consequence of stochastic damage accumulation 1 .Ageing is unique in that it does not seem to be subject to evolutionary selection, as it occurs after the reproductive phase, suggesting that it may occur by default 2 .Nevertheless, it is apparent from studies in many systems that ageing is subject to regulation by evolutionarily highly conserved molecular pathways [3][4][5] .As such, damage drives functional decline with advancing age; however, the existence of universal mechanisms that are able to promote longevity may set the pace on how rapidly damage builds up and function is lost.We discuss the nature of the processes that determine the length and the quality of the thread of life woven by Lakhesis and ultimately snipped by Atropos.Damage and ageing: the DNA perspective Within the complex chemical machinery of each cell, all biomolecules (proteins, lipids and nucleic acids) are subject to indiscriminate damage caused by spontaneous reactions (mostly hydrolysis) and by numerous endogenous and exogenous reactive agents.It is therefore plausible that damage to multiple cellular constituents accounts for ageing 1 .However, damage to certain macromolecules may play a more prominent part than damage to others.The almost exclusive link between an extending class George A." + }, + { + "document_id": "1ccb0d11-1c88-4b08-b40d-4039a954745f", + "section_type": "main", + "text": "Why does ageing evolve? The intrinsic decline in function that occurs during ageing appears to be caused by the accumulation of damage, particularly at the molecular level.As far as we know, no genes have evolved specifically because they cause damage to accumulate, and the evolution of ageing can therefore be understood only as a side-effect of other causes of evolutionary change.The mechanisms by which ageing can evolve were first elucidated by J.B.S. Haldane [14], P.B. Medawar [15] and G.C. Williams [16].Extrinsic hazards from disease, predation and accidents mean that even potentially immortal organisms will die.Genetic effects that become apparent only later in life encounter a reduced force of natural selection, because not all their bearers will survive to express them.Haldane pointed out that late-onset genetic diseases in humans, such as Huntington's disease, encounter only weak selection, because most reproduction is complete by the age of onset [14].Ageing could therefore result from the accumulation under mutation pressure of age-specific, deleterious mutations.In addition, if some mutations have pleiotropic effects, with beneficial effects in youth, such as high fecundity, but also with a higher subsequent rate of ageing, then they could be incorporated into the population by natural selection, which will act more strongly on the early, beneficial effect.Thus, variation in the rate of ageing would result from the readjustment of a tradeoff between youthful benefits and the subsequent rate of ageing.Both processes imply that faster ageing will evolve where the extrinsic hazard to adults is greatest, a hypothesis in general supported by the data [1,2,17]." + } + ], + "document_id": "0DC57EB6A882DE1734E915DE035D2296", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "genes", + "aging", + "DNA&damage", + "free&radicals", + "telomeres", + "oxidative&stress", + "environmental&factors", + "genetic&factors", + "cellular&repair", + "protein&synthesis" + ], + "metadata": [ + { + "object": "Transient overexpression of WRKY79 in protoplasts results in up-regulation of Gene:542165, Gene:541974, Gene:100274033, Gene:542688, Gene:542150, Gene:542151, Gene:100273457, Gene:100285509, Gene:103626248, Gene:103646045, Gene:100217270, Gene:100279981, Gene:100281950, Gene:542476, Gene:542369, Gene:100281950, and Gene:542260.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab969966" + }, + { + "object": "Uniform Mu insertion results in up-regulation of cytokinin synthesis genes and down-regulation of cytokinin degradation genes. The protein binds to Gene:103632693, Gene:100502174, Gene:100283866, Gene:542044, and Gene:100037786.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab983367" + }, + { + "object": "Part of autosomal recessive retinitis pigmentosa gene network established using RetNet info; Part of autosomal recessive cone_cone-rod gene network established using RetNet info; Part of age-related macular degeneration gene network, cone-dystrophy gene network, and retinitis pigmentosa gene network established using GeneNetwork info -ILMN_2829604\\r\\nused by Irene Whitney", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab4267" + }, + { + "object": "TET1 regulates numerous genes defining differentiation programs in the epiblast and extraembryonic ectoderm. In epiblasts, TET1 demethylates gene promoters via hydroxymethylation and maintains telomere stability. It represses a majority of epiblast target genes independent of methylation, partly by regulation of the JMJD8 gene. Dysregulated gene expression in the absence of TET1 causes embryonic defects.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab769005" + }, + { + "object": "Genome-wide associations P < 5 x 10-8 were found at the PCSK9 gene, the APOB gene, theLPL gene, the APOA1-APOA5 locus, the LIPC gene, the CETP gene, the LDLR gene, and the APOE locus.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab320109" + }, + { + "object": "Genetic risk score GRSNPY analysis found twelve significant P<0.05 serum NPY concentration related SNPs among alpha7 nicotinic acetylcholine receptor gene CHRNA7, insulin receptor gene INSR, leptin receptor gene LEPR, glucocorticoid receptor GR gene NR3C1, and NPY gene. However, after permutation test of gene score the predictive value of GRSNPY remained non-significant P=0.078. CONCLUSIONS: Serum NPY level ...", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab318213" + }, + { + "object": "TYROBP influences a batch of genes that are related to Alzheimer's disease; ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors. By merely leveraging gene expression data, Context Based Dependency Network CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab980273" + }, + { + "object": "Correlation analyses showed that 5hmC enrichment in gene body is positively associated with gene expression level in mouse kidney. Moreover, ischemia reperfusion IR injury-associated genes both up- and down-regulated genes during renal IR injury in mouse kidney exhibit significantly higher 5hmC enrichment in their gene body regions when compared to those un-changed genes.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab157853" + }, + { + "object": "PI3/PI4-kinase family, and is closely related to ATM, a protein kinase encoded by the gene mutated in ataxia telangiectasia. Shares similarity with S. pombe rad3, a cell cycle checkpoint gene required for cell cycle arrest and DNA damage repair in response to DNA damage. This kinase has been shown to phosphorylate checkpoint kinase CHK1, checkpoint proteins RAD17, and RAD9, as well as tumor suppressor protein BRCA1. Mutations of this gene are associated with Seckel syndrome.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab4171" + }, + { + "object": "seems that ZnSO4 as a proper antioxidant could improve the aging-related features due to lengthening of the telomeres, increasing the telomerase gene expression, telomerase activity, decreasing aging, and changing the methylation status of hTERT promoter; it could potentially beneficial for enhancing the application of aged-MSCs", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab694596" + } + ], + "question": "what causes the aging process", + "subquestions": null, + "task_id": "0DC57EB6A882DE1734E915DE035D2296", + "usage": { + "chatgpt": 8117, + "gpt-4": 4643, + "gpt-4-turbo-preview": 3712 + }, + "user_id": 2 + }, + "document_id": "0DC57EB6A882DE1734E915DE035D2296", + "task_id": "0DC57EB6A882DE1734E915DE035D2296" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/08.json b/gnqa/paper1_eval/src/data/responses/aging/experts/08.json new file mode 100644 index 0000000..3bf70fb --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/08.json @@ -0,0 +1,406 @@ +{ + "created_at": "2024-05-31T21:16:38.963061Z", + "data": { + "amplify": false, + "answer": "The genes associated with aging include the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, HLA-DQA1/DRB1, LPA, CHRNA3/5, CDKN2A/B, SH2B3, and AKT1. Other genes involved in processes like growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition are also associated with aging.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\n\nStudies revealed from 300 to 750 genes related to longevity that are critically involved in a variety of life activities, such as growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition [4].These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [5,6].Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability, and the endocrine-related pathway [7][8][9].In addition, the candidates for longevity encompass genes related to drug metabolism, the ones involved in protein folding, stabilization, and degradation, as well those related to coagulation and regulation of circulation [10], etc.In most cases, these genes or their polymorphic sites were examined in multiple population replication studies, which discovered certain longevity-associated genes or pathways [4][5][6][7][8][9][10]." + } + ], + "4a27da1c-b184-47e8-bef2-de6435d7c3f5": [ + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + } + ], + "520b36a2-4c9c-4894-a818-9917bd357982": [ + { + "document_id": "520b36a2-4c9c-4894-a818-9917bd357982", + "text": "\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases." + } + ], + "555a1533-2905-4d91-a3b6-2fca3679ab02": [ + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nInvolvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes." + } + ], + "5e6ad994-9cad-4b8b-903d-2d5c350e25dc": [ + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + } + ], + "99a35e24-bbd2-495b-82dc-53d7e2075191": [ + { + "document_id": "99a35e24-bbd2-495b-82dc-53d7e2075191", + "text": "\n\nThus, substantially more work is needed in this area to establish whether longevity is driven by nuclear genomic stability.Diverse and unexpected bits of evidence support a relationship.For example, a disproportionate number of genes identified in unbiased and targeted genome-wide association studies (GWASs) as associated with longevity are involved in genome maintenance (75).One study involved age of natural menopause in ∼70,000 women and led to the identification of 44 genetic variants associated with early or late menopause, a strong biomarker of healthy TIFs (telomere dysfunction-induced foci): co-localization of multiple DNA damage response factors and repair proteins on uncapped telomeric DNA aging (76).Approximately two-thirds of these are associated with genome maintenance genes.Seven of ten significantly associated pathways are involved in DNA repair.The highly significant overrepresentation of DNA repair pathways indicates an intimate connection between genome maintenance and aging phenotypes.From unrelated studies, we know that reduced expression of the repair endonuclease ERCC1-XPF causes accelerated aging (3), whereas ERCC1 is one of the top genes under positive selective pressure in the longest-lived mammalian species, the bowhead whale (77).Intriguingly, hepatocytes from old rats have impaired NER, whereas caloric restriction, which extends longevity, restored the NER capacity of old rats to that of youthful levels (42).In a human interventional study, brief caloric restriction increased NER capacity in PBMCs of individuals who had low NER prior to dietary intervention (78).Therefore, increased DNA repair capacity could promote longevity and may even prove amenable to improvement." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nIn addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha ˜es et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations." + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nGenes/loci identified by genome-wide association studies of longevity and lifespan traits." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + } + ], + "efd5747f-9e8b-45e8-9e04-bb31131d44fa": [ + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "text": "\n\nSince many alleles will fit the two patterns just described, it follows that we expect many genetic and biochemical mechanisms of aging.There are some experiments that have attempted to estimate the number of genes involved in aging, particularly in Drosophila.Quantitative genetic estimates of gene number have probably been subject to artifacts, [6,8] and are highly imprecise.Molecular genetic estimates using 2-D gels [3] and high-density geneexpression arrays [12] indicate the involvement of at least 300 genetic loci in Drosophila aging, and that estimate is highly conservative.For now, the best conclusion is probably that many genes are involved in aging in fruit flies.Vertebrates are unlikely to have fewer genes involved in aging, in view of their larger genomes." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "\n\nGenAge consists of several searchable data sets.Considering the extraordinary discoveries in the genetics of aging in model organisms, GenAge includes a data set of genes associated with longevity and/or aging in model organisms.We consider a given gene for inclusion in GenAge if genetic manipulations of the gene result in noticeable changes in the aging phenotype and/or longevity.Most genes in GenAge are from the four typical model organisms: mice, worms, fruit flies and yeast (Table 1).Strikingly, homologues of many genes -such as insulin receptors and sirtuins -have been shown to regulate aging in model organisms separated by large evolutionary distances (Kenyon, 2005;Liu et al ., 2005;Smith et al ., 2008).Moreover, we have shown that genes associated with aging and/or longevity in model organisms are evolutionary conserved in terms of having more homologues than predicted by chance (Budovsky et al ., 2007(Budovsky et al ., , 2008) ) and exhibiting slower molecular evolution rates (de Magalhães & Church, 2007).Therefore, it is now clear that at least some genes identified in model organisms may be relevant to human aging." + } + ], + "f3610ccc-2831-42f6-a3d3-1a0feeba4902": [ + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + } + ] + }, + "data_source": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nGenes/loci identified by genome-wide association studies of longevity and lifespan traits." + }, + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "section_type": "main", + "text": "\n\nIn addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha ˜es et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations." + }, + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "section_type": "main", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "\n\nGenAge consists of several searchable data sets.Considering the extraordinary discoveries in the genetics of aging in model organisms, GenAge includes a data set of genes associated with longevity and/or aging in model organisms.We consider a given gene for inclusion in GenAge if genetic manipulations of the gene result in noticeable changes in the aging phenotype and/or longevity.Most genes in GenAge are from the four typical model organisms: mice, worms, fruit flies and yeast (Table 1).Strikingly, homologues of many genes -such as insulin receptors and sirtuins -have been shown to regulate aging in model organisms separated by large evolutionary distances (Kenyon, 2005;Liu et al ., 2005;Smith et al ., 2008).Moreover, we have shown that genes associated with aging and/or longevity in model organisms are evolutionary conserved in terms of having more homologues than predicted by chance (Budovsky et al ., 2007(Budovsky et al ., , 2008) ) and exhibiting slower molecular evolution rates (de Magalhães & Church, 2007).Therefore, it is now clear that at least some genes identified in model organisms may be relevant to human aging." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nInvolvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "main", + "text": "\n\nStudies revealed from 300 to 750 genes related to longevity that are critically involved in a variety of life activities, such as growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition [4].These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [5,6].Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability, and the endocrine-related pathway [7][8][9].In addition, the candidates for longevity encompass genes related to drug metabolism, the ones involved in protein folding, stabilization, and degradation, as well those related to coagulation and regulation of circulation [10], etc.In most cases, these genes or their polymorphic sites were examined in multiple population replication studies, which discovered certain longevity-associated genes or pathways [4][5][6][7][8][9][10]." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "section_type": "main", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "section_type": "main", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "section_type": "main", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "abstract", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "main", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "section_type": "main", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "section_type": "main", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "Candidate gene studies identified APOE and FOXO3A as human longevity genes\n\nThe first genetic longevity studies mainly focused on lifespan regulating loci that emerged from animal models [22].Lifespan Prospects & Overviews .... extension in animal models was obtained by applying caloric restriction or by modifying gene functions (mutagenesis) using RNA interference, knock-out or overexpression of single genes (GenAge; http://genomics.senescence.info/genes/)[23].The most interesting pathways identified using these models are the growth hormone (GH)/insulin/insulin-like growth factor 1 (IGF-1) signaling and mammalian target of rapamycin (mTOR) signaling pathways [24].Thus far, lifespan has been the main phenotype investigated in animal models.In order to make these models more translatable to human studies research should focus on defining the parameters that reflect the physiology and pathology of aging in both animals and humans [25,26]." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010)." + }, + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "section_type": "main", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "99a35e24-bbd2-495b-82dc-53d7e2075191", + "section_type": "main", + "text": "\n\nThus, substantially more work is needed in this area to establish whether longevity is driven by nuclear genomic stability.Diverse and unexpected bits of evidence support a relationship.For example, a disproportionate number of genes identified in unbiased and targeted genome-wide association studies (GWASs) as associated with longevity are involved in genome maintenance (75).One study involved age of natural menopause in ∼70,000 women and led to the identification of 44 genetic variants associated with early or late menopause, a strong biomarker of healthy TIFs (telomere dysfunction-induced foci): co-localization of multiple DNA damage response factors and repair proteins on uncapped telomeric DNA aging (76).Approximately two-thirds of these are associated with genome maintenance genes.Seven of ten significantly associated pathways are involved in DNA repair.The highly significant overrepresentation of DNA repair pathways indicates an intimate connection between genome maintenance and aging phenotypes.From unrelated studies, we know that reduced expression of the repair endonuclease ERCC1-XPF causes accelerated aging (3), whereas ERCC1 is one of the top genes under positive selective pressure in the longest-lived mammalian species, the bowhead whale (77).Intriguingly, hepatocytes from old rats have impaired NER, whereas caloric restriction, which extends longevity, restored the NER capacity of old rats to that of youthful levels (42).In a human interventional study, brief caloric restriction increased NER capacity in PBMCs of individuals who had low NER prior to dietary intervention (78).Therefore, increased DNA repair capacity could promote longevity and may even prove amenable to improvement." + }, + { + "document_id": "0fc75a0d-3aa3-481a-8c0f-689bd7ae6104", + "section_type": "abstract", + "text": "\nAging is a complex process affecting different species and individuals in different ways.Comparing genetic variation across species with their aging phenotypes will help understanding the molecular basis of aging and longevity.Although most studies on aging have so far focused on short-lived model organisms, recent comparisons of genomic, transcriptomic, and metabolomic data across lineages with different lifespans are unveiling molecular signatures associated with longevity.Here, we examine the relationship between genomic variation and maximum lifespan across primate species.We used two different approaches.First, we searched for parallel amino-acid mutations that co-occur with increases in longevity across the primate linage.Twenty-five such amino-acid variants were identified, several of which have been previously reported by studies with different experimental setups and in different model organisms.The genes harboring these mutations are mainly enriched in functional categories such as wound healing, blood coagulation, and cardiovascular disorders.We demonstrate that these pathways are highly enriched for pleiotropic effects, as predicted by the antagonistic pleiotropy theory of aging.A second approach was focused on changes in rates of protein evolution across the primate phylogeny.Using the phylogenetic generalized least squares, we show that some genes exhibit strong correlations between their evolutionary rates and longevity-associated traits.These include genes in the Sphingosine 1-phosphate pathway, PI3K signaling, and the Thrombin/protease-activated receptor pathway, among other cardiovascular processes.Together, these results shed light into human senescence patterns and underscore the power of comparative genomics to identify pathways related to aging and longevity." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "abstract", + "text": "\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "abstract", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "section_type": "main", + "text": "Murabito JM, Yuan R, Lunetta KL (2012) The search for\nlongevity and healthy aging genes: insights from epidemiological\nstudies and samples of long-lived individuals. J Gerontol A Biol\nSci Med Sci 67(5):470–479. doi:10.1093/gerona/gls089\n20. Nuzhdin SV, Pasyukova EG, Dilda CL et al (1997) Sex-specific\nquantitative trait loci affecting longevity in Drosophila melanogaster. Proc Natl Acad Sci USA 94(18):9734–9739\n21. Gems D, Riddle DL (2000) Genetic, behavioral and environmental determinants of male longevity in Caenorhabditis elegans.\n Genetics 154(4):1597–1610\n\n123\n\n22." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "\n\nAlthough the models data set comprises all genes (to our knowledge) shown by the time of the latest update to statistically increase longevity or alter the aging process in a noticeable way, in the human data set we try to evaluate whether a given intervention is affecting the aging process itself or not.For example, many mutations may increase longevity by decreasing the incidence of specific diseases, rather than by altering the basic process of aging (de Magalhães et al ., 2005a(de Magalhães et al ., , 2005b)).Therefore, the human data set is not merely an extension of the work conducted in model organisms and of its bibliography, but a manually selected list of the most pertinent human aging candidate genes, each presented with a higher annotation level.We cite studies on whether the functions of aging-associated genes in model organisms are conserved in their human orthologues.Likewise, we cite flaws in previous studies based on new published observations, although we have a neutral stance on conflicting findings from different research groups.Our policy is to cite all conflicting reports and let visitors make their own decisions on how to interpret them.By contrast, each entry in GenAge model organisms has only one reference: the first publication reporting an association of the gene with longevity or aging.Moreover, one of the latest enhancements in the human data set was the inclusion of Gene Ontology annotation.Gene Ontology terms and annotation files were obtained from the Gene Ontology Consortium website (http://www.geneontology.org/ ) and provide an additional layer of description for the gene products in a cellular context (Ashburner et al ., 2000)." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "0fc75a0d-3aa3-481a-8c0f-689bd7ae6104", + "section_type": "main", + "text": "\n\nAging is a complex process affecting different species and individuals in different ways.Comparing genetic variation across species with their aging phenotypes will help understanding the molecular basis of aging and longevity.Although most studies on aging have so far focused on short-lived model organisms, recent comparisons of genomic, transcriptomic, and metabolomic data across lineages with different lifespans are unveiling molecular signatures associated with longevity.Here, we examine the relationship between genomic variation and maximum lifespan across primate species.We used two different approaches.First, we searched for parallel amino-acid mutations that co-occur with increases in longevity across the primate linage.Twenty-five such amino-acid variants were identified, several of which have been previously reported by studies with different experimental setups and in different model organisms.The genes harboring these mutations are mainly enriched in functional categories such as wound healing, blood coagulation, and cardiovascular disorders.We demonstrate that these pathways are highly enriched for pleiotropic effects, as predicted by the antagonistic pleiotropy theory of aging.A second approach was focused on changes in rates of protein evolution across the primate phylogeny.Using the phylogenetic generalized least squares, we show that some genes exhibit strong correlations between their evolutionary rates and longevity-associated traits.These include genes in the Sphingosine 1-phosphate pathway, PI3K signaling, and the Thrombin/protease-activated receptor pathway, among other cardiovascular processes.Together, these results shed light into human senescence patterns and underscore the power of comparative genomics to identify pathways related to aging and longevity." + }, + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "section_type": "main", + "text": "[PubMed: 18208581]\n3. de Magalhães JP, Wuttke D, Wood SH, Plank M & Vora C Genome-environment interactions that\nmodulate aging: Powerful targets for drug discovery. Pharmacol. Rev. 64, 88–101 (2012). [PubMed:\n22090473]\n4. McDaid AFet al.Bayesian association scan reveals loci associated with human lifespan and linked\nbiomarkers. Nat. Commun. 8, 15842 (2017). [PubMed: 28748955]\n5. Fontana L & Partridge L Promoting health and longevity through diet: From model organisms to\nhumans. Cell 161, 106–118 (2015). [PubMed: 25815989]\n6." + }, + { + "document_id": "7291ceb2-482a-4f9b-a116-2b68ff24854f", + "section_type": "main", + "text": "\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10)." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "main", + "text": "\n\nResults: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "section_type": "main", + "text": "\n\nSince many alleles will fit the two patterns just described, it follows that we expect many genetic and biochemical mechanisms of aging.There are some experiments that have attempted to estimate the number of genes involved in aging, particularly in Drosophila.Quantitative genetic estimates of gene number have probably been subject to artifacts, [6,8] and are highly imprecise.Molecular genetic estimates using 2-D gels [3] and high-density geneexpression arrays [12] indicate the involvement of at least 300 genetic loci in Drosophila aging, and that estimate is highly conservative.For now, the best conclusion is probably that many genes are involved in aging in fruit flies.Vertebrates are unlikely to have fewer genes involved in aging, in view of their larger genomes." + }, + { + "document_id": "29c57767-2e2c-4fbe-a8b2-629e1abd5628", + "section_type": "main", + "text": "\n\nLongevity-associated genes I Figure 6 Longevity-associated genes I. Listed genes are those that are differentially expressed with respect to each of four-long lived dwarf models (Snell, Ames, Little, GHR-KO).Each row corresponds to an individual candidate gene, while each column corresponds to one of the contrasts listed in" + }, + { + "document_id": "520b36a2-4c9c-4894-a818-9917bd357982", + "section_type": "abstract", + "text": "\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases." + }, + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "section_type": "main", + "text": "\n\nIn conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "section_type": "main", + "text": "\n\nOne way to overcome (part of) this problem is by using a family-based study design (Box 1 and Fig. 1), in which the offspring of long-lived individuals -representing ''healthy agers'' -are compared to similar-aged controls from the general population.The differential gene expression profiles identified using this design may represent markers of healthy aging and familial longevity.This approach has been applied in the LLS to explore the transcriptome in whole blood for association with human familial longevity.Genes belonging to the mTOR pathway, as well as ASF1A and IL7R, were differentially expressed between offspring and controls [59,60].In addition, the expression of mTOR genes in blood associated to prevalent diabetes and serum glucose.However, the association with familial longevity was not dependent on this.Thus, gene expression profiles in blood mark human longevity in middle age and potentially provide information on the pathways that contribute to healthy aging and longevity." + }, + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "section_type": "main", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + }, + { + "document_id": "520b36a2-4c9c-4894-a818-9917bd357982", + "section_type": "main", + "text": "\n\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases." + }, + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "section_type": "main", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "document_id": "B0164472D40098296DA0836E50978AC8", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "HLA-DQA1&DRB1", + "LPA", + "CHRNA3&5", + "CDKN2A&B", + "SH2B3", + "AKT1", + "ERCC1-XPF", + "MTP" + ], + "metadata": [ + { + "object": "Data suggest that the redox status of serum apoE might be related to the synthesis of HDL; the cysteine-thiol residue of reduced-apoE is in a naive state, while that of non-reduced-apoE is in a reversibly or irreversibly oxidized state. Data suggest that apoE homodimer and apoE-AII complex are typical reversibly oxidized forms of apoE. apoE-AII complex = a complex of apolipoprotein E and apolipoprotein A-II", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab212832" + }, + { + "object": "Low apoE and mir-650 plasma concentrations were risk factors for developing Alzheimer's disease AD and were particularly pronounced in severe dementia. APOE E4 allele in both AD patients and controls led to a reduction in apoE, while APOE E3/E3 genotype was associated with an increased apoE concentration and level of miR-107 in AD, which inversely correlated with the number of APOE E4 alleles.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab459467" + }, + { + "object": "Neuronal expression of apoE is controlled by transcription of apoE-intron3 apoE-I3 under normal conditions and by processing of apoE-I3 into mature apoE mRNA in response to injury.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab522285" + }, + { + "object": "FoxO3a was overexpressed in 64.71% cases of hepatocellular carcinoma HCC. FoxO3a overexpression was associated with aggressive phenotypes of HCC, such as histologic grade, stage, and small vessel invasion. FoxO3a overexpression was also correlated with poor disease-free survival. Downregulation of FoxO3a in a HepG2 cell line inhibited cell proliferation and migration.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab303610" + }, + { + "object": "T-type channel signaling is redirected towards the activation of the kinase Akt1, leading to increased expression of the anti-apoptotic protein survivin, and a decrease in the pro-apoptotic mediator FoxO3A. Finally, in iPAH cells, Akt1 is no longer able to regulate caspase 9 activation, whereas T-type channel overexpression reverses PP2A defect in iPAH cells but reinforces the deleterious effects of Akt1 activation", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab762059" + }, + { + "object": "These findings indicate that maternal apo B levels are significantly associated with apo B levels in their pre-school age children, adjusted for confounding variables. Furthermore, the mother-child correlations in apo B levels were independent of mother-child adiposity. Measurement of apo B levels in mothers may identify both high-risk children and mothers who may benefit from intervention.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab902074" + }, + { + "object": "APOE genotype status moderated the age-related declines in episodic memory: APOE-epsilon4+ middle-aged adults exhibited impairments relative to both APOE-epsilon4- middle-aged participants, and APOE-epsilon4+ younger adults.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab77520" + }, + { + "object": "The APOE epsilon2 allele may be protective on cognitive decline among the oldest old. A 22% increased mortality risk for APOE epsilon4 carriers was found. No protective effect of the APOE epsilon2 allele on mortality compared with the APOE epsilon3 allele.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab780736" + }, + { + "object": "Emerging evidences indicate that FOXO3a acts as a tumor suppressor in cancer. FOXO3a is frequently inactivated in cancer cell lines by mutation of the FOXO3a gene or cytoplasmic sequestration of FOXO3a protein. And its inactivation is associated with the initiation and progression of cancer. [review]", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab255659" + }, + { + "object": "The preferential interaction of the P. falciparum PFE1590w protein with the human ApoE epsilon3 and ApoE epsilon4 isoforms, but not the ApoE epsilon2 isoform, supports the hypothesis that ApoE genotype affects risk of malaria infection.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab847419" + } + ], + "question": "What genes are associated with aging?", + "subquestions": null, + "task_id": "B0164472D40098296DA0836E50978AC8", + "usage": { + "chatgpt": 7627, + "gpt-4": 4474, + "gpt-4-turbo-preview": 3537 + }, + "user_id": 2 + }, + "document_id": "B0164472D40098296DA0836E50978AC8", + "task_id": "B0164472D40098296DA0836E50978AC8" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/09.json b/gnqa/paper1_eval/src/data/responses/aging/experts/09.json new file mode 100644 index 0000000..fb82f6d --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/09.json @@ -0,0 +1,400 @@ +{ + "created_at": "2024-05-31T21:57:15.838942Z", + "data": { + "amplify": false, + "answer": "Several genetic factors are associated with aging. These include the APOE, FOXO3A, and CHRNA3/5 genes, as well as the CDKN2A/B, SH2B3, and MTP genes. Other factors include the HLA-DQA1/DRB1 and LPA regions, and the AKAP2 gene. Additionally, genes highly expressed in the brain, genes involved in neural development and function, and genes associated with autophagy have been linked to aging.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + } + ], + "0942fb8b-731c-4d6e-9b5a-8a303012eec6": [ + { + "document_id": "0942fb8b-731c-4d6e-9b5a-8a303012eec6", + "text": "\nBackground: Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality.Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.Results: Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively.We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function.Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs.A polygenic score for GrimAge acceleration showed strong associations with adiposityrelated traits, educational attainment, parental longevity, and C-reactive protein levels.Conclusion: This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity." + } + ], + "1386c8ad-297d-48b1-aa34-41659a9f6544": [ + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + } + ], + "555a1533-2905-4d91-a3b6-2fca3679ab02": [ + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010)." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nOn the other hand, the same evolutionary-motivated strategy suggesting to focus on more heterogeneous phenotypes (as opposite to more homogenous) can be highly beneficial for unraveling genetic predisposition to fundamental mechanisms of intrinsic biological aging and, consequently, to geriatric diseases.Indeed, aging is associated with systemic remodeling of an organism's functioning which increases chances of virtually all geriatric disorders (Franco et al. 2009;Franceschi et al. 2000;Martin et al. 2007;Cutler and Mattson 2006).Experiments with laboratory animals (Johnson 2006) and heritability estimates in humans (Christensen et al. 2006;Iachine et al. 1998) show that aging can be genetically regulated (Finch and Tanzi 1997;Martin et al. 2007;Vaupel 2010).Accordingly, yielding insights in genetic predisposition to aging-related processes in an organism could be a major breakthrough in preventing and/or ameliorating not one geriatric trait, but perhaps a major subset of such traits (Martin et al. 2007) that can greatly advance progress in solving the problem of extending healthy lifespan in humans." + } + ], + "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4": [ + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "text": "\n\nIn conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies." + } + ], + "7291ceb2-482a-4f9b-a116-2b68ff24854f": [ + { + "document_id": "7291ceb2-482a-4f9b-a116-2b68ff24854f", + "text": "\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10)." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + } + ], + "ca76f85d-9f72-4e15-8ba9-3bf94308c449": [ + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + } + ], + "db90a971-e55a-4ab0-a3b1-05908d6771a4": [ + { + "document_id": "db90a971-e55a-4ab0-a3b1-05908d6771a4", + "text": "Introduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go ¨gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha ¨chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + } + ], + "f4e2fa75-559b-4fa9-b722-bdac03f7715a": [ + { + "document_id": "f4e2fa75-559b-4fa9-b722-bdac03f7715a", + "text": "\n\nI NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity." + } + ] + }, + "data_source": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + }, + { + "document_id": "7291ceb2-482a-4f9b-a116-2b68ff24854f", + "section_type": "main", + "text": "\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10)." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "main", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "section_type": "abstract", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "section_type": "main", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010)." + }, + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "section_type": "main", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nOn the other hand, the same evolutionary-motivated strategy suggesting to focus on more heterogeneous phenotypes (as opposite to more homogenous) can be highly beneficial for unraveling genetic predisposition to fundamental mechanisms of intrinsic biological aging and, consequently, to geriatric diseases.Indeed, aging is associated with systemic remodeling of an organism's functioning which increases chances of virtually all geriatric disorders (Franco et al. 2009;Franceschi et al. 2000;Martin et al. 2007;Cutler and Mattson 2006).Experiments with laboratory animals (Johnson 2006) and heritability estimates in humans (Christensen et al. 2006;Iachine et al. 1998) show that aging can be genetically regulated (Finch and Tanzi 1997;Martin et al. 2007;Vaupel 2010).Accordingly, yielding insights in genetic predisposition to aging-related processes in an organism could be a major breakthrough in preventing and/or ameliorating not one geriatric trait, but perhaps a major subset of such traits (Martin et al. 2007) that can greatly advance progress in solving the problem of extending healthy lifespan in humans." + }, + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "section_type": "main", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + }, + { + "document_id": "ea036684-619d-4b82-9242-c0b220f2d8df", + "section_type": "main", + "text": "The mechanisms that underlie healthy aging—particularly, the cognitive as-\n\npects—remain poorly understood. Research suggests that genetics play a significant role in determining an individual’s\nsusceptibility or resilience to cognitive decline and dementia\n(Harris and Deary 2011; Ridge et al. , 2013). Identification of precise genetic factors involved would provide insight into\n\nCell Reports 32, 108091, September 1, 2020 ª 2020 The Author(s). 1\nThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).\n ll\nOPEN ACCESS\n\nReport\n\nFigure 1." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "Discussion\n\nIn our analyses of over 25,000 individuals of 55 years and older followed for an average of 11 years, we did not identify genome-wide significant associations for all-cause mortality and survival free of major diseases.However, both traits highlighted loci with suggestive significance that were in the neighborhood of genes related to neural regulation.In addition, our pathway and network analyses identified an enrichment of genes associated with cellular and neural development and function, and cell communication that may contribute to variation in human aging.Brain development might be responsible for the creation of redundancy in brain circuitry, which is associated with functional reserve and resiliency.Brain function regulates most of the compensatory strategy supporting maintenance of homeostatic equilibrium.Both of these processes are essential to healthy aging and longevity." + }, + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "section_type": "main", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + }, + { + "document_id": "593b752f-f448-47be-8b83-13bc5e9eb0d4", + "section_type": "main", + "text": "\n\nIn this light, we pursued a genomic study of an alternate but related aging phenotype-healthy aging-in order to expose its potential to uncover genetic factors for protection against age-associated disease.It is important to differentiate longevity from our healthy aging phenotype, which, as we have defined it for our healthy aging cohort (Wellderly), attempts to understand the genetics of disease-free aging in humans without medical interventions.Toward this end, we performed whole-genome sequencing (WGS) of the Wellderly and compared their genetic characteristics to an ethnicity-matched population control.Our findings suggest that healthy aging is associated with a diseaseprotective genetic profile that overlaps with but differs from that observed in exceptional longevity cohorts.These findings include no enrichment of true longevity variants, a lower genetic risk from common susceptibility alleles for Alzheimer and coronary artery disease, and no decrease in the rate of rare pathogenic variants.We identify suggestive common and rare variant genetic associations that implicate genetic protection against cognitive decline in healthy aging.Our data are made available for the discovery of additional disease protective genetic factors by the research community." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "section_type": "main", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + }, + { + "document_id": "99a35e24-bbd2-495b-82dc-53d7e2075191", + "section_type": "main", + "text": "\n\nThus, substantially more work is needed in this area to establish whether longevity is driven by nuclear genomic stability.Diverse and unexpected bits of evidence support a relationship.For example, a disproportionate number of genes identified in unbiased and targeted genome-wide association studies (GWASs) as associated with longevity are involved in genome maintenance (75).One study involved age of natural menopause in ∼70,000 women and led to the identification of 44 genetic variants associated with early or late menopause, a strong biomarker of healthy TIFs (telomere dysfunction-induced foci): co-localization of multiple DNA damage response factors and repair proteins on uncapped telomeric DNA aging (76).Approximately two-thirds of these are associated with genome maintenance genes.Seven of ten significantly associated pathways are involved in DNA repair.The highly significant overrepresentation of DNA repair pathways indicates an intimate connection between genome maintenance and aging phenotypes.From unrelated studies, we know that reduced expression of the repair endonuclease ERCC1-XPF causes accelerated aging (3), whereas ERCC1 is one of the top genes under positive selective pressure in the longest-lived mammalian species, the bowhead whale (77).Intriguingly, hepatocytes from old rats have impaired NER, whereas caloric restriction, which extends longevity, restored the NER capacity of old rats to that of youthful levels (42).In a human interventional study, brief caloric restriction increased NER capacity in PBMCs of individuals who had low NER prior to dietary intervention (78).Therefore, increased DNA repair capacity could promote longevity and may even prove amenable to improvement." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nInvolvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "abstract", + "text": "\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "0942fb8b-731c-4d6e-9b5a-8a303012eec6", + "section_type": "abstract", + "text": "\nBackground: Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality.Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.Results: Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively.We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function.Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs.A polygenic score for GrimAge acceleration showed strong associations with adiposityrelated traits, educational attainment, parental longevity, and C-reactive protein levels.Conclusion: This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "section_type": "main", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "section_type": "main", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "section_type": "abstract", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "593b752f-f448-47be-8b83-13bc5e9eb0d4", + "section_type": "main", + "text": "\n\nAge at death in adulthood has a moderate genetic component overall, with a heritability of approximately 25% (Murabito et al., 2012).Heritability of longevity increases with age, with a negligible genetic contribution to survival up to approximately 60 years of age, after which an increasing genetic component to survival is observed (Brooks-Wilson, 2013;Christensen et al., 2006).Most genetic studies of aging have focused on long-lived individuals, typically defined as centenarians 100 years or older, who may have had exceptional survival due to medical interventions (Murabito et al., 2012).A number of genetic associations with exceptional longevity have been made (Atzmon et al., 2006;Bojesen and Nordestgaard, 2008;Hurme et al., 2005;Kuningas et al., 2007;Melzer et al., 2007;Pawlikowska et al., 2009;Sanders et al., 2010;Suh et al., 2008;Willcox et al., 2008), with only markers at APOE and FOXO3A being well replicated (Murabito et al., 2012).Overall, the results of genetic and epidemiological longevity studies suggest aging is a complex trait and that achievement of exceptional longevity may not best capture the genetics of resistance to or delay of age-associated disease (Christensen et al., 2006)." + }, + { + "document_id": "da4a9500-831f-48ab-acea-5ec7097276ed", + "section_type": "main", + "text": "\n\nStudies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "section_type": "main", + "text": "Conclusions and Perspectives\n\nThe advent of new technologies has allowed the identification of conserved pathways involved in the aging process, as well as the association of genomic variants with human longevity.Nevertheless, heritability of human longevity has been estimated from 20% to 30%, reinforcing the fact that external factors such as diet, environment, and physical activity play a critical role in the human life span." + }, + { + "document_id": "0fc75a0d-3aa3-481a-8c0f-689bd7ae6104", + "section_type": "abstract", + "text": "\nAging is a complex process affecting different species and individuals in different ways.Comparing genetic variation across species with their aging phenotypes will help understanding the molecular basis of aging and longevity.Although most studies on aging have so far focused on short-lived model organisms, recent comparisons of genomic, transcriptomic, and metabolomic data across lineages with different lifespans are unveiling molecular signatures associated with longevity.Here, we examine the relationship between genomic variation and maximum lifespan across primate species.We used two different approaches.First, we searched for parallel amino-acid mutations that co-occur with increases in longevity across the primate linage.Twenty-five such amino-acid variants were identified, several of which have been previously reported by studies with different experimental setups and in different model organisms.The genes harboring these mutations are mainly enriched in functional categories such as wound healing, blood coagulation, and cardiovascular disorders.We demonstrate that these pathways are highly enriched for pleiotropic effects, as predicted by the antagonistic pleiotropy theory of aging.A second approach was focused on changes in rates of protein evolution across the primate phylogeny.Using the phylogenetic generalized least squares, we show that some genes exhibit strong correlations between their evolutionary rates and longevity-associated traits.These include genes in the Sphingosine 1-phosphate pathway, PI3K signaling, and the Thrombin/protease-activated receptor pathway, among other cardiovascular processes.Together, these results shed light into human senescence patterns and underscore the power of comparative genomics to identify pathways related to aging and longevity." + }, + { + "document_id": "593b752f-f448-47be-8b83-13bc5e9eb0d4", + "section_type": "abstract", + "text": "\nHighlights d Healthy aging is a complex polygenic trait related but distinct from longevity d Healthy aging is associated with decreased genetic risk for select diseases d Healthy aging is potentially linked to protection against cognitive decline d Genome data are made available for further analysis Authors" + }, + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "section_type": "main", + "text": "This population genetic\nmechanism also can maintain genetic variability for aging, like antagonistic pleiotropy.\n LARGE-EFFECT MUTANTS AND THE GENETICS OF AGING\n\nOne approach that has become increasingly common in the characterization of the genetics of aging is to isolate aging mutants, usually from mutagenesis experiments, and\nthen to determine the mechanistic basis for the unusual life span in the mutants. This\napproach has led to the discovery of genes that can enhance (e.g. , Maynard Smith 1958;\nLin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span\n(e.g. , Pearl and Parker 1922)." + }, + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "section_type": "main", + "text": "This population genetic\nmechanism also can maintain genetic variability for aging, like antagonistic pleiotropy.\n LARGE-EFFECT MUTANTS AND THE GENETICS OF AGING\n\nOne approach that has become increasingly common in the characterization of the genetics of aging is to isolate aging mutants, usually from mutagenesis experiments, and\nthen to determine the mechanistic basis for the unusual life span in the mutants. This\napproach has led to the discovery of genes that can enhance (e.g. , Maynard Smith 1958;\nLin et al. 1988; reviewed in Guarente and Kenyon 2000, Kim 2007) or reduce life span\n(e.g. , Pearl and Parker 1922)." + }, + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "section_type": "main", + "text": "\n\nIn conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "section_type": "main", + "text": "\n\nGenes/loci identified by genome-wide association studies of longevity and lifespan traits." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "\n\nSeveral explanations are possible for the lack of genomewide significant findings.First, mortality is arguably 1 of the most complex phenotypes, and several trajectories toward extreme old age have been identified (Evert et al., 2003).Multiple genes could mediate the aging process but would have their effects through numerous different patho-physiological processes and diseases that act as intermediate factors on the pathway to death (de Magalhaes et al., 2010).Therefore, any common variation in genes associated with aging probably has a small effect." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "section_type": "main", + "text": "\n\nAging is an extremely complex process associated with interplay of genetic, biochemical, and metabolic factors in an organism in a given environment.Although genetic studies of various animal models suggest that even a single-gene mutation can remarkably extend lifespan (Kenyon 2005;Johnson 2006) and, thus, modulate aging, no such genes are revealed in humans so far.Given that a human organism is a much more complex system than a model organism (Christensen et al. 2006), it is evident that genetic effects on the aging process should be mediated via coordinate action of a large number of inter-related processes (Kirkwood 2011).Coordinated function is rather relevant to complex biological (Soltow et al. 2010;Slagboom et al. 2011) and genetic (Bloss et al. 2011) networks than to individual genes." + }, + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "section_type": "main", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "section_type": "main", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + }, + { + "document_id": "a6bc2efd-61a7-4e07-ad5c-49234aa89431", + "section_type": "main", + "text": "\n\nIn 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13]." + }, + { + "document_id": "f4e2fa75-559b-4fa9-b722-bdac03f7715a", + "section_type": "main", + "text": "\n\nI NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity." + }, + { + "document_id": "db90a971-e55a-4ab0-a3b1-05908d6771a4", + "section_type": "main", + "text": "Introduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go ¨gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha ¨chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches." + } + ], + "document_id": "22C4FE902B2E9A047267A56F6096B4C6", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "longevity", + "aging", + "genetic", + "SNPs", + "DNA&methylation", + "epigenetic&clock", + "GWAS", + "chromosome&5q33.3" + ], + "metadata": [ + { + "object": "APOE genotype status moderated the age-related declines in episodic memory: APOE-epsilon4+ middle-aged adults exhibited impairments relative to both APOE-epsilon4- middle-aged participants, and APOE-epsilon4+ younger adults.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab77520" + }, + { + "object": "Data suggest that the redox status of serum apoE might be related to the synthesis of HDL; the cysteine-thiol residue of reduced-apoE is in a naive state, while that of non-reduced-apoE is in a reversibly or irreversibly oxidized state. Data suggest that apoE homodimer and apoE-AII complex are typical reversibly oxidized forms of apoE. apoE-AII complex = a complex of apolipoprotein E and apolipoprotein A-II", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab212832" + }, + { + "object": "Low apoE and mir-650 plasma concentrations were risk factors for developing Alzheimer's disease AD and were particularly pronounced in severe dementia. APOE E4 allele in both AD patients and controls led to a reduction in apoE, while APOE E3/E3 genotype was associated with an increased apoE concentration and level of miR-107 in AD, which inversely correlated with the number of APOE E4 alleles.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab459467" + }, + { + "object": "study investigated DNA methylation of the imprinted IGF2/H19 locus; data suggest aging more than population genetics is responsible for the inter-individual variability in DNA methylation patterns; DNA methylation variability appears to be highly region-specific", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab744889" + }, + { + "object": "BDNF mRNA expression and DNA methylation of seven CpG sites were not associated with schizophrenia after accounting for age and PMI effects. BDNF mRNA expression and DNA methylation were not altered by Val66Met after accounting for age and PMI effects. Schizophrenia risk was not associated with differential BDNF mRNA expression and DNA methylation.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab97590" + }, + { + "object": "the minimum alleles of rs10895322, rs1784424, rs3781788, and rs1573954 correlated with an increased risk of alcohol-induced ONFH P<0.05. Genetic model analysis revealed significant associations of 9 SNPs with alcohol-induced ONFH occurrence after adjustment for age P<0.05: 2 protective SNPs rs1711423 and rs1784418 and 7 high-risk SNPs rs10895322, rs1784424, rs3781788, rs7126560, rs1573954, rs1711399, rs2292730.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab834824" + }, + { + "object": "1443823_s_at: short probe set - potential SNPs could affect mapping result; 1427465_at: 3 SNPs in target area affect the hybridization of 5 probes; 1434893_at: 6 SNPs in target area could affect the hybridization of 7 probes; 1455136_at generate true cisQTL even 3 SNPs in target area affect mapping accuracy of 4 probes - BUT probes without any SNPs reveal the presence of an eQTL.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab43" + }, + { + "object": "These findings indicate that maternal apo B levels are significantly associated with apo B levels in their pre-school age children, adjusted for confounding variables. Furthermore, the mother-child correlations in apo B levels were independent of mother-child adiposity. Measurement of apo B levels in mothers may identify both high-risk children and mothers who may benefit from intervention.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab902074" + }, + { + "object": "Study of genetic risk of prevalent hrHPV infections in Nigerian women found significant associations with SNPs on ribosomal protein gene S19 RPS19 and Thymidylate Synthase gene TYMS, in an allelic model. This risk remained significant, after adjusting for age, body mass index, smoking, age at menarche, age at sexual debut, lifetime total number of sexual partners and the total number of pregnancies.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab745428" + }, + { + "object": "Study surveyed the non-synonymous SNPs of DNASE1L2: 19 SNPs originating from frameshift/nonsense mutations found in DNASE1L2 resulted in loss of function of the enzyme. Thus, the present findings suggest that each of the minor alleles for these SNPs may serve as one of genetic risk factors for parakeratotic skin diseases such as psoriasis, even though they lack a worldwide genetic distribution.", + "predicate": "http://www.w3.org/2000/01/rdf-schema#comment", + "subject": "ndd791caee50643ad90a986f563d2a0dab752673" + } + ], + "question": "what genetic factor are associated with aging", + "subquestions": null, + "task_id": "22C4FE902B2E9A047267A56F6096B4C6", + "usage": { + "chatgpt": 8077, + "gpt-4": 5976, + "gpt-4-turbo-preview": 5057 + }, + "user_id": 2 + }, + "document_id": "22C4FE902B2E9A047267A56F6096B4C6", + "task_id": "22C4FE902B2E9A047267A56F6096B4C6" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_01.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_01.json new file mode 100644 index 0000000..32fe672 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_01.json @@ -0,0 +1,144 @@ +{ + "created_at": "2024-05-31T12:56:07.845098Z", + "data": { + "amplify": false, + "answer": "The mouse genes associated with longevity include the C3H allele at D2Mit58, the BALB allele at D16Mit182, the C57BL/6 allele at D4Mit84, the C3H allele at D9Mit110, and the C57BL/6 and C3H alleles at D12Mit167. Additionally, a locus on chromosome 2 and another on chromosome 6 have been associated with longevity.", + "context": { + "0c6c0977-2cf9-4bbf-bc31-fe025f008089": [ + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n24 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10006, 10006\ngenenetwork.org/\nshow_trait?trait_id=\n10006&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10010, 10010\ngenenetwork.org/\nshow_trait?trait_id=\n10010&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10011, 10011\ngenenetwork.org/\nshow_trait?trait_id=\n10011&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10021, 10021\ngenenetwork.org/\nshow_trait?trait_id=\n10021&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10022, 10022\ngenenetwork.org/\nshow_trait?trait_id=\n10022&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10025, 10025\ngenenetwork.org/\nshow_trait?trait_id=\n10025&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10066, 10066\ngenenetwork.org/\nshow_trait?trait_id=\n10066&dataset=BXD-\nLongevityPublish\n\nReferences\nAlbertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P,\nCarlson M. 1994." + } + ], + "2464a084-1a11-44eb-8bce-4b344de049ff": [ + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "text": "DOI: https://doi.org/10.7554/eLife.75244\n\n\b\n\n24 of 30\nChromosomes and Gene Expression | Genetics and Genomics\n\nResearch article\nContinued\nAuthor(s)\n\nYear\n\nDataset title\n\nDataset URL\n\nDatabase and Identifier\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10006, 10006\ngenenetwork.org/\nshow_trait?trait_id=\n10006&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10010, 10010\ngenenetwork.org/\nshow_trait?trait_id=\n10010&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10011, 10011\ngenenetwork.org/\nshow_trait?trait_id=\n10011&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10021, 10021\ngenenetwork.org/\nshow_trait?trait_id=\n10021&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10022, 10022\ngenenetwork.org/\nshow_trait?trait_id=\n10022&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2020\n\nGenetics of longevity in\nBXD mice\n\nhttp://www.\nBDL_10025, 10025\ngenenetwork.org/\nshow_trait?trait_id=\n10025&dataset=BXD-\nLongevityPublish\n\nLongevityteam\n\n2021\n\nGenetics and epigenetics\nof aging and longevity in\nBXD mice\n\nhttp://www.\nBDL_10066, 10066\ngenenetwork.org/\nshow_trait?trait_id=\n10066&dataset=BXD-\nLongevityPublish\n\nReferences\nAlbertsen HM, Smith SA, Mazoyer S, Fujimoto E, Stevens J, Williams B, Rodriguez P, Cropp CS, Slijepcevic P,\nCarlson M. 1994." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Leduc MS, Hageman RS, Meng Q et al (2010) Identification of\ngenetic determinants of IGF-1 levels and longevity among mouse\ninbred strains. Aging Cell 9(5):823–836. doi:10.1111/j.14749726.2010.00612.x\n10. Lang DH, Gerhard GS, Griffith JW et al (2010) Quantitative trait\nloci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin Exp Res 22(1):8–19\n11. Gelman R, Watson A, Bronson R et al (1988) Murine chromosomal\nregions\ncorrelated\nwith\nlongevity. Genetics\n118(4):693–704\n12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci\nassociated with life span exhibit sex-specific and epistatic effects." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Conclusions These results suggest a novel locus influencing survival in the B6/D2 genetic background, perhaps\nvia a metabolic disorder that emerges by 200 days of age in\nmale animals. Keywords\nPathology\n\nLongevity \\ Lifespan \\ Mouse \\ Linkage \\\n\nIntroduction\nLongevity, the quintessential complex trait, likely reflects\nall aspects of an organism’s life history. In humans, the\nestimated heritability of age at death is estimated at\n25–33 % [1]. Genetic contributions to mortality rates are thus of great interest and may aid in the understanding of\ndisease etiology and the process of aging itself [2]." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Here, we have extended this analysis to search for\ngenotypes related to survival to the age of 800 days in a\npopulation of a reciprocal F2 cross between (B6) and (D2)\nmice. Since QTL for longevity in mice have shown strong\nsex specificity [10, 12], we conducted sex-specific analyses. In addition, we also determined whether there were\nany change in pathology changes associated with the loci\nthat showed frequency distortions with aging. To confirm\nthe associations of the loci of interest with longevity and\npathology, we performed replication analyses on a panel of\nBXD recombinant inbred strains." + } + ], + "64886b4e-8599-4f61-84e6-9add7663a1b3": [ + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": "352(6291): p. aad0189. Liao, C.Y. , et al. , Genetic variation in the murine lifespan response to dietary restriction: from life extension to life\nshortening. Aging Cell, 2010. 9(1): p. 92-5. Johnson, M., Laboratory Mice and Rats. Mater. Methods, 2012. 2: p. 113. Fontaine, D.A. and D.B. Davis, Attention to Background Strain Is Essential for Metabolic Research: C57BL/6 and\nthe International Knockout Mouse Consortium. Diabetes, 2016. 65(1): p. 25-33. Simon, M.M. , et al. , A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains. Genome Biol, 2013. 14(7): p. R82. Lilue, J., et al." + } + ], + "8dad24f7-b658-44fa-af65-6f33db69c15a": [ + { + "document_id": "8dad24f7-b658-44fa-af65-6f33db69c15a", + "text":"Mamm Genome 2001;12: 930–2. 21 Gelman R, Watson A, Bronson R, Yunis E. Murine chromosomal\nregions correlated with longevity. Genetics 1988;118:693–704. 22 Peirce JL, Lu L, Gu J, Silver LM, Williams RW. A new set of BXD\nrecombinant inbred lines from advanced intercross populations in\nmice. BMC Genet 2004;5:7. 23 Rahman ZS, Tin SK, Buenaventura PN et al. A novel susceptibility\nlocus on chromosome 2 in the (New Zealand Black \\ New Zealand\nWhite) F1 hybrid mouse model of systemic lupus erythematosus. J Immunol 2002;168:3042–9. 24 Kono DH, Burlingame RW, Owens DG et al." + } + ], + "958b37c9-9bd5-4e84-939d-8f12dccf1055": [ + { + "document_id": "958b37c9-9bd5-4e84-939d-8f12dccf1055", + "text": "Conversely, the BXD strain with the shortest life span\n(BXD14) has the lowest responsiveness to the stimulatory effect of\nTGF-2 when old (48). The region on chromosome 2 where a\nsuggestive QTL regulating the responsiveness to TGF-2 in old\nmice is located also contains two QTL for longevity (32). Finally,\nthe strongest support for this hypothesis is the correlation between\nlongevity and the age-related increase in the serum-dependent effect of TGF-2 on LSK cells, the extent of which may determine\nstem cell function in aged mice." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nFIGURE 8-5 Genetic regulation of longevity in mice stratified by cause of death.Female mice that inherit the C3H allele at D2Mit58 plus the BALB allele at D16Mit182 (light gray bars) have significantly higher longevity than their sisters (dark gray bars) with the C57BL/6 plus DBA/2 allele combination (\"all causes\" of death combined).Subsets of mice that died either of cancer or of a nonneoplastic (\"benign\") illness both show the association between genotype and longevity.Among the mice dying of neoplasia, subsets dying of lymphoma or of fibrosarcoma show equivalent, and significant, genotypic effects.Bars indicate means plus standard error of the mean.SOURCE:Miller et al. (unpublished results)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nThe available dataset also provides examples in which genetic variants seem to influence the risk of specific late-life diseases.Figure 8-6, for example, shows longevity results for mice stratified by their inheritance at the 12th chromosome locus D12Mit167.This is a locus associated with differential longevity in both male and female mice, with the strongest effect (adjusted p < 0.01) seen in those mice living more than 657 days (Jackson et al., unpublished results).The longest-lived mice are those that inherit both the C57BL/6 allele from their mother and the C3H allele from their father; on average, they survive 93 days longer than siblings with the BALB plus C3H combination.Figure 8-6 shows that the D12Mit167, like the pair of loci illustrated in Figure 8-5, has significant and similar effects in mice dying of cancer (85 days) and in mice dying of non-neoplastic diseases (126 days).A more detailed analysis of the cancers, however, suggests that while lymphoma and hepatoma victims are equally protected by the favorable alleles (effect sizes of 93 and 167 days, respec- mice of two subgroups: those dying of the urinary syndrome MUS, and those dying of all other causes.The genetic analysis contrasts mice with both the C57BL/6 allele at D4Mit84 and the C3H allele at D9Mit110 to mice with any of the three other allele combinations.In the males dying of causes other than MUS, this allele pair is associated with a 170-day increment in longevity (post-hoc p < 0.00003).But for males that do die of MUS, the same allele combination is associated with a 187-day decline in mean life span (post-hoc p < 0.03).This effect is thus pleiotropic, in that these alleles accelerate death in mice susceptible to MUS, while postponing death for all other males in the population.Although these loci are associated with differential longevity in mice that do develop MUS, they do not have a significant effect on the chances that MUS will indeed occur (not shown).The risk of developing MUS seems to be under control of a separate locus on chromosome 6.As shown in the bottom panel of Figure 8-7, males that inherit the C3H allele at D6Mit268 are far more likely to develop MUS (28 percent risk) than are their brothers who receive the DBA/2 allele at this locus (7 percent risk; p = 0.012 by two-tailed Fisher's exact test)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nHigh levels of CD8M cells are associated with diminished longevity in mated females (left panel; p < 0.001), but not in virgin females (center panel).Among virgin males, those dying of diseases other than the urinary syndrome MUS show no association between CD8M and longevity (open circles, upper line), but those dying because of MUS show a nonsignificant trend (filled circles, lower line, R = -0.27,p = 0.13) similar to the relationship observed in mated females.SOURCE : Miller et al. (unpublished results).Male or female mice that inherit the C57BL/6 (maternal) and C3H (paternal) alleles at D12Mit167 (light gray bars) are longer lived than their siblings that inherit the BALB plus C3H combination.The \"effect size\" shown at the right represents that difference in mean longevity between mice in the two genetically different groups, with (**) = p < 0.01 and (*) = p < 0.05 by t-test.Similar effect sizes are seen for mice dying of cancer or of non-neoplastic illnesses (\"benign\"), and among the cancer deaths the genetic effect is similar for deaths due to lymphoma and hepatoma.The genetic effect on longevity seems to be minimal, however, for mice dying of fibrosarcoma.Bars show means plus standard errors.SOURCE : Miller et al. (unpublished results)." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nOur own work has taken a different tack: we have attempted to determine whether mutations with differential effects on aging may be present within the many available populations of laboratory-adopted inbred mice.The goal is not so much to clone these genes-if indeed they existbecause positional cloning strategies of this kind require many thousands of animals and would be extremely expensive using an assay, age at death, that is itself so costly.Instead, the goal has been to use gene mapping methods to test hypotheses about aging and to develop new animal models that will be useful for testing well-specified hypotheses about the molecular basis for age-dependent changes.In the absence of a validated battery of biomarkers of aging, we (like most others) have reluctantly decided to use mouse life span as a crude surrogate for aging itself, reasoning that genetic alleles that extend life span well beyond the median for the tested population may be operating via an influence on aging itself.Work conducted using recombinant inbred mouse stocks (Gelman et al., 1988;de Haan and Van Zant, 1999) has suggested that life-span differences between pairs of inbred mouse lines might reflect the influence of as few as 4-7 polymorphic loci, providing some basis for hope that some of these would have an effect large enough to be detected by a genome scan experiment involving 300-1,200 mice." + } + ], + "9ac0b7e7-6294-4cfb-97e3-e5a4546af324": [ + { + "document_id": "9ac0b7e7-6294-4cfb-97e3-e5a4546af324", + "text": ", Vogler, G.P. , Vandenbergh,\nD.J. , Blizard, D.A. , Stout, J.T. & McClearn, G.E. Quantitative Trait\nLocus (QTL) Analysis of Longevity in C57BL/6J byDBA/2J (BXD)\nRecombinant Inbred Mice. Aging Clin Exp Res (in press). Lionikas, A., Blizard, D.A. , Vandenbergh, D.J. , Glover, M.G. ,\nStout, J.T. , Vogler, G.P. , McClearn, G.E. & Larsson, L. (2003)\nGenetic architecture of fast- and slow-twitch skeletal muscle\nweight in 200-day-old mice of the C57BL/6J and DBA/2J lineage. Physiol Genomics 16, 141–152. Lionikas A., Blizard D.A. , Gerhard G.S. , Vandenbergh D.J. , Stout J.T. ,\nVogler G.P. , McClearn G.E." + } + ], + "cb3f9967-9762-4a9b-96cb-0acccdc316d2": [ + { + "document_id": "cb3f9967-9762-4a9b-96cb-0acccdc316d2", + "text": "Deficiency mapping of quantitative trait loci affecting longevity\nin Drosophila melanogaster. Genetics 2000;156:1129–1146. [PubMed: 11063689]\n33. Ma RZ, et al. Identification of Bphs, an autoimmune disease locus, as histamine receptor H1. Science\n2002;297:620–623. [PubMed: 12142541]\n\nNat Rev Genet. Author manuscript; available in PMC 2007 November 5. Page 12\n\nNIH-PA Author Manuscript\n\n34. Vivian JL, Chen Y, Yee D, Schneider E, Magnuson T. An allelic series of mutations in Smad2 and\nSmad4 identified in a genotype-based screen of N-ethyl-N-nitrosourea-mutagenized mouse\nembryonic stem cells. Proc. Natl Acad. Sci. USA 2002;99:15542–15547. [PubMed: 12432092]\n35. Vogel G. Scientists dream of 1001 complex mice." + } + ], + "ce2c68bf-878d-460c-8d9b-d45ce3034ef7": [ + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "text": "34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with\nlongevity. Genetics 118, 693–704 (1988). [PubMed: 3163317]\n35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011). 36. Houtkooper RHet al.Mitonuclear protein imbalance as a conserved longevity mechanism. Nature497, 451–457 (2013). [PubMed: 23698443]\n37. Williams EGet al.An Evolutionarily conserved role for the aryl hydrocarbon receptor in the\nregulation of movement. PLOS Genet. 10, e1004673 (2014). [PubMed: 25255223]\n38. Lang DHet al.Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin. Exp. Res. 22, 8–19 (2010)." + } + ], + "db0459f8-6602-48d7-be9b-14863a88bbe1": [ + { + "document_id": "db0459f8-6602-48d7-be9b-14863a88bbe1", + "text": "In addition,\nthe B6 mouse strain is one of the longest-lived mouse strains with a mean lifespan of 3\nyears versus other mouse strains with mean lifespan from 1.5-2 years. Therefore, it is\nevident that the genetic background of a particular mouse strain can have a profound\neffect on the biology of the HSC population as well as organismal longevity. Indeed, it is\nfor this reason that it is difficult to compare findings from various laboratories where\ndifferent mouse strains are used." + } + ], + "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748": [ + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "NIH-PA Author Manuscript\n\nThis study indicated a large amount of genetic variation for mouse longevity; heritability\nwas 34% for AL and 36% for DR (60% of AL food intake). There was no significant\ncorrelation between mean longevity under these two conditions, although maximum\nlifespans of the AL and DR mice were significantly correlated. Similar observations were\nmade at the UTHSCSA on the ILSXISS RI mice (Liao et al. , 2010a, b; Mattson 2010),\nwhere they also observed similar heritability (28% AL males, 36% AL females, 55% DR\nmales, 53% DR females)." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "For females, hairs of the congenic mice grew 31% faster, also highly significant (P =\n0.0006, 1-tailed). These results validated the presence of a gene in the differential region\naffecting FE. Discussion\nWe report the outcomes of a quantitative genetic study on aging and longevity in the mouse. We studied an extant series of recombinant inbred strains (ILSXISS) that have been used\nboth in DR aging studies as well as to study alcohol sensitivity (Williams et al. , 2004)." + }, + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "(2007) is a separate issue from the analyses conducted in this\nstudy (the AL efficiency model will be tested in future studies). Exp Gerontol. Author manuscript; available in PMC 2011 September 1. Rikke et al. Page 8\n\nNIH-PA Author Manuscript\n\nOther studies have also reported that individual mice that maintained the highest BW were\nlikely to be the longest-lived individuals among cohorts of genetically identical mice\n(Weindruch et al. , 1986; Harper et al. , 2006)." + } + ], + "f116ee1c-b275-4239-98e9-c2032b8f05c5": [ + { + "document_id": "f116ee1c-b275-4239-98e9-c2032b8f05c5", + "text": "Age-associated changes are conserved between mouse strains\n\nLife span and aging vary between mouse strains.For example, C57BL/6 mice are long-lived compared to the short-lived DBA/2 mice (Turturro et al. 1999).To test the generality of our observations, we also examined LT-HSCs, ST-HSC and MPPs in young and old mice from the DBA/2 strain, which originates from a distinct breeding lineage (Fox 1997)." + } + ] + }, + "data_source": [], + "document_id": "2D2D12594F1A6AC91E150695D70A4FFA", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "C57BL/6&allele", + "C3H&allele", + "BALB&allele", + "D2Mit58", + "D16Mit182", + "longevity", + "mouse", + "genetic", + "D12Mit167", + "IGF-1" + ], + "metadata": [], + "question": "Which mouse genes have been associated with longevity?", + "subquestions": null, + "task_id": "2D2D12594F1A6AC91E150695D70A4FFA", + "usage": { + "chatgpt": 9852, + "gpt-4": 6314, + "gpt-4-turbo-preview": 4895 + }, + "user_id": 2 + }, + "document_id": "2D2D12594F1A6AC91E150695D70A4FFA", + "task_id": "2D2D12594F1A6AC91E150695D70A4FFA" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_02.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_02.json new file mode 100644 index 0000000..238aa61 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_02.json @@ -0,0 +1,146 @@ +{ + "created_at": "2024-05-31T13:01:42.014610Z", + "data": { + "amplify": false, + "answer": "Lifespan is partially determined by genetics, with studies estimating that genetics contribute to about 25-30% of the variation in human lifespan. However, it is also significantly influenced by non-heritable factors such as environment and lifestyle.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nOur results show that longevity is partly determined by the predisposition to common diseases and, to an even greater extent, by modifiable risk factors.The genetic architecture of lifespan appears complex and diverse and there appears to be no single genetic elixir of long life." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nL ongevity is of interest to us all, and philosophers have long speculated on the extent to which it is pre-determined by fate.Here we focus on a narrower question-the extent and nature of its genetic basis and how this inter-relates with that of health and disease traits.In what follows, we shall use longevity as an umbrella term.We shall also more specifically refer to lifespan (the duration of life) and long-livedness (living to extreme old age, usually defined by a threshold, such as 90 years).Up to 25% of the variability in human lifespan has been estimated to be genetic 1 , but genetic variation at only three loci (near APOE, FOXO3A and CHRNA3/5) [2][3][4][5] have so far been demonstrated to be robustly associated with lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "GENETICS OF LIFE SPAN IN HUMANS\n\nMost studies of human twins agree that the heritability of life span is less than 50% (45,68).Of particular interest is an ongoing study of aging in Swedish twins that includes a large group of adopted twins who were reared separately.Ljungquist et al. (68) concluded that \"a maximum of one-third the variance in integrated mortality risk is attributable to genetic factors and that almost all of the remaining variance is due to nonshared, individually unique environmental factors. \"Moreover, this heritability declined with age and was negligible after the age of 85 in men and 90 in women." + } + ], + "1ccb0d11-1c88-4b08-b40d-4039a954745f": [ + { + "document_id": "1ccb0d11-1c88-4b08-b40d-4039a954745f", + "text": "\n\nHow can lifespan be controlled by a single gene?Two possibilities are, first, that the mutations that extend lifespan are in genes whose products regulate the activity of many other genes and, second, that these genes do not in fact control the rate of ageing." + } + ], + "4ca8d070-8b58-4bd5-86be-127089b70324": [ + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "text": "\n\nSince that time, observations across species have shown that life span can be extended by genetic factors.One of the first demonstrations of this entailed the study of recombinant inbred populations of the nematode worm Caenorhabditis elegans by Thomas E. Johnson.Then a postdoc in William (Bill) Wood's lab at the University of Colorado Boulder, Tom and Bill demonstrated that crosses of C. elegans strains did not display the heterosis effect that interfered with many other studies, \"As predicted, we found significant genetic effects on life span as well as other life history traits. \"This finding established a method for evaluating genetic factors that influenced life-span variation.In fact, their measurements of life span of the recombinant inbred strains demonstrated the heritability of life span to be 19%-51% (1).Consistent with theories of the 1970s and 1980s, it was concluded that these genetic factors were a collection of small influences across many genes.This finding was one of the first steps in demonstrating that genetic factors influence aging.As genetic analysis was making great progress in understanding other biological processes, such as developmental programming, the realization that aging could be investigated using the same tools was highly significant." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nAlthough it is known that health and lifespan are heavily influenced by genetics [14], variations in the lifespan of different individuals within the same species seem to be more the result of the accumulation over time of molecular damage that compromises the function of the cells [15].These molecular alterations can occur both at the genetic and epigenetic levels and depend on genetic, environmental, and stochastic factors [16].This complex multifactorial mix determined characteristics, such as longevity and a healthy lifespan, which are central concerns of human existence (Fig. 13.1).This chapter describes different types of tools in genomics used in ageing research and their different applications in clinical scenarios." + } + ], + "593b752f-f448-47be-8b83-13bc5e9eb0d4": [ + { + "document_id": "593b752f-f448-47be-8b83-13bc5e9eb0d4", + "text": "\n\nAge at death in adulthood has a moderate genetic component overall, with a heritability of approximately 25% (Murabito et al., 2012).Heritability of longevity increases with age, with a negligible genetic contribution to survival up to approximately 60 years of age, after which an increasing genetic component to survival is observed (Brooks-Wilson, 2013;Christensen et al., 2006).Most genetic studies of aging have focused on long-lived individuals, typically defined as centenarians 100 years or older, who may have had exceptional survival due to medical interventions (Murabito et al., 2012).A number of genetic associations with exceptional longevity have been made (Atzmon et al., 2006;Bojesen and Nordestgaard, 2008;Hurme et al., 2005;Kuningas et al., 2007;Melzer et al., 2007;Pawlikowska et al., 2009;Sanders et al., 2010;Suh et al., 2008;Willcox et al., 2008), with only markers at APOE and FOXO3A being well replicated (Murabito et al., 2012).Overall, the results of genetic and epidemiological longevity studies suggest aging is a complex trait and that achievement of exceptional longevity may not best capture the genetics of resistance to or delay of age-associated disease (Christensen et al., 2006)." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "Introduction\n\nWorldwide human populations have shown an increase in mean life expectancy in the past two centuries (Oeppen & Vaupel, 2002).This is mainly because of environmental factors such as improved hygiene, nutrition, and health care.The large variation in healthy lifespan among the elderly has prompted research into the determinants of aging and lifespan regulation.The genetic contribution to human lifespan variation was estimated at 25-30% in twin studies (Gudmundsson et al., 2000;Skytthe et al., 2003;Hjelmborg et al., 2006).The most prominent genetic influence is observed in families in which the capacity to attain a long lifespan clusters (Perls et al., 2000;Schoenmaker et al., 2006).Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al., 2008;Terry et al., 2008), raising the question whether protective mechanisms against disease exist in long-lived subjects." + } + ], + "78a43a45-84b0-4d73-9396-95b99cfd3983": [ + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "text": "Introduction\n\nHuman lifespan is a highly complex trait, the product of myriad factors involving health, lifestyle, genetics, environment, and chance.The extent of the role of genetic variation in human lifespan has been widely debated (van den Berg et al., 2017), with estimates of broad sense heritability ranging from around 25% based on twin studies (Ljungquist et al., 1998;Herskind et al., 1996;McGue et al., 1993) (perhaps over-estimated [Young et al., 2018]) to around 16.1%, (narrow sense 12.2%) based on large-scale population data (Kaplanis et al., 2018).One very recent study suggests it is much lower still (<7%) (Ruby et al., 2018), pointing to assortative mating as the source of resemblance amongst kin." + }, + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "text": "\n\nMany factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone.Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases.If such genes exist, their effects were too small to be detected in this study.The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "Life Span\n\nDuring the last decade a variety of twin studies have shown that approximately 25 percent of the variation in life span is caused by genetic differences.This seems to be a rather consistent finding in various Nordic countries in different time periods and even so among other species not living in the wild (Herskind et al., 1996;Iachine et al., 1999;Finch and Tanzi, 1997).their relative magnitude and pattern depend on sex and on the socioeconomic environment experienced by successive birth cohorts.Genetic effects were most pronounced in periods with consciously controlled fertility, suggesting that the genetic disposition primarily affects fertility behavior and motivation for having children.Analyses of fertility motivation in some of the more recent twin cohorts, measured by age at first attempt to have children, supported this interpretation." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "The Height-Life Span Nexus\n\nSeveral observations and lines of experimentation have raised the issue of whether interindividual differences in aging rate are influenced by genes that modulate body size and early-life growth patterns.These include (a) the association between small stature and exceptional longevity in calorically restricted rodents (Yu et al., 1985), methionine-restricted rats (Orentreich et al., 1993), and mutant dwarf mice (Brown-Borg et al., 1996;Miller, 1999); and (b) the association between small body size and longer life span in natural populations of mice (Falconer et al., 1978), flies (Hillesheim and Stearns, 1992), dogs (Li et al., 1996), and, possibly, people (Samaras andStorms, 1992).The correlation in dogs is particularly striking: selective breeding for dogs of different body size has produced breeds varying in size from Chihuahua to Irish wolfhound.These breeds also vary greatly in mean longevity, from approximately 7 to 10.5 years, and the correlation between breed longevity and breed body weight (Miller, 1999) is a remarkable R 2 = 0.56.These differences are genetic and affect stature rather than obesity: no amount of overeating will convert a West Highland white terrier to a St. Bernard.The selective pressures applied were designed to create dogs of specific sizes and temperaments and were not intended to influence aging rate or life span.The clear implication is that the effects on longevity are pleiotropic, i.e., that genes selected for their effect on body size and conformation influenced life span as a side effect.It is of interest to note that the few analyses (Eigenmann et al., 1984(Eigenmann et al., , 1988) ) of the hormonal basis for interbreed differences in body size have shown that the genes in question influence levels of IGF-1, the most likely mediator of the life-span effects in the long-lived df/df and dw/dw mouse mutants.Could it be mere coincidence that long-lived mutant nematode worms (Kimura et al., 1997) also show mutations in genes related to insulin and IGF-1 receptors?" + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nAltogether, the twin and genealogical studies have shown that human lifespan is heritable, but is significantly influenced by non-heritable factors, which may explain why genetic studies of lifespan have proven to be challenging." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nTwin studies have shown that the heritability of lifespan ranges between 0.01 and 0.27 in various European populations (Ljungquist et al., 1998;van den Berg et al., 2017).Large genealogical studies are more powered to address questions FIGURE 1 | Relationship between aging and lifespan variation versus species defining lifespan. (A) Lifespan comparisons within species, measured as mean (50%) or portion of a population living till extended limits of lifespan (90-95%).Differences between populations (orange and green) can identify specific genetic or environmental changes associating with long life.These factors promote viability and often associate with increasing healthspan.Mutant analysis within a particular model organism often encompasses these types of changes as it relates to lifespan. (B) Maximum lifespans recorded for different species (A-E).While lifespan variation within a species is capped to a certain extent, variation between species can range dramatically.Changes to maximum lifespan often are associated with protective mechanisms for genomic and genetic fidelity as well as life history changes as they relate to maturation and reproduction." + } + ], + "c7361625-831a-44a2-b04d-157a49d00c6a": [ + { + "document_id": "c7361625-831a-44a2-b04d-157a49d00c6a", + "text": "\n\nThe genetic component of human lifespan based on twin studies has been estimated to be around 20-30 percent in the normal population [7], but higher in long-lived families [8][9][10].Furthermore, siblings, parents, and offspring of centenarians also live well beyond average [11,12].Lifestyle choices in terms of smoking, alcohol consumption, exercise, or diet does not appear to differ between centenarians and controls [13].Taken together, these findings provide ample evidence that extreme longevity has a genetic component ." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "Introduction\n\nHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006)." + } + ], + "dbf4c446-7c25-470a-9532-a564b8683eef": [ + { + "document_id": "dbf4c446-7c25-470a-9532-a564b8683eef", + "text": "\n\nUnraveling the heritability of human longevity was one of the first problems faced by geneticists.Just over a century ago, Mary Beeton and Karl Pearson [1] described a resemblance among relatives for the duration of life.A short time later, Yule [2] and Fisher [3] proved that the correlation is to be expected if lifespan is influenced by what had recently been termed 'genes' [4].Indeed, a century of correlation studies have established that something on the order of 30-50% of the total variation in human life span is attributable to genetic variation [5].Despite the wealth of diversity, specific genes contributing to this variation have proven notoriously difficult to identify.Sample size and issues of shared environment limit family-based methods such as linkage analysis, where rough genomic positions of important genetic variants are identified by comparing a small number of exceptionally long-lived people in defined pedigrees." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nStudies of mono-and dizygous twins have revealed that the genetic contribution to the variation in human lifespan is about 25-30% [12,13], and is most prominent in families clustered for longevity [14,15].This genetic contribution is mainly apparent after the age of 60 years and seems to increase with age [13,16].Furthermore, human lifespan is a complex trait which is assumed to be determined by many genes with small individual effects [17], although the polygenic architecture still needs to be characterized [18,19].The diverse health features of long-lived families illustrate that different age-related diseases have common determinants and implicate that pathways can be identified that attenuate aging and delay age-related disease.From a genomic perspective, individuals from long-lived families are assumed to be characterized by a decreased prevalence of disease-promoting variants (referred to as disease-susceptibility alleles) and an increased prevalence of variants conferring maintenance of health and protection from disease, when compared to population controls.In the last 5 years, many diseasesusceptibility alleles have been identified (National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) Catalog; http://www.genome.gov/gwastudies/)[20].A first comparison between long-lived individuals, selected from both long-lived families (LLS) and the general population (Leiden 85-plus study), and young controls showed no difference in the distribution or frequency of disease-susceptibility alleles identified in cancer, coronary artery disease and type 2 diabetes [21].The search for lifespan regulating loci -contributing to longevity and population mortality -must therefore extend beyond a focus on disease-susceptibility alleles.We will first discuss the efforts to identify longevity loci by genetics approaches." + } + ] + }, + "data_source": [], + "document_id": "5AE03C65B85643330DE58348F4946E8A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "genetics", + "lifespan", + "heritability", + "environmental&factors", + "twin&studies", + "genealogical&studies", + "longevity", + "genomic&research", + "biomarker&research", + "aging" + ], + "metadata": [], + "question": "Is lifespan determined by genetics?", + "subquestions": null, + "task_id": "5AE03C65B85643330DE58348F4946E8A", + "usage": { + "chatgpt": 8198, + "gpt-4": 4730, + "gpt-4-turbo-preview": 3782 + }, + "user_id": 2 + }, + "document_id": "5AE03C65B85643330DE58348F4946E8A", + "task_id": "5AE03C65B85643330DE58348F4946E8A" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_03.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_03.json new file mode 100644 index 0000000..3f80e3c --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_03.json @@ -0,0 +1,138 @@ +{ + "created_at": "2024-05-31T20:36:11.762580Z", + "data": { + "amplify": false, + "answer": "Yes, there is a direct association between aging and susceptibility to having diabetes. The risk of onset of type 2 diabetes increases with age, and most diabetic patients in certain regions are 40 years old or more. Additionally, aging is associated with changes in body composition and glucose tolerance, which can contribute to the development of diabetes.", + "context": { + "29e232a4-a580-411d-83a3-7ff6a4e8f0ad": [ + { + "document_id": "29e232a4-a580-411d-83a3-7ff6a4e8f0ad", + "text": "\n\nOur result provides a novel hypothesis on the mechanism for the connection between two aging-related diseases: Alzheimer's disease and type 2 diabetes." + } + ], + "6e570a0b-a876-4263-b32f-cee85088756d": [ + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "text": "\n\nThere are two major factors that underlie these alarming projections.The first is T2D is associated with age, and Western populations are aging rapidly.The second major explanation is our lifestyles have changed dramatically in recent years.Epidemiological studies have identified strong T2D risk relationships for obesity, sedentary behavior [2][3][4], and diets rich in energy [5], processed carbohydrates [6], and animal fats [7].Collectively, these lifestyle factors impede the actions of insulin and raise hepatic glucose production, which can result in the diminution of endogenous insulin production and T2D.The strongest evidence for a causal relationship between adverse lifestyle behaviors and T2D comes from randomized controlled trials that show intensive lifestyle interventions involving structured exercise regimes which promote habitual physical activity (PA) and have a major beneficial impact on diabetes incidence in high-risk individuals [8,9]." + }, + { + "document_id": "6e570a0b-a876-4263-b32f-cee85088756d", + "text": "\n\nEpidemiological studies examining the associations between lifestyle behaviors and diabetes risk have reached similar conclusions as the clinical trials described above.For example, the 14-year follow-up University of Pennsylvania Alumni Health Study [52] (n = 5,990 men aged 39-68 years) showed PA (leisure time physical activity [LTPA] expressed in kcal expended per week through walking, stair climbing, and sports) was inversely associated with the incidence of T2D.Incidence rates declined as energy expenditure rose from 500 through 3,500 kcal/week.The age-adjusted relative risk ratio (RR) of T2D was reduced by about 6% for each 500 kcal increment increase in PA energy expenditure." + } + ], + "71172700-7bcc-42f5-9354-d8e9290e8743": [ + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "\n\nOverall, results were similar in analyses restricted to diabetes mellitus identified at baseline only, although the confidence interval included 1.These results suggest that diabetes mellitus is related to risk of AD in old age.These findings are consistent with the results of 2 large longitudinal cohort studies. 5,6In one study, 5 diabetes mellitus doubled the risk of AD during 2 years of follow-up in a sample of more than 6000 older persons from a defined cohort.The other study, 6 using data from about 2500 Japanese American men, found a similar result: diabetes mellitus approximately doubled the risk of AD.In contrast, 2 other longitudinal studies 7,8 did not demonstrate a significant association between diabetes mellitus and incident AD, but in both, the results were in the direction of increased risk.Some, [9][10][11] but not all, 12 previous studies found that diabetes mellitus was related to change in cognitive function.One factor that may contribute to variability from study to study is that diabetes mellitus may be related to decline in some cognitive systems but not others.4][15] Although diabetes mellitus was related to level of global cognition and multiple cognitive domains at baseline, we found that diabetes mellitus was only related to decline in perceptual speed.The one study 12 that did not find a relation between diabetes mellitus and cognitive decline did not include a measure of perceptual speed." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "COMMENT\n\nIn a cohort of more than 800 older persons, we found that diabetes mellitus sometime in the study was associated with an increased risk of developing AD during a mean of 5.5 years of observation.The risk of incident AD was 65% higher in those with diabetes mellitus than in those without it." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "\n\nIn summary, these findings suggest that diabetes mellitus is associated with AD and decline in cognitive function in older persons.December 12, 2003." + }, + { + "document_id": "71172700-7bcc-42f5-9354-d8e9290e8743", + "text": "DIABETES MELLITUS AND RISK OF AD\n\nDuring the follow-up evaluations, 151 persons developed AD, of whom 31 had diabetes mellitus.In a proportional hazards model adjusted for age, sex, and educational level, there was a 65% increase in the risk of developing AD in those with diabetes mellitus compared with those without diabetes mellitus (hazard ratio, 1.65; 95% confidence interval, 1.10-2.47).The cumulative hazard of AD over time, adjusted for age, sex, and educational level, is shown graphically in Figure 1 for typical participants with and without diabetes mellitus.Similar results were found in analyses with diabetes mellitus identified at baseline only (hazard ratio, 1.53; 95% confidence interval, 0.96-2.45)." + } + ], + "77daf125-3e88-41fe-92fd-71a9ce9c6671": [ + { + "document_id": "77daf125-3e88-41fe-92fd-71a9ce9c6671", + "text": "\n\nAge. Age is another factor that has a considerable effect on outcomes in obesity and T2DM research.In humans, body weight increases with age and peaks at ~55 years in both men and women.Ageing per se is associated with a redistribution of both the fat-free mass and the fat mass, with the latter increase starting at ~30 years of age 129 .Intramuscular and intrahepatic fat are particularly increased in older persons, and this increase has been linked to insulin resistance 130 .Partially on the basis of these changes, ageing has been proposed to be an independent determinant of glucose tolerance, which progressively worsens with age 131,132 ." + } + ], + "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a": [ + { + "document_id": "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a", + "text": "\n\nAge also plays a vital role in the onset of diabetes (Cowie & Eberhardt, 1995).In south-east Asia almost 97% diabetic patients are 40 years old or more (IDF Atlas, 2017).In Bangladesh, the reported age of diabetes is ≥40 years in 71% urban and 85% rural female, while in the case of male the proportion is 85.5% urban and 86.5% in rural population (IDF Atlas, 2017).The current study also pinpointed an exponential increase in the risk of onset of T2DM with the increase of age when 40 years was chosen as the reference (Table S4)." + }, + { + "document_id": "94e153f4-bc43-4e5b-99d4-6bb64ed24e4a", + "text": "\n\nWhether age and stress variables are risk factors for type 2 diabetes incidence was assessed by multivariate logistic regression (Table S4).Subjects in the age groups of (40-60) and >60 years had 1.78× (p = .005)and 3.19× (p = .006)greater risk for type 2 diabetes respectively than group of <40 years.Overall, patients under stressful condition are more likely to develop T2DM than that of nonstressed respondent (p = .000).Moreover, when stress is divided into two groups-low stress and high stress, we found that both males (p = .000)and females (p = .000)with high stress were at high risk of diabetes mellitus, whereas the association between low stress and T2DM incidence was significant only among males (Male: p = .002;Female: p = .115).The distribution and association of the genotypes, age, and stress with T2DM have been summarized in Table 3 and Figure 3.There was no difference in T2DM incidence between CT (p = .030)and TT/CC (p = .034)genotype containing people who were in age group of 40-60 years (Table 3).In contrast, people who were more than 60 years old with CT genotype (OR = 4.636, p = .029)were more prone to T2DM than that of TT/CC genotype (OR = 3.714, p = .007)subjects (Table 3)." + } + ], + "9c9cc0b3-5dde-4077-ae41-1410db9aeb24": [ + { + "document_id": "9c9cc0b3-5dde-4077-ae41-1410db9aeb24", + "text": "Research Gaps\n\nThere is a clear correlation of environmental influences to diabetes risk.Yet, the assembled experts agreed that hypothesis-driven research is needed to define direct causal relationships between specific environmental factors and pathophysiologies leading to diabetes.Research efforts need to address environmental etiologies of type 1 diabetes and determine their relative contribution to onset of autoimmunity and progression to symptomatic disease.Whether there is a direct causal role of the intestinal microbiota in pathogenesis of type 1 and type 2 diabetes and response to therapies needs to be determined.Public health interventions that successfully reduce the levels of consumption of energy-dense foods and/or reduce sedentary time and increase time spent in physical activity need to be evaluated to determine whether they can reduce type 2 diabetes incidence at a population level." + } + ], + "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6": [ + { + "document_id": "afe6a42e-2c8b-4cfd-9334-157d1b9d15b6", + "text": "\n\nIn sum, it is clear that multiple risk factors are involved in diabetes-associated cognitive decrements as well as in dementia in relation to diabetes 38 .On the basis of our assessment of the literature, it is also clear that there are still substantial knowledge gaps on how the risk factors interconnect, how the risk factors translate to potentially modifiable mechanisms and which genetic factors are involved." + } + ], + "b21bbbce-b53f-416b-8378-b635f4270ace": [ + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\n\nThe aim of this study was to investigate the association between age at natural menopause and risk of developing type 2 diabetes, and to assess whether this association is independent of potential intermediate risk factors for type 2 diabetes.Furthermore, we examined the role of endogenous sex hormone levels in the association between age at natural menopause and type 2 diabetes." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\n\nAims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\nAims/hypothesis In this study, we aimed to examine the association between age at natural menopause and risk of type 2 diabetes, and to assess whether this association is independent of potential mediators.Methods We included 3639 postmenopausal women from the prospective, population-based Rotterdam Study.Age at natural menopause was self-reported retrospectively and was treated as a continuous variable and in categories (premature, <40 years; early, 40-44 years; normal, 45-55 years; and late menopause, >55 years [reference]).Type 2 diabetes events were diagnosed on the basis of medical records and glucose measurements from Rotterdam Study visits.HRs and 95% CIs were calculated using Cox proportional hazards models, adjusted for confounding factors; in another model, they were additionally adjusted for potential mediators, including obesity, C-reactive protein, glucose and insulin, as well as for levels of total oestradiol and androgens.Results During a median follow-up of 9.2 years, we identified 348 individuals with incident type 2 diabetes.After adjustment for confounders, HRs for type 2 diabetes were 3.7 (95% CI 1.8, 7.5), 2.4 (95% CI 1.3, 4.3) and 1.60 (95% CI 1.0, 2.8) for women with premature, early and normal menopause, respectively, relative to those with late menopause (ptrend <0.001).The HR for type 2 diabetes per 1 year older at menopause was 0.96 (95% CI 0.94, 0.98).Further adjustment for BMI, glycaemic traits, metabolic risk factors, C-reactive protein, endogenous sex hormone levels or shared genetic factors did not affect this association.Conclusions/interpretation Early onset of natural menopause is an independent marker for type 2 diabetes in postmenopausal women." + }, + { + "document_id": "b21bbbce-b53f-416b-8378-b635f4270ace", + "text": "\n\nassociation and explore whether the timing of natural menopause can add value to diabetes prediction and prevention." + } + ], + "d1449eee-d4ec-4886-87d1-835fb54a5f56": [ + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\n\nAlthough drawing of definitive conclusions is difficult from these observational studies, their results suggest that young-onset type 2 diabetes is associated with a much more frequent occurrence of adverse macrovascular and microvascular outcomes and a more rapidly progressing severity of complications than is seen in type 1 diabetes or later-onset type 2 diabetes." + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\n\nIn a study of the age-specific incidence of type 2 diabetes in the UK (a retrospective cohort study of patients with newly diagnosed type 2 diabetes between 1990 and 2010), the investigators reported a substantial increase in the proportion of people aged 40 years or younger at diagnosis" + }, + { + "document_id": "d1449eee-d4ec-4886-87d1-835fb54a5f56", + "text": "\nThe prevalence of type 2 diabetes in adolescents and young adults is dramatically increasing.Similar to older-onset type 2 diabetes, the major predisposing risk factors are obesity, family history, and sedentary lifestyle.Onset of diabetes at a younger age (defined here as up to age 40 years) is associated with longer disease exposure and increased risk for chronic complications.Young-onset type 2 diabetes also affects more individuals of working age, accentuating the adverse societal effects of the disease.Furthermore, evidence is accumulating that young-onset type 2 diabetes has a more aggressive disease phenotype, leading to premature development of complications, with adverse effects on quality of life and unfavourable effects on long-term outcomes, raising the possibility of a future public health catastrophe.In this Review, we describe the epidemiology and existing knowledge regarding pathophysiology, risk factors, complications, and management of type 2 diabetes in adolescents and young adults." + } + ], + "f53cd4d6-2d42-47e1-b58f-5bf8f2d65ef5": [ + { + "document_id": "f53cd4d6-2d42-47e1-b58f-5bf8f2d65ef5", + "text": "\n\nThe biological processes linking aging and disease risk are poorly understood.Still, aging is considered to date as one of the main factors responsible for several complex diseases including cancer, cardiovascular diseases, and diabetes." + } + ] + }, + "data_source": [], + "document_id": "B627313B69829B6ACD923E8E6588A622", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "aging", + "diabetes", + "T2DM", + "age", + "susceptibility", + "onset", + "risk&factors", + "incidence", + "type&2&diabetes", + "age-related&diseases" + ], + "metadata": [], + "question": "Is there a direct association between aging and susceptibility to having diabetes?", + "subquestions": null, + "task_id": "B627313B69829B6ACD923E8E6588A622", + "usage": { + "chatgpt": 5686, + "gpt-4": 3837, + "gpt-4-turbo-preview": 2918 + }, + "user_id": 2 + }, + "document_id": "B627313B69829B6ACD923E8E6588A622", + "task_id": "B627313B69829B6ACD923E8E6588A622" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_04.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_04.json new file mode 100644 index 0000000..18145a9 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_04.json @@ -0,0 +1,144 @@ +{ + "created_at": "2024-05-31T20:43:30.117900Z", + "data": { + "amplify": false, + "answer": "The genes associated with aging in humans are APOE and FOXO3A.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Genetic\nlinkage studies of long-lived human families identified a\nlongevity locus while candidate gene approaches have been\nused to identify and confirm the association between\nspecific variants in the FOXO3A gene and human\nlongevity [3–7]. Genome-wide association studies have\nalso been used to identify the association of APOE with life\n\n123\nAging Clin Exp Res\n\nspan and have yielded insights into potential biological\npathways and processes related to aging. Despite these\nsuccesses, several problems are inherent in human\nlongevity studies including potentially high degrees of\nenvironmental heterogeneity, genetic diversity, and lack of\nbirth matched controls, among others [8]." + } + ], + "4a27da1c-b184-47e8-bef2-de6435d7c3f5": [ + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + } + ], + "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4": [ + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "text": "\n\nIn conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies." + } + ], + "5e6ad994-9cad-4b8b-903d-2d5c350e25dc": [ + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\n\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nIn addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha ˜es et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + } + ], + "f3610ccc-2831-42f6-a3d3-1a0feeba4902": [ + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\n\nThe only two genes associated with human longevity that have been replicated in multiple populations are FOXO3A and APOE [11,12,15,26,28 -31].The effect sizes of these two genes for longevity are small with odds ratios of 1.26 and 1.45 for survival to age 100 in replicate studies for FOXO3A and APOE, respectively [10,29].These genes account for only a small portion of the genetic contribution to longevity measured through family heritability studies [4,5].Therefore, much of the heritability of lifespan remains to be explained." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + }, + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\n\nAgeing in humans is typified by the decline of physiological functions in various organs and tissues leading to an increased probability of death.Some individuals delay, escape or survive much of this age-related decline and live past age 100.Studies comparing centenarians to average-aged individuals have found polymorphisms in genes that are associated with long life, including APOE and FOXOA3, which have been replicated many times.However, the associations found in humans account for small percentages of the variance in lifespan and many other gene associations have not been replicated in additional populations.Therefore, ageing is probably a highly polygenic trait.In humans, it is important to also consider differences in age-related decline that occur within and among tissues.Longitudinal data of age-related traits can be used in association studies to test for polymorphisms that predict how an individual will change over time.Transcriptional and genetic association studies of different tissues have revealed common and unique pathways involved in human ageing.Genomic convergence is a method that combines multiple types of functional genomic information such as transcriptional profiling, expression quantitative trait mapping and gene association.The genomic convergence approach has been used to implicate the gene MMP20 in human kidney ageing.New human genetics technologies are continually in development and may lead to additional breakthroughs in human ageing in the near future." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + } + ] + }, + "data_source": [], + "document_id": "9AA0126F9464E89A7B057D231376A79A", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "longevity", + "human", + "aging", + "genes", + "GWAS", + "SNP", + "centenarians", + "genetic&variants" + ], + "metadata": [], + "question": "Which genes are associated with aging in humans?", + "subquestions": null, + "task_id": "9AA0126F9464E89A7B057D231376A79A", + "usage": { + "chatgpt": 7766, + "gpt-4": 5012, + "gpt-4-turbo-preview": 4076 + }, + "user_id": 2 + }, + "document_id": "9AA0126F9464E89A7B057D231376A79A", + "task_id": "9AA0126F9464E89A7B057D231376A79A" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_05.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_05.json new file mode 100644 index 0000000..d25aaa8 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_05.json @@ -0,0 +1,146 @@ +{ + "created_at": "2024-05-31T20:40:14.163893Z", + "data": { + "amplify": false, + "answer": "Several genetic factors influence aging in humans. These include the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling pathway, and the exonuclease 1 (EXO1) gene. Other genes associated with aging are those involved in pathways such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response. Additionally, genes related to immune response, energy metabolism, signal transduction pathways, and cellular senescence also play a role in aging.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + }, + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "Introduction\n\nWith the development of human genomics research, a large number of studies of the genetics of longevity have been conducted.Scientists from various countries have proposed many different theories concerning the mechanisms of aging from different perspectives, involving oxidative stress, energy metabolism, signal transduction pathways, immune response, etc. [1,2].These mechanisms interact with each other and are influenced by heredity to some degree [2,3].The identification of longevity-related biological markers is critical to an indepth understanding of the mechanisms of carrier protection against common disease and/or of the retardation of the process of aging." + } + ], + "1386c8ad-297d-48b1-aa34-41659a9f6544": [ + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Influence of Genetic Factors in Ageing and Lifespan\n\nAgeing is defined as the decline of physiological functions in several tissues and organs inducing an increasing probability of death [17].The understanding of genetic factors involved in ageing has been limited due to the complexity of this process and the heterogeneity among individuals and even among tissues [18][19][20].Tissue cells adopt a senescent phenotype as a consequence of multiple intrinsic, extrinsic, and stochastic factors [21].The combination of these genetic factors is related to longevity and healthy ageing [22].Although this decline is somewhat predictable, some individuals show a much slower decline and get to live past the age of 100.Studies in these individuals showed polymorphisms in some genes which are associated with long life, such as APOE and FOXO3.However, these associations have not been consistent across different populations, suggesting that ageing is rather polygenic [23]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + } + ], + "7291ceb2-482a-4f9b-a116-2b68ff24854f": [ + { + "document_id": "7291ceb2-482a-4f9b-a116-2b68ff24854f", + "text": "\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10)." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + } + ], + "c8fbb24d-0a72-4a45-a552-6cd98a4a25a2": [ + { + "document_id": "c8fbb24d-0a72-4a45-a552-6cd98a4a25a2", + "text": "Translational\n\nA LTHOUGH there is much debate about the processes driving human aging, there is little doubt that genetic influences play a significant role (1).Humans clearly live very much longer than the currently favored laboratory models of aging, and such interspecies differences in reproductively 'fit' life span must have an inherited genetic foundation.Within human populations, environmental and behavioral exposures are important but at least a quarter of life expectancy variation in twin or family studies is attributable to inherited genetic or epigenetic factors (2).Age-related conditions such as type 2 diabetes, myocardial infarction, common cancers, and Alzheimer's disease (AD) typically have onsets after the fourth decade of life; \"successful\" agers delay these onsets until relatively late in life (3).Many aging traits and diseases show moderate heritability, including cardiovascular disease (CVD) (4) and impaired physical functioning (5), independent of known environmental risk factors." + } + ], + "ca76f85d-9f72-4e15-8ba9-3bf94308c449": [ + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + }, + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "Introduction\n\nHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006)." + } + ], + "db90a971-e55a-4ab0-a3b1-05908d6771a4": [ + { + "document_id": "db90a971-e55a-4ab0-a3b1-05908d6771a4", + "text": "Introduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go ¨gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha ¨chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + } + ], + "f4e2fa75-559b-4fa9-b722-bdac03f7715a": [ + { + "document_id": "f4e2fa75-559b-4fa9-b722-bdac03f7715a", + "text": "\n\nI NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + } + ] + }, + "data_source": [], + "document_id": "7530EBCCAFF1750013433CA62E07A82F", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "IGF", + "insulin", + "GH", + "LPA", + "HLA-DQA1/DRB1", + "CHRNA3/5", + "CDKN2A/B", + "SH2B3" + ], + "metadata": [], + "question": "What genetic factors influence aging in humans?", + "subquestions": null, + "task_id": "7530EBCCAFF1750013433CA62E07A82F", + "usage": { + "chatgpt": 9172, + "gpt-4": 5514, + "gpt-4-turbo-preview": 4604 + }, + "user_id": 2 + }, + "document_id": "7530EBCCAFF1750013433CA62E07A82F", + "task_id": "7530EBCCAFF1750013433CA62E07A82F" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_06.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_06.json new file mode 100644 index 0000000..e47f351 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_06.json @@ -0,0 +1,150 @@ +{ + "created_at": "2024-05-31T20:53:55.182883Z", + "data": { + "amplify": false, + "answer": "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and numerous different pathophysiological processes and diseases. Secondly, any common variation in genes associated with aging probably has a small effect, requiring large studies for identification. Thirdly, human studies face issues like environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of environmental factors and human-specific confounders like psychosocial, economic, and cultural factors can potentially mask purely biological aging mechanisms, making the analysis more difficult.", + "context": { + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "0d6942b4-e75f-4f44-98ac-b8378ef1d3c7": [ + { + "document_id": "0d6942b4-e75f-4f44-98ac-b8378ef1d3c7", + "text": "FUTURE DIRECTIONS: HIGHER RESOLUTION DATA VIA HIGHER THROUGHPUT ASSAYS\n\nOne inescapable conclusion of the aggregate results of genome-wide studies of aging to date (see summary Table 1) is that we have not come close to saturating the number of potentially lifespan-altering genes in any organism.This is in no small part because directly generating survival curves is a relatively time-consuming process in most model organisms using current methods.There are several possible ways to address this.One way that has been tried is by attempting to find surrogate phenotypes [72,73,126] that can be screened more rapidly, or even scored under selection.Another is mining candidates from the many whole-genome expression profiles.Results to date with these have been very fruitful, but have not suggested that these methods alone will rapidly saturate our search for lifespan-and healthspan-altering genes in tractable model organisms." + } + ], + "113cb521-b79d-4b44-8250-dc1013ea2cb3": [ + { + "document_id": "113cb521-b79d-4b44-8250-dc1013ea2cb3", + "text": "\n\nChromosome mapping of genes that were differentially expressed in mice of different ages and/or in response to CR revealed a wide distribution of genes with some physical clustering of responsive genes within the genome.The latter findings are consistent with the concept that aging is a complex process and that evolutionary adaptations to aging, if they exist, may or may not involve geographic clustering of functionally related genes." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Genetic\nlinkage studies of long-lived human families identified a\nlongevity locus while candidate gene approaches have been\nused to identify and confirm the association between\nspecific variants in the FOXO3A gene and human\nlongevity [3–7]. Genome-wide association studies have\nalso been used to identify the association of APOE with life\n\n123\nAging Clin Exp Res\n\nspan and have yielded insights into potential biological\npathways and processes related to aging. Despite these\nsuccesses, several problems are inherent in human\nlongevity studies including potentially high degrees of\nenvironmental heterogeneity, genetic diversity, and lack of\nbirth matched controls, among others [8]." + } + ], + "4ca8d070-8b58-4bd5-86be-127089b70324": [ + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "text": "\n\nThe aging process most certainly is under highly polygenic controls… This should not discourage us from pursuing a search for those loci which may be of profound importance to human aging as it ordinarily occurs in most human beings." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "606c59c5-5ae4-47e9-b3eb-58afa55669d1": [ + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + } + ], + "690a2ae6-962a-438c-91ca-60425a0c8d02": [ + { + "document_id": "690a2ae6-962a-438c-91ca-60425a0c8d02", + "text": "Accepted Article\n\n© 2013 The Authors Aging Cell © 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland over 90 years and 1,955 controls between 55 and 80 years did not reveal genome-wide significant loci (Newman et al., 2010) and neither did the analyses of all-cause mortality and survival free of major disease in this cohort (Walter et al., 2011).A smaller Dutch study of 403 nonagenarians and 1,670 controls younger than 65 years identified the APOE gene as a mortality locus (Deelen et al., 2011), which was confirmed in a German study of 763 long-lived individuals and 1,085 younger controls (Nebel et al., 2011) and a longitudinal study of 1,606 Danes showed that the effect size of this association increases at the highest ages (Jacobsen et al., 2010).Apparently, the influence of the common genetic variation on longevity is small which requires large meta-GWA studies for identification.Alternatively, rare genetic variants may play a more important role in longevity.Since the previous linkage studies showed contradictory results potentially due to heterogeneity in the longevity phenotype, it is expected that longevity is influenced by many private rare variants." + } + ], + "78a43a45-84b0-4d73-9396-95b99cfd3983": [ + { + "document_id": "78a43a45-84b0-4d73-9396-95b99cfd3983", + "text": "\n\nAgeing is complex and takes a long time to study -a lifetime in fact.This makes it difficult to discern its causes, among the countless possibilities based on an individual's genes, behaviour or environment.While thousands of regions in an individual's genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle.Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nSeveral explanations are possible for the lack of genomewide significant findings.First, mortality is arguably 1 of the most complex phenotypes, and several trajectories toward extreme old age have been identified (Evert et al., 2003).Multiple genes could mediate the aging process but would have their effects through numerous different patho-physiological processes and diseases that act as intermediate factors on the pathway to death (de Magalhaes et al., 2010).Therefore, any common variation in genes associated with aging probably has a small effect." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nSecond, the largely negative findings of this and other studies contrast with the intriguing animal studies of longevity.Very large effects of single genes on lifespan have indeed been observed in laboratory animals, but humans often have several homologues of these genes which might significantly differ in function or compensate for mutated genes through redundant mechanisms (Kuningas et al., 2008).This could explain why our top findings did not include genes in these pathways found in animal models.Animal models also represent genetically homogenous populations and are exposed to controlled environmental influences.The lack of replication of animal model findings in humans suggests that the use of knockout animals may not provide the optimal approach to understanding the variation in survival in humans as interactions with environmental factors may obscure the associations and prevent the identification of loci in humans." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + } + ], + "97290894-086d-438a-bbd2-907dd4cea2ab": [ + { + "document_id": "97290894-086d-438a-bbd2-907dd4cea2ab", + "text": "\n\nIn addition to timing differences, a small proportion of genes (10%-15%) exhibit opposite trends of expression changes with age in humans and macaques (Supplemental Fig. S13).Interestingly, such differences are ;1.5 times more common in aging than in development, an observation consistent with the lower strength of purifying selection on the gene regulation at old age (discussed below).These differences could also reflect extreme shifts in developmental timing between species, as well as technical artifacts.Future studies, using additional species and alternative methodology, are needed to address this issue." + } + ], + "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed": [ + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nThe remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha ˜es et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design." + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "TRANSLATION OF LONGEVITY MODEL ORGANISMS AND CORE AGING PATHWAYS\n\nGenetic studies on lifespan have proven to be challenging.While longevity is a defining trait for a given species, the lifespan of individuals is of limited heritability, making analyses more difficult.Exceptional human life span, although a rare phenotype, is likely multifactorial; refined analyses are required to obtain statistically robust genomic signatures of longevity (Zhang et al., 2020) and these have proven elusive.Unlike laboratory models, the effect of environmental variance cannot be controlled in human studies, potentially masking purely biological aging mechanisms.Even laboratory models cannot replicate the complex \"environment\" of humans; it includes psychosocial, economic, and cultural factors, rather than strictly biological.These human-specific confounders are difficult or impossible to target in traditional model organisms.Despite these limitations, experimentally tractable model organisms have proven invaluable in deciphering the purely genetic contribution to lifespan, including genes and pathways conserved across the tree of life." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY\n\nHeritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE ε2), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans." + } + ], + "efd5747f-9e8b-45e8-9e04-bb31131d44fa": [ + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "text": "\n\nWith modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "Conclusions and prospects\n\nOver the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges." + } + ] + }, + "data_source": [], + "document_id": "E1F24400EE215327FE987A4DDC0768C8", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "GWAS", + "longevity", + "aging", + "human", + "gene", + "lifespan", + "genetic", + "environment" + ], + "metadata": [], + "question": "Why is it so diffuclut to map gene loci that control aging in humans?", + "subquestions": null, + "task_id": "E1F24400EE215327FE987A4DDC0768C8", + "usage": { + "chatgpt": 7866, + "gpt-4": 4669, + "gpt-4-turbo-preview": 3717 + }, + "user_id": 2 + }, + "document_id": "E1F24400EE215327FE987A4DDC0768C8", + "task_id": "E1F24400EE215327FE987A4DDC0768C8" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_07.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_07.json new file mode 100644 index 0000000..3848934 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_07.json @@ -0,0 +1,150 @@ +{ + "created_at": "2024-05-31T20:55:47.964141Z", + "data": { + "amplify": false, + "answer": "The aging process is caused by a combination of factors including genetic influences, environmental conditions, and stochastic processes. It involves the accumulation of molecular damage, mutations, incomplete repair, and genetic programs. Other factors include wear and tear on cells, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown. Aging is also associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.", + "context": { + "18e216d9-ea5c-4dfe-a30d-632163fcf39e": [ + { + "document_id": "18e216d9-ea5c-4dfe-a30d-632163fcf39e", + "text": "\n\nThere are multiple definitions of the aging process.Aging may be perceived as the random, systemic loss of molecular fidelity that, after reproductive maturity, accumulates to levels that eventually exceed tissue repair, turnover, or maintenance capacity (Hayflick 2004).The underlying molecular mechanisms of aging remain a subject of debates (de Magalhaes et al. 2009): tissue deterioration might not be programmed, being just a function of increase in entropy (Hayflick 2004).No genes are necessary to drive a stochastic process; however, there are genes that act to prevent an organism from destruction and disorganization.It may be due to the absence of specific disease-causing alleles or due to the presence of favorable alleles (Halaschek-Wiener et al. 2009).These genes may inhibit entropy, regulate inflammation, maintain DNA repair (such as telomere maintenance factors), or provide antioxidant functions (e.g., antagonists of reactive oxygen species).As healthy cells adapt to degeneration, differential expression of genes with age may indicate a transcriptional response to aging rather than a deleterious mechanism of aging per se (de Magalhaes et al. 2009).It might be postulated that there exist alleles that confer a pleiotropic effect on structure and function during aging (Lunetta et al. 2007).These alleles should regulate the ability of an organism to withstand challenging endogenous and exogenous influences." + } + ], + "1ccb0d11-1c88-4b08-b40d-4039a954745f": [ + { + "document_id": "1ccb0d11-1c88-4b08-b40d-4039a954745f", + "text": "Why does ageing evolve? The intrinsic decline in function that occurs during ageing appears to be caused by the accumulation of damage, particularly at the molecular level.As far as we know, no genes have evolved specifically because they cause damage to accumulate, and the evolution of ageing can therefore be understood only as a side-effect of other causes of evolutionary change.The mechanisms by which ageing can evolve were first elucidated by J.B.S. Haldane [14], P.B. Medawar [15] and G.C. Williams [16].Extrinsic hazards from disease, predation and accidents mean that even potentially immortal organisms will die.Genetic effects that become apparent only later in life encounter a reduced force of natural selection, because not all their bearers will survive to express them.Haldane pointed out that late-onset genetic diseases in humans, such as Huntington's disease, encounter only weak selection, because most reproduction is complete by the age of onset [14].Ageing could therefore result from the accumulation under mutation pressure of age-specific, deleterious mutations.In addition, if some mutations have pleiotropic effects, with beneficial effects in youth, such as high fecundity, but also with a higher subsequent rate of ageing, then they could be incorporated into the population by natural selection, which will act more strongly on the early, beneficial effect.Thus, variation in the rate of ageing would result from the readjustment of a tradeoff between youthful benefits and the subsequent rate of ageing.Both processes imply that faster ageing will evolve where the extrinsic hazard to adults is greatest, a hypothesis in general supported by the data [1,2,17]." + } + ], + "4f010a74-a9b4-4538-94f7-ae8f35c8b96e": [ + { + "document_id": "4f010a74-a9b4-4538-94f7-ae8f35c8b96e", + "text": "A. Theories\n\nIn looking back at the development of aging studies, we can see that it did not follow a straight or logical course.On the contrary, it can be compared with the flow of several convergent streams winding in their course.To date, numerous proposals have been made for the paradigm of aging.These include Hayflick's contributions (153) on programmed cellular incapacitation derived from flbroblast studies, a decrease in immunologic response, deleterious endocrinological changes, nuclear somatic gene mutation, mitochondrial somatic gene mutation, oxygen free radical damage to proteins and nucleic acids, molecular instabilities, molecular cross-linking, glycation reactions, and so on.There is little doubt that many of these factors contribute to the overall aging, but what are primary causes, and what are secondary outcomes?" + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Ageing Is Adjusted by Genetic, Environmental, and Stochastic Processes\n\nEnough evidence suggests that ageing is the result of different events such as molecular damage, mutations, incomplete repair, genetic programs, and continued development, among others [16].These events, in turn, are caused by genetic factors, environmental conditions, and even stochastic factors, which are mentioned below in this chapter." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nDifferent stochastic theories of ageing focus on specific mechanisms that may lead to ageing.The catastrophic error theory poses that the accumulation of errors in protein synthesis causes damage in cell function.The theory of cross-linking holds this process between proteins and other macromolecules responsible for ageing, while the theory of free radicals suggests that ageing is the result of inadequate protection against cell and tissue damage by free radicals and oxidative stress throughout life.Finally, the wear-and-tear theory poses that the cumulative damage that eventually leads to ageing and death is, in fact, the result of the continuous functioning of vital processes, during which stochastic errors gradually arise." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Introduction\n\nAging is a natural and irreversible process characterized by a progressive decay in physiological, biochemical, and structural functions of individuals.Aging is a multifactorial process that can be affected by two main factors: environmental and genetic.Environmental factors are nutrition, pathologies, pollution exposure, physical activity, and microbiota, while genetic factors are issues that have been associated with antioxidant and DNA damage responses, the fidelity of genetic information transfer, the efficiency of protein degradation, the extent of cellular responsiveness to stress, the mechanisms of epigenetic regulation, and the ability to elongate telomeres.All of them can determine how fast we age.Traditionally, aging studies had used several model organisms, from yeast to mammals, especially rodents (rats and mice).Most of the studies are made under controlled conditions, where only a few variables are observed, and the subjects are members of the same strain with the same genetic backgrounds or the same mutations.The information that so far has been obtained about aging has helped us to describe different factors that influence this process and that are the fundamental concepts of the various theories of aging.However, these theories do not fully explain the aging process in the different models of aging study.This is the case of the study of aging in humans, where it is very difficult to control the environmental and genetic variables.That is why issues haven't been solved such as the following: How does time influence aging?When do we start to age?How do we know we are old?Is it possible to delay aging?Those and more questions are the cornerstones for aging studies.Biological aging has been associated with the decrease in the repair and regeneration capacity of tissues and organs; it is a time-dependent process.This reduction can be observed by an increase in the acquisition of diseases and functional and reproductive disability, which eventually lead to death.On the other hand, it has been observed that in humans, people with the same chronological age exhibit different trajectories in the decrease of physiological functions associated with biological aging and what complicates the understanding of the molecular and physiological phenomena that drive the complex and multifactorial processes that underlie biological aging in humans." + } + ], + "5030cbc8-e02c-4e3a-8cbc-0156ce123c99": [ + { + "document_id": "5030cbc8-e02c-4e3a-8cbc-0156ce123c99", + "text": "\nThe underlying cause of aging remains one of the central mysteries of biology.Recent studies in several different systems suggest that not only may the rate of aging be modified by environmental and genetic factors, but also that the aging clock can be reversed, restoring characteristics of youthfulness to aged cells and tissues.This Review focuses on the emerging biology of rejuvenation through the lens of epigenetic reprogramming.By defining youthfulness and senescence as epigenetic states, a framework for asking new questions about the aging process emerges." + } + ], + "5e157c2e-91b8-466d-a9fd-f91f8f432f0c": [ + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "text": "\n\nAging does not happen in a vacuum.Aging must be the result of changes that occur in molecules that have existed at one time with no age changes.It is the state of these pre-existing molecules that governs longevity determination.The pre-existing state is, as I have already described, maintained by repair and turnover systems that themselves eventually succumb to irreparable age changes.Longevity determination is the state of all molecules prior to succumbing to irreparable loss of molecular structure." + }, + { + "document_id": "5e157c2e-91b8-466d-a9fd-f91f8f432f0c", + "text": "\n\nBiological aging is more than simply the occurrence of random changes in molecules.It also includes the role of the many repair systems found within cells.Thus, a more complete, but less concise, explanation of the first causes of aging in biological systems is the following:" + } + ], + "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c": [ + { + "document_id": "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c", + "text": "U\n\nnderstanding the deleterious processes that cause aging has been a human endeavor ever since we figured out that we grew old and that we didn't like it.Many hypotheses have been proposed to explain the root cause of aging (1).One broad-based hypothesis is that generalized homeostatic failure leads to age-related decline.Although notions of time-and use-related deterioration may be applicable to mechanical objects, they fall short as analogies to biological systems because energy input should theoretically maintain living systems indefinitely.Yet, despite the regenerative potential of biological organisms, progressive deterioration accompanies postmaturational aging.That the organism's repair capabilities cannot keep up with wear and tear is, according to evolutionary theory, explained by the inevitable declining force of natural selection with age.According to this reasoning, there is no selective advantage to maintaining somatic cells in perfect order much beyond reproductive maturation (1).Hence, a long life depends on the timing of maturation and the quality of somatic cell maintenance." + }, + { + "document_id": "5f434783-db8a-409e-a1c6-1dc1c5e2ba1c", + "text": "\n\nWear and tear on the DNA often has been touted as a possible basis for our progressive age-related decline.Supporting this notion is the work of de Boer et al. (2) reported on page 1276 of this week's issue.They reveal important evidence for imperfect genome maintenance of DNA damage as a possible causal factor in aging.Harman, with his \"free radical theory of aging\" (3), was the first to propose that metabolic by-products called reactive oxygen species (ROS) continually damage cellular macromolecules, including DNA.Incomplete repair of such damage would lead to its accumulation over time and eventually result in age-related deterioration.A number of observations support the free radical theory, including the discovery that dietary restriction delays aging and extends life-span in a wide range of rodents and other species, possibly by reducing free radical damage.The notion that genomic DNA could be a major target of continual free radical attack over time is supported by the recent observation that genetic lesions accumulate with age and that dietary restriction reduces this accumulation in rodents (4).In addition, deletion of p66 shc , a signaling protein that maintains oxidant levels, increases resistance to oxidative damage and extends the life-span of mice (5)." + } + ], + "606c59c5-5ae4-47e9-b3eb-58afa55669d1": [ + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "text": "Instead, aging is expected to\nbe a pervasive failure of adaptation across most, if not all, of the physiological mechanisms\nthat sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute “the cause of\naging” to any one physiological mechanism or gene for aging or programmed death. Although common genetic pathways might be identified that contribute to aging among a\nvariety of organisms (cf." + } + ], + "846ae0a9-165f-4b25-8bcb-310c7da5eb44": [ + { + "document_id": "846ae0a9-165f-4b25-8bcb-310c7da5eb44", + "text": "Background\n\nAging is a complex process characterized by the progressive degeneration of a healthy phenotype and correlated with a decline in the ability to withstand cellular stress and damage.The subject of investigation for decades, the underlying molecular genetic causes of and responses to aging remain an area of active study.Research from model systems has characterized a range of physiological and molecular phenotypes associated with aging.These include genomic instability caused by accumulation of DNA damage, dysregulation of repair mechanisms, and telomere attrition; epigenetic alterations; dysregulation of transcription; loss of proteostasis; cellular senescence; and deregulated nutrient sensing, metabolic pathways, and energy use (reviewed in [1]).Separating causation from correlation between these phenotypes and aging remains a challenge, however." + } + ], + "870798fd-2c26-4819-9403-fe52836770eb": [ + { + "document_id": "870798fd-2c26-4819-9403-fe52836770eb", + "text": "Introduction\n\nUnderstanding what actually causes ageing remains admittedly a fundamental and fascinating problem in biology [1].Experimental data accumulated in the last three decades have led to the identification of various environmental and genetic factors, as well as chemical substances that influence lifespan in divergent eukaryotic species [1,2].Organisms normally age faster and hence live shorter under stress conditions that can lead to the generation of DNA mutations and, often as a consequence of mutations, damaged cytoplasmic constituents (including injured proteins, lipids, carbohydrates and organelles).Such types of damage can interfere with cellular functioning; thereby, they should be eliminated by effective repair and self-cleaning mechanisms to maintain cellular homeostasis.These mechanisms include DNA repair pathways, molecular chaperons, as well as the proteasome-ubiquitin system and lysosome-mediated autophagy, the main forms of cellular self-degradation [3].This has led to the attractive model that the gradual, lifelong accumulation of unrepaired cellular damage drives the ageing process and determines the incidence of age-related fatal diseases [4,5]." + } + ], + "996e02bf-91b2-4e81-89ba-1f661dfc662a": [ + { + "document_id": "996e02bf-91b2-4e81-89ba-1f661dfc662a", + "text": "\n\nIn conclusion, aging may not be primarily due to damage accumulating from the basic biochemical reactions that make up life but rather the result of the developmental program or of changes brought about by it.Our hypothesis is that the timing of development regulates the rate of aging among mammals, with a subset of developmental mechanisms determining the pace and causing most agerelated changes.Maybe people change as they grow old due to the same mechanisms that drive changes throughout the earlier stages in life." + } + ], + "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed": [ + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "text": "Instead, aging is expected to\nbe a pervasive failure of adaptation across most, if not all, of the physiological mechanisms\nthat sustain survival and reproduction among young individuals. For this reason, evolutionary biologists have generally been skeptical of proposals that attribute “the cause of\naging” to any one physiological mechanism or gene for aging or programmed death. Although common genetic pathways might be identified that contribute to aging among a\nvariety of organisms (cf." + } + ], + "a6bc2efd-61a7-4e07-ad5c-49234aa89431": [ + { + "document_id": "a6bc2efd-61a7-4e07-ad5c-49234aa89431", + "text": "\n\nIn 2021, Science published a special issue entitled \"125 Questions: Exploration and Discovery.\" One of these 125 questions was \"Can we stop ourselves from aging? \"The U.S. National Institute on Aging (NIA) at the National Institutes of Health (NIH) states that \"aging is associated with changes in dynamic biological, physiological, environmental, psychological, behavioral, and social processes.\" Although geneticists and epidemiologists have long debated the relative importance of the role played by genotype or the environment in the development of age-related diseases, it is apparent that both can play substantial roles in this process [6,7].However, most etiological studies have concentrated on the role of genotype and have considered the environment to play a secondary role.Nevertheless, an analysis of GBD data showed that nearly 50% of deaths worldwide are attributable to environmental exposure, primarily exposure to airborne particulates (including household air pollution and occupational exposure; 14% of all deaths), smoking and secondhand smoke (13%), plasma sodium concentrations (6%), and alcohol consumption (5%) [8].In contrast, a recent analysis of 28 chronic diseases in identical twins showed that the genetic-related risks of developing one of five age-related diseases were 33.3%, 10.6%, 36.3%, 19.5%, and 33.9% for AD, PD, CAD, COPD, and T2DM, respectively, with a mean of only 26% [9].The results of over 400 genome-wide association studies (GWASs) have also elucidated that the heritability of degenerative diseases is only approximately 10% [10,11].Consequently, nongenetic drivers, such as environmental factors, are now recognized as major risk factors for age-related diseases.The contributions of environmental factors to the development of age-related diseases can be revealed by analyses of all of the factors to which individuals are exposed in their life and the relationships between these exposures and age-related diseases [12,13]." + } + ], + "ab6a47ba-2131-4fc5-be5e-b81dd80d2a65": [ + { + "document_id": "ab6a47ba-2131-4fc5-be5e-b81dd80d2a65", + "text": "Introduction\n\nThe fundamental manifestation of the aging process is a progressive decline in the functional maintenance of tissue homeostasis and an increasing propensity to degenerative diseases and death [1].It has attracted significant interest to study the underlying mechanisms of aging, and many theories have been put forward to explain the phenomenon of aging.There is an emerging consensus that aging is a multifactorial process, which is genetically determined and influenced epigenetically by environment [2].Most aging theories postulate a single physiological cause of aging, and likely these theories are correct to a certain degree and in certain aspects of aging." + } + ], + "ca76f85d-9f72-4e15-8ba9-3bf94308c449": [ + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + } + ], + "f4dd6a1d-062b-42bc-8e22-83fcb3135578": [ + { + "document_id": "f4dd6a1d-062b-42bc-8e22-83fcb3135578", + "text": "\n\nTrying to explain aging in terms of a singular process would be in conflict with evolutionary theory.Even if loss of genome sequence integrity was the most conserved cause of aging, already active in the first replicators (Vijg, 2007), natural selection would allow a multitude of mutations with late adverse effects to accumulate in the germline, many of which would be positively selected for because of their beneficial effects early in life (Williams, 1957), In this respect, somatic mutation accumulation could be a conserved, inevitable cause of aging but superposed on multiple other processes that usually cause the earlier demise of an individual." + } + ] + }, + "data_source": [], + "document_id": "0DC57EB6A882DE1734E915DE035D2296", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "genes", + "aging", + "DNA&damage", + "free&radicals", + "telomeres", + "oxidative&stress", + "environmental&factors", + "genetic&factors", + "cellular&repair", + "protein&synthesis" + ], + "metadata": [], + "question": "what causes the aging process", + "subquestions": null, + "task_id": "0DC57EB6A882DE1734E915DE035D2296", + "usage": { + "chatgpt": 8117, + "gpt-4": 4643, + "gpt-4-turbo-preview": 3712 + }, + "user_id": 2 + }, + "document_id": "0DC57EB6A882DE1734E915DE035D2296", + "task_id": "0DC57EB6A882DE1734E915DE035D2296" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_08.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_08.json new file mode 100644 index 0000000..abbd2b8 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_08.json @@ -0,0 +1,154 @@ +{ + "created_at": "2024-05-31T21:16:38.963061Z", + "data": { + "amplify": false, + "answer": "The genes associated with aging include the apolipoprotein E gene (APOE), the forkhead box O3A (FOXO3A) gene, HLA-DQA1/DRB1, LPA, CHRNA3/5, CDKN2A/B, SH2B3, and AKT1. Other genes involved in processes like growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition are also associated with aging.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\n\nStudies revealed from 300 to 750 genes related to longevity that are critically involved in a variety of life activities, such as growth and development, energy metabolism, oxidative stress, genomic stability maintenance, and neurocognition [4].These candidate genes include mainly APOE, a gene involved in lipoprotein metabolism [5,6].Others are those involved in cell cycle regulation, cell growth and signal transduction, the maintenance of genome stability, and the endocrine-related pathway [7][8][9].In addition, the candidates for longevity encompass genes related to drug metabolism, the ones involved in protein folding, stabilization, and degradation, as well those related to coagulation and regulation of circulation [10], etc.In most cases, these genes or their polymorphic sites were examined in multiple population replication studies, which discovered certain longevity-associated genes or pathways [4][5][6][7][8][9][10]." + } + ], + "4a27da1c-b184-47e8-bef2-de6435d7c3f5": [ + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + } + ], + "520b36a2-4c9c-4894-a818-9917bd357982": [ + { + "document_id": "520b36a2-4c9c-4894-a818-9917bd357982", + "text": "\nUnbiased genome-wide studies of longevity in S. cerevisiae and C. elegans have led to the identification of more than one hundred genes that determine life span in one or both organisms.Key pathways have been uncovered linking nutrient and growth factor cues to longevity.Quantitative measures of the degree to which aging is evolutionary conserved are now possible.A major challenge for the future is determining which of these genes play a similar role in human aging and using that information to develop therapies toward age-associated diseases." + } + ], + "555a1533-2905-4d91-a3b6-2fca3679ab02": [ + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nInvolvement of genes in a wide range of fundamental biological processes suggests also a broad role of these genes in regulating the aging-related phenotypes." + } + ], + "5e6ad994-9cad-4b8b-903d-2d5c350e25dc": [ + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + } + ], + "99a35e24-bbd2-495b-82dc-53d7e2075191": [ + { + "document_id": "99a35e24-bbd2-495b-82dc-53d7e2075191", + "text": "\n\nThus, substantially more work is needed in this area to establish whether longevity is driven by nuclear genomic stability.Diverse and unexpected bits of evidence support a relationship.For example, a disproportionate number of genes identified in unbiased and targeted genome-wide association studies (GWASs) as associated with longevity are involved in genome maintenance (75).One study involved age of natural menopause in ∼70,000 women and led to the identification of 44 genetic variants associated with early or late menopause, a strong biomarker of healthy TIFs (telomere dysfunction-induced foci): co-localization of multiple DNA damage response factors and repair proteins on uncapped telomeric DNA aging (76).Approximately two-thirds of these are associated with genome maintenance genes.Seven of ten significantly associated pathways are involved in DNA repair.The highly significant overrepresentation of DNA repair pathways indicates an intimate connection between genome maintenance and aging phenotypes.From unrelated studies, we know that reduced expression of the repair endonuclease ERCC1-XPF causes accelerated aging (3), whereas ERCC1 is one of the top genes under positive selective pressure in the longest-lived mammalian species, the bowhead whale (77).Intriguingly, hepatocytes from old rats have impaired NER, whereas caloric restriction, which extends longevity, restored the NER capacity of old rats to that of youthful levels (42).In a human interventional study, brief caloric restriction increased NER capacity in PBMCs of individuals who had low NER prior to dietary intervention (78).Therefore, increased DNA repair capacity could promote longevity and may even prove amenable to improvement." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nIn addition to aging-and CR-related genes, another source of candidate genes and pathways for drug design are human longevity-associated genes (Barzilai and Shuldiner, 2001;Browner et al., 2004;Kenyon, 2010).Dozens of genes have now been associated with human longevity (de Magalha ˜es et al., 2009a), although only a handful of genes have been shown to have consistent effects across populations." + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nGenes/loci identified by genome-wide association studies of longevity and lifespan traits." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + } + ], + "efd5747f-9e8b-45e8-9e04-bb31131d44fa": [ + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "text": "\n\nSince many alleles will fit the two patterns just described, it follows that we expect many genetic and biochemical mechanisms of aging.There are some experiments that have attempted to estimate the number of genes involved in aging, particularly in Drosophila.Quantitative genetic estimates of gene number have probably been subject to artifacts, [6,8] and are highly imprecise.Molecular genetic estimates using 2-D gels [3] and high-density geneexpression arrays [12] indicate the involvement of at least 300 genetic loci in Drosophila aging, and that estimate is highly conservative.For now, the best conclusion is probably that many genes are involved in aging in fruit flies.Vertebrates are unlikely to have fewer genes involved in aging, in view of their larger genomes." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "\n\nGenAge consists of several searchable data sets.Considering the extraordinary discoveries in the genetics of aging in model organisms, GenAge includes a data set of genes associated with longevity and/or aging in model organisms.We consider a given gene for inclusion in GenAge if genetic manipulations of the gene result in noticeable changes in the aging phenotype and/or longevity.Most genes in GenAge are from the four typical model organisms: mice, worms, fruit flies and yeast (Table 1).Strikingly, homologues of many genes -such as insulin receptors and sirtuins -have been shown to regulate aging in model organisms separated by large evolutionary distances (Kenyon, 2005;Liu et al ., 2005;Smith et al ., 2008).Moreover, we have shown that genes associated with aging and/or longevity in model organisms are evolutionary conserved in terms of having more homologues than predicted by chance (Budovsky et al ., 2007(Budovsky et al ., , 2008) ) and exhibiting slower molecular evolution rates (de Magalhães & Church, 2007).Therefore, it is now clear that at least some genes identified in model organisms may be relevant to human aging." + } + ], + "f3610ccc-2831-42f6-a3d3-1a0feeba4902": [ + { + "document_id": "f3610ccc-2831-42f6-a3d3-1a0feeba4902", + "text": "\n\nGene associations with age-related traits found using longitudinal study data." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + } + ] + }, + "data_source": [], + "document_id": "B0164472D40098296DA0836E50978AC8", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "HLA-DQA1&DRB1", + "LPA", + "CHRNA3&5", + "CDKN2A&B", + "SH2B3", + "AKT1", + "ERCC1-XPF", + "MTP" + ], + "metadata": [], + "question": "What genes are associated with aging?", + "subquestions": null, + "task_id": "B0164472D40098296DA0836E50978AC8", + "usage": { + "chatgpt": 7627, + "gpt-4": 4474, + "gpt-4-turbo-preview": 3537 + }, + "user_id": 2 + }, + "document_id": "B0164472D40098296DA0836E50978AC8", + "task_id": "B0164472D40098296DA0836E50978AC8" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_09.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_09.json new file mode 100644 index 0000000..75ebba9 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_09.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-05-31T21:57:15.838942Z", + "data": { + "amplify": false, + "answer": "Several genetic factors are associated with aging. These include the APOE, FOXO3A, and CHRNA3/5 genes, as well as the CDKN2A/B, SH2B3, and MTP genes. Other factors include the HLA-DQA1/DRB1 and LPA regions, and the AKAP2 gene. Additionally, genes highly expressed in the brain, genes involved in neural development and function, and genes associated with autophagy have been linked to aging.", + "context": { + "022c37a3-3ea8-4bb7-9997-98ed87635770": [ + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\n\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + }, + { + "document_id": "022c37a3-3ea8-4bb7-9997-98ed87635770", + "text": "\nGenomic analysis of longevity offers the potential to illuminate the biology of human aging.Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA).We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity.Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated.We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD.Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan." + } + ], + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nRecent developments on the genetics of aging can be seen as several streams of effort.In general, humans show a relatively modest (<50%) heritability of life spans (results obtained from twin studies discussed below).The apoE polymorphisms are remarkable for their influence on both cardiovascular disease and Alzheimer disease.In contrast, rare mutant genes with high penetrance cause these same diseases but with early onset and a major shortening of the life span.Shortlived laboratory models (fruit flies, nematodes, mice) are yielding rapid advances, with the discovery of mutants that increase life spans in association with altered metabolism, which leads to questions on the physiological organization of aging processes.Although these early findings do not show that a conserved genetic program actually controls aging processes across animal phylogeny, it is striking how frequently findings of metabolic rate, insulin signaling, and free radicals have emerged from very different approaches to aging in nematodes and mammals, for example.These findings hint that the genetic control of life span was already developed in the common ancestor of modern animals so that subsequent evolution of life spans was mediated by quantitative changes in the control of metabolism through insulin and the production of free radicals." + } + ], + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "\nBackground: Genetic research on longevity has provided important insights into the mechanism of aging and aging-related diseases.Pinpointing import genetic variants associated with aging could provide insights for aging research.Methods: We performed a whole-genome sequencing in 19 centenarians to establish the genetic basis of human longevity.Results: Using SKAT analysis, we found 41 significantly correlated genes in centenarians as compared to control genomes.Pathway enrichment analysis of these genes showed that immune-related pathways were enriched, suggesting that immune pathways might be critically involved in aging.HLA typing was next performed based on the whole-genome sequencing data obtained.We discovered that several HLA subtypes were significantly overrepresented.Conclusions: Our study indicated a new mechanism of longevity, suggesting potential genetic variants for further study." + } + ], + "0942fb8b-731c-4d6e-9b5a-8a303012eec6": [ + { + "document_id": "0942fb8b-731c-4d6e-9b5a-8a303012eec6", + "text": "\nBackground: Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality.Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.Results: Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively.We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function.Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs.A polygenic score for GrimAge acceleration showed strong associations with adiposityrelated traits, educational attainment, parental longevity, and C-reactive protein levels.Conclusion: This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity." + } + ], + "1386c8ad-297d-48b1-aa34-41659a9f6544": [ + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nBefore the advent of NGS technologies, several scientists were interested in the study of allele variants associated with aging, but they were limited by the lack of aging rate biomarkers.Now with NGS technologies, these biomarkers have been emerged such as the epigenetic clock that is described in the DNA methylation sequencing section of this chapter.In this post-genomic era, different strategies have been developed in order to understand the genetic factors involved in aging [17].One strategy used is the study of aging in extreme longevity groups of people, called centenarians.Centenarians are a group that can reach an age above 100 years and has an incidence of 1 every 10,000 people [18].In a pioneering study using extreme longevity people (308 individuals belonging to 137 sibships showing extreme longevity), genome-wide scan analysis identified a region on chromosome 4 associated with extreme longevity [19] that corresponds to the microsomal transfer protein (MTP) [20], which is associated with abetalipoproteinemia and hypobeta lipoproteinemia in humans [21,22].Another approach to study the genetic factors involved in longevity consists in assessing allele frequencies from people of different ages, looking for those polymorphisms (SNPs) with enhanced allele frequencies in high-longevity individuals.Those alleles with diminished frequencies in aged individuals may be associated with age-related diseases.Using this approximation, an SNP that shifts isoleucine to valine was identified in the PKA-anchoring protein (AKAP2) gene.This polymorphism is associated with reduced longevity and cardiac disease [23].Genome-wide association studies (GWAS) have confirmed only three loci that affect longevity: FOXO3A, APOE, and an intergenic locus on chromosome 5q33.3[24][25][26]." + } + ], + "555a1533-2905-4d91-a3b6-2fca3679ab02": [ + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nEven more disappointing result is that some genes predisposing to geriatric diseases discovered by GWAS appear to be not correlated with human longevity (Beekman et al. 2010;Deelen et al. 2011).This result questions whether findings obtained from GWAS may provide insights into the bio-genetic mechanisms underlying a healthy lifespan.In fact, this finding is very surprising because (1) genetic studies of non-human species have discovered numerous genes predisposing to aging-related processes (Cutler and Mattson 2006;Vijg and Suh 2005;Kenyon 2005;Johnson 2006;Greer and Brunet 2008), (2) nongenetic association studies show that the long-living individuals are typically in better health compared to the short-living individuals (Barzilai et al. 2003;Willcox et al. 2008b;Willcox et al. 2008a;Evert et al. 2003), and (3) candidate-gene studies (but not GWAS) document that the same genes can affect diseases and lifespan (Koropatnick et al. 2008;Kulminski et al. 2011).This is an apparent paradox which has to be carefully examined.A prominent geneticist and evolutionary biologist T. G. Dobzhansky asserts that \"nothing in biology makes sense except in the light of evolution. \"Evolution primarily maximizes fitness of individuals of reproductive age.The classical evolutionary biological theory of aging claims that aging occurs because of decline in the force of natural selection with age (Kirkwood and Austad 2000).Then, according to that theory, aging-related (senescent) phenotypes with post-reproductive manifestation are non-adaptive and subject to stochastic variation.Therefore, at a first glance evolution should not be relevant to senescent phenotypes (apart so-called grandmother hypothesis; Hawkes et al. 1998).Such phenotypes, however, can be caused by reproductive-age-related risk factors making, thus, evolution to be relevant to them (Vijg and Suh 2005;Di Rienzo and Hudson 2005;Drenos and Kirkwood 2010)." + }, + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nOn the other hand, the same evolutionary-motivated strategy suggesting to focus on more heterogeneous phenotypes (as opposite to more homogenous) can be highly beneficial for unraveling genetic predisposition to fundamental mechanisms of intrinsic biological aging and, consequently, to geriatric diseases.Indeed, aging is associated with systemic remodeling of an organism's functioning which increases chances of virtually all geriatric disorders (Franco et al. 2009;Franceschi et al. 2000;Martin et al. 2007;Cutler and Mattson 2006).Experiments with laboratory animals (Johnson 2006) and heritability estimates in humans (Christensen et al. 2006;Iachine et al. 1998) show that aging can be genetically regulated (Finch and Tanzi 1997;Martin et al. 2007;Vaupel 2010).Accordingly, yielding insights in genetic predisposition to aging-related processes in an organism could be a major breakthrough in preventing and/or ameliorating not one geriatric trait, but perhaps a major subset of such traits (Martin et al. 2007) that can greatly advance progress in solving the problem of extending healthy lifespan in humans." + } + ], + "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4": [ + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "text": "\n\nIn conclusion, we performed a genome-wide association study of longevity-related phenotypes in individuals of European, East Asian and African American ancestry and identified the APOE and GPR78 loci to be associated with these phenotypes in our study.Moreover, our gene-level association analyses highlight a role for tissue-specific expression of genes at chromosome 5q13.3,12q13.2,17q21.31,and 19q13.32 in longevity.Genetic correlation analyses show that our longevity-related phenotypes are genetically correlated with several disease-related phenotypes, which in turn could help to identify phenotypes that could be used as potential biomarkers for longevity in future (genetic) studies." + } + ], + "7291ceb2-482a-4f9b-a116-2b68ff24854f": [ + { + "document_id": "7291ceb2-482a-4f9b-a116-2b68ff24854f", + "text": "\n\nM OST genetic studies involved with aging have focused on identifying genes contributing to particular diseases.More recently, it has been recognized that it is also valuable to examine genetic factors related to diseasefree or healthy aging (1,2).Utilizing twins from the National Academy of Sciences-National Research Council (NAS-NRC) twin panel, we have demonstrated that healthy physical aging is under a significant degree of genetic influence, with a heritability over 50% (3).Our definition of healthy aging focused principally on freedom from cardiovascular disease, and has received considerable support in the more recent literature.Brand and colleagues (4) reported that parental age at death was a significant predictor of coronary heart disease death in the Framingham offspring study and concluded that familial similarities for age at death may be mediated through shared coronary heart disease risk factors.Frederiksen and colleagues (5) reported that increased parental life was associated with a reduction in odds ratio for their children to have diabetes, ischemic heart disease, heart failure, stroke, and hypertension.We have found that better midlife lipid levels and blood pressures were associated with increased parental longevity in the National Heart, Lung, and Blood Institute twin study (6).Centenarian siblings and offspring, besides having increased longevity, have been shown to have better health and better cardiovascular risk factor profiles (7)(8)(9)(10)." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\nHuman longevity and healthy aging show moderate heritability (20%-50%).We conducted a meta-analysis of genome-wide association studies from 9 studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium for 2 outcomes: (1) all-cause mortality, and (2) survival free of major disease or death.No single nucleotide polymorphism (SNP) was a genome-wide significant predictor of either outcome (p Ͻ 5 ϫ 10 Ϫ8 ).We found 14 independent SNPs that predicted risk of death, and 8 SNPs that predicted event-free survival (p Ͻ 10 Ϫ5 ).These SNPs are in or near genes that are highly expressed in the brain (HECW2, HIP1, BIN2, GRIA1), genes involved in neural development and function (KCNQ4, LMO4, GRIA1, NETO1) and autophagy (ATG4C), and genes that are associated with risk of various diseases including cancer and Alzheimer's disease.In addition to considerable overlap between the traits, pathway and network analysis corroborated these findings.These findings indicate that variation in genes involved in neurological processes may be an important factor in regulating aging free of major disease and achieving longevity." + } + ], + "ca76f85d-9f72-4e15-8ba9-3bf94308c449": [ + { + "document_id": "ca76f85d-9f72-4e15-8ba9-3bf94308c449", + "text": "\n\nMany factors contribute to aging, including genes.This is the first article in a 10-part series that highlight some of what is known about the influence of genes on aging and emerging treatment options that may slow down or potentially reverse the aging process.The series will address \\genes, adducts, and telomeres, decreased immune defenses, oxidation and inefficient mitochondria, toxins and radiation, glycosylation, caloric intake and sirtuin production, neurotransmitter imbalance, hormone mechanisms, reduced nitric oxide, and stem cell slowdown.Underpinning these factors are wear and tear on cells and aging as a result of inability to repair or replace these affected cells.These topics have been addressed in research, health magazines, and even by talk show hosts.There is even a LongevityMap website addressing significant and nonsignificant genetic association studies in aging across the human genome (http://genomics.senescence.info/longevity/).The series will address a scientific and clinical approach to genome-related aging topics." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "\n\nThe genetic basis of human longevity has so far been primarily investigated by association studies.Most results from these experiments have been difficult to confirm in independent samples, probably owing to the modest heritability, multifactorial nature, and heterogeneity of the phenotype (Christensen et al., 2006).To date, variation in only two genes has been identified, which has an effect on longevity in various populations: (i) the apolipoprotein E gene (APOE) (Scha ¨chter et al., 1994;Christensen et al., 2006) and (ii) the forkhead box O3A (FOXO3A) gene in the insulin-IGF1 signaling (IIS) pathway (Willcox et al., 2008;Flachsbart et al., 2009).Given the apparent lack of susceptibility candidates, it is conceivable that other genetic factors influence the function or expression of genes relevant for human longevity." + } + ], + "db90a971-e55a-4ab0-a3b1-05908d6771a4": [ + { + "document_id": "db90a971-e55a-4ab0-a3b1-05908d6771a4", + "text": "Introduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go ¨gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha ¨chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "GenAge: the aging gene database Philosophy and overview of resources\n\nIt is undisputed that genetic factors influence aging.In a remarkable series of recent breakthroughs, a number of genes capable of altering the aging process as a whole -or at least to a large degree -have been identified in animal models and even a few in humans (Finch & Ruvkun, 2001;de Magalhães, 2005;Kenyon, 2005).Furthermore, multiple alleles have been examined for their association with human exceptional longevity (Vijg & Suh, 2005).This is a fascinating and important area of research, yet there are now so many genes being associated with aging and longevity that keeping track of them all is becoming increasingly more difficult.Moreover, it is necessary now to study not only individual genes but their interactions with each other and with the environment, and how together genes give rise to a given phenotype: the so-called systems biology approach.To help researchers address these issues we created GenAge, a database of genes related to longevity and/or aging." + } + ], + "f4e2fa75-559b-4fa9-b722-bdac03f7715a": [ + { + "document_id": "f4e2fa75-559b-4fa9-b722-bdac03f7715a", + "text": "\n\nI NCREASES in longevity of the general population world- wide are an unprecedented phenomenon with significant health and social impact.Although environmental factors have led to an increase in life span, there is ample evidence that genetic factors are involved in extreme longevity both in humans (1-7) and in other organisms (8).The protective genetic factors that lead to longevity are likely to involve fundamental processes of aging that may be different from those associated with early mortality or premature onset of age-related diseases in younger individuals.The mechanisms of aging in humans are far from understood, but available evidence suggests that several pathways-inflammation, oxidative stress and stress responses, cellular senescence, DNA damage and repair, and the growth hormone or insulinlike growth factor and insulin (GH, IGF, INS) axis-may play key roles (9)(10)(11)(12).Model organisms suggest that inhibiting the GH, IGF, or INS axis, which is involved in regulating cell proliferation, cell death, wound repair, and metabolism, may promote longevity by reducing oxidative stress and slowing the rate of cell replication and the accumulation of somatic-cell DNA mutations (13).There is also evidence for other important pathways such as the heatshock proteins and heat-shock factors that are highly conserved across species and play a role in prolongevity transcription pathways.Clinical and epidemiological investigations, including candidate gene studies, have suggested that inflammation pathways may affect life span and risk of age-related conditions such as cardiovascular disease (CVD) and its risk factors (14)(15)(16)(17)(18)(19).A combination of multiple genetic variants may be required for an individual to achieve exceptional longevity, which may account in part for its rarity." + } + ] + }, + "data_source": [], + "document_id": "22C4FE902B2E9A047267A56F6096B4C6", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "longevity", + "aging", + "genetic", + "SNPs", + "DNA&methylation", + "epigenetic&clock", + "GWAS", + "chromosome&5q33.3" + ], + "metadata": [], + "question": "what genetic factor are associated with aging", + "subquestions": null, + "task_id": "22C4FE902B2E9A047267A56F6096B4C6", + "usage": { + "chatgpt": 8077, + "gpt-4": 5976, + "gpt-4-turbo-preview": 5057 + }, + "user_id": 2 + }, + "document_id": "22C4FE902B2E9A047267A56F6096B4C6", + "task_id": "22C4FE902B2E9A047267A56F6096B4C6" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_10.json b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_10.json new file mode 100644 index 0000000..d400a87 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/experts/expert_aging_10.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-05-31T21:59:24.668100Z", + "data": { + "amplify": false, + "answer": "The genes involved in the aging process include daf-16, daf-2, BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, ZKSCAN1, DBH, TPO, LSS, GPER, HCRTR2, ATG2A, NEDD4L, PSMB1, UBXN4, USP6, EEF1A2, ITGB2, TUBB2C, WRN, ABCA7, AZGP1, CD36, DEGS2, PI4KA, SOAT2, APOE, LDLR, CDKN2B, RBM38, IGF1R, FOXO3, SNCA, NAP1L4, GAB2, QKI, and many others.", + "context": { + "0af83a97-18ef-47f4-9f0c-872633ca3414": [ + { + "document_id": "0af83a97-18ef-47f4-9f0c-872633ca3414", + "text": "\n\nIndicative biological pathways associated with the candidate aging genes" + }, + { + "document_id": "0af83a97-18ef-47f4-9f0c-872633ca3414", + "text": "\n\nFig. 2 Significant biological processes associated with the candidate aging genes" + }, + { + "document_id": "0af83a97-18ef-47f4-9f0c-872633ca3414", + "text": "\n\nFollowing are examples of the identified genes and experimental or GWAS link between these genes and aging.On the list of the 25 top genes, NAP1L4 encodes a member of the nucleosome assembly protein (NAP) family, which interacts with both core and linker histones, and shuttles between the cytoplasm and nucleus, suggesting a role as histone chaperone.Histone protein levels decline during aging, and dramatically affect chromatin structure.Remarkably, the lifespan can be extended by manipulations that reverse the age-dependent changes to chromatin structure, indicating the pivotal role of chromatin structure in aging [32].In another example, gene expression of NAP1L4 increases with age in the skin tissue [33].Findings of GWAS link a number of the identified genes to age-related disorders, such as GAB2 and late onset Alzheimer's disease [86], and QKI and coronary heart disease/myocardial infarction [79].Interestingly, GWAS reports also link QKI to successful aging [87]." + } + ], + "18e216d9-ea5c-4dfe-a30d-632163fcf39e": [ + { + "document_id": "18e216d9-ea5c-4dfe-a30d-632163fcf39e", + "text": "\n\nExamples of biological candidate genes with pleiotropic functions, which are involved in aging in general and in musculoskeletal aging in particular, are numerous: (a) in addition to the IGF-1 and vitamin D genes, estrogen metabolism pathway genes, including estrogen receptors and aromatase (CYP19), are associated with fat-free mass (Walsh et al. 2005) and BMD (Shearman et al. 2004), prostate and breast cancer (Gallicchio et al. 2006), and cardiovascular disease risk (Shearman et al. 2003)." + } + ], + "271236e4-60b1-4fe9-a3cc-11748e3cc718": [ + { + "document_id": "271236e4-60b1-4fe9-a3cc-11748e3cc718", + "text": "\n\nIn-depth analysis of the age-regulated genes revealed that multiple genes in the DNA damage response pathway were upregulated with age including those that function in non-homologous end-joining repair (mre11, rad50, Ku80 and mus308) and in translesion DNA synthesis (mus205 and DNApol-eta) [44][45][46].Genes that encoded enzymes with antioxidant properties, such as the thioredoxin reductase Trxr-1, and antioxidant genes involved in glutamate metabolism, such as GlnRS, isoQC and QC, were also upregulated with age [47][48][49][50].We also observed increased age-associated expression of chaperone genes (Cct1, Cct4, Cct5, Cct6, Hsc70-4) and the unfolded protein response transcription factor Xbp1, consistent with an induction of the unfolded protein response [51][52][53].Under stress conditions, there is a translational switch that favors production of stressrelated proteins while decreasing translation of other proteins [54].Paralogs of canonical translation factors such as NAT1 and Rack1, which were both upregulated, promote this switch to cap-independent translation [55,56].Notably, Rheb, which is downregulated with age, positively regulates ribosome production and capdependent translation by activating the mechanistic target of rapamycin (mTOR) kinase pathway [57].Thus, decreased Rheb levels during aging could decrease mTOR pathway activity, which extends lifespan and is protective against age-related pathology [58].Together, these data suggest that multiple genes are induced in aging photoreceptors to mitigate the effects of oxidative stress, protein misfolding and DNA damage." + } + ], + "3a9e80fc-b20d-4828-aaed-1a6ad490020a": [ + { + "document_id": "3a9e80fc-b20d-4828-aaed-1a6ad490020a", + "text": "CellAge vs human orthologues of longevity-associated model organism genes\n\nTo understand how senescence is linked to the genetics of aging processes, we looked at the intersection of CellAge genes and the 869 genes in the human orthologues of model organisms' longevity-associated genes (LAGs) dataset, collected based on quantitative changes in lifespan [34].Like CellAge, where genes are classified based on whether their upregulation induces, inhibits, or has an unknown impact on CS, the longevity orthologues dataset also provides information on the effect of upregulation of its genes, namely whether it promotes (pro, 421) or inhibits (anti, 448) longevity (Additional file 1: Table S7; Additional file 2: Fig. S2)." + }, + { + "document_id": "3a9e80fc-b20d-4828-aaed-1a6ad490020a", + "text": "\n\nUsing network biology, we implicated the CellAge genes in various processes, particularly cell division and immune system processes.We used network topology to identify potential regulators of CS and bottlenecks that could impact various downstream processes if deregulated.Indeed, we identified 11 genes that have already been shown to contribute towards CS, which will be added to future versions of CellAge.Finally, we experimentally verified 26 genes that induce CS morphology or biomarkers when knocked down in human mammary fibroblasts.Of these, 13 genes (C9orf40, CDC25A, CDCA4, CKAP2, GTF3C4, HAUS4, IMMT, MCM7, MTHFD2, MYBL2, NEK2, NIPA2, and TCEB3) were strong hits in inducing a senescent phenotype." + }, + { + "document_id": "3a9e80fc-b20d-4828-aaed-1a6ad490020a", + "text": "\n\nResults: We develop CellAge (http://genomics.senescence.info/cells),a manually curated database of 279 human genes driving cellular senescence, and perform various integrative analyses.Genes inducing cellular senescence tend to be overexpressed with age in human tissues and are significantly overrepresented in anti-longevity and tumor-suppressor genes, while genes inhibiting cellular senescence overlap with pro-longevity and oncogenes.Furthermore, cellular senescence genes are strongly conserved in mammals but not in invertebrates.We also build cellular senescence protein-protein interaction and co-expression networks.Clusters in the networks are enriched for cell cycle and immunological processes.Network topological parameters also reveal novel potential cellular senescence regulators.Using siRNAs, we observe that all 26 candidates tested induce at least one marker of senescence with 13 genes (C9orf40, CDC25A, CDCA4, CKAP2, GTF3C4, HAUS4, IMMT, MCM7, MTHFD2, MYBL2, NEK2, NIPA2, and TCEB3) decreasing cell number, activating p16/p21, and undergoing morphological changes that resemble cellular senescence.Conclusions: Overall, our work provides a benchmark resource for researchers to study cellular senescence, and our systems biology analyses reveal new insights and gene regulators of cellular senescence." + } + ], + "42cbc297-d57c-4c1f-8d3f-f9e52748b823": [ + { + "document_id": "42cbc297-d57c-4c1f-8d3f-f9e52748b823", + "text": "Genomics-a fundamental basis for understanding skin aging\n\nIn the last decade, genomic tools such as gene chips have been widely developed.This accomplishment has provided us with deeper insights into the molecular events underlying skin aging. 137Gene expression profiling has led to identification of pathways affected by aging, and this information has led to the development of new strategies to enable better skin repair and antiaging benefits. 138ene expression patterns were examined in sun-protected (buttocks) and sun-exposed skin (extensor forearm) from 10 young (age 19 to 20 years) and 10 older women (age 63 to 67 years) to examine gene expression profiles associated with chronologic skin aging and photoaging.Chronologic and photoaging were both associated with downregulation of the biologic process of lipid synthesis.In particular, genes involved in cholesterol and fatty acid synthesis were downregulated, as were genes associated with epidermal differentiation, including keratin filaments and cornified envelope components.An upregulation of the biologic processes of inflammatory response and wound healing, the molecular functions of cytokine activity and protease activity and the cellular component theme of extracellular matrix was also observed in both skin aging types.Elastin gene expression was upregulated with aging only in the photodamaged arm and remained unchanged in the sunprotected buttock.This finding corresponds to the histopathologic findings that show typical elastotic changes, the \"solar elastosis,\" in photoaged skin. 139urther studies conducted to investigate changes in gene expression during skin aging have been performed on naturally aged human foreskin obtained from children and elderly men.Some of the mechanisms proposed to be involved in the induction of aging comprise disturbed lipid metabolism, altered insulin and STAT3 signalling, upregulation of apoptotic genes partly due to the deregulation of FOXO1, downregulation of members of the jun and fos family, differential expression of cytoskeletal proteins (eg, keratin 2A, 6A, and 16A), extracellular matrix components (eg, PI3, S100A2, A7, A9, SPRR2B), and proteins involved in cell-cycle control (eg, CDKs, GOS2). 140Similar results have been presented by a study related to aging of skeletal muscle. 141n a previous study, we proposed that one of the factors significantly involved in the initiation of aging might be the physiologic decline of hormones occurring with age.Human SZ95 sebocytes in vitro treated with hormone levels that can be found in 60 year-old women produce less lipids than sebocytes treated with a hormone mixture representing that found in the serum of 20 year-old women. 6A differential gene expression between SZ95 sebocytes under the 20 and 60 year-old hormone mixture detected differentially expressed genes that are involved in biologic processes such as DNA repair and stability, mitochondrial function, oxidative stress, cell cycle and apoptosis, ubiquitin-induced proteolysis, and transcriptional regulation. 139,140A comparison of these results with data obtained from the aged kidney 142 identified key genes that may be of great importance for global aging.The most significantly altered signalling pathway was that of TGF-β.A disturbed function of this cascade has been also c-Fos, which heterodimerize to form the activator protein 1 (AP-1) complex.AP-1 is a key regulator of skin aging, because it induces the expression of the MMP family and inhibits type I procollagen gene expression through interference with TGF-β signalling pathway.It has been postulated that MAP kinases may be activated by excess production of reactive oxygen species (ROS) that occurs with advanced age and may be superimposed by extrinsic factors such as ultraviolet irradiation.Excess ROS production also leads to accumulation of cellular damage, which includes oxidation of DNA resulting in mutations, oxidation of proteins leading to reduced function, and oxidation of membrane lipids resulting in reduced transport efficiency and altered transmembrane signalling.IL, interleukin; NF-κB, nuclear factor-κB; TGF-β, transforming growth factor-β; TSP-1, thrombospondin-1; TSP-2, thrombospondin-2; VEGF, vascular endothelial growth factor.associated with tumorigenesis, such as in pancreatic, prostate, intestine, breast, and uterine cancer." + } + ], + "555a1533-2905-4d91-a3b6-2fca3679ab02": [ + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nAnalysis of prior research (Online Resource 5) shows that the revealed genes can be explicitly involved in other key biological processes in an organism whose role is known to be changing with aging.Specifically, ten genes (BAZ2B, HMGB4, NOC2L, RAI1, SIK1, SMARCA2, SPZ1, TBP, TRIP13, and ZKSCAN1) regulate transcription which is believed to be disrupted when an organism is getting older (Roy et al. 2002).The DBH, TPO, and LSS genes are involved in synthesis of catecholamine, thyroid, and vitamin D hormones, respectively.The GPER binds estrogen and HCRTR2 binds orexin-A and orexin-B neuropeptid hormones.Hormonal deregulation with aging is considered to be one of the major components of senescent processes in an organism (Barzilai and Gabriely 2010).Five genes (ATG2A, NEDD4L, PSMB1, UBXN4, and USP6) are involved in degradation of proteins through ubiquitin-proteasome and the lysosomal/autophagic system.Dysfunction of this system leads to accumulation of damaged proteins in an organism that is associated with aging (Koga et al. 2011).Protein degradation through ubiquitin-mediated proteolysis plays an important role in cell-cycle regulation (Reed 2003).The PSMB1, SIK1, TRIP13, and TTN genes in the revealed set coordinate cell cycle.Cell cycle is linked with the aging-related processes in humans through a gradual increase in cell division errors in all tissues in an organism (Ly et al. 2000).Five genes (EEF1A2, DBH, ITGB2, TUBB2C, and WRN) take part in regulation of apoptosis which plays an important role in the aging process and tumorigenesis (Salvioli et al. 2008).Seven genes (ABCA7, AZGP1, CD36, DEGS2, LSS, PI4KA, and SOAT2) are involved in lipid metabolism which plays one of the key roles in human longevity and healthy aging (Barzilai et al. 2003)." + } + ], + "5e6ad994-9cad-4b8b-903d-2d5c350e25dc": [ + { + "document_id": "5e6ad994-9cad-4b8b-903d-2d5c350e25dc", + "text": "\n\nGenes that are age-regulated in all tissues would reveal genes involved in core mechanisms that underlie cellular ageing.Zahn et al. [63] discovered genetic pathways that show common age regulation in human kidney, brain and muscle.They used microarrays to analyse expression in 81 skeletal muscle samples from patients aged 16 -86 years and found 250 age-regulated muscle genes [63].Similar to the ageing expression profile for the kidney, the overall expression behaviour of this set of age-regulated muscle genes correlated with the physiological as well as chronological age of the muscle sample.Next, they compared their muscle-ageing results to previously published data on kidney and brain ageing of similarly large sample size [56,60].Although most of the age-related changes were tissue specific, they found evidence for common age regulation of six genetic pathways in all three tissues.Specifically, there is an overall increase in expression of the extracellular matrix genes, the ribosomal genes, the cell growth genes and the complement activation genes in all three tissues.Increased overall expression of the extracellular matrix and complement activation gene sets with advancing age may contribute to widespread fibrosis and inflammation in the elderly.There is an overall decrease in expression of the chloride transport genes and the electron transport genes in all three tissues.Decreased overall expression of electron transport chain genes with age might support the mitochondrial free-radical theory of ageing [67], as free-radical generation by mitochondria would preferentially damage the electron transport chain protein complexes.Decreased expression of the electron transport genes (encoded in the nucleus) might be caused by feedback regulation from damage to the electron transport chain protein complexes [63].However, it is also possible that increased oxidative damage occurs as a consequence of the decreased expression of the electron transport chain genes.In addition, an increasing number of studies in model organisms have critically challenged the mitochondrial free-radical theory of ageing [68]." + } + ], + "6ac32a33-e2af-40bb-aad6-9971c46023d4": [ + { + "document_id": "6ac32a33-e2af-40bb-aad6-9971c46023d4", + "text": "Discussion\n\nAging studies from model organisms such as yeast, worms, and flies have repeatedly shown that changes in the expression of certain genes have an effect upon longevity.Although similar aging processes are likely to operate across multiple species [30], it has been much more difficult to identify longevity candidate genes in human studies [30].A key question in human aging is to what extent a signature of aging may be detectable across tissues.Until now there has been a lack of large transcriptional profiles from the same human individuals in multiple tissues.The MuTHER study provides insight into the human aging process by interrogating the largest multiple human tissue gene expression resource to identify genes in which expression was affected by chronological age.The analysis of the skin and adipose tissues samples identified several hundred genes responsive to changes in chronological age.However, the 43 shared genes in skin and adipose tissue showed a single common identifiable pathway related to the stress response.From over 1,800 transcripts that have altered expression with age in skin and adipose tissues, 14 also had age-related differential expression in brain.The limited overlap in these two experiments may partly reflect the smaller sample size of the brain expression dataset, the differences in age range between the studies (16 to 83 years for brain samples; 39 to 85 years for MUTHER samples), or the inclusion of males in the brain samples.But it may also imply, as other studies have suggested, that the effects of age on gene transcription are tissue specific [6,31,32].This hypothesis was supported by the comparison with known related aging genes from the GenAge database, which identified an overlap for a small number of aging-related genes with our data.The GenAge database was the result of a meta-analysis using age-related expression profiles from human brain, kidney, and skeletal muscle, and several expression profiles from mouse and rat; no adipose tissue or skin samples were included (Additional file, Table 1 in [7]).The limited overlap between these datasets supports the idea that molecular signatures of aging reflect predominantly a tissue-specific transcriptional response.The lack of age-related genes in transformed LCLs, suggest that the transformation to immortalize a cell line may mask or even remove the age-related signatures in gene expression.The transformation of primary B lymphocytes into LCLs requires infection by the Epstein-Barr virus which has the effect of disrupting the p53 signaling pathway in order to induce growth and survival [33].Joehanes et al. [15] identified only five genes with age-associated expression in LCLs, including p53 itself (TP53).Although the authors attribute the lack of age-affected genes to their small sample size (n=50) and narrow age range, our analysis with a much larger sample size found even fewer age-related changes, suggesting a lack of detectable aging signature in LCLs.The analysis in the subset of fresh lymphocytes suggested an age influence in fresh lymphocytes may potentially be detectable with a larger sample size." + } + ], + "71cc1ce5-d23c-42cf-97b8-bb6110ed8d72": [ + { + "document_id": "71cc1ce5-d23c-42cf-97b8-bb6110ed8d72", + "text": "\n\nGenes Whose Expression Decreased with Age.Of the 26 genes that decreased expression with age in control mice, 23% are involved in DNA replication and the cell cycle (Table 2).Most of these have a negative effect on cell growth and division.Among these, the product of phosphatase and tensin homolog (Pten) gene is a tumor suppressor that induces cell-cycle arrest through inhibition of the phosphoinositide 3-kinase pathway (28).B cell translocation gene 2 (Btg2) is a tumor suppressor that increases expression in response to DNA damage (29).The murine gene product of the amino-terminal enhancer of split (Aes) is a potent corepressor of gene expression and cellular proliferation (30).Calcium-binding protein A11 (S100a10) binds to and regulates the activity of annexin II, which is involved in the transduction of calcium-related mitogenic signals (31).Insulin-like growth factor (IGF) binding protein 1 (Igfbp1) plays an important role in the negative regulation of the IGF-1 system, a stimulator of mitogenesis (32)." + } + ], + "8a8bea99-d3b9-4109-88e4-ad459dcd7173": [ + { + "document_id": "8a8bea99-d3b9-4109-88e4-ad459dcd7173", + "text": "daf-16 dependent genes\n\nAmong the 52 genes that we have tested, 29 genes act almost completely in a daf-16 dependent manner, to regulate lifespan (Table 2).One of the genes identified was daf-2 (Y55D5A_391.b).This serves as a proof of principle that our screen is effective in identification of aging genes." + } + ], + "9fed8fd1-fce5-4fc1-9911-05d312f88521": [ + { + "document_id": "9fed8fd1-fce5-4fc1-9911-05d312f88521", + "text": "\n\nSeveral of the genes we identify have previously been shown to influence lifespan in experiments on model organisms.For example, knockouts of the orthologs of APOE, LDLR, CDKN2B, and RBM38 in mice shortens their lifespan [24][25][26][27] , while knockout of IGF1R has the opposite effect 28 .Similarly, overexpression of the FOXO3 orthologue in Drosophila melanogaster 29 and the SNCA orthologue in Caenorhabditis elegans 30 have shown to extend their respective lifespans.Many of our genes are also enriched for pathways previously related to ageing in eukaryotic model organisms, including genomic stability, cellular senescence, and nutrient sensing 31 .For example, FOXO3 and IGF1R are well-known players modulating survival in response to dietary restriction 32 , but we also highlight genes involved in the response to DNA damage and apoptosis, such as CDKN2B, USP28, E2F2, and BCL3.In addition to hallmarks discovered in model organisms, our results suggest that haem metabolism may play a role in human ageing.This pathway includes genes involved in processing haem and differentiation of erythroblasts 33 .Although the enrichment is largely driven by genes linked to the LDLR locus, genes linked to other loci of interest (such as FOXO3, CDKN2B, LINC02513) are involved in similar biological pathways: myeloid differentiation, erythrocyte homeostasis, and chemical homeostasis." + } + ], + "adf2d31e-e83d-47df-97af-3764e42aa80e": [ + { + "document_id": "adf2d31e-e83d-47df-97af-3764e42aa80e", + "text": "\n\nHundreds of genes in several pathways act as regulators of ageing (1,32).However, analysis of DrugAge and other HAGR databases has revealed that the overlap between the targets of lifespan-extending drugs and known ageing related genes is modest (31).This indicates that most ageing-related pathways have yet to be targeted pharmacologically; DrugAge may aid in guiding further assays.This was recently demonstrated in one study where machine learning was used to predict whether a compound would increase lifespan in worms using data from Dru-gAge.The best model had 80% prediction accuracy and the top hit compounds could broadly be divided into compounds affecting mitochondria, inflammation, cancer, and gonadotropin-releasing hormone (33)." + } + ], + "b1ffece8-f805-4d99-8e3b-402df309f1ed": [ + { + "document_id": "b1ffece8-f805-4d99-8e3b-402df309f1ed", + "text": "\n\nTop 25genes co-expressed with aging related genes" + }, + { + "document_id": "b1ffece8-f805-4d99-8e3b-402df309f1ed", + "text": "Aging-related gene prediction and putative transcriptional mechanisms\n\nGeneFriends was used to identify genes related to aging.A seed list of genes known to be consistently overexpressed with age in mammals was used [18].In total, 1119 genes were co-expressed with the aging seed list at p <10 -6 ; Table 1 shows the top 25 genes.Many of these genes have been associated with age-related diseases.Several other genes that have been shown to play a role in aging such as lysosomal-associated membrane protein-2 Lamp2 [19] (p = 5.68 -30 ), Fas [20] (p = 2.70 -31 ) and growth hormone receptor Ghr [21] (p = 1.34 -19 ) also showed a significant co-expression.Anxa2, Anxa3 and Anxa4 also show a low p-value (p < 10 -25 ) as well as several S100 calcium binding proteins which have been shown to interact with annexins [22]." + } + ], + "dc322053-2672-4c26-b739-5b58c50476ed": [ + { + "document_id": "dc322053-2672-4c26-b739-5b58c50476ed", + "text": "\n\nGenetic studies have shown that aging can be slowed in mutants that are defective in a wide range of cellular processes (such as mitochondrial function, chromatin regulation, insulin signaling, transcriptional regulation, and genome stability).This indicates that aging is a complex process driven by diverse molecular pathways and biochemical events.As such, a powerful approach to study aging is to use systems biology, which allows a multitude of factors affecting aging to be analyzed in parallel.For example, DNA microarrays and gene expression chips have been used to perform a genome-wide analysis of changes in gene expres-sion in old age.Extensive studies in Caenorhabditis elegans and Drosophila melanogaster have identified hundreds of ageregulated genes (Hill et al. 2000;Zou et al. 2000;Lund et al. 2002;Pletcher et al. 2002;Murphy et al. 2003).Several studies have described age-regulated genes in the muscle and brain of mice (Lee et al. 1999(Lee et al. , 2000) ) and the retina and muscle of humans (Yoshida et al. 2002;Welle et al. 2003Welle et al. , 2004).These age-regulated genes may serve as markers of aging, enabling one to assess physiological age independently of chronological age.Analysis of the functions of these age-regulated genes has identified specific biochemical mechanisms that change toward the end of life." + } + ], + "e562a7f1-f43a-4ca4-bf18-4196276b6170": [ + { + "document_id": "e562a7f1-f43a-4ca4-bf18-4196276b6170", + "text": "\n\nAge-Regulated Genes Involved in Reproductive Capacity.Decline in reproductive capacity is an age-related phenotype, and the reproductive system seems to play an important role in longevity (22).For example, signals from germ cells can affect lifespan in C. elegans (23).In our study, we observed decreased RNA levels for several genes involved in reproduction (Fig. 3).These include two genes that encode members of the Acp family.The Acp from male flies stimulates female egg-laying and facilitates storage of sperm in the female genital tract (24).In addition, two ESTs showing age-regulated decrease of transcript levels represent different genes with homology to Arabidopsis MALE STERIL- In Northern analysis, the ratios were calculated by dividing mRNA levels at 25-, 40-, and 50-day time points by those at 3-day time points after normalization with mRNA levels of the control gene rp49.Ratios in microarray analysis are provided from each of the duplicate experiments for comparison.ITY 2 (MS2; ref. 25), a gene involved in gametogenesis.Furthermore, an EST with homology to peanut, a member of the septin family (26), is down-regulated in older flies.This downregulation may reflect a decrease in spermatogenesis." + } + ] + }, + "data_source": [], + "document_id": "A1B97AFC3BB6C31EBB65B7E046A0A55E", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "daf-16", + "aging", + "genes", + "lifespan", + "daf-2", + "transcription", + "protein°radation", + "apoptosis", + "lipid&metabolism", + "cell&cycle" + ], + "metadata": [], + "question": "which genes are involved in the aging process", + "subquestions": null, + "task_id": "A1B97AFC3BB6C31EBB65B7E046A0A55E", + "usage": { + "chatgpt": 10445, + "gpt-4": 6328, + "gpt-4-turbo-preview": 5402 + }, + "user_id": 2 + }, + "document_id": "A1B97AFC3BB6C31EBB65B7E046A0A55E", + "task_id": "A1B97AFC3BB6C31EBB65B7E046A0A55E" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_01.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_01.json new file mode 100644 index 0000000..5d51bcf --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_01.json @@ -0,0 +1,130 @@ +{ + "created_at": "2024-04-01T22:21:34.595376Z", + "data": { + "amplify": false, + "answer": "1. A study assessing the impact of a rapamycin derivative on healthy aging in people (Mannick et al. 2014).\n2. A veterinary clinical trial testing rapamycin as a treatment for osteosarcoma (Paoloni et al. 2010).\n3. A veterinary clinical trial assessing the side effect profile and effects on age-associated cardiac function following 10 weeks of rapamycin treatment in healthy elderly dogs (Kaeberlein 2015).\n4. A study on the pharmacokinetic analysis of rapamycin treatment in healthy dogs (Larson et al. 2016).\n5. A study on the effects of rapamycin in aging mice (Johnson et al. 2015).\n6. A study on the effects of rapamycin in rodents (Johnson et al. 2013).\n7. A study on the effects of rapamycin on the replicative life of yeast (Reference [57]).\n8. A study on the effects of rapamycin in 20-month-old mice (Reference [58]).\n9. A study on the combination of rapamycin with other compounds for anti-aging activity (Reference [59]).", + "context": { + "00591f6a-0d6f-4993-ae6c-e9a8109a95ec": [ + { + "document_id": "00591f6a-0d6f-4993-ae6c-e9a8109a95ec", + "text": "\n\nIntervention trials and cell-based monotherapy" + } + ], + "3043efd1-4b13-4300-b2a7-d1992c8d4e47": [ + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "\n\nRapamycin has been shown to be well tolerated in dogs, improves outcome in a glycogen storage disease model (Yi et al. 2014), and is currently being tested in veterinary clinical trials as a treatment for osteosarcoma (Paoloni et al. 2010).A veterinary clinical trial is underway to assess the side effect profile and effects on age-associated cardiac function following 10 weeks of rapamycin treatment in healthy elderly dogs (Kaeberlein 2015)." + }, + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "\n\nRapamycin is used clinically to prevent organ transplant rejection, for some forms of cancer, and to prevent restenosis in cardiac stents (Kaeberlein 2013b).Shortterm treatment with the rapamycin derivative RAD001 improves ageassociated decline in immune function, as measured by antibody response to an influenza vaccine, in healthy elderly people (Mannick et al. 2014)." + }, + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "\n\nTo date, only one study has been performed assessing the impact of a rapamycin derivative on healthy aging in people.In this trial, it was observed that 6 weeks of treatment with the rapamycin derivative RAD001 (everolimus) was sufficient to enhance function of the aged immune system, as assessed by response to an influenza vaccine (Mannick et al. 2014).This recapitulates what was observed in elderly mice (Chen et al. 2009), and suggests that at least some of the mechanisms by which rapamycin delays aging in mice work similarly in humans.Although both compounds have essentially identical biological activities, RAD001 was used in this study instead of rapamycin because the study was funded by Novartis, who holds the patent rights for RAD001 (rapamycin is now off patent and sold as a generic drug).The doses of RAD001 used in the human immune aging study were lower than those typically used to prevent organ transplant rejection and showed improved side effect profiles, although some adverse effects, including the presence of mouth sores in a subset of the patients, were noted." + }, + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "\n\nThis trial is designed to determine whether treatment with the drug rapamycin (see Table 1) can significantly reduce age-related disease and disability as well as mortality in middle-aged large dogs.The initial phase of this trial, which is in progress at the time of this writing, is intended to enroll at least 32 dogs 6 years of age or older and 40 lb in weight or greater.Each animal receives an initial veterinary exam and comprehensive blood work along with a cardiac exam including echocardiography (Fig. 3).Those dogs that do not present with any abnormalities or significant pre-existing health conditions are randomized into either placebo or rapamycin treatment groups for a 10-week treatment period.Initial rapamycin dosing regimens were determined, in part, based on pharmacokinetic analysis of rapamycin treatment in healthy dogs (Larson et al. 2016).After 10 weeks in the study, each dog receives another full exam and blood chemistry panel as well as repeat cardiac exam.The primary goals of this first phase are to establish appropriate dosing of rapamycin in the absence of significant adverse events and to determine whether similar improvements in cardiac function are achieved in aged dogs after 10 weeks of rapamycin treatment, as has been observed in laboratory mice (Dai et al. 2014;Flynn et al. 2013)." + }, + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "\n\nFig. 3 Design of the current short-term rapamycin intervention trial.Dogs must weigh at least 40 pounds and be at least 6 years old at time of entry into the study.If no significant pre-existing health conditions are detected at the first exam, dogs are randomized into either placebo or one of the rapamycin treatment groups.Red indicates the 10-week period during which the dogs receive either rapamycin or placebo.Dogs receive the same generic rapamycin (sirolimus) pill that is provided to human patients.Asterisk Serum and feces are collected at each appointment for future metabolomic and microbiome analyses and for quantitation of circulating rapamycin levels" + }, + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "\n\nPending the outcome of phase 1, we anticipate enrolling several hundred additional dogs with similar entry criteria into a longer-term, 3-5 year study, to carefully assess the extent to which rapamycin improves health and reduces mortality in middle-age companion dogs.In addition to cardiac function, assessments of multiple age-related phenotypes will be performed including measures of cognitive function, muscle function, kidney function, glucose homeostasis, and cancer incidence.Many of these parameters are beneficially impacted by rapamycin in aging mice (Johnson et al. 2015), and we predict that rapamycin will induce similar improvements in aging dogs." + }, + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "\n\nRapamycin is currently the most effective pharmacological intervention for extending lifespan and delaying a broad range of age-related functional declines in rodents (Johnson et al. 2013).However, the doses used clinically to prevent organ transplant rejection are associated with side effects, such as impaired wound healing, edema, elevated circulating triglycerides, impaired glucose homeostasis, gastrointestinal discomfort, and mouth ulcers (Augustine et al. 2007;de Oliveira et al. 2011).These adverse side effects would likely preclude long-term use of rapamycin at these levels in otherwise healthy people.With the possible exception of impaired glucose homeostasis (Lamming et al. 2012), these side effects have not been observed at doses that are associated with increased lifespan and healthspan in mice, however, raising the possibility that lower doses of this drug could promote healthy aging with minimal adverse effects." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Rapamycin\n\nRapamycin is a macrolide isolated from Streptomyces hygroscopicus, a bacteria from Pascua Island (Rapa Nui).It has functions as an antibiotic, an immune suppressant drug, and it is also proposed as a CRM.After the first studies, it was found that rapamycin could induce the extension of the replicative life of yeast through the inhibition of TOR signaling [57].This compound could extend the lifetime useful in 20-month-old mice in correlation with TOR activity [58].These studies were the basis of the research to determine the function of rapamycin as a CRM, due to its modulating properties over proteostasis.In addition, studies suggest that rapamycin can be combined with other compounds (metformin, losartan, statins, propranolol, and aspirin among others) to potentiate their anti-aging activity [59]." + } + ], + "7f23af74-95a3-46aa-bd61-629d2cfc2073": [ + { + "document_id": "7f23af74-95a3-46aa-bd61-629d2cfc2073", + "text": "Rapamycin\n\nRapamycin is a macrolide isolated from Streptomyces hygroscopicus, a bacteria from Pascua Island (Rapa Nui).It has functions as an antibiotic, an immune suppressant drug, and it is also proposed as a CRM.After the first studies, it was found that rapamycin could induce the extension of the replicative life of yeast through the inhibition of TOR signaling [57].This compound could extend the lifetime useful in 20-month-old mice in correlation with TOR activity [58].These studies were the basis of the research to determine the function of rapamycin as a CRM, due to its modulating properties over proteostasis.In addition, studies suggest that rapamycin can be combined with other compounds (metformin, losartan, statins, propranolol, and aspirin among others) to potentiate their anti-aging activity [59]." + } + ], + "7fc7babc-51be-4358-bae4-ca1058c36da7": [ + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "One out of the 25 FDA approved Breast cancer\ndrugs (Gemcitabine), was found in the top 20 drug list from LINCS from breast cancer stage I (dark magenta). As shown in Fig. 12, one drug out of 25 FDA approved Breast cancer drugs, Gemcitabine, was found as\nrepurposed drug from LINCS for breast cancer stage III. Letrozole (Breast cancer drug) has similar structure\n(greater than 60%) with Ruxolitinib (repurposed drug from LINCS) a drug for the treatment of intermediate or\nhigh-risk myelofibrosis (Fig. 13)." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "One out of the 25 FDA approved Breast\ncancer drugs (Palbociclib), was found in the top 20 drug list from LINCS from breast cancer stage II (deep pink). Scientific Reports | 6:20518 | DOI: 10.1038/srep20518\n\n13\nwww.nature.com/scientificreports/\n\nFigure 11. Highlighted target genes that physically interact with genes from the breast cancer stage\nII common network pattern and their corresponding repurposed drugs from LINCS, along with their\nstructurally similar Breast cancer drugs. As shown in Figs 16–17 two target genes (TOP2A and TYMS) are also involved in the Triple Negative pattern." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "Two of them (Gemcitabine and Palbociclib) are included in the list of the 25 known\nFDA-approved Breast cancer therapeutic drugs. We performed a Hypergeometric distribution test in order to\nfind the statistical significance of this drug overlapping. More precisely, LINCS_L1000 database is comprised\nfrom 20,413 chemical reagents. Twenty two out of twenty five breast cancer drugs are also included in LINCS\ndatabase. Finally, from the 105 drugs that were found from our analysis, the probability of finding two drugs to\noverlap with the Breast Cancer drugs in LINCS is 0.005471157, pointing out that there is statistical significance\nin their selection." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "Two from the 25 FDA\napproved Breast cancer drugs (Gemcitabine and Palbociclib), was found in the top 20 drug list from LINCS\nfrom Luminal A breast cancer (dark magenta and deep pink respectively)." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "18 two drugs out of 25 FDA approved Breast cancer drugs – Gemcitabine and Palbociclib –\nwere also found as repurposed drugs from LINCS for breast cancer Luminal A (Fig. 18). Two genes from the\nLuminal A network pattern physically interact with four genes that involved in Histone deacetylases class\n(HDAC1, HDAC2, HDAC3 and HDAC8), which are target genes of Vorinostat (repurposed drug from LINCS). Vorinostat is a member of a larger class of compounds that inhibit histone deacetylases (HDAC) and it is used\nto treat cutaneous T cell lymphoma (CTCL)." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "One out of the 25 FDA\napproved Breast cancer drugs (Gemcitabine), was found in the top 20 drug list from LINCS from breast cancer\nstage III (dark magenta). that was found from the drug repurposing analysis of HER2 pattern. It has similar structure - 75% with\nWZ-4002 repurposed drug, which is a novel mutant-selective inhibitor of EGFR. Finally, both Palbociclib\nand WZ-4002 are structurally similar to Dasatinib (more than 60%), which is a cancer drug used to treat\nacute lymphoblastic leukemia." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "Network pattern for each breast cancer subtype and the common interactions across Luminal A\nand Luminal B. As shown in Fig. 8, one drug out of 25 FDA approved Breast cancer drugs, Gemcitabine, was proposed as\nrepurposed drug by the LINCS for breast cancer stage I. Furthermore, Gemcitabine is quite similar (tanimoto31\nsimilarity greater than 80%) with Clofarabine and Kinetin-riboside (repurposed drugs from LINCS). Clofarabine\nis also an anti-cancer, antineoplastic chemotherapy drug and is classified as an antimetabolite." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "Hierarchical clustering using tanimoto similarity (Soergel\ndistance) was applied to each of the top 20 drug list from LINCS and the 25 known FDA-approved Breast cancer\ntherapeutic drugs (Supplementary Figs 54–61). LINCS Drug Names were transformed into ChemSpider IDs (see\nSupplementary Table 1)\nIn synopsis, the unique drugs for the breast cancer stages were 63 and for the breast cancer subtypes 58, as we\nhave located common drugs across them. Taking their union and removing the duplicates we conclude to a total\nof 105 repurposed drugs." + }, + { + "document_id": "7fc7babc-51be-4358-bae4-ca1058c36da7", + "text": "13, is also\nstructurally similar (greater than 60%) with 6-(1,3-Benzodioxol-5-yl)-N-(cyclopentylmethyl)-4-quinazolinamine\n(repurposed small molecule from LINCS). As in breast cancer stages I and III one drug out of 25 FDA approved Breast cancer drugs – Gemcitabine – was\nfound as repurposed drug from LINCS for breast cancer stage IV (Fig. 14). A repurposed drug from LINCS –\nHomoharringtonine was found to be structurally similar with Everolimus and Vinblastine Breast cancer drugs\n(greater than 70%). On the other hand, as shown in Fig." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nRapamycin has serious side effects, particularly as an immunosuppressor, and thus it is not suitable as an antiaging drug.As in sirtuins, however, these studies highlight the road from basic discovery on the biology of aging to antiaging interventions.Further studies of the TOR pathway and of repressors more specific of its downstream signaling pathway are ongoing.Whether rapamycin produces a change in another parameter related to energy uptake or utilization is unknown, and determining which of its effects modulate lifespan is an important unsolved question.Like resveratrol, TOR has attracted considerable attention from the pharmaceutical industry, particularly in the context of cancer (Meric-Bernstam and Gonzalez-Angulo, 2009)." + } + ] + }, + "data_source": [], + "document_id": "8444FD3208D16364524ABE2CCAA9544D", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "rapamycin", + "dogs", + "osteosarcoma", + "cardiac&function", + "aging", + "immune&system", + "influenza&vaccine", + "lifespan", + "cancer", + "breast&cancer" + ], + "metadata": [], + "question": "List as many studies are you can that include rapamycin", + "subquestions": null, + "task_id": "8444FD3208D16364524ABE2CCAA9544D", + "usage": { + "chatgpt": 7060, + "gpt-4": 3910, + "gpt-4-turbo-preview": 2966 + }, + "user_id": 2 + }, + "document_id": "8444FD3208D16364524ABE2CCAA9544D", + "task_id": "8444FD3208D16364524ABE2CCAA9544D" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_02.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_02.json new file mode 100644 index 0000000..e80f554 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_02.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-04-02T09:32:25.786728Z", + "data": { + "amplify": false, + "answer": "Mapping gene loci that control aging in humans is difficult due to several reasons. Firstly, aging is a complex phenotype influenced by multiple genes and their interactions with each other and the environment. Secondly, the effects of common genetic variations on longevity are small, requiring large studies for identification. Thirdly, human studies often face issues such as environmental heterogeneity, genetic diversity, and lack of birth matched controls. Lastly, the influence of social, economic, and cultural factors, which are unique to humans, can't be replicated in traditional model organisms, adding to the complexity.", + "context": { + "0d6942b4-e75f-4f44-98ac-b8378ef1d3c7": [ + { + "document_id": "0d6942b4-e75f-4f44-98ac-b8378ef1d3c7", + "text": "FUTURE DIRECTIONS: HIGHER RESOLUTION DATA VIA HIGHER THROUGHPUT ASSAYS\n\nOne inescapable conclusion of the aggregate results of genome-wide studies of aging to date (see summary Table 1) is that we have not come close to saturating the number of potentially lifespan-altering genes in any organism.This is in no small part because directly generating survival curves is a relatively time-consuming process in most model organisms using current methods.There are several possible ways to address this.One way that has been tried is by attempting to find surrogate phenotypes [72,73,126] that can be screened more rapidly, or even scored under selection.Another is mining candidates from the many whole-genome expression profiles.Results to date with these have been very fruitful, but have not suggested that these methods alone will rapidly saturate our search for lifespan-and healthspan-altering genes in tractable model organisms." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Genetic\nlinkage studies of long-lived human families identified a\nlongevity locus while candidate gene approaches have been\nused to identify and confirm the association between\nspecific variants in the FOXO3A gene and human\nlongevity [3–7]. Genome-wide association studies have\nalso been used to identify the association of APOE with life\n\n123\nAging Clin Exp Res\n\nspan and have yielded insights into potential biological\npathways and processes related to aging. Despite these\nsuccesses, several problems are inherent in human\nlongevity studies including potentially high degrees of\nenvironmental heterogeneity, genetic diversity, and lack of\nbirth matched controls, among others [8]." + } + ], + "4a27da1c-b184-47e8-bef2-de6435d7c3f5": [ + { + "document_id": "4a27da1c-b184-47e8-bef2-de6435d7c3f5", + "text": "\n\nAdditional association studies with these families and replication of these results with an independent data set should facilitate the positional cloning of a gene that influences the ability to age well and achieve exceptional longevity.Identification of the genes in humans that allow certain individuals to live to extreme old age should lead to insights on cellular pathways that are important to the aging process." + } + ], + "4ca8d070-8b58-4bd5-86be-127089b70324": [ + { + "document_id": "4ca8d070-8b58-4bd5-86be-127089b70324", + "text": "\n\nThe aging process most certainly is under highly polygenic controls… This should not discourage us from pursuing a search for those loci which may be of profound importance to human aging as it ordinarily occurs in most human beings." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "606c59c5-5ae4-47e9-b3eb-58afa55669d1": [ + { + "document_id": "606c59c5-5ae4-47e9-b3eb-58afa55669d1", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + } + ], + "690a2ae6-962a-438c-91ca-60425a0c8d02": [ + { + "document_id": "690a2ae6-962a-438c-91ca-60425a0c8d02", + "text": "Accepted Article\n\n© 2013 The Authors Aging Cell © 2013 Blackwell Publishing Ltd/Anatomical Society of Great Britain and Ireland over 90 years and 1,955 controls between 55 and 80 years did not reveal genome-wide significant loci (Newman et al., 2010) and neither did the analyses of all-cause mortality and survival free of major disease in this cohort (Walter et al., 2011).A smaller Dutch study of 403 nonagenarians and 1,670 controls younger than 65 years identified the APOE gene as a mortality locus (Deelen et al., 2011), which was confirmed in a German study of 763 long-lived individuals and 1,085 younger controls (Nebel et al., 2011) and a longitudinal study of 1,606 Danes showed that the effect size of this association increases at the highest ages (Jacobsen et al., 2010).Apparently, the influence of the common genetic variation on longevity is small which requires large meta-GWA studies for identification.Alternatively, rare genetic variants may play a more important role in longevity.Since the previous linkage studies showed contradictory results potentially due to heterogeneity in the longevity phenotype, it is expected that longevity is influenced by many private rare variants." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nThe lack of success in the identification of genes related to aging in humans may be due to the complexity of the phenotype.One approach to investigate aging and longevity is to compare frequencies of genetic variants between nonagenarians or centenarians and the general population.This approach led to the discovery of an association between APOE (Deelen et al., 2011;Ewbank, 2007;Gerdes et al., 2000) and more recently FOXO3A (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009a;Pawlikowska et al., 2009;Willcox et al., 2008) and human aging and longevity.However, a recent genome-wide association study (GWAS) of individuals reaching the age of 90 or older failed to identify genome-wide significant variants (Newman et al., 2010)." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nSeveral explanations are possible for the lack of genomewide significant findings.First, mortality is arguably 1 of the most complex phenotypes, and several trajectories toward extreme old age have been identified (Evert et al., 2003).Multiple genes could mediate the aging process but would have their effects through numerous different patho-physiological processes and diseases that act as intermediate factors on the pathway to death (de Magalhaes et al., 2010).Therefore, any common variation in genes associated with aging probably has a small effect." + }, + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "\n\nSecond, the largely negative findings of this and other studies contrast with the intriguing animal studies of longevity.Very large effects of single genes on lifespan have indeed been observed in laboratory animals, but humans often have several homologues of these genes which might significantly differ in function or compensate for mutated genes through redundant mechanisms (Kuningas et al., 2008).This could explain why our top findings did not include genes in these pathways found in animal models.Animal models also represent genetically homogenous populations and are exposed to controlled environmental influences.The lack of replication of animal model findings in humans suggests that the use of knockout animals may not provide the optimal approach to understanding the variation in survival in humans as interactions with environmental factors may obscure the associations and prevent the identification of loci in humans." + } + ], + "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed": [ + { + "document_id": "a440a3fa-74e7-4fd8-8a7f-d0391300d6ed", + "text": "1993), and\ngene expression microarrays (Pletcher et al. 2002). Given the ambiguities and limitations of large-effect mutant studies of aging, discussed earlier, those publications do not\nprovide very useful evidence with respect to the question of the number of loci that\naffect aging. At present, the best answer to the question of the number of genes controlling aging is many (Rose and Long 2002), in keeping with the original expectations of\nevolutionary biologists. However, studies of the genetics of the experimental evolution of aging are now\namenable to the application of genomic methods." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nThe remarkable discoveries of the past 2 decades showing that single genes can regulate aging in model organisms demonstrate that aging can be genetically manipulated (Finch and Ruvkun, 2001;Kenyon, 2010).Hundreds of genes that modulate longevity have now been identified in model organisms (de Magalha ˜es et al., 2009a).In some cases (e.g., in worms), mutations in single genes can extend lifespan by almost 10-fold (Ayyadevara et al., 2008).Nonetheless, aging is a complex process that derives not from single genes but from the interactions of multiple genes with each other and with the environment.Evidence from animal systems shows a major impact of the environment on aging, yet environmental manipulations of aging act through genes and proteins, usually by triggering signaling pathways and modulating gene expression.In fact, some genes have been shown in model organisms to have varying effects on lifespan depending on diet (Heikkinen et al., 2009).Genes that can regulate aging in model organisms cannot be directly applied to humans through genetic manipulations for numerous legal, ethical, and technical reasons.If we could understand how the environment modulates these aging-related genes, we might be able to create antiaging therapies applicable to humans, potentially through diet, lifestyle, and even pharmacological interventions.Therefore, understanding genome-environment interactions in the context of aging can be a powerful approach to identify attractive targets for drug design." + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nResults from mutational analysis across eukaryote model organisms have shown unexpected conservation of genes and processes regulating aging.While unique properties exist within particular organisms that modulate these foundational networks, the conservation provides a tool to refine human genetic studies.As noted, GWAS for human longevity metrics suffer from large sample size requirements to obtain statistical resolution due to multiple hypothesis testing across the genome.Assuming that evolutionary genesets for longevity could be generated with confidence, an intersection of them with human variation data would increase the sensitivity of association studies.This would serve as a selective filter to refine the number of loci investigated for association in human populations.Similarly, such evolutionary filters could refine analysis of rare, unique variation within genome sequence data from extremely long-lived cohorts.A similar approach to refine human longevity GWAS used an intersection with age-related disease datasets.This 'disease-informed' GWAS helped refine candidates (iGWAS, Fortney et al., 2015), though, it should be noted that this particular strategy would further blur the distinction between aging and longevity as discussed above.The definition of gene sets from evolutionary experiments in longevity, across clades, would similarly empower detection of networks previously hidden under GWAS in human population analyses (Figure 3)." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY\n\nHeritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE ε2), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "TRANSLATION OF LONGEVITY MODEL ORGANISMS AND CORE AGING PATHWAYS\n\nGenetic studies on lifespan have proven to be challenging.While longevity is a defining trait for a given species, the lifespan of individuals is of limited heritability, making analyses more difficult.Exceptional human life span, although a rare phenotype, is likely multifactorial; refined analyses are required to obtain statistically robust genomic signatures of longevity (Zhang et al., 2020) and these have proven elusive.Unlike laboratory models, the effect of environmental variance cannot be controlled in human studies, potentially masking purely biological aging mechanisms.Even laboratory models cannot replicate the complex \"environment\" of humans; it includes psychosocial, economic, and cultural factors, rather than strictly biological.These human-specific confounders are difficult or impossible to target in traditional model organisms.Despite these limitations, experimentally tractable model organisms have proven invaluable in deciphering the purely genetic contribution to lifespan, including genes and pathways conserved across the tree of life." + } + ], + "c7361625-831a-44a2-b04d-157a49d00c6a": [ + { + "document_id": "c7361625-831a-44a2-b04d-157a49d00c6a", + "text": "\n\nOur analyses show that it is extremely unlikely that there is a single gene harboring rare protein-altering variants shared by all supercentenarians but no controls.It is not surprising that a highly complex trait such as longevity is not explained by a single Mendelian gene." + } + ], + "efd5747f-9e8b-45e8-9e04-bb31131d44fa": [ + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "text": "\n\nWith modern genomic technologies and largescale data analysis methods, it is possible to sift through the genes of populations to find the loci that act to postpone aging. [3]There are uncertainties with the comparison of populations with different rates of aging.However, it is superior to experimental designs that only consider age-dependence or dietary-response, without determining causal mechanisms." + } + ], + "f2b8524b-501d-4ec7-a3d7-048aab67ce05": [ + { + "document_id": "f2b8524b-501d-4ec7-a3d7-048aab67ce05", + "text": "\n\nAlthough the models data set comprises all genes (to our knowledge) shown by the time of the latest update to statistically increase longevity or alter the aging process in a noticeable way, in the human data set we try to evaluate whether a given intervention is affecting the aging process itself or not.For example, many mutations may increase longevity by decreasing the incidence of specific diseases, rather than by altering the basic process of aging (de Magalhães et al ., 2005a(de Magalhães et al ., , 2005b)).Therefore, the human data set is not merely an extension of the work conducted in model organisms and of its bibliography, but a manually selected list of the most pertinent human aging candidate genes, each presented with a higher annotation level.We cite studies on whether the functions of aging-associated genes in model organisms are conserved in their human orthologues.Likewise, we cite flaws in previous studies based on new published observations, although we have a neutral stance on conflicting findings from different research groups.Our policy is to cite all conflicting reports and let visitors make their own decisions on how to interpret them.By contrast, each entry in GenAge model organisms has only one reference: the first publication reporting an association of the gene with longevity or aging.Moreover, one of the latest enhancements in the human data set was the inclusion of Gene Ontology annotation.Gene Ontology terms and annotation files were obtained from the Gene Ontology Consortium website (http://www.geneontology.org/ ) and provide an additional layer of description for the gene products in a cellular context (Ashburner et al ., 2000)." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "Conclusions and prospects\n\nOver the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nMost of the human candidate gene studies were performed in cross-sectional designs (Box 1 and Fig. 1), comparing allele frequencies of potential longevity loci between highly aged individuals and young controls.The candidate gene studies based on single genes have pointed a role for genes involved in, e.g., GH/insulin/IGF-1 signaling, immune regulation, and lipoprotein metabolism (Supporting Information Table S1), although most of these results have not (yet) been confirmed in sufficient independent studies.The most convincing human longevity loci today are APOE and FOXO3A which have frequently been associated with longevity in cross-sectional studies (see for a review [26]) and survival in prospective studies [27][28][29] (Fig. 3).APOE encodes the protein apolipoprotein E which seems to play a role in e.g., lipoprotein metabolism, cognitive function, and immune regulation [30].FOXO3A encodes the protein forkhead box O3 which acts as a transcription factor for many different genes involved in processes like apoptosis and oxidative stress [31]." + } + ] + }, + "data_source": [], + "document_id": "2B64FFA2A5559907DDEC720E3CC23553", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE", + "FOXO3A", + "GWAS", + "longevity", + "aging", + "human", + "gene", + "variants", + "lifespan", + "genome" + ], + "metadata": [], + "question": "Why is it so difficult to map gene loci that control aging in humans?", + "subquestions": null, + "task_id": "2B64FFA2A5559907DDEC720E3CC23553", + "usage": { + "chatgpt": 8006, + "gpt-4": 4826, + "gpt-4-turbo-preview": 3875 + }, + "user_id": 2 + }, + "document_id": "2B64FFA2A5559907DDEC720E3CC23553", + "task_id": "2B64FFA2A5559907DDEC720E3CC23553" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_03.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_03.json new file mode 100644 index 0000000..619e6ee --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_03.json @@ -0,0 +1,146 @@ +{ + "created_at": "2024-04-02T11:58:00.471434Z", + "data": { + "amplify": false, + "answer": "One would evaluate a candidate gene for mouse lifespan by conducting gene mapping methods and using mouse life span as a surrogate for aging. This involves studying the genetic contributions to mortality rates, conducting sex-specific analyses, and determining any changes in pathology associated with the loci. The study would involve a large population of mice, and the results would be compared to Mendelian expectations. Additionally, epigenetic predictors and differential methylation analysis could be used to assess the relationship between the gene and lifespan. The gene's influence on specific late-life diseases and its effect on the risk of these diseases would also be evaluated.", + "context": { + "0c6c0977-2cf9-4bbf-bc31-fe025f008089": [ + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "text": "Funding: See page 22\nPreprinted: 24 June 2021\nReceived: 03 November 2021\nAccepted: 01 April 2022\nPublished: 07 April 2022\nReviewing Editor: Joris Deelen,\nMax Planck Institute for Biology\nof Ageing, Germany\n Copyright Mozhui et al. This\narticle is distributed under the\nterms of the Creative Commons\nAttribution License, which\npermits unrestricted use and\nredistribution provided that the\noriginal author and source are\ncredited. Editor's evaluation\nThis article used three newly generated epigenetic predictors to test how they differ between\ngenetically diverse mice from the BXD family (by looking at metabolic traits and lifespan)." + }, + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "text": "Longevity data\nwas obtained from a parallel cohort of BXD mice housed in the same UTHSC colony, and members\nof this ‘longevity cohort’ were allowed to age until natural death (more detail on the longevity cohort\ncan be found in Roy et al. , 2021). Males were excluded and strain-by-diet lifespan summary statistics\nwere derived. Only strain-by-diet groups with five or more observations for lifespan were included in\nthe correlational analyses with the epigenetic predictors. Multivariable EWAS\nSite-by-site differential methylation analysis (EWAS) was performed on the 27,966 CpGs using a\nmultivariable regression model." + } + ], + "2464a084-1a11-44eb-8bce-4b344de049ff": [ + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "text": "Funding: See page 22\nPreprinted: 24 June 2021\nReceived: 03 November 2021\nAccepted: 01 April 2022\nPublished: 07 April 2022\nReviewing Editor: Joris Deelen,\nMax Planck Institute for Biology\nof Ageing, Germany\n Copyright Mozhui et al. This\narticle is distributed under the\nterms of the Creative Commons\nAttribution License, which\npermits unrestricted use and\nredistribution provided that the\noriginal author and source are\ncredited. Editor's evaluation\nThis article used three newly generated epigenetic predictors to test how they differ between\ngenetically diverse mice from the BXD family (by looking at metabolic traits and lifespan)." + }, + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "text": "Longevity data\nwas obtained from a parallel cohort of BXD mice housed in the same UTHSC colony, and members\nof this ‘longevity cohort’ were allowed to age until natural death (more detail on the longevity cohort\ncan be found in Roy et al. , 2021). Males were excluded and strain-by-diet lifespan summary statistics\nwere derived. Only strain-by-diet groups with five or more observations for lifespan were included in\nthe correlational analyses with the epigenetic predictors. Multivariable EWAS\nSite-by-site differential methylation analysis (EWAS) was performed on the 27,966 CpGs using a\nmultivariable regression model." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text":"Conclusions These results suggest a novel locus influencing survival in the B6/D2 genetic background, perhaps\nvia a metabolic disorder that emerges by 200 days of age in\nmale animals. Keywords\nPathology\n\nLongevity ␁ Lifespan ␁ Mouse ␁ Linkage ␁\n\nIntroduction\nLongevity, the quintessential complex trait, likely reflects\nall aspects of an organism’s life history. In humans, the\nestimated heritability of age at death is estimated at\n25–33 % [1]. Genetic contributions to mortality rates are\nthus of great interest and may aid in the understanding of\ndisease etiology and the process of aging itself [2]." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Leduc MS, Hageman RS, Meng Q et al (2010) Identification of\ngenetic determinants of IGF-1 levels and longevity among mouse\ninbred strains. Aging Cell 9(5):823–836. doi:10.1111/j.14749726.2010.00612.x\n10. Lang DH, Gerhard GS, Griffith JW et al (2010) Quantitative trait\nloci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin Exp Res 22(1):8–19\n11. Gelman R, Watson A, Bronson R et al (1988) Murine chromosomal\nregions\ncorrelated\nwith\nlongevity. Genetics\n118(4):693–704\n12. Jackson AU, Galecki AT, Burke DT et al (2002) Mouse loci\nassociated with life span exhibit sex-specific and epistatic effects." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Here, we have extended this analysis to search for\ngenotypes related to survival to the age of 800 days in a\npopulation of a reciprocal F2 cross between (B6) and (D2)\nmice. Since QTL for longevity in mice have shown strong\nsex specificity [10, 12], we conducted sex-specific analyses. In addition, we also determined whether there were\nany change in pathology changes associated with the loci\nthat showed frequency distortions with aging. To confirm\nthe associations of the loci of interest with longevity and\npathology, we performed replication analyses on a panel of\nBXD recombinant inbred strains." + }, + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Methods We examined a population of 1200 mice that\nwere F2 generation offspring of a 4-way reciprocal cross\nbetween C57BL6/J and DBA2/J strains. Animals were\nsacrificed at age 200, 500, or 800 days and genotyped at 96\nmarkers. The 800 days old cohort, which were the survivors of a much larger breeding group, were examined for\nenriched frequency of alleles that benefit survival and depletion of alleles that reduce survival. Results Loci on Chr 13 in males and on Chr X in females\nwere significantly distorted from Mendelian expectations,\neven after conservative correction for multiple testing." + } + ], + "4851405f-bb2b-4406-a218-ffe408d257f8": [ + { + "document_id": "4851405f-bb2b-4406-a218-ffe408d257f8", + "text": "Assessing epigenetic age in long-lived mice\n\nThe epigenetic-aging model was applied to the methylation profiles of long-lived mice and the age-matched controls not used for training (Additional file 2: Datasets used summary).Reductions in age were calculated by subtracting the epigenetic ages of the untreated, wild-type mice from those of the treated mice of the same genetic background.To assess the significance, we used an ANOVA for all 22-month-old mice or only 22-month-old UM-HET3 mice.We also compared the epigenetic ages between treatments with their agematched controls from the same genetic background using a t-test (Additional file 4: Treatment vs wild type stats)." + } + ], + "5b2055ca-65c0-49a5-a442-e4ea8d5e8efb": [ + { + "document_id": "5b2055ca-65c0-49a5-a442-e4ea8d5e8efb", + "text": "Editor's evaluation\n\nThis article used three newly generated epigenetic predictors to test how they differ between genetically diverse mice from the BXD family (by looking at metabolic traits and lifespan).The authors subsequently identified several quantitative trait loci for the different predictors, using linkage analysis, and performed transcriptome and proteome analyses of liver and adipose tissue.The described results provide some important new insights on the underlying biology of epigenetic mouse aging and may be used to inform future studies in other model organisms and humans focused on studying the relationship between epigenetic aging and metabolism." + } + ], + "64886b4e-8599-4f61-84e6-9add7663a1b3": [ + { + "document_id": "64886b4e-8599-4f61-84e6-9add7663a1b3", + "text": "352(6291): p. aad0189. Liao, C.Y. , et al. , Genetic variation in the murine lifespan response to dietary restriction: from life extension to life\nshortening. Aging Cell, 2010. 9(1): p. 92-5. Johnson, M., Laboratory Mice and Rats. Mater. Methods, 2012. 2: p. 113. Fontaine, D.A. and D.B. Davis, Attention to Background Strain Is Essential for Metabolic Research: C57BL/6 and\nthe International Knockout Mouse Consortium. Diabetes, 2016. 65(1): p. 25-33. Simon, M.M. , et al. , A comparative phenotypic and genomic analysis of C57BL/6J and C57BL/6N mouse strains. Genome Biol, 2013. 14(7): p. R82. Lilue, J., et al." + } + ], + "71cc1ce5-d23c-42cf-97b8-bb6110ed8d72": [ + { + "document_id": "71cc1ce5-d23c-42cf-97b8-bb6110ed8d72", + "text": "Materials and Methods\n\nStudy Design.Female mice of the long-lived F 1 hybrid strain C3B10RF1 were fed and maintained as described (7).Briefly, mice were weaned at 28 days, individually housed, given free access to water, and randomly assigned to study groups.Comparisons between five groups of mice were used to determine the effects of aging and CR on gene expression.Control young (7-month-old; n ϭ 3) and old (27-month-old; n ϭ 3) mice were fed 95 kcal of a semipurified control diet (Harlan Teklad, Madison, WI; no.TD94145) per week after weaning.Long-term CR (LT-CR) young (7-month-old; n ϭ 3) and old (27-month-old; n ϭ 3) mice were fed 53 kcal of a semipurified CR diet (Harlan Teklad; no.TD94146) per week after weaning.Short-term CR (ST-CR) mice were 34-monthold control mice that were switched to 80 kcal of CR diet for 2 weeks, followed by 53 kcal for 2 weeks (n ϭ 3).The effects of age on gene expression in control mice were determined by comparison between results from the young control and the old control groups.The effects of LT-CR on gene expression were determined by comparison between results from the young control and the young LT-CR groups, and from the old control and the old LT-CR groups.The effects of ST-CR were determined by comparison between results from the old control and the ST-CR groups.Mice were fasted for 48 h before killing.Mice were killed by cervical dislocation, and the livers were rapidly excised and flash frozen in liquid nitrogen.No signs of pathology were detected in any of the animals used.All animal use protocols were approved by the institutional animal use committee of the University of California, Riverside." + } + ], + "75813bc2-f0b5-400c-92d7-0958df97a04f": [ + { + "document_id": "75813bc2-f0b5-400c-92d7-0958df97a04f", + "text": "Accessing data resources in the mouse\nphenome database for genetic analysis of murine life span and health span. J.\nGerontol. A Biol. Sci. Med. Sci. 71 (2), 170–177. Brown, R.E. , Stanford, L., Schellinck, H.M., 2000. Developing standardized behavioral\ntests for knockout and mutant mice. ILAR J. 41 (3), 163–174. Bubier, J.A. , Jay, J.J., Baker, C.L. , Bergeson, S.E. , Ohno, H., Metten, P., Crabbe, J.C.,\nChesler, E.J. , 2014. Identification of a QTL in Mus musculus for alcohol preference,\nwithdrawal, and Ap3m2 expression using integrative functional genomics and precision genetics. Genetics 197 (4), 1377–1393. Burn, C.C. , 2008." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nOur own work has taken a different tack: we have attempted to determine whether mutations with differential effects on aging may be present within the many available populations of laboratory-adopted inbred mice.The goal is not so much to clone these genes-if indeed they existbecause positional cloning strategies of this kind require many thousands of animals and would be extremely expensive using an assay, age at death, that is itself so costly.Instead, the goal has been to use gene mapping methods to test hypotheses about aging and to develop new animal models that will be useful for testing well-specified hypotheses about the molecular basis for age-dependent changes.In the absence of a validated battery of biomarkers of aging, we (like most others) have reluctantly decided to use mouse life span as a crude surrogate for aging itself, reasoning that genetic alleles that extend life span well beyond the median for the tested population may be operating via an influence on aging itself.Work conducted using recombinant inbred mouse stocks (Gelman et al., 1988;de Haan and Van Zant, 1999) has suggested that life-span differences between pairs of inbred mouse lines might reflect the influence of as few as 4-7 polymorphic loci, providing some basis for hope that some of these would have an effect large enough to be detected by a genome scan experiment involving 300-1,200 mice." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nThe available dataset also provides examples in which genetic variants seem to influence the risk of specific late-life diseases.Figure 8-6, for example, shows longevity results for mice stratified by their inheritance at the 12th chromosome locus D12Mit167.This is a locus associated with differential longevity in both male and female mice, with the strongest effect (adjusted p < 0.01) seen in those mice living more than 657 days (Jackson et al., unpublished results).The longest-lived mice are those that inherit both the C57BL/6 allele from their mother and the C3H allele from their father; on average, they survive 93 days longer than siblings with the BALB plus C3H combination.Figure 8-6 shows that the D12Mit167, like the pair of loci illustrated in Figure 8-5, has significant and similar effects in mice dying of cancer (85 days) and in mice dying of non-neoplastic diseases (126 days).A more detailed analysis of the cancers, however, suggests that while lymphoma and hepatoma victims are equally protected by the favorable alleles (effect sizes of 93 and 167 days, respec- mice of two subgroups: those dying of the urinary syndrome MUS, and those dying of all other causes.The genetic analysis contrasts mice with both the C57BL/6 allele at D4Mit84 and the C3H allele at D9Mit110 to mice with any of the three other allele combinations.In the males dying of causes other than MUS, this allele pair is associated with a 170-day increment in longevity (post-hoc p < 0.00003).But for males that do die of MUS, the same allele combination is associated with a 187-day decline in mean life span (post-hoc p < 0.03).This effect is thus pleiotropic, in that these alleles accelerate death in mice susceptible to MUS, while postponing death for all other males in the population.Although these loci are associated with differential longevity in mice that do develop MUS, they do not have a significant effect on the chances that MUS will indeed occur (not shown).The risk of developing MUS seems to be under control of a separate locus on chromosome 6.As shown in the bottom panel of Figure 8-7, males that inherit the C3H allele at D6Mit268 are far more likely to develop MUS (28 percent risk) than are their brothers who receive the DBA/2 allele at this locus (7 percent risk; p = 0.012 by two-tailed Fisher's exact test)." + } + ], + "ce270796-8098-48e6-afe2-ad285a75bce2": [ + { + "document_id": "ce270796-8098-48e6-afe2-ad285a75bce2", + "text": "Previously, the methylation status of CpG sites within the genes Prima1, Hsf4,\nKcns1 was shown to qualify as a reliable predictor of\nchronological age of B6 mice.10 This same study also\nrevealed enhanced epigenetic aging of the D2 strain in\naccordance with its general reduced mean life span, supporting the possibility that the panel might also serve as a\nmarker for the biological age in mice. Applying this B6trained marker panel to our (congenic) experimental\nstrains, we observed that epigenetic age predictions correlated with chronological age in B6 (R2=0.93) and line A\nmice (R2=0.89)." + } + ], + "ce2c68bf-878d-460c-8d9b-d45ce3034ef7": [ + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "text": "34. Gelman R, Watson A, Bronson R & Yunis E Murine chromosomal regions correlated with\nlongevity. Genetics 118, 693–704 (1988). [PubMed: 3163317]\n35. Houtkooper RHet al.The metabolic footprint of aging in mice. Sci. Rep1, (2011). 36. Houtkooper RHet al.Mitonuclear protein imbalance as a conserved longevity mechanism. Nature497, 451–457 (2013). [PubMed: 23698443]\n37. Williams EGet al.An Evolutionarily conserved role for the aryl hydrocarbon receptor in the\nregulation of movement. PLOS Genet. 10, e1004673 (2014). [PubMed: 25255223]\n38. Lang DHet al.Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD)\nrecombinant inbred mice. Aging Clin. Exp. Res. 22, 8–19 (2010)." + } + ], + "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748": [ + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "For females, hairs of the congenic mice grew 31% faster, also highly significant (P =\n0.0006, 1-tailed). These results validated the presence of a gene in the differential region\naffecting FE. Discussion\nWe report the outcomes of a quantitative genetic study on aging and longevity in the mouse. We studied an extant series of recombinant inbred strains (ILSXISS) that have been used\nboth in DR aging studies as well as to study alcohol sensitivity (Williams et al. , 2004)." + } + ], + "efd5747f-9e8b-45e8-9e04-bb31131d44fa": [ + { + "document_id": "efd5747f-9e8b-45e8-9e04-bb31131d44fa", + "text": "FOURTH STEP: MEDICAL TESTING OF CANDIDATE DRUGS\n\nMany genes are common between fruit flies and mammals, but by no means all.Therefore, it is important to test biochemical pathways that work in fruit flies with mammals.Mice are the system of choice, as they have relatively short lifespans (2 -3 years) and a great deal is known of their genetics.Mortality rate measurements, like those studied in fruit flies, [10] might speed up mouse trials to just 6-12 months.Mouse trials would also help address issues of safety, such as liver and kidney toxicity, before going on to human trials." + } + ], + "f377428d-564d-4d5b-b86c-515b9325134f": [ + { + "document_id": "f377428d-564d-4d5b-b86c-515b9325134f", + "text": "Experimental Procedures\n\nMouse Breeding, Maintenance, and Longevity.Cdc42GAP Ϫ/Ϫ and p53 Ϫ/Ϫ mice were generated as previously described (6,35), and the mice used in the studies were mixed C57BL/6 ϩ/Ϫ 129/Sv inbred.Littermates of different genotypes were housed and fed freely with standard mouse chow over their life span in a pathogen-free environment and were monitored for vitality and longevity.Mice exhibiting extreme morbidity were euthanized and subjected to necropsy.All animal procedures were approved by the Institutional Animal Care and Use Committee at the Children's Hospital Research Foundation." + } + ] + }, + "data_source": [], + "document_id": "7163C22AFA8EE292590331B6E6B7B173", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "C57BL6/J&DBA2/J", + "F2&generation", + "mice", + "lifespan", + "longevity", + "genetic", + "alleles", + "chromosome&13&males", + "chromosome&X&females", + "Mendelian&expectations" + ], + "metadata": [], + "question": "How would one evaluate a candidate gene for mouse lifespan?", + "subquestions": null, + "task_id": "7163C22AFA8EE292590331B6E6B7B173", + "usage": { + "chatgpt": 7619, + "gpt-4": 5028, + "gpt-4-turbo-preview": 4003 + }, + "user_id": 2 + }, + "document_id": "7163C22AFA8EE292590331B6E6B7B173", + "task_id": "7163C22AFA8EE292590331B6E6B7B173" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_04.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_04.json new file mode 100644 index 0000000..a0ea882 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_04.json @@ -0,0 +1,154 @@ +{ + "created_at": "2024-04-02T12:00:22.254831Z", + "data": { + "amplify": false, + "answer": "Yes, significant quantitative trait loci (QTLs) have been found in aging studies. Three examples include:\n\n1. Lfdr1: This QTL was found on chromosome 7 and affects lifespan and fertility in response to dietary restriction. It also has suggestive effects on fuel efficiency (FE).\n\n2. Fedr1 and Fedr2: These QTLs were identified on chromosomes 9 and 15 respectively. They have significant effects on fuel efficiency in response to dietary restriction.\n\n3. QTLs associated with age-related thymic involution: These were identified in a study using C57BL/6 J X DBA/2 J (BXD) recombinant inbred (RI) strains of mice.", + "context": { + "0c6c0977-2cf9-4bbf-bc31-fe025f008089": [ + { + "document_id": "0c6c0977-2cf9-4bbf-bc31-fe025f008089", + "text": "Genetic associations for two biological age measures point\nto distinct aging phenotypes. Aging Cell 20:e13376. DOI: https://doi.org/10.1111/acel.13376, PMID:\n34038024\nLang DH, Gerhard GS, Griffith JW, Vogler GP, Vandenbergh DJ, Blizard DA, Stout JT, Lakoski JM, McClearn GE. 2010. Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clinical and Experimental Research 22:8–19. DOI: https://doi.org/10.1007/BF03324809, PMID:\n20305363\nLappalainen T. 2015. Functional genomics bridges the gap between quantitative genetics and molecular\nbiology. Genome Research 25:1427–1431." + } + ], + "1fb6e4db-79c1-49c9-a358-3414f6a674da": [ + { + "document_id": "1fb6e4db-79c1-49c9-a358-3414f6a674da", + "text": "Pharmacol Biochem Behav 81, 764–768. Hsu, H.C., Lu, L., Yi, N., Van Zant, G., Williams, R.W. & Mountz, J.D. (2007) Quantitative trait locus (QTL) mapping in aging systems. Methods Mol Biol 371, 321–348. Hurlin, P.J. & Huang, J. (2006) The MAX-interacting transcription\nfactor network. Semin Cancer Biol 16, 265–274. Jones, B.C. , Tarantino, L.M. , Rodriguez, L.A., Reed, C.L. , McClearn,\nG.E. , Plomin, R. & Erwin, V.G. (1999) Quantitative-trait loci analysis\nof cocaine-related behaviours and neurochemistry. Pharmacogenetics 9, 607–617. Jones, B.C. , Beard, J.L. , Gibson, J.N. , Unger, E.L., Allen, R.P. ,\nMcCarthy, K.A. & Earley, C.J." + } + ], + "2464a084-1a11-44eb-8bce-4b344de049ff": [ + { + "document_id": "2464a084-1a11-44eb-8bce-4b344de049ff", + "text": "Genetic associations for two biological age measures point\nto distinct aging phenotypes. Aging Cell 20:e13376. DOI: https://doi.org/10.1111/acel.13376, PMID:\n34038024\nLang DH, Gerhard GS, Griffith JW, Vogler GP, Vandenbergh DJ, Blizard DA, Stout JT, Lakoski JM, McClearn GE. 2010. Quantitative trait loci (QTL) analysis of longevity in C57BL/6J by DBA/2J (BXD) recombinant inbred mice. Aging Clinical and Experimental Research 22:8–19. DOI: https://doi.org/10.1007/BF03324809, PMID:\n20305363\nLappalainen T. 2015. Functional genomics bridges the gap between quantitative genetics and molecular\nbiology. Genome Research 25:1427–1431." + } + ], + "47c12133-5a30-45b9-bcb8-b96f00737f31": [ + { + "document_id": "47c12133-5a30-45b9-bcb8-b96f00737f31", + "text": "Interestingly, the correlation analysis indicates\nQTL Mapping in Aging Systems\n\n333\n\nFig. 5. Basic statistics provided by the WebQTL GeneNetwork website. The strain\ndistribution pattern (SDP) of the quantitative trait is presented in the basic statistics page\nof WebQTL in the following ways: (A) the raw data of the quantitative trait obtained\nfrom each BXD recombinant inbred (RI) strain, (B) data mean and distribution, (C) bar\ngraph showing the mean and variable of each strain, and (D) the normal probability plot\nof the SDP." + }, + { + "document_id": "47c12133-5a30-45b9-bcb8-b96f00737f31", + "text": "23\nQuantitative Trait Locus (QTL) Mapping in Aging\nSystems\nHui-Chen Hsu, Lu Lu, Nengjun Yi, Gary Van Zant, Robert W. Williams,\nand John D. Mountz\nSummary\nUnderstanding the genetic basis of the effects of aging on the decline in the immune\nresponse is an enormous undertaking. The most prominent age-related change in the\nimmune system is thymic involution. This chapter will focus on the use of C57BL/6 J X\nDBA/2 J (BXD) recombinant inbred (RI) strains of mice to map genetic loci associated\nwith age-related thymic involution in mice." + } + ], + "5b2055ca-65c0-49a5-a442-e4ea8d5e8efb": [ + { + "document_id": "5b2055ca-65c0-49a5-a442-e4ea8d5e8efb", + "text": "\n\nFor further prioritization, we converted the mouse QTL regions to the corresponding syntenic regions in the human genome and retrieved GWAS annotations for these intervals (Buniello et al., 2019).We specifically searched for the traits: epigenetic aging, longevity, age of menarche/menopause/puberty, Alzheimer's disease, and age-related cognitive decline and dementia.This highlighted five genes in Eaa11 and three genes in Eaa19 (Supplementary file 4c).We also identified a GWAS that found associations between variants near Myof-Cyp26a1 and human longevity (Yashin et al., 2018), and a meta-GWAS that found gene-level associations between Nkx2-3 and Cutc, and epigenetic aging (Supplementary file 4c; McCartney et al., 2021)." + } + ], + "5edf84d0-c2d9-45eb-91b9-c35743b6a463": [ + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "Jiang, C. and Zeng, Z. B. (1995). Multiple trait analysis of genetic mapping for quantitative\ntrait loci. Genetics 140, 1111–1127. Jin, W., Riley, R. M., Wolfinger, R. D.et al. (2001). The contributions of sex, genotype and age\nto transcriptional variance in Drosophila melanogaster. Nat Genet 29, 389–395. Kempermann, G., Chesler, E. J., Lu, L. et al. (2006). Natural variation and genetic covariance\nin adult hippocampal neurogenesis. Proc Natl Acad Sci U S A 103, 780–785. Kendziorski, C. M., Chen, M., Yuan, M. et al. (2006). Statistical methods for expression\nquantitative trait loci (eQTL) mapping. Biometrics 62, 19–27." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nHypothesis-free genome-wide approaches have also been undertaken.Genome-wide linkage scans reported evidence for linkage with longevity on chromosome 4q25 (Puca et al., 2001), 3p24-22, 9q31-34, and12q24 (Boyden &Kunkel, 2010).However, the evidence for these loci is still very weak as the results, obtained in centenarians and their families, could not be replicated in nonagenarian sibling pairs (Beekman et al., 2006) or have yet to be tested in other studies.A meta GWAS of survival to 90 years or older in 1836 cases and 1955 controls did not find any significant genome-wide associations (Newman et al., 2010).Thus far, hypothesis-free approaches have not identified any loci involved in longevity." + } + ], + "75e0ffe8-7675-4e11-be3e-880bfeb3dabd": [ + { + "document_id": "75e0ffe8-7675-4e11-be3e-880bfeb3dabd", + "text": "Abiola O, Angel JM, Avner P, Bachmanov AA, Belknap JK, Bennett B, et al. The nature and identification of quantitative trait loci: a community’s view. Nat Rev Genet. Nature Publishing Group; 2003; 4:\n911–916. https://doi.org/10.1038/nrg1206 PMID: 14634638\n\n18. Grupe A, Germer S, Usuka J, Aud D, Belknap JK, Klein RF, et al. In silico mapping of complex diseaserelated traits in mice. Science. American Association for the Advancement of Science; 2001; 292:\n1915–1918. https://doi.org/10.1126/science.1058889 PMID: 11397946\n\n19. Pletcher MT, McClurg P, Batalov S, Su AI, Barnes SW, Lagler E, et al." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\ncoid levels, etc.The mapping project should thus help to guide the search for human genes that regulate these interesting phenotypes and at the same time spark new investigations, in animal models, for the biochemical differences that mediate the genetic effects we detect.At the same time, the dataset that emerges should also allow us to test more general questions about the nature of aging and its genetic control.We may, for example, be able to identify QTLs that not only retard the development of one or more age-sensitive T-cell subsets, but also retard age-dependent changes in protein conformation, bone matrix turnover, and brain GFAP levels.Such a finding would imply that these changes are influenced, together, by a common biochemical pathway, and the corresponding QTLs would be excellent candidates for genes that regulate aging per se, rather than merely one among the many more agesensitive traits.In the same way, it will be of particular interest to determine if QTLs that regulate age-sensitive traits also are associated with differences in life span, and conversely if QTLs identified on the basis of longevity effects modify one (or nearly all?) of the age-sensitive traits in our test battery." + }, + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "\n\nThe strategy for mapping such quantitative trait loci (QTL) involves looking for preferential segregation of specific alleles or allele combina-tions in mice that differ in life span (or, more generally, any age-sensitive trait of interest).Our test population, called UM-HET3, consisted of a group of mice bred as the progeny of females of the (BALB/c × C57BL/6)F1 genotype and males of the (C3H/HeJ × DBA/2)F1 genotype.Mice bred in this way are, from a genetic perspective, all siblings; each shares a random half of its alleles with every other animal in the UM-HET3 population.The current set of analyses was conducted when genotype and longevity data were available from a group of 110 virgin males and 143 virgin females.The analytical method adjusted, by permutation testing, for Type I errors attributable to the simultaneous evaluation of multiple linkage hypotheses, and also included gender as a covariate to look for instances of sex-specific genetic effects.Because we had particular interest in regulation of late-life diseases rather than in causes of premature death, and because of evidence that genetic influences on mouse longevity were particularly strong when early deaths were not considered (Covelli et al., 1989), we repeated each analysis after exclusion of those animals dying before 657 days of age, i.e., the age at which 20 percent of the animals had already died." + } + ], + "9ac0b7e7-6294-4cfb-97e3-e5a4546af324": [ + { + "document_id": "9ac0b7e7-6294-4cfb-97e3-e5a4546af324", + "text": "The proportion of the phenotypic variance accounted for by\nthe QTL yield for Hbact and Hbrear was substantial and of the\nsame order of magnitude as that contributed by age. A small\nnumber of age-dependent QTL were found in the midst of\na majority of age-stable QTL (see discussion above). These\nage-sensitive loci point toward genes whose functions are\ncorrelated with important behavioral changes during aging." + } + ], + "9fed8fd1-fce5-4fc1-9911-05d312f88521": [ + { + "document_id": "9fed8fd1-fce5-4fc1-9911-05d312f88521", + "text": "\n\nAgeing genes and pathways.Assessing the loci of interest for colocalisation with gene expression quantitative trait loci (eQTL), we find strong evidence (FDR SMR < 5%; P HEIDI > 1%; see \"Methods\") of cis-acting eQTL colocalisation for eight out of 10 loci.In total, we highlight 27 unique genes acting across 32 tissues, especially whole blood (12 genes) and the tibial nerve (7 genes) (Supplementary Data 5).In blood, higher expression levels of BCL3 and CKM (near APOE); CTC-510F12.2, ILF3, KANK2 and PDE4A (near LDLR); USP28 and ANKK1 (near ZW10); and CDKN2B are linked to an increase in multivariate ageing traits (i.e.improved survival), while the opposite is true for EXOC3L2 (near APOE), TTC12 (near ZW10), and FOXO3.For the multivariate signal near SLC4A7 we find colocalisation with expression of NEK10 (liver); for the signal near LPA we find colocalisation with expression of SLC22A1/A3 (multiple tissues) and MAP3K4 (pituitary); and for the signal near FGD6 we find colocalisation with expression of FGD6 itself (adipose/arterial).Including trans-acting eQTL from blood, while keeping the same thresholds for colocalisation, we additionally discover higher expression levels of FOXO3B colocalises with the life-extending signal near FOXO3.When we include genes which could not be tested for heterogeneity (N eQTL < 3), we identify one additional cis-acting and 49 additional trans-acting genes (of which 10 colocalise with the signal near LINC02513) (Table 2; Supplementary Data 5)." + } + ], + "c12e853e-4f0d-48f9-93af-15db9ad2dfae": [ + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "Jiang, C. and Zeng, Z. B. (1995). Multiple trait analysis of genetic mapping for quantitative\ntrait loci. Genetics 140, 1111–1127. Jin, W., Riley, R. M., Wolfinger, R. D.et al. (2001). The contributions of sex, genotype and age\nto transcriptional variance in Drosophila melanogaster. Nat Genet 29, 389–395. Kempermann, G., Chesler, E. J., Lu, L. et al. (2006). Natural variation and genetic covariance\nin adult hippocampal neurogenesis. Proc Natl Acad Sci U S A 103, 780–785. Kendziorski, C. M., Chen, M., Yuan, M. et al. (2006). Statistical methods for expression\nquantitative trait loci (eQTL) mapping. Biometrics 62, 19–27." + } + ], + "cb3f9967-9762-4a9b-96cb-0acccdc316d2": [ + { + "document_id": "cb3f9967-9762-4a9b-96cb-0acccdc316d2", + "text": "Quantitative trait loci (QTLs) can be identified in several ways, but is\nthere a definitive test of whether a candidate locus actually corresponds to a specific QTL? NIH-PA Author Manuscript\n\nMuch of the genetic variation that underlies disease susceptibility and morphology is complex\nand is governed by loci that have quantitative effects on the phenotype. Gene-gene and geneenvironment interactions are common and make these loci difficult to analyse. Here, we present\na community’s view on the steps that are necessary to identify genetic loci that govern\nquantitative traits, along with a set of interpretive guidelines." + } + ], + "d1f04d58-2589-4183-aee4-569820dae052": [ + { + "document_id": "d1f04d58-2589-4183-aee4-569820dae052", + "text": "QTL Analysis in Hematopoiesis\n\n47\n\n3\nQuantitative Trait Analysis in the Investigation\nof Function and Aging of Hematopoietic Stem Cells\nHans-Willem Snoeck\nSummary\nExtensive genetically determined quantitative variation exists in the number and function of hematopoietic stem cells in inbred mouse strains. Furthermore, aging of hematopoietic stem cells is genetically determined. Gene identification of quantitative trait loci\ninvolved in the regulation and aging of hematopoietic stem cells would provide novel\ninsights into regulatory mechanisms that are relevant in vivo and may be clinically important." + } + ], + "dbfe8986-e861-496f-a534-7bb9ca061ad6": [ + { + "document_id": "dbfe8986-e861-496f-a534-7bb9ca061ad6", + "text": "\n\nIn order to find the causal loci for heritable differences in transcript levels and possible interactions between age and genotype, we applied a two-time-point model.In this model, we used three factors-(1) relative age, (2) genotype (marker), and (3) the interaction between factors 1 and 2-to explain the differences in gene expression between RILs and age groups.With this mapping procedure, we found almost 900 genes that had an eQTL or gxa eQTL in developing and/or aging worms (P < 0.0001; Fig. 2).Almost half of these genes with heritable transcript differences were found to have a genotype-by-age effect (396 at P < 0.0001; Table 1) allocated to a specific marker, which we coined genotype-by-age expression-QTL ( gxa eQTL).One specific hotspot (trans-band) for gxa eQTL was found on chromosome IV for aging worms and a trans-band for eQTL on chromosome I was detected in developing worms (Fig. 2)." + } + ], + "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748": [ + { + "document_id": "e2eaa1f2-1a1c-42b7-ab7f-e69a0394f748", + "text": "NIH-PA Author Manuscript\n\nWe found three significant QTLs (genetic regions harboring genes controlling these various\naging traits, Supplementary Table 5). On chromosome 7, we found a QTL affecting lifespan\nand fertility after DR that we have named Lfdr1 for “longevity and fertility response to\ndietary restriction, QTL 1; this QTL also has suggestive effects on FE (Fig. 5D). Two QTLs\nhaving significant effects on FE were identified on chromosomes 9 and 15. These we have\nnamed Fedr1 and Fedr2, respectively, for “fuel efficiency response to dietary restriction”\nQTLs 1 and 2." + } + ], + "f041550e-5f2d-430e-8f46-15ebea6ca496": [ + { + "document_id": "f041550e-5f2d-430e-8f46-15ebea6ca496", + "text": "Quantitative trait locus (QTL) mapping in\naging systems. Methods in Molecular Biology (Clifton, NJ ). 2007; 371:321–348. Hunter KW, Crawford NPS. The future of mouse QTL mapping to diagnose disease in mice in the age\nof whole-genome association studies. Annual Review of Genetics. 2008; 42:131–141. Ito R, Robbins TW, Everitt BJ. Differential control over cocaine-seeking behavior by nucleus\naccumbens core and shell. Nature Neuroscience. 2004; 7:389–397. [PubMed: 15034590]\nKapp MB. Ethical and legal issues in research involving human subjects: do you want a piece of me? Journal of Clinical Pathology. 2006; 59:335–339." + } + ], + "f35e02a1-3314-4663-913f-38a3fc072aa8": [ + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "Jiang, C. and Zeng, Z. B. (1995). Multiple trait analysis of genetic mapping for quantitative\ntrait loci. Genetics 140, 1111–1127. Jin, W., Riley, R. M., Wolfinger, R. D.et al. (2001). The contributions of sex, genotype and age\nto transcriptional variance in Drosophila melanogaster. Nat Genet 29, 389–395. Kempermann, G., Chesler, E. J., Lu, L. et al. (2006). Natural variation and genetic covariance\nin adult hippocampal neurogenesis. Proc Natl Acad Sci U S A 103, 780–785. Kendziorski, C. M., Chen, M., Yuan, M. et al. (2006). Statistical methods for expression\nquantitative trait loci (eQTL) mapping. Biometrics 62, 19–27." + } + ] + }, + "data_source": [], + "document_id": "F609F53687E61A2158330F914066FE89", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "aging", + "QTL", + "quantitative&trait&loci", + "longevity", + "epigenetic&aging", + "Alzheimers&disease", + "cognitive&decline", + "dementia", + "GWAS", + "eQTL" + ], + "metadata": [], + "question": "Do we find significant quantitative trait loci in aging studies, if so give the top three examples with descriptions?", + "subquestions": null, + "task_id": "F609F53687E61A2158330F914066FE89", + "usage": { + "chatgpt": 7623, + "gpt-4": 5054, + "gpt-4-turbo-preview": 3989 + }, + "user_id": 2 + }, + "document_id": "F609F53687E61A2158330F914066FE89", + "task_id": "F609F53687E61A2158330F914066FE89" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_05.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_05.json new file mode 100644 index 0000000..7d5ca5e --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_05.json @@ -0,0 +1,148 @@ +{ + "created_at": "2024-04-02T12:04:11.979513Z", + "data": { + "amplify": false, + "answer": "The most fundamental human life expectancy extending geroscience studies include genetic and genomic research into longevity. These studies use both hypothesis-driven candidate gene approaches and explorative genome-wide analyses. They also involve the study of biomarkers related to aging, deep phenotyping, and the use of novel technologies like next-generation sequencing. Additionally, they examine the role of environmental factors, lifestyle, and heritability in human lifespan variation.", + "context": { + "04c5378f-40dc-4690-af03-e5205779b881": [ + { + "document_id": "04c5378f-40dc-4690-af03-e5205779b881", + "text": "Introduction\n\nWith the development of human genomics research, a large number of studies of the genetics of longevity have been conducted.Scientists from various countries have proposed many different theories concerning the mechanisms of aging from different perspectives, involving oxidative stress, energy metabolism, signal transduction pathways, immune response, etc. [1,2].These mechanisms interact with each other and are influenced by heredity to some degree [2,3].The identification of longevity-related biological markers is critical to an indepth understanding of the mechanisms of carrier protection against common disease and/or of the retardation of the process of aging." + } + ], + "1386c8ad-297d-48b1-aa34-41659a9f6544": [ + { + "document_id": "1386c8ad-297d-48b1-aa34-41659a9f6544", + "text": "INTRODUCTION\n\nHuman aging is affected by genes, life style, and environmental factors.The genetic contribution to average human aging can be modest with genes explaining ∼20-25% of the variability of human survival to the mid-eighties (Herskind et al., 1996;Fraser and Shavlik, 2001).By contrast, genetic factors may have greater impact on survival to the ninth through eleventh decades (Tan et al., 2008).Notably, exceptional longevity is rare and may involve biological mechanisms that differ from those implicated in usual human aging." + } + ], + "3043efd1-4b13-4300-b2a7-d1992c8d4e47": [ + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "Introduction\n\nGeroscience refers to research aimed at understanding the mechanisms of biological aging (Kennedy et al. 2014).A major goal of geroscience is to define the genetic, epigenetic, and environmental features that determine individual rates of aging.From a translational perspective, a further goal is to use this knowledge to develop interventions that can slow or delay aging in order to promote healthy longevity and increase healthspan, the period of life spent in good health free from chronic disease and disability (Burch et al. 2014;Pitt and Kaeberlein 2015)." + } + ], + "3bf70612-23e6-41b8-9b88-ce9ba23c1edf": [ + { + "document_id": "3bf70612-23e6-41b8-9b88-ce9ba23c1edf", + "text": "\nthe maximum human life span.Several avenues to studying aging have placed us on Department of Biology Massachusetts Institute of Technology the threshold of understanding basic underlying mechanisms.These approaches include the identification of Cambridge, Massachusetts 02139 key genes and pathways important in aging; genetic studies of heritable diseases that cause the appearance of premature aging in affected people; physiological ex-Introduction periments that relate the pace of aging to caloric intake; Is aging the final act in the script of developmental bioland advances in human genetics, as well as cell and ogy?The characteristic changes that are part and parcel molecular biology leading to an understanding of the of aging appear similar to developmentally regulated basis of many diseases of aging.Strikingly, single gene programs.But why would aging mechanisms have been mutations have been found to significantly extend the evolutionarily selected as advantageous?Indeed, evolife span in C. elegans, yeast, and, most recently, Drolutionary biologists might argue that aging occurs by sophila, suggesting that aging may be relatively simple, default due to the absence of selection in the postreproat least in these organisms.Further, the limited replicaductive phase of life.By this view, the aging process is tion potential of human cells in culture has been attribnot programmed, but, rather, the detritus of the absence uted to a specific mechanism (i.e., the shortening of of selection for maintenance (Medawar, 1952; Kirkwood, telomeric ends of chromosomes).An important chal- 1977).However, it is quite reasonable that any mechalenge is now to relate these recent findings to the more nisms that sprang up to slow or regulate the pace of complex case of human aging.aging would be selected, because lucky individualsIn this review, we will discuss several important mocould potentially give rise to more progeny.Therefore, lecular models of aging that come from current research.it is reasonable to suppose that life span extending pro-These are damage by reactive oxygen species (ROS) cesses have been selected and that these can be viewed generated by metabolism, genome instability, genetias an elaboration of development itself.In principle, cally programmed extension mechanisms, cell death, such extension mechanisms may act to slow or forestall and systemic aging.Questions to be posed include the deleterious changes in an organism that progressively following.What evidence exists for and against these lead to death.The life span of an organism, therefore, models?Can more than one of these models apply to is the sum of deleterious changes and counteracting aging of different tissues in humans-specifically do repair and maintenance mechanisms that respond to organs with continually dividing cells age by the same the damage (Figure 1).mechanism as organs that are postmitotic?Finally, is A priori, one imagines such longevity mechanisms to aging amenable to therapeutic intervention, and would be much less complex than those regulating embryonic such intervention be advisable?development.The spatial and temporal constraints on embryonic development are many, while requirements Oxidative Damage for longevity mechanisms might be much more specific One theory of aging proposes that ROS which are generif there were a single process (or a few processes) whose ated by metabolism cause cumulative damage over a breakdown is the limiting event in longevity (i.e., the lifetime (Harman, 1981).Roughly two to three percent Achilles heel).of oxygen taken up is chemically reduced by the addition Aging is defined when two criteria are met.First, the of single electrons, which are sequentially converted probability of death at any point in time increases with into ROS, including the superoxide anion, hydrogen perthe age of the organism.This statistical definition applies oxide, and the hydroxyl radical.ROS have been shown from yeast to mammals and reflects the progressive to cause molecular damage relatively indiscriminately nature of aging.Second, characteristic changes in pheto proteins, lipids, and nucleic acids.In addition, specific notype occur in all individuals over time due to the limdamage has been observed in the mitochondrial DNA, iting processes.which we consider below in Genome Instability.The phenotypic definition is equally general and is What is the evidence that oxidative damage causes useful in distinguishing the aging process itself from aging?One category of study that is supportive of this diseases of aging, such as cancer and heart disease.view involves animals transgenic for genes encoding Phenotypes of aging affect all of the individuals in a antioxidants.Transgenic Drosophila overexpressing both population, while diseases of aging affect only a subset.Cu/Zn SOD and catalase live 34% longer than controls Both impact on life span, but in different ways.For exam-(Orr and Sohal, 1994).A more recent study shows that ple, the many advances in medicine and public health expression of human SOD1 exclusively in Drosophila in this century have caused a large increase in the averadult motor neurons leads to a 40% extension in life age life span of humans in developed countries.Howspan (Parkes et al., 1998).Further experiments are necever, because these advances have not altered the aging essary to clarify the nature of this primary role of motor neurons in life span.Conversely, mice knocked out for either GPX1 (encoding glutathione peroxidase), SOD1," + }, + { + "document_id": "3bf70612-23e6-41b8-9b88-ce9ba23c1edf", + "text": "\n\nthe maximum human life span.Several avenues to studying aging have placed us on Department of Biology Massachusetts Institute of Technology the threshold of understanding basic underlying mechanisms.These approaches include the identification of Cambridge, Massachusetts 02139 key genes and pathways important in aging; genetic studies of heritable diseases that cause the appearance of premature aging in affected people; physiological ex-Introduction periments that relate the pace of aging to caloric intake; Is aging the final act in the script of developmental bioland advances in human genetics, as well as cell and ogy?The characteristic changes that are part and parcel molecular biology leading to an understanding of the of aging appear similar to developmentally regulated basis of many diseases of aging.Strikingly, single gene programs.But why would aging mechanisms have been mutations have been found to significantly extend the evolutionarily selected as advantageous?Indeed, evolife span in C. elegans, yeast, and, most recently, Drolutionary biologists might argue that aging occurs by sophila, suggesting that aging may be relatively simple, default due to the absence of selection in the postreproat least in these organisms.Further, the limited replicaductive phase of life.By this view, the aging process is tion potential of human cells in culture has been attribnot programmed, but, rather, the detritus of the absence uted to a specific mechanism (i.e., the shortening of of selection for maintenance (Medawar, 1952; Kirkwood, telomeric ends of chromosomes).An important chal- 1977).However, it is quite reasonable that any mechalenge is now to relate these recent findings to the more nisms that sprang up to slow or regulate the pace of complex case of human aging.aging would be selected, because lucky individuals" + } + ], + "555a1533-2905-4d91-a3b6-2fca3679ab02": [ + { + "document_id": "555a1533-2905-4d91-a3b6-2fca3679ab02", + "text": "\n\nCurrently prevailing studies of genetic and biological origin of human health and longevity follow largely two approaches which focus on the aging-related diseases and on individuals with exceptionally long lives (Martin et al. 2007).This study provides de facto the rationale for a new approach.Specifically, Fig. 2 suggests that a promising strategy could be to focus on individuals who died prematurely.Studies of genetic profiles of short-lived subjects compared to those who aged more successfully (i.e., those who lived longer and perhaps healthier lives) can be a core of this strategy.Importantly, this strategy can be naturally implemented in longitudinal studies of aging and longevity by focusing on individuals who died first." + } + ], + "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4": [ + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "text": "\n\nT he average human life expectancy has been increasing for centuries 1 .Based on twin studies, the heritability of human lifespan has been estimated to be ~25%, although this estimate differs among studies 2 .On the other hand, the heritability of lifespan based on the correlation of the mid-parent (i.e., the average of the father and mother) and offspring difference between age at death and expected lifespan was estimated to be 12% 3 .A recent study has indicated that the different heritability estimates may be inflated due to assortative mating, leaving a true heritability that is below 10% 4 .The heritability of lifespan, estimated using the sibling relative risk, increases with age 5 and is assumed to be enriched in long-lived families, particularly when belonging to the 10% longest-lived of their generation 6 .To identify genetic associations with human lifespan, several genome-wide association (GWA) studies have been performed [7][8][9][10][11][12][13][14][15][16][17][18][19][20] .These studies have used a discrete (i.e., older cases versus younger controls) or a continuous phenotype (such as age at death of individuals or their parents).The selection of cases for the studies using a discrete longevity phenotype has been based on the survival to ages above 90 or 100 years or belonging to the top 10% or 1% of survivors in a population.Studies defining cases using a discrete longevity phenotype often need to rely on controls from more contemporary birth cohorts, because all others from the case birth cohorts have died before sample collection.Previous GWA studies have identified several genetic variants, but the only locus that has shown genome-wide significance (P ≤ 5 × 10 −8 ) in multiple independent meta-analyses of GWA studies is apolipoprotein E (APOE) 21 , where the ApoE ε4 variant is associated with lower odds of being a long-lived case." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "Introduction\n\nWorldwide human populations have shown an increase in mean life expectancy in the past two centuries (Oeppen & Vaupel, 2002).This is mainly because of environmental factors such as improved hygiene, nutrition, and health care.The large variation in healthy lifespan among the elderly has prompted research into the determinants of aging and lifespan regulation.The genetic contribution to human lifespan variation was estimated at 25-30% in twin studies (Gudmundsson et al., 2000;Skytthe et al., 2003;Hjelmborg et al., 2006).The most prominent genetic influence is observed in families in which the capacity to attain a long lifespan clusters (Perls et al., 2000;Schoenmaker et al., 2006).Exceptional longevity can be reached with a low degree of age-related disability (Christensen et al., 2008;Terry et al., 2008), raising the question whether protective mechanisms against disease exist in long-lived subjects." + } + ], + "6005d141-8758-44b5-9baa-d553da68d167": [ + { + "document_id": "6005d141-8758-44b5-9baa-d553da68d167", + "text": "Introduction\n\nHuman life expectancies are increasing almost everywhere in the world where socio-economic circumstances are permissive (Tuljapurkar et al., 2000) and there is no evidence that a limit to life is anywhere near (Oeppen and Vaupel, 2002).While this increase in life span would prevent a proposed compression of morbidity (Fries, 1980), there is no evidence that higher average life spans are associated with an extension of the period of increased morbidity (Manton and Gu, 2001).On the contrary, older individuals have never been so healthy and further improvements in life style, environmental conditions and medical care are likely to help this trend to continue.Especially the medical sciences now seem poised to push the biological limits of longevity further by a number of innovations that seem to affect basic mechanisms of ageing and disease rather than merely alleviating its symptoms.While in the past medicine contributed mainly to public health advances by redu-cing infectious diseases, thereby helping infant mortality to decline, more recent developments hold promise for a more basic intervention in the processes that underlie age-related decline.An example is atherosclerosis, a common problem in ageing and, along with hypertension, the cause of most cardiovascular disease.Basic medical research has likely contributed significantly to the current dramatic decline in cardiovascular disease by actively intervening in some of its main risk factors, i.e., lipid levels and hypertension (Levi et al., 2002).However, one could question whether age-related diseases should be seen as separate from ageing.In this respect, ageing has been considered as a process of cellular degeneration and death universal to all or most species, increasing the risk of fatal disease in humans and other mammals.Would it be possible to define such a process and ultimately understand it in terms of the timedependent, coordinated action of the products of multiple genes interacting with the environment?If so, then ageing per se rather than the diseases associated with it, may offer a more logical starting point for further increasing healthy life expectancies through prevention and therapy.This is especially true now that we have a working draft of the human genome and are in a position to determine the functional significance of each gene as part of the dynamic network of all genes that ultimately determine the physiology of an organism.Termed 'Functional Genomics', this new discipline is now often called upon to solve the complex problems in biology, such as to understand functional control mechanisms and investigate the role that genotype and environment play in determining disease phenotypes.The question is then if this same approach would apply to ageing as a complex phenotype.What is ageing, how does it differ from its diametrical opposite, i.e., organismal development, and what role can functional genomics play in unraveling the basic causes of ageing and exploit such knowledge for developing new, rational strategies for extending healthy life span?" + } + ], + "6df20592-9856-49a6-8bf3-f6a701ff3b56": [ + { + "document_id": "6df20592-9856-49a6-8bf3-f6a701ff3b56", + "text": "Introduction\n\nAs a result of improvements in health care and living conditions over the past two centuries, the average human life expectancy has dramatically increased in many regions of the world [1].This major success reflects the great malleability of the ageing process.Unfortunately, for most people, ageing is accompanied with an increased risk of developing age-related illnesses/disabilities and frailty.Therefore new approaches are required to understand the genetic, cellular, and molecular factors controlling ageing to identify strategies to extend healthy life span." + } + ], + "79ae7122-3716-498b-9b9a-dd0960e33f99": [ + { + "document_id": "79ae7122-3716-498b-9b9a-dd0960e33f99", + "text": "\nThe search for the genetic determinants of extreme human longevity has been challenged by the phenotype's rarity and its nonspecific definition by investigators.To address these issues, we established a consortium of four studies of extreme longevity that contributed 2,070 individuals who survived to the oldest one percentile of survival for the 1900 U.S. birth year cohort.We conducted various analyses to discover longevity-associated variants (LAV) and characterized those LAVs that differentiate survival to extreme age at death (eSAVs) from those LAVs that become more frequent in centenarians because of mortality selection (eg, survival to younger years).The analyses identified new rare variants in chromosomes 4 and 7 associated with extreme survival and with reduced risk for cardiovascular disease and Alzheimer's disease.The results confirm the importance of studying truly rare survival to discover those combinations of common and rare variants associated with extreme longevity and longer health span." + }, + { + "document_id": "79ae7122-3716-498b-9b9a-dd0960e33f99", + "text": "\n\nThe search for the genetic determinants of extreme human longevity has been challenged by the phenotype's rarity and its nonspecific definition by investigators.To address these issues, we established a consortium of four studies of extreme longevity that contributed 2,070 individuals who survived to the oldest one percentile of survival for the 1900 U.S. birth year cohort.We conducted various analyses to discover longevity-associated variants (LAV) and characterized those LAVs that differentiate survival to extreme age at death (eSAVs) from those LAVs that become more frequent in centenarians because of mortality selection (eg, survival to younger years).The analyses identified new rare variants in chromosomes 4 and 7 associated with extreme survival and with reduced risk for cardiovascular disease and Alzheimer's disease.The results confirm the importance of studying truly rare survival to discover those combinations of common and rare variants associated with extreme longevity and longer health span." + } + ], + "932ef21b-9235-4210-a99c-6153a901bb89": [ + { + "document_id": "932ef21b-9235-4210-a99c-6153a901bb89", + "text": "Introduction\n\nThe recent, remarkable extension of life expectancy is largely attributed to the postponement of mortality at old age (Vaupel, 1997(Vaupel, , 2010)).The years of life gained in the older population residing in developed nations are a success story of public health measures and improved health care.In addition to such external factors, longevity and healthy aging consistently show a modest heritability between 20% and 50% and aging-associated genetic research may provide further insights into the mechanisms of aging (Herskind et al., 1996;McGue et al., 1993;Reed and Dick, 2003).It has been postulated that genes involved in pathways associated with aging identified in animal models, such as insulin-like growth factor (IGF)-insulin signaling, regulation of lipoprotein metabolism, the mTOR pathway, and the oxidative stress response may also influence survival to old or even exceptionally old age in humans (Christensen et al., 2006;Kenyon, 2010;Vellai et al., 2003).However, in humans, common variants within genes involved in these pathways have not been consistently associated with lifespan (Chris-tensen et al., 2006;Kenyon, 2010;Kuningas et al., 2008;Vijg and Suh, 2005)." + } + ], + "ae9d5a74-24c1-43f1-b514-5e3f10c91284": [ + { + "document_id": "ae9d5a74-24c1-43f1-b514-5e3f10c91284", + "text": "DESIGNS TO STUDY PARAMETERS OF HEALTHY AGEING, MORBIDITY, MORTALITY AND LONGEVITY\n\nHuman cohorts may vary considerably in their morbidity, mortality and longevity characteristics and yet they have shown a common increase in mean life expectancy in the past two centuries [5].This is mainly due to improved hygiene, nutrition and healthcare.There is a large variation in healthy lifespan among the elderly and remarkably exceptional longevity (EL) can be reached with a low degree of agerelated disability [6,7].Heritability studies comparing the concordance of lifespan in monozygous and dizygous twins estimated a 25 -30% genetic contribution to human lifespan variation [8 -11], which becomes increasingly important at higher ages.The most prominent genetic influence is present in families in which survival to high ages clusters [12,13].Unlike model systems where single-gene mutations have major life extension effects, human longevity is presumed to be a complex trait [14]." + }, + { + "document_id": "ae9d5a74-24c1-43f1-b514-5e3f10c91284", + "text": "INTRODUCTION\n\nGenomic studies into human longevity are inspired by the fact that, in animal models, healthy lifespan has proved to be remarkably plastic, and major pathways of lifespan regulation have been identified.Considerable lifespan extension has been induced in models as diverse as yeast, worms, fish, flies and rodents by applying genetic manipulation and dietary restriction (DR) (see [1] for review).Reduced activity of nutrient-sensing pathways such as insulin/insulin-like growth factor (IGF-1) signalling (IIS) and target of rapamycin (TOR) signalling mediated lifespan extension, and also the extension of lifespan by DR [2].An interesting observation from the perspective of human ageing is that, in rodents and monkeys, diets restricted in glucose, fat or protein uptake reduced or delayed the risk of cancer and metabolic disease, thus extending the healthspan of the animals [2].Following the discovery of genes and pathways involved in animal lifespan extension, human research has focused on the corresponding candidate human genes with genetic, genomic and epigenetic studies into ageing and longevity.The designs of these studies differ with respect to the selection of naturally occurring phenotypes and the study populations, which include population-based, patient-based, family-based and exposure-based cohorts." + }, + { + "document_id": "ae9d5a74-24c1-43f1-b514-5e3f10c91284", + "text": "GENETIC STUDIES OF HUMAN LONGEVITY\n\nGenetic and genomic studies into longevity have been performed based on a hypothesis, referred to as a candidate gene approach.Alternatively, explorative genome-wide analyses have been applied in which genetic variation and gene transcription across the complete genome are being studied for associations with longevity and related traits.Genetic studies into human disease and longevity include candidate gene approaches, genome-wide association studies (GWASs) and genome-wide linkage studies." + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY\n\nHeritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE ε2), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans." + } + ], + "d174ea46-2c88-4047-a333-cb66e483a51f": [ + { + "document_id": "d174ea46-2c88-4047-a333-cb66e483a51f", + "text": "Introduction\n\nHuman longevity is influenced by multiple genetic and environmental factors.Approximately 25-32% of the overall variation in adult lifespan is because of genetic variation that becomes particularly important for survival at advanced age (Hjelmborg et al., 2006).Epidemiological studies have revealed that long-lived individuals (LLI), that is, people surviving to the 95th percentile of the respective birth cohort-specific age distributions (Gudmundsson et al., 2000), frequently show a favorable ('healthy') course of the aging process, with the absence or a delayed onset of agerelated diseases (Hitt et al., 1999).Hence, the LLI offer the key to elucidate the molecular mechanisms underlying the 'healthy aging' phenotype (Perls, 2006)." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "Conclusions and prospects\n\nOver the past two decades the human aging field has built up the necessary resources to study the biology of aging and longevity by establishing human populations with a diversity of designs.Meta-analyses integrating genetic and phenotypic datasets have successfully identified variants associated with a range of age-related traits and diseases.Despite these accomplishments, the number of novel leads contributing to human lifespan regulation is limited.Although positive regions of linkage and suggestive GWAS hits have been reported, the field has not yet identified the loci that explain the clustering of longevity in families and the variation in biological aging rate in the population.As for animal models, down-signaling of the IIS and mTOR pathway appeared to be relevant in humans.These findings are being followed up by molecular and physiological profiling using skin, fat and muscle tissue of long-lived family members and controls.Human studies now also include the response of nutrient sensing systems to the application of dietary and physical challenges." + }, + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nHuman lifespan variation is mainly determined by environmental factors, whereas the genetic contribution is 25-30% and expected to be polygenic.Two complementary fields go hand in hand in order to unravel the mechanisms of biological aging: genomic and biomarker research.Explorative and candidate gene studies of the human genome by genetic, transcriptomic, and epigenomic approaches have resulted in the identification of a limited number of interesting positive linkage regions, genes, and pathways that contribute to lifespan variation.The possibilities to further exploit these findings are rapidly increasing through the use of novel technologies, such as next-generation sequencing.Genomic research is progressively being integrated with biomarker studies on aging, including the application of (noninvasive) deep phenotyping and omics data -generated using novel technologies -in a wealth of studies in human populations.Hence, these studies may assist in obtaining a more holistic perspective on the role of the genome in aging and lifespan regulation." + } + ] + }, + "data_source": [], + "document_id": "9329B7F018681D21C6BF01A0ACF8A9FF", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "aging", + "longevity", + "genes", + "genetic", + "human", + "GWAS", + "APOE", + "IIS&mTOR", + "pathways", + "ROS" + ], + "metadata": [], + "question": "What are the most fundamental human life expectancy extending geroscience studies?", + "subquestions": null, + "task_id": "9329B7F018681D21C6BF01A0ACF8A9FF", + "usage": { + "chatgpt": 9018, + "gpt-4": 6435, + "gpt-4-turbo-preview": 5512 + }, + "user_id": 2 + }, + "document_id": "9329B7F018681D21C6BF01A0ACF8A9FF", + "task_id": "9329B7F018681D21C6BF01A0ACF8A9FF" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_06.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_06.json new file mode 100644 index 0000000..78b6bd7 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_06.json @@ -0,0 +1,140 @@ +{ + "created_at": "2024-04-02T12:10:07.521330Z", + "data": { + "amplify": false, + "answer": "The grand challenges related to aging research in genetics and genomics include:\n\n1. Lack of awareness and interest: Health professionals, governments, and researchers often lack awareness and interest in genetic diseases, which hinders progress.\n\n2. Limited funding and access to technology: Research funding is often limited, and access to high-end technology is not always available, which restricts the scope of research.\n\n3. Economic and health disparities: These disparities can affect the application and benefits of genomic medicine, making it less accessible to those in lower income brackets.\n\n4. Ethical, legal, and social issues: The interpretation, storage, and sharing of genetic data raise ethical and legal concerns, including issues of privacy and consent.\n\n5. Rapid advancements: The fast pace of developments in genetics and genomics can outpace the ability to address emerging ethical, legal, and social questions.\n\n6. Public knowledge and trust: There is a need to increase public knowledge about genomic research and address issues of trust in medicine and scientific research.\n\n7. Integration of genomics into public health: This is a major future challenge for healthcare systems, requiring dialogue with all stakeholders in society.", + "context": { + "16a19a42-1f27-43e4-a4df-46d5e9c81096": [ + { + "document_id": "16a19a42-1f27-43e4-a4df-46d5e9c81096", + "text": "\n\nThere is a great need for continuing efforts to increase public knowledge about genomic research.As individuals and communities from diverse social backgrounds become more aware of genomic research and the potential role of genetics in contributing to health outcomes, the public will hopefully be more informed about the implications of genomic research for personal medical care, public health and more broadly the public representation of diverse population groups based on genetic findings.This knowledge should reinforce the ability of potential participants to make informed choices about joining a genetic study.There are complicated issues underlying public trust in medicine as well as scientific and genetic research that must be addressed.Innovative strategies for public education and community engagement should take into account cultural settings and historical experiences that have contributed to distrust in the past." + } + ], + "64d87c52-1185-4080-8d06-134c32dae5fd": [ + { + "document_id": "64d87c52-1185-4080-8d06-134c32dae5fd", + "text": "\n\nThe issues discussed in this section refl ect key current concerns, but, given the rapid advances in genetic and genomic research, new issues will continue to confront families in the next few years.For example, major advances in the developing area of neuropsychiatric genetics, studies of the heritable nature of psychiatric and other nervous system disorders, characterized at the molecular, cellular, or behavioral levels, will challenge family members to address the potential role genes play in the development of schizophrenia, bipolar, or affective disorders (Genomics Network, n.d.)." + }, + { + "document_id": "64d87c52-1185-4080-8d06-134c32dae5fd", + "text": "Future Implications and Communication Research Directions\n\nGiven ever-expanding research on genetics and genomics, scholars interested in family interaction will be challenged to stay abreast of the implications for family disclosure and discussion of genetic health.We believe that the following issues will emerge as key concerns:" + } + ], + "855e497d-7305-4154-b395-283992ddc4d0": [ + { + "document_id": "855e497d-7305-4154-b395-283992ddc4d0", + "text": "Conclusion\n\nAfter more than four decades of working, genetics and genomic medicine still faces a considerable challenge to be addressed.Lack of awareness of health professionals and government, lack of interest of researcher on genetic diseases, limited research funding, limited access to high technology, low national health budget and low income family are seem to be the main obstacles to be overcome in implementation of genetics and genomic medicine.Despite these conditions, several research centers still managed to do some studies and few numbers of genetic testing.Several collaborations with countries abroad have been done to overcome some obstacles.Yet, Indonesia still has to accelerate this effort to be able to catch up its lag.Mentoring and collaborations are needed to enable Indonesia in doing so." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "Opportunities for Population-Based Research on Aging Human Subjects:\n\nPathology and Genetics" + } + ], + "9e513fea-5257-4887-9802-57d416f21dfc": [ + { + "document_id": "9e513fea-5257-4887-9802-57d416f21dfc", + "text": "Concluding remarks\n\nThe next decade will provide a window of opportunity to prepare health professionals, public health practitioners, the public and policy makers for the advent of genomics on health and health care.This will be a doable project but will require regional, national, European and global coordination on both the vertical and horizontal levels.We argue that there is an ethical obligation to prepare society to meet this challenge and to take up the opportunities provided by the science in a medically useful, effective, efficient, socially desirable and ethically justifiable manner.Here, health literacy, health communication and empowerment in managing risks are key for opening the doors to a truly beneficial Public Health Genomics practice.This can be facilitated by implementing ethical benchmarks and legal safeguards 70 such as respect for autonomy and social justice in the context of policy development." + }, + { + "document_id": "9e513fea-5257-4887-9802-57d416f21dfc", + "text": "\n\nClarifying the general conditions under which genomic knowledge can be put to best practice in the field of public health, paying particular consideration to the ethical, legal and social implications 12,17,35 is currently the most pressing task in Public Health Genomics.Aiming the application of genetic and molecular science to the promotion of health and disease prevention through the organised efforts of society, integral to its activities is a dialogue with all stakeholders in society, including industry, governments, health professionals and the general public. 18Thus, the integration of genomics into public health research, policy and practice is one of the major future challenges for our health-care systems. 36,37Expertise is already feasible and can be clustered and evaluated for a socially accountable use." + }, + { + "document_id": "9e513fea-5257-4887-9802-57d416f21dfc", + "text": "\n\nPublic health needs to prepare itself for the upcoming challenges, which derive from genomics.In this sense, it needs to strengthen the communication efforts among all sciences involved.Public health can serve as the umbrella, that spans the disciplines such as genetics, ethics, law and all other stakeholders." + } + ], + "9f21007a-1487-46d8-8e9e-cde8df4af6d5": [ + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "text": "\n\nEconomic and health disparities related to genetics and genomics." + }, + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "text": "\n\nCapabilities and limitations of current genetic/genomic technologies." + }, + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "text": "\n\nIdentify ethical, legal, and social issues associated with genetic/genomic information." + }, + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "text": "\n\nOngoing research contributing to improved understanding of the genetic/genomic influences on health." + }, + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "text": "Economic and health disparities related to genetics and genomics. Integrate knowledge from psychology, history, politics, sociology and culture when delivering genetic and genomic care." + }, + { + "document_id": "9f21007a-1487-46d8-8e9e-cde8df4af6d5", + "text": "\n\nEthical and legal issues surrounding genetic and genomic information and services." + } + ], + "a4e27158-1e54-4ee2-9cc1-049489a628bc": [ + { + "document_id": "a4e27158-1e54-4ee2-9cc1-049489a628bc", + "text": "\n\nDevelopments in genetics and genomics occur very rapidly and bring with them new ethical, legal and social questions that need swift, sensible and responsible responses (Pepper, 2011).Examples include next-generation sequencing, genetic cohort studies and biobanks, which have raised questions about data management, including quality of interpretation of data, data storage, data sharing, consent for re-use of data, as well as concerns about identifiability and privacy interests of those who provide samples (Kaye, 2012;Wolf, 2013;Pinxten and Howard, 2014).However, the rapidity of advancement poses difficulties for those who must determine the responses to these questions.They are often slow or even overtaken by further advancements.Ethical, legal and social-related challenges should be prioritised for policymakers, researchers, clinicians and public health practitioners to maximise the benefits of genomic and genetic applications while minimising the risk of harm to people (Geller et al., 2014).Any education strategy developed should therefore be dynamic." + } + ], + "af3d7cd3-40ec-4a86-a473-89f83da250e4": [ + { + "document_id": "af3d7cd3-40ec-4a86-a473-89f83da250e4", + "text": "Query 2. Perceptions of Genetics and Genomics\n\nAwareness of Genetic and Genomic Advancements." + } + ], + "be3e9fcb-5469-48eb-bc1b-118e58f82cc5": [ + { + "document_id": "be3e9fcb-5469-48eb-bc1b-118e58f82cc5", + "text": "\n\nIn addition, 4 scholarly commentaries in this issue provide insights into several current practical issues and developments in genetics and genomics.Feero and colleagues 11 describe advances in genomics science and explore many of the issues surrounding translation of these advances to routine \"personalized\" patient care.Offit 12 discusses the increasing availability of direct-to-consumer marketing of genomic and genetic testing and sounds an appropriately cautionary note about the need for standards, quality control, and appropriate regulation.Uhlmann and Guttmacher 13 present a useful collection of practical Internet genetics resources for clinicians and patients, including genetics information on specific diseases; guidelines for genetic testing; and educational resources to help clinicians integrate genetics into patient care.Ginsberg and colleagues 14 discuss the importance of centralized biorepositories for genetics and genomics research and empha-size the need to develop and implement standards for informed consent, informatics, and governance." + } + ], + "cb76344a-9307-4a44-b6b2-455b728bb249": [ + { + "document_id": "cb76344a-9307-4a44-b6b2-455b728bb249", + "text": "\n\nKey Themes Relevant To Genomic Research . . . . . . . . . . . . . . . . . . . . . . . . . . 3" + } + ], + "e8be2280-10e9-4b62-af14-0772947d2d7e": [ + { + "document_id": "e8be2280-10e9-4b62-af14-0772947d2d7e", + "text": "\n\nA first step is to define the challenges that stand in the way of realizing the promise of genomic medicine.These include addressing gaps in the oversight of genetic testing (including regulation of companies providing test interpretation services), ensuring that realistic claims are made in promotional materials for genetic testing, determining the appropriate role of new genomic technologies in patient care, ensuring the privacy of patients' genomic data, and improving insurance coverage and reimbursement for genetic services.The Secretary's Advisory Committee on Genetics, Health, and Society (SACGHS), on which two of us serve, advises the secretary of health and human services and reports on these issues." + }, + { + "document_id": "e8be2280-10e9-4b62-af14-0772947d2d7e", + "text": "\n\nHow can we maximize the benefits of these new developments and minimize the harms?How can we encourage patients' involvement and autonomy yet establish appropriate safeguards while avoiding inappropriate paternalism?How do we promote Preparing for a Consumer-Driven Genomic Age the understanding that interpretations of genomic information may evolve as research unravels the meaning of gene-gene and gene-environment interactions and the roles of noncoding DNA sequences, copy-number variants, epigenetic mechanisms, and behavioral factors in health and disease?" + } + ] + }, + "data_source": [], + "document_id": "8FE04B7D11697E4EF20F0147B082EC5B", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "genetics&genomics", + "public&health", + "ethical&issues", + "legal&issues", + "genetic&testing", + "data&management", + "privacy", + "health&disparities", + "genomic&medicine", + "patient&care" + ], + "metadata": [], + "question": "List and describe the grand challenges related to aging research in genetics and genomics.", + "subquestions": null, + "task_id": "8FE04B7D11697E4EF20F0147B082EC5B", + "usage": { + "chatgpt": 3769, + "gpt-4": 2657, + "gpt-4-turbo-preview": 1711 + }, + "user_id": 2 + }, + "document_id": "8FE04B7D11697E4EF20F0147B082EC5B", + "task_id": "8FE04B7D11697E4EF20F0147B082EC5B" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_07.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_07.json new file mode 100644 index 0000000..aeb904c --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_07.json @@ -0,0 +1,134 @@ +{ + "created_at": "2024-04-02T12:30:28.749434Z", + "data": { + "amplify": false, + "answer": "Gene-by-drug effects (GxD) can modulate life expectancies and health by influencing how individuals respond to medications. Genetic variations can affect how drugs are absorbed, disseminated, metabolized, and excreted in the body, which can lead to differences in drug efficacy and the occurrence of adverse drug reactions. For example, a genetic variant in the HMG-CoA reductase gene can modify the LDL-C response to pravastatin. Additionally, certain genes are involved in DNA damage repair and oxidative stress, which can influence susceptibility to adverse drug effects. Therefore, understanding these genetic variations can help in personalizing treatment and potentially improving health outcomes.", + "context": { + "0bc591e0-bd1c-4c15-9e1e-3aa4499ad270": [ + { + "document_id": "0bc591e0-bd1c-4c15-9e1e-3aa4499ad270", + "text": "\n\nA supervised (pathway driven) approach was used to specifically query three general gene ontology (GO) areas of interest, namely xenobiotic metabolism, DNA damage repair, and oxidative stress-related genes (Table 1).These gene categories are hypothesized to play important roles in sex-and age-related susceptibility to adverse drug effects [18,30].Of the 122 genes included in the xenobiotic metabolism gene list in the Ingenuity Knowledge Base, 61 were differentially expressed.These included Cyp2d4, the rat ortholog of human gene CYP2D6, which is speculated to metabolize up to 25% of commonly prescribed drugs [31].Genes involved in DNA Damage Repair, derived from Ingenuity, were combined with the list by Wood et al. [32] to give 222 genes involved in DNA damage repair.Sixty-five of these genes (approximately 25%) were found to be differentially expressed in the liver.Oxidative Stress genes were defined by 68 genes included in \"response to oxidative stress\" (IPA) of which 23 genes were differentially expressed (Table 1)." + } + ], + "17cd95a4-6e8e-4696-8881-ea43fa80ccce": [ + { + "document_id": "17cd95a4-6e8e-4696-8881-ea43fa80ccce", + "text": "\n\nPharmacogenomics has advanced the field of drug-response assessment.For example, the first experiences with guiding vitamin K antagonist therapy with the aid of CYP2C9 (cytochrome P450, family 2, subfamily C, polypeptide 9) or VKORC1 (vitamin K epox- ide reductase complex, subunit 1) polymorphisms (93 ), and the use of cytochrome P450 polymorphisms for assessing clopidogrel response have entered US Food and Drug Administration recommendations (94 ).Disease prevention lags behind.Gene chips and modern sequencing approaches that allow largescale interrogation of the genome at the population level will generate novel hypotheses of disease causation.Furthermore, with the continuing drop in the costs of whole-genome sequencing, the practicing physician may soon be faced with having to comment on the disease risks of a patient's Ͼ4 ϫ 10 6 sequence variants before any clinical signs occur, a task that no certified genetic counselor could fulfill at present.With advent of GWASs, ethical and practical concerns of reporting genetic research results have become apparent.Initial efforts at defining rules of reporting large-scale association results and assessing the level of evidence also apply to nextgeneration large-scale genomics (95,96 ).Reports have suggested that on the consumer side, genomewide genetic profiling of employees of health and technology companies does not change anxiety symptoms, dietary fat intake, or exercise behavior (i.e., lifestyle factors) over a 6-month period (97 ); however, the association of genetic variation with risk and the dissection of objective markers of risk and risk factors that reside in the causal pathways of disease will need careful assessment before these approaches can enter clinical decision making (98 ).A data set containing 80 genes associated with coronary heart disease in GWASs was uploaded and overlaid onto the molecular networks developed from information contained in the Ingenuity Knowledge Base.Networks of Network Eligible Molecules were then algorithmically generated on the basis of their connectivity.The most substantially enriched network, as shown, comprises 36 genes, of which 20 are coronary heart disease genes." + } + ], + "5edf84d0-c2d9-45eb-91b9-c35743b6a463": [ + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "19.3.1 An environmental or pharmacogenetic basis for drug\nefficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many\nnon-genetic factors also influence the efficacy of medications, including the patient’s\nage, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit\njuice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the\nfirst-pass metabolism of many medications." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "Finally, it is possible that other\nmolecules (or drugs) might modulate the biological context within which the drug–\ntarget interaction takes place. Variation in any of the elements that control these\ntypes of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related\ncandidates. 19.3 PHARMACOGENETICS (PGx)\n\n519\n\n19.3.5 Using bioinformatics to gain understanding of adverse\ndrug reaction (ADR)\nOne of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "19.3 Pharmacogenetics (PGx)\nIt is well known that after exposure to a drug, almost any given cohort of patients show\na wide variety of responses. In an ideal situation, patients show a beneficial response\nto the therapy, although they may also show no response or a weak response, and\nperhaps most worryingly, they may experience an adverse drug reaction (ADR),\nwhich in extreme situations could lead to serious illness or even death. ADR is an\nincreasingly serious problem with a huge toll in lives and health-care costs every year." + }, + { + "document_id": "5edf84d0-c2d9-45eb-91b9-c35743b6a463", + "text": "A good understanding of disease biology and effective chemistry is not the\nonly requirement for an efficacious drug; we also must understand how variation\nat the target affects drug action, and how variation in other genes affects the way\ndrugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the\ndrug development paradigm also faces some unique challenges; for example, the\nexquisite rarity of some adverse reactions makes collection of sufficient samples for\nwell-powered genetic analysis almost impossible." + } + ], + "c12e853e-4f0d-48f9-93af-15db9ad2dfae": [ + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "19.3.1 An environmental or pharmacogenetic basis for drug\nefficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many\nnon-genetic factors also influence the efficacy of medications, including the patient’s\nage, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit\njuice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the\nfirst-pass metabolism of many medications." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "Finally, it is possible that other\nmolecules (or drugs) might modulate the biological context within which the drug–\ntarget interaction takes place. Variation in any of the elements that control these\ntypes of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related\ncandidates. 19.3 PHARMACOGENETICS (PGx)\n\n519\n\n19.3.5 Using bioinformatics to gain understanding of adverse\ndrug reaction (ADR)\nOne of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "19.3 Pharmacogenetics (PGx)\nIt is well known that after exposure to a drug, almost any given cohort of patients show\na wide variety of responses. In an ideal situation, patients show a beneficial response\nto the therapy, although they may also show no response or a weak response, and\nperhaps most worryingly, they may experience an adverse drug reaction (ADR),\nwhich in extreme situations could lead to serious illness or even death. ADR is an\nincreasingly serious problem with a huge toll in lives and health-care costs every year." + }, + { + "document_id": "c12e853e-4f0d-48f9-93af-15db9ad2dfae", + "text": "A good understanding of disease biology and effective chemistry is not the\nonly requirement for an efficacious drug; we also must understand how variation\nat the target affects drug action, and how variation in other genes affects the way\ndrugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the\ndrug development paradigm also faces some unique challenges; for example, the\nexquisite rarity of some adverse reactions makes collection of sufficient samples for\nwell-powered genetic analysis almost impossible." + } + ], + "cea13566-9d52-4423-9280-d46da486dd7f": [ + { + "document_id": "cea13566-9d52-4423-9280-d46da486dd7f", + "text": "Drug-Gene Interactions Predicting Efficacy\n\nIn 1 candidate gene study, a genetic variant in the HMG-CoA reductase gene, present in 6.7% of patients, modified the LDL-C response to pravastatin by 6.4 mg/dL. 244][247] However, these effect sizes are small and difficult to distinguish from random variation in individual patients.Indeed, the metformin finding is less important for its potential clinical applications than for the biological insight provided by this link between glucose control and a gene involved in the response to DNA damage. 245,246" + } + ], + "d2bbd79c-672b-4c18-8b37-717b9be32877": [ + { + "document_id": "d2bbd79c-672b-4c18-8b37-717b9be32877", + "text": "Nutrition and metabolism\n\nThe power of these new experimental protocols, comparing gene expression profiles to understand spontaneous differences in phenotype due to disease, was extended by inducing phenotypic differences using creative molecular intervention.The first experiments to manipulate phenotype in this way used drugs.A comparison of the gene expression of a drug-induced phenotype with that of the normal phenotype was brilliantly executed in a single study that simultaneously identified a mechanism for the regulation of sterol uptake in the intestine and a genetic disease, sitosterolemia [17 • ], mice were treated with a lipid-metabolism altering compound and the expression profiles of various tissues compared with normal mice using gene arrays.Differentially expressed genes were evaluated 'in silico,' and an unknown gene was found using bioinformatic tools to be homologous to the ATP-binding cassette (ABC) family of genes.Members of the ABC family include cellular cholesterol transport proteins.Defects in a member of this family (ABCA1) form the basis for the poor cholesterol delivery to high-density lipoprotein (HDL) that underlies Tangiers disease [18], another cholesterol-related disease [19].Through the use of a variety of in silico techniques, Berge et al. [17 •• ] concluded that the proteins produced from the newly discovered genes, ABCG5 and ABCG8, were responsible for the regulated reverse transport of newly absorbed cholesterol and phytosterols out of the apical surface of intestinal cells.Using public gene databases, a human homolog of the putative mouse transporter was identified, cloned and used to screen sitosterolemic humans.Dysfunctional mutations were found in these genes in all individuals suffering from sitosterolemia.Thus, individuals suffering from sitosterolemia lack the machinery responsible for the selective and controlled transport of cholesterol, and therefore hyperabsorb various sterols (including plant sterols).This study illustrated many of the strengths of genomic experimentation: the identification of phenotypically important genes using global differential gene expression analysis; querying internet databases to deduce structure/function relationships from sequence comparison; and the characterization of individual variation (polymorphism) linked to health.These findings have transformed our understanding of lipid absorption and metabolism, begging the question: how long would this knowledge have waited to be discovered without genomics?" + } + ], + "f35e02a1-3314-4663-913f-38a3fc072aa8": [ + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "19.3.1 An environmental or pharmacogenetic basis for drug\nefficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many\nnon-genetic factors also influence the efficacy of medications, including the patient’s\nage, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit\njuice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the\nfirst-pass metabolism of many medications." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "Finally, it is possible that other\nmolecules (or drugs) might modulate the biological context within which the drug–\ntarget interaction takes place. Variation in any of the elements that control these\ntypes of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related\ncandidates. 19.3 PHARMACOGENETICS (PGx)\n\n519\n\n19.3.5 Using bioinformatics to gain understanding of adverse\ndrug reaction (ADR)\nOne of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "19.3 Pharmacogenetics (PGx)\nIt is well known that after exposure to a drug, almost any given cohort of patients show\na wide variety of responses. In an ideal situation, patients show a beneficial response\nto the therapy, although they may also show no response or a weak response, and\nperhaps most worryingly, they may experience an adverse drug reaction (ADR),\nwhich in extreme situations could lead to serious illness or even death. ADR is an\nincreasingly serious problem with a huge toll in lives and health-care costs every year." + }, + { + "document_id": "f35e02a1-3314-4663-913f-38a3fc072aa8", + "text": "A good understanding of disease biology and effective chemistry is not the\nonly requirement for an efficacious drug; we also must understand how variation\nat the target affects drug action, and how variation in other genes affects the way\ndrugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the\ndrug development paradigm also faces some unique challenges; for example, the\nexquisite rarity of some adverse reactions makes collection of sufficient samples for\nwell-powered genetic analysis almost impossible." + } + ], + "fca531d0-d45b-495f-a02c-fbd437617b20": [ + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "19.3.1 An environmental or pharmacogenetic basis for drug\nefficacy and ADR? Before getting into the complexities of PGx, it is important to recognize that many\nnon-genetic factors also influence the efficacy of medications, including the patient’s\nage, sex and general health, but also environmental factors, such as concomitant therapies, drug interactions and diet. To give a seemingly innocuous example, grapefruit\njuice is an inhibitor of intestinal cytochrome P-450 3A4, which is responsible for the\nfirst-pass metabolism of many medications." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "Finally, it is possible that other\nmolecules (or drugs) might modulate the biological context within which the drug–\ntarget interaction takes place. Variation in any of the elements that control these\ntypes of processes can lead to variability in drug action, which might well confound the search for causative genes among the usual ADME and target-related\ncandidates. 19.3 PHARMACOGENETICS (PGx)\n\n519\n\n19.3.5 Using bioinformatics to gain understanding of adverse\ndrug reaction (ADR)\nOne of the biggest concerns during the development of any medication is the possibility of unintended consequences in the patient." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "19.3 Pharmacogenetics (PGx)\nIt is well known that after exposure to a drug, almost any given cohort of patients show\na wide variety of responses. In an ideal situation, patients show a beneficial response\nto the therapy, although they may also show no response or a weak response, and\nperhaps most worryingly, they may experience an adverse drug reaction (ADR),\nwhich in extreme situations could lead to serious illness or even death. ADR is an\nincreasingly serious problem with a huge toll in lives and health-care costs every year." + }, + { + "document_id": "fca531d0-d45b-495f-a02c-fbd437617b20", + "text": "A good understanding of disease biology and effective chemistry is not the\nonly requirement for an efficacious drug; we also must understand how variation\nat the target affects drug action, and how variation in other genes affects the way\ndrugs are absorbed, disseminated, metabolized and excreted. Genetic analysis in the\ndrug development paradigm also faces some unique challenges; for example, the\nexquisite rarity of some adverse reactions makes collection of sufficient samples for\nwell-powered genetic analysis almost impossible." + } + ] + }, + "data_source": [], + "document_id": "56BFA4C5360F4028B70961B34F0F40D0", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "pharmacogenetics&PGx", + "gene-by-drug&effects&GxD", + "life&expectancies", + "health", + "adverse&drug&reaction&ADR", + "cytochrome&P-450&3A4", + "HMG-CoA&reductase&gene", + "LDL-C", + "metformin", + "CYP2C9" + ], + "metadata": [], + "question": "How do gene-by-drug effects (GxD) modulate life expectancies\nand health?", + "subquestions": null, + "task_id": "56BFA4C5360F4028B70961B34F0F40D0", + "usage": { + "chatgpt": 6822, + "gpt-4": 4211, + "gpt-4-turbo-preview": 3234 + }, + "user_id": 2 + }, + "document_id": "56BFA4C5360F4028B70961B34F0F40D0", + "task_id": "56BFA4C5360F4028B70961B34F0F40D0" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_08.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_08.json new file mode 100644 index 0000000..d9851e6 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_08.json @@ -0,0 +1,150 @@ +{ + "created_at": "2024-04-02T12:31:43.259395Z", + "data": { + "amplify": false, + "answer": "The study identified 10 genomic loci which influence healthspan, parental lifespan, and longevity. These include loci near FOXO3, SLC4A7, LINC02513, ZW10, and FGD6. The life-extending variant near FOXO3 is associated with a delay in the age at menarche, suggesting a possible sex-specific effect. However, the study did not perform sex-stratified analyses, so it's unclear if there are other sex-specific longevity-related genetic variants.", + "context": { + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nIn one case, a gene identified by mutation recovered from a genetic screen in the laboratory, methuselah, may have variants in natural populations.In particular, the common ATATC haplotype has a sharp geographic (north-south) cline in U.S. populations, which, intriguingly, is associated with an 18% difference in life span (97).It would be interesting to examine these natural populations for differences in their reproductive schedule.Extensive studies show that life span can be rapidly selected as an indirect outcome of artificial selection for age at reproduction.Samples from natural populations of Drosophila contain genetic variants that can be rapidly selected, within 15 generations, for 50% or greater differences in life span on the basis of choosing individuals that are reproductive at early versus later ages (93).Selection was reversible, indicating that these life history variants depended on existing gene combinations not new mutations.Among the genes that differed in quantitative expression between young-and old-selected lines were heat shock proteins, e.g., hsp 22 (60).An overarching conclusion from fly aging genetics is that stress resistance is coupled to longevity (94), as in C. elegans.Other gene candidates are being sought by QTL analysis and show complex interactions with gender and population density (17,115)." + } + ], + "43d5140a-ad39-438e-8ba6-76dd3c7c42bc": [ + { + "document_id": "43d5140a-ad39-438e-8ba6-76dd3c7c42bc", + "text": "Murabito JM, Yuan R, Lunetta KL (2012) The search for\nlongevity and healthy aging genes: insights from epidemiological\nstudies and samples of long-lived individuals. J Gerontol A Biol\nSci Med Sci 67(5):470–479. doi:10.1093/gerona/gls089\n20. Nuzhdin SV, Pasyukova EG, Dilda CL et al (1997) Sex-specific\nquantitative trait loci affecting longevity in Drosophila melanogaster. Proc Natl Acad Sci USA 94(18):9734–9739\n21. Gems D, Riddle DL (2000) Genetic, behavioral and environmental determinants of male longevity in Caenorhabditis elegans. Genetics 154(4):1597–1610\n\n123\n\n22." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + } + ], + "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4": [ + { + "document_id": "57e2d0f5-c5eb-4ba6-8101-5bacaed53cb4", + "text": "\n\nOur study has several limitations.First, we did not analyse the sex and mitochondrial chromosomes, since we were unable to gather enough cohorts that could contribute to the analysis of these chromosomes.However, these chromosomes may harbour loci associated with longevity that we thus have missed.Second, although we included as many cohorts as possible, the sample size of our study is still relatively small (especially for the 99th percentile analysis) in comparison to GWA studies of age-related diseases, such as T2D and cardiovascular disease, and parental age at death 11,51,52 .Hence, this limited our power to detect loci with a low MAF (<1%) that contribute to longevity.Third, we did not perform sex-stratified analyses and may thus have missed sexspecific longevity-related genetic variants.The reason for this is that (1) we only identified a limited number of suggestive significant associations in our unstratified 90th and 99th percentile analyses, (2) our sample size is modest (especially when stratified by sex), and (3) thus far, there has been no report of any genomewide significant sex-specific longevity locus." + } + ], + "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7": [ + { + "document_id": "5fefb0e4-e7f9-4df3-a984-ad4f61756cf7", + "text": "\n\nIn most experimentally modified animal model systems, single-gene mutations in many different genes have major life extension effects (Fontana et al., 2010;Kenyon, 2010).However, natural human and animal longevity is presumed to be a complex trait (Finch & Tanzi, 1997).In humans, both candidate gene and genome-wide genetic association approaches have been applied in an attempt to identify longevity loci.The frequency of genetic variants has been typically compared between nonagenarian cases and young controls, revealing loci at which genetic variants may contribute to a higher or lower probability of survival into old age.The initial candidate gene studies aimed at finding human longevity genes were dominated by contradictory results (Christensen et al., 2006).The more consistent evidence obtained by repeated observation in independent cohort studies for association with longevity has so far only been observed for three loci, the apolipoprotein E (APOE) locus (Schachter et al., 1994;Christensen et al., 2006), the FOXO3A locus (Willcox et al., 2008;Flachsbart et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010), and the AKT1 locus (Pawlikowska et al., 2009).Thus, despite the expectation that longevity would be influenced by many genetic variants with small effect sizes, the effect of variants has consistently been shown in only three genes." + } + ], + "690a2ae6-962a-438c-91ca-60425a0c8d02": [ + { + "document_id": "690a2ae6-962a-438c-91ca-60425a0c8d02", + "text": "\n\nPreviously, it has been suggested that genetic variation in the FOXO1 gene is specifically contributing to human female longevity (reviewed in Chung et al., 2010).However, at chromosome 13q14.11harboring the FOXO1 gene we found no evidence for linkage with female longevity (LOD<0.05)and at the gene position of FOXO1 we found no evidence for association in the females-only metaanalysis (p-values>0.042) in the GEHA Study.Potentially, the effect of this locus is not only influenced by gender but also by genetic background." + } + ], + "6b2dba7c-0249-448e-9e84-92de7088109b": [ + { + "document_id": "6b2dba7c-0249-448e-9e84-92de7088109b", + "text": ", 2003), to study GXE and\nconsequences of treatments as a function of age, diet, and sex (Fleet et al. , 2016; Philip et\nal. , 2010; Roy et al. , 2020; Sandoval-Sierra et al. , 2020; Williams et al. , 2016, 2020), gene\npleiotropy (Wang et al. , 2016a), and to test behavioral predictions based on differences in\nbrain architecture (Yang et al. , 2008). Author Manuscript\nAuthor Manuscript\n\nHere we summarize the current status of this resource with a focus on genetic structure, and\non the power and precision of mapping trait variance to loci and genes." + } + ], + "7f23af74-95a3-46aa-bd61-629d2cfc2073": [ + { + "document_id": "7f23af74-95a3-46aa-bd61-629d2cfc2073", + "text": "\n\nSomatic mutations with the inherited gene variations of each individual cumulatively or synergistically influence the health span and life span [11].Very few genetic variants have been associated with human longevity, but those found include the transcription factor FOXO3 gene, the APOE/TOMM40 and the CDKN2B/ ANRIL loci, which are associated with Alzheimer's disease and cellular senescence [12][13][14].In fact, the heritability for human longevity has been estimated to be approximately 20-30%, according to studies of twins, suggesting that external factors such as diet, environment, physical activity and microbiomes are important factors that influence the health span [14][15][16].The increase in the rate of retrotranscription reflects genome deregulation, creating additional mutations, DNA damage, and other forms of genome instability.For instance, the expression of several families of retrotransposable elements increases with age, as observed in mouse skeletal muscle and human fibroblasts, particularly the long interspersed nuclear element-1 (L1 LINE) [17,18]." + } + ], + "98ce73c6-a53b-486f-8326-4b0bd47ec22e": [ + { + "document_id": "98ce73c6-a53b-486f-8326-4b0bd47ec22e", + "text": "The Height-Life Span Nexus\n\nSeveral observations and lines of experimentation have raised the issue of whether interindividual differences in aging rate are influenced by genes that modulate body size and early-life growth patterns.These include (a) the association between small stature and exceptional longevity in calorically restricted rodents (Yu et al., 1985), methionine-restricted rats (Orentreich et al., 1993), and mutant dwarf mice (Brown-Borg et al., 1996;Miller, 1999); and (b) the association between small body size and longer life span in natural populations of mice (Falconer et al., 1978), flies (Hillesheim and Stearns, 1992), dogs (Li et al., 1996), and, possibly, people (Samaras andStorms, 1992).The correlation in dogs is particularly striking: selective breeding for dogs of different body size has produced breeds varying in size from Chihuahua to Irish wolfhound.These breeds also vary greatly in mean longevity, from approximately 7 to 10.5 years, and the correlation between breed longevity and breed body weight (Miller, 1999) is a remarkable R 2 = 0.56.These differences are genetic and affect stature rather than obesity: no amount of overeating will convert a West Highland white terrier to a St. Bernard.The selective pressures applied were designed to create dogs of specific sizes and temperaments and were not intended to influence aging rate or life span.The clear implication is that the effects on longevity are pleiotropic, i.e., that genes selected for their effect on body size and conformation influenced life span as a side effect.It is of interest to note that the few analyses (Eigenmann et al., 1984(Eigenmann et al., , 1988) ) of the hormonal basis for interbreed differences in body size have shown that the genes in question influence levels of IGF-1, the most likely mediator of the life-span effects in the long-lived df/df and dw/dw mouse mutants.Could it be mere coincidence that long-lived mutant nematode worms (Kimura et al., 1997) also show mutations in genes related to insulin and IGF-1 receptors?" + } + ], + "9fed8fd1-fce5-4fc1-9911-05d312f88521": [ + { + "document_id": "9fed8fd1-fce5-4fc1-9911-05d312f88521", + "text": "\n\nThe antagonistic pleiotropy and hyperfunction theories of ageing predict the presence of genetic variants important for growth and development in early life with deleterious effects towards the end of the reproductive window 19,20 .While we are unable to directly capture the genetic effects on individuals before age 40 due to the study design of our datasets, we found that the life-extending variant near FOXO3 is associated with a delay in the age at menarche and a decrease in intracranial volume and cognitive abilities.It thus appears that there are loci exhibiting antagonistic effects, although we are unable to discern whether this is due to true pleiotropy or due to linkage of causal variants within a region Genes which showed a significant effect (FDR < 5%) of gene expression on ageing traits are displayed here.Gene names are annotated with the direction of effect, where + andindicate whether the life-extending association of the locus is linked with higher or lower gene expression, respectively.Locus: nearest gene to lead variant in the multivariate analysis, Chr: chromosome, Position: base-pair position of lead variant (GRCh37), Cis-genes: genes in physical proximity (<500 kb) to the lead variant of the locus which colocalise with the multivariate signal, Trans-genes: genes located more than 500 kb from the lead variant of the locus." + }, + { + "document_id": "9fed8fd1-fce5-4fc1-9911-05d312f88521", + "text": "\nAgeing phenotypes, such as years lived in good health (healthspan), total years lived (lifespan), and survival until an exceptional old age (longevity), are of interest to us all but require exceptionally large sample sizes to study genetically.Here we combine existing genome-wide association summary statistics for healthspan, parental lifespan, and longevity in a multivariate framework, increasing statistical power, and identify 10 genomic loci which influence all three phenotypes, of which five (near FOXO3, SLC4A7, LINC02513, ZW10, and FGD6) have not been reported previously at genome-wide significance.The majority of these 10 loci are associated with cardiovascular disease and some affect the expression of genes known to change their activity with age.In total, we implicate 78 genes, and find these to be enriched for ageing pathways previously highlighted in model organisms, such as the response to DNA damage, apoptosis, and homeostasis.Finally, we identify a pathway worthy of further study: haem metabolism." + }, + { + "document_id": "9fed8fd1-fce5-4fc1-9911-05d312f88521", + "text": "\n\nHere, we assess the degree of genetic overlap between published GWAS of three different kinds of ageing phenotypeshealthspan, parental lifespan, and longevity (defined as survival to an age above the 90th percentile)-and perform a multivariate meta-analysis to identify genetic variants related to healthy ageing.We subsequently characterise the sex-and age-specific effects of loci which affect all three ageing traits and look up reported associations with age-related phenotypes and diseases.Finally, we link the observed signal in these loci to the expression of specific genes, including some that are currently studied in model organisms, and identify pathways involved in healthy ageing." + }, + { + "document_id": "9fed8fd1-fce5-4fc1-9911-05d312f88521", + "text": "\n\nAgeing phenotypes, such as years lived in good health (healthspan), total years lived (lifespan), and survival until an exceptional old age (longevity), are of interest to us all but require exceptionally large sample sizes to study genetically.Here we combine existing genome-wide association summary statistics for healthspan, parental lifespan, and longevity in a multivariate framework, increasing statistical power, and identify 10 genomic loci which influence all three phenotypes, of which five (near FOXO3, SLC4A7, LINC02513, ZW10, and FGD6) have not been reported previously at genome-wide significance.The majority of these 10 loci are associated with cardiovascular disease and some affect the expression of genes known to change their activity with age.In total, we implicate 78 genes, and find these to be enriched for ageing pathways previously highlighted in model organisms, such as the response to DNA damage, apoptosis, and homeostasis.Finally, we identify a pathway worthy of further study: haem metabolism." + } + ], + "adf2d31e-e83d-47df-97af-3764e42aa80e": [ + { + "document_id": "adf2d31e-e83d-47df-97af-3764e42aa80e", + "text": "LongevityMap--human genetic variants associated with longevity\n\nVariation in human lifespan has been found to be 20-30% heritable, with increasing heritability at advanced ages (27).As next-generation sequencing and genome-wide approaches advance, so does the capacity for performing longevity association studies.To catalog the increasing volume of data in genetic studies of human longevity, we created LongevityMap (http://genomics.senescence.info/longevity/), a database of genes, gene variants and chromosomal locations associated with longevity (28).This differs from the GenAge database, which focuses mostly on data from model organisms and the few genes associated with human ageing (e.g.genes causing progeroid syndromes)." + } + ], + "b0e49b4c-954d-476a-ba3a-0215e63c98b6": [ + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "\n\nGenes/loci identified by genome-wide association studies of longevity and lifespan traits." + }, + { + "document_id": "b0e49b4c-954d-476a-ba3a-0215e63c98b6", + "text": "ANALYSIS OF HUMAN VARIATION IN THE GENETIC CONTROL OF LONGEVITY\n\nHeritability studies have convincingly demonstrated that at least some fraction of human lifespan is heritable.In tandem, large-scale genome-wide association studies (GWAS) have identified numerous loci associated with age-related traits (Buniello et al., 2019).While genetic studies have functionally shown an inverse effect of multiple age-related, diseaseassociated variants on lifespan regulation, the number of well-replicated longevity-conferring variants remains limited to variants in APOE (ApoE ε2), and more recently, CDKN2A/B and IL6 (see Table 1).To date, studies in humans have been hampered by the specific phenotype definitions used, sample sizes of the extreme phenotypes, and modest heritability of the longevity-related traits (Breitbach et al., 2019).This is due to the complex interplay of biological and social factors involved in human aging, as well as the limited power of GWAS, which require sampling thousands of subjects to achieve statistical significance (Breitbach et al., 2019).Genetic studies of aging have also been hindered by an inconsistent use of definitions of aging (reviewed in Baghdadi et al., 2020).The two main ways of conducting research on the genetics of longevity in human populations are by studying (i) the lifespan (continuous trait, years lived) and (ii) the longevity (dichotomous trait, i.e., being among the longest-lived individuals within a specific population).These complexities have limited the resolution and capability of broad association studies of human longevity.Importantly, these genomic analyses focus on a shift of survival in a population; these variables may be genetically distinct from the mechanisms establishing potential for longevity overall (Figure 1A).We argue that an understanding of this shift in lifespan as well as genetic mechanisms of regulating a species specific 'set points' (Figure 1B) will aid in the conceptual distinction of aging and longevity in humans." + } + ], + "ce2c68bf-878d-460c-8d9b-d45ce3034ef7": [ + { + "document_id": "ce2c68bf-878d-460c-8d9b-d45ce3034ef7", + "text": "Put more simply: What is the strength of evidence in favor of GXE effects on\nlifespan? We ask if youthful adult body weight (~120 days) predicts lifespan. Is the change\nin body weight in adults in response to a HFD a causal predictor of lifespan? Finally,\nwe ask whether levels of classic serum metabolites or metabolic hormones measured in\nmiddle-age or old-age predict variation in lifespan? Our focus is both on overall effects and\non strain-specific difference in effect of diet on lifespan and weight gain, rather than on\nspecific genetic modifiers or loci of lifespan." + } + ], + "da4a9500-831f-48ab-acea-5ec7097276ed": [ + { + "document_id": "da4a9500-831f-48ab-acea-5ec7097276ed", + "text": "\n\nStudies in various models have revealed that genetic differences and somatic mutations underlie longevity, but non-genetic contributions also play a major role (Cournil and Kirkwood, 2001).Calorie restriction (Bordone and Guarente, 2005), lowering of basal metabolic rate (Ruggiero et al., 2008), upregulated stress response (Migliaccio et al., 1999), restoration of mi-tonuclear protein balance (Houtkooper et al., 2013), and reduced fertility (Westendorp and Kirkwood, 1998) have all been shown to correlate with lifespan extension.These observations illuminate the role of ''epi''-genetic mechanisms in modulating longevity pathways." + } + ], + "db90a971-e55a-4ab0-a3b1-05908d6771a4": [ + { + "document_id": "db90a971-e55a-4ab0-a3b1-05908d6771a4", + "text": "Introduction\n\nApproximately 25-30% of the variation in adult lifespan is attributable to genetic factors that become more important with increasing age and exert their strongest effects in nonagenarians and centenarians (Go ¨gele et al., 2010;Hjelmborg et al., 2006).As yet, however, only a few genetic variants have been found consistently to influence longevity.The first to be discovered was the e4 allele of the apolipoprotein E (APOE) gene, a mortality factor that predisposes to both Alzheimer's and cardiovascular diseases (Corder et al., 1993; Panza et al., 2004).APOE e4 is the only variant with a reportedly large adverse effect upon survival at advanced age (Scha ¨chter et al., 1994), and this association has been replicated in several populations (Christensen et al., 2006).Variation in the human forkhead box O3A gene (FOXO3A), in contrast, has been found to be associated with the ability to live long, an effect corroborated by studies in Japanese, German, Italian, US-American, Jewish, Chinese and Danish populations (Anselmi et al., 2009;Flachsbart et al., 2009;Li et al., 2009;Pawlikowska et al., 2009;Soerensen et al., 2010;Willcox et al., 2008).More recently, we have identified exonuclease 1 (EXO1) as a potential novel longevity gene (Nebel et al., 2009).All three genes were detected through candidate-gene approaches." + } + ], + "f6bde053-64e5-42d9-966d-9d5d5d82a068": [ + { + "document_id": "f6bde053-64e5-42d9-966d-9d5d5d82a068", + "text": "\n\nStudies of mono-and dizygous twins have revealed that the genetic contribution to the variation in human lifespan is about 25-30% [12,13], and is most prominent in families clustered for longevity [14,15].This genetic contribution is mainly apparent after the age of 60 years and seems to increase with age [13,16].Furthermore, human lifespan is a complex trait which is assumed to be determined by many genes with small individual effects [17], although the polygenic architecture still needs to be characterized [18,19].The diverse health features of long-lived families illustrate that different age-related diseases have common determinants and implicate that pathways can be identified that attenuate aging and delay age-related disease.From a genomic perspective, individuals from long-lived families are assumed to be characterized by a decreased prevalence of disease-promoting variants (referred to as disease-susceptibility alleles) and an increased prevalence of variants conferring maintenance of health and protection from disease, when compared to population controls.In the last 5 years, many diseasesusceptibility alleles have been identified (National Human Genome Research Institute (NHGRI) genome-wide association study (GWAS) Catalog; http://www.genome.gov/gwastudies/)[20].A first comparison between long-lived individuals, selected from both long-lived families (LLS) and the general population (Leiden 85-plus study), and young controls showed no difference in the distribution or frequency of disease-susceptibility alleles identified in cancer, coronary artery disease and type 2 diabetes [21].The search for lifespan regulating loci -contributing to longevity and population mortality -must therefore extend beyond a focus on disease-susceptibility alleles.We will first discuss the efforts to identify longevity loci by genetics approaches." + } + ] + }, + "data_source": [], + "document_id": "2FE689C771242014845BDF2008542DAA", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "FOXO3", + "APOE", + "AKT1", + "SLC4A7", + "LINC02513", + "ZW10", + "FGD6", + "CDKN2B", + "ANRIL", + "FOXO1" + ], + "metadata": [], + "question": "What loci and gene variants modulate normal variation in lifespan,\nand in which cases is this sex-specific (GxS)?", + "subquestions": null, + "task_id": "2FE689C771242014845BDF2008542DAA", + "usage": { + "chatgpt": 8066, + "gpt-4": 5645, + "gpt-4-turbo-preview": 4696 + }, + "user_id": 2 + }, + "document_id": "2FE689C771242014845BDF2008542DAA", + "task_id": "2FE689C771242014845BDF2008542DAA" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_09.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_09.json new file mode 100644 index 0000000..4346780 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_09.json @@ -0,0 +1,146 @@ +{ + "created_at": "2024-04-02T13:11:59.579145Z", + "data": { + "amplify": false, + "answer": "Rapamycin, an mTOR inhibitor, has been shown to extend the lifespan of various organisms, including mice, by delaying or reversing aging in multiple cell types. It does this by regulating rates of protein synthesis and energy utilization, which are processes associated with aging. In the hematopoietic system, rapamycin limits age-related increases in stem cells and biomarkers of aging, enhancing the performance of these cells. However, rapamycin also has potential unwanted metabolic effects, such as insulin resistance and glucose intolerance, though these effects are controversial and potentially reversible. The mTOR pathway, which rapamycin targets, integrates signals from insulin, cytokines, nutrients, oxygen, and mitogenic stimuli, and its regulation has implications for longevity and against the negative effects of aging. Rapamycin also induces autophagy, a process important for cellular homeostasis and damage prevention. Despite these benefits, the exact mechanisms by which rapamycin extends lifespan and whether it delays aging or affects specific diseases remain unclear.", + "context": { + "198bd45c-e3a3-4937-b83a-61914b64e43b": [ + { + "document_id": "198bd45c-e3a3-4937-b83a-61914b64e43b", + "text": "\n\nOne surprising result of our experiment was the relatively weak support for involvement of the insulin/insulin-like signaling (IIS) or target-of-rapamycin (TOR) pathways in the evolution of late-life performance.Mutations in genes within these pathways can alter life span and fertility in flies and other organisms (Partridge and Gems 2002); natural genetic variation in expression of IIS/TOR-pathway genes has been reported to predict agingrelated phenotypes (Nuzhdin et al. 2009), and natural clinal variation in the insulin receptor gene InR has been associated with variation in stress resistance and fecundity (Paaby et al. 2010).We therefore expected that some of these genes would contribute to the evolution of life span and late-life fecundity in our experiment.Only one gene previously annotated with the Gene Ontology biological function \"determination of adult life span\" (Cct1) was among the genes bearing the strongest signature of selection, no more than would be expected by chance (1/96 of the candidate genes that had some biological process annotation, compared to 116/10,792 of all genes with some biological-process annotation, χ [1] 2 = 0.002, P > 0.96).Genes annotated with the functions \"aging\" or \"determination of adult life span\" were also significantly underrepresented among differentially expressed genes (43/215 transcripts with these annotations had P < 0.05 for line or line-by-age effects, compared to 4488/13,258 of all annotated transcripts, χ [1] 2 = 18.1, P < 0.0001).Most of the genes we identified are therefore novel candidates for the regulation of life span and late-age performance." + } + ], + "3043efd1-4b13-4300-b2a7-d1992c8d4e47": [ + { + "document_id": "3043efd1-4b13-4300-b2a7-d1992c8d4e47", + "text": "Rapamycin\n\nRapamycin has been shown to robustly increase lifespan in at least three different mouse strains and to improve healthspan measures including cognitive function, cardiac function, immune function, obesity, and cancer incidence (Johnson et al. 2015;Kaeberlein 2014)." + } + ], + "4f709611-ea0b-4bcc-a634-df5d518ccb54": [ + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nmTOR activates the kinase S6K, which phosphorylates S6, inhibiting autophagy [92].Rapamycin can extend the life span of organisms from yeast to mammals in a dose-dependent manner [95].However, some data suggest that rapamycin has unwanted metabolic effects, including insulin resistance, hyperlipidemia, glucose intolerance, and hypophosphatemia; however, whether rapamycin is responsible for these effects remains controversial, and some of the effects are reversible [96,97].The mTOR pathway integrates different signals from insulin, cytokines, nutrients, oxygen, and mitogenic stimuli, and its regulation has important implications for longevity and against the negative effects of aging [92]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "\n\nThe molecular mechanisms that drive cellular senescence in proliferative and nonproliferative cells are being discovered.One of the metabolic pathways associated with aging is the growth-promoting mitogen/nutrient-sensing pathway, in which the target of rapamycin (mTOR) is considered a central signaling molecule that affects multiple cellular pathways associated with aging [137].In particular, mTOR participates in the transition of cells from quiescence to senescence [138]." + }, + { + "document_id": "4f709611-ea0b-4bcc-a634-df5d518ccb54", + "text": "Inductors of Autophagy and its Impact on Aging\n\nAutophagy has a role in homeostasis, which plays an essential role in the maintenance of cellular physiology and the prevention of cellular damage.Among the inducers of autophagy have been described the already-mentioned rapamycin, resveratrol, and polyamines; however, only polyamines have demonstrated results in clinical research in humans [65].It is known that these compounds can induce the canonical autophagy pathway, which includes inactivation of the mammalian objective of the rapamycin complex 1 (mTORC1), allowing phosphorylation and activation of the Unc-51 complex (Ulk1/2), where the cascade of the other members of the complex is subsequently activated, ULK as FIP200 and ATG13 [65]." + } + ], + "5030cbc8-e02c-4e3a-8cbc-0156ce123c99": [ + { + "document_id": "5030cbc8-e02c-4e3a-8cbc-0156ce123c99", + "text": "\n\nA third example illustrates that pharmacological targeting of pathways that have been implicated in promoting aging may also restore youthfulness at cellular and biochemical levels.Among the key regulators associated with interventions that extend life span is the enzyme mTOR, which senses cellular nutrient levels and in turn regulates rates of protein synthesis and energy utilization.Notably, administration of rapamycin, an mTOR inhibitor, starting at midlife can extend the life span of mice, suggesting that aging can be delayed or reversed in multiple cell types (Harrison et al., 2009).In the hematopoietic system, aging is associated with an increase in mTOR activation in stem cells and progenitors (Chen et al., 2009).Administration of rapamycin to old mice to inhibit mTOR not only limited the normal age-related increases in hematopoietic stem cells and biomarkers of aging in those cells, but also enhanced the performance of the stem cells to become as effective as young stem cells in heterochronic transplantation experiments (Chen et al., 2009) (Figure 1)." + } + ], + "6ee86c77-b359-45f1-bd54-b1cd9b260ae6": [ + { + "document_id": "6ee86c77-b359-45f1-bd54-b1cd9b260ae6", + "text": "Rapamycin inhibits TOR signalling to alter nDNA\ntranslation, inducing mitonuclear protein imbalance35, and increases\nlifespan in various species, including mice33. Rapamycin also\nincreased mean worm lifespan (by 16%)34 in a ubl-5-dependent manner, induced UPRmt, but not UPRER or heat shock response, and\nincreased respiration (Fig. 6a, c and Supplementary Fig. 9a). This\nwas associated with increased ATP levels, equal citrate synthase activity and altered nDNA/mtDNA oxidative phosphorylation protein\nratio (Fig. 6d, e). Additionally, rapamycin changed the balance\nbetween nDNA- and mtDNA-encoded oxidative phosphorylation\nsubunits in mouse hepatocytes in a dose dependent manner (Fig. 6f,\ng)." + }, + { + "document_id": "6ee86c77-b359-45f1-bd54-b1cd9b260ae6", + "text": "Zylbee, E., Vesco, C. & Penman, S. Selective inhibition of the synthesis of\nmitochondria-associated RNA by ethidium bromide. J. Mol. Biol. 44, 195–204\n(1969). 33. Harrison, D. E. et al. Rapamycin fed late in life extends lifespan in genetically\nheterogeneous mice. Nature 460, 392–395 (2009). 34. Robida-Stubbs, S. et al. TOR signaling and rapamycin influence longevity by\nregulating SKN-1/Nrf and DAF-16/FoxO. Cell Metab. 15, 713–724 (2012). 35. Zid, B. M. et al. 4E-BP extends lifespan upon dietary restriction by enhancing\nmitochondrial activity in Drosophila. Cell 139, 149–160 (2009). 36. Schulz, T. J. et al." + }, + { + "document_id": "6ee86c77-b359-45f1-bd54-b1cd9b260ae6", + "text": "a, Rapamycin (Rapa, 1 nM) extends worm lifespan in a\nubl-5-dependent manner; b, ubl-5-dependently induced UPRmt (hsp-6::GFP)\nbut not UPRER (hsp-4::GFP) (n 5 4). c–e, Rapamycin increased respiration\n(c, n 5 10) and ATP content but not citrate synthase activity (d, n 5 3) and\ninduced mitonuclear protein imbalance (e). f–h, In mouse hepatocytes,\nrapamycin induces mitonuclear protein imbalance (f, g) and induces UPRmt as\n\nshown at the protein (f, g, n 5 3), and transcriptional (h, n 5 8) level. i, Resveratrol (Resv, 25 mM) induced mitonuclear protein imbalance in mouse\nhepatocytes (n 5 4)." + } + ], + "7c2732db-ed6e-419a-8256-537b4dc68072": [ + { + "document_id": "7c2732db-ed6e-419a-8256-537b4dc68072", + "text": "\n\npivotal in this aspect providing molecular insights and having huge conceptual contributions in the field.Characterising the contribution of individual mutants in ageing is a continuously active and informative activity in the field.On top of these studies, genome-wide screens have provided insights on the role of evolutionarily conserved processes and signalling pathways in ageing such as nutrient response [17,18], protein translation, oxidative damage [19,20], mitochondrial function [21,22] and autophagy [22,23] opening new avenues for biogerontology research.Yeasts have proved informative and helped in understanding mechanisms of highly conserved pathways (from yeast to human) in physiology, health and disease such as the Target of Rapamycin (TOR) [24], glucose sensing (PKA) and stress response pathways (Sty1/p38) [25]." + } + ], + "7f23af74-95a3-46aa-bd61-629d2cfc2073": [ + { + "document_id": "7f23af74-95a3-46aa-bd61-629d2cfc2073", + "text": "\n\nmTOR activates the kinase S6K, which phosphorylates S6, inhibiting autophagy [92].Rapamycin can extend the life span of organisms from yeast to mammals in a dose-dependent manner [95].However, some data suggest that rapamycin has unwanted metabolic effects, including insulin resistance, hyperlipidemia, glucose intolerance, and hypophosphatemia; however, whether rapamycin is responsible for these effects remains controversial, and some of the effects are reversible [96,97].The mTOR pathway integrates different signals from insulin, cytokines, nutrients, oxygen, and mitogenic stimuli, and its regulation has important implications for longevity and against the negative effects of aging [92]." + }, + { + "document_id": "7f23af74-95a3-46aa-bd61-629d2cfc2073", + "text": "\n\nThe molecular mechanisms that drive cellular senescence in proliferative and nonproliferative cells are being discovered.One of the metabolic pathways associated with aging is the growth-promoting mitogen/nutrient-sensing pathway, in which the target of rapamycin (mTOR) is considered a central signaling molecule that affects multiple cellular pathways associated with aging [137].In particular, mTOR participates in the transition of cells from quiescence to senescence [138]." + }, + { + "document_id": "7f23af74-95a3-46aa-bd61-629d2cfc2073", + "text": "Inductors of Autophagy and its Impact on Aging\n\nAutophagy has a role in homeostasis, which plays an essential role in the maintenance of cellular physiology and the prevention of cellular damage.Among the inducers of autophagy have been described the already-mentioned rapamycin, resveratrol, and polyamines; however, only polyamines have demonstrated results in clinical research in humans [65].It is known that these compounds can induce the canonical autophagy pathway, which includes inactivation of the mammalian objective of the rapamycin complex 1 (mTORC1), allowing phosphorylation and activation of the Unc-51 complex (Ulk1/2), where the cascade of the other members of the complex is subsequently activated, ULK as FIP200 and ATG13 [65]." + } + ], + "844ab36b-9239-4d73-a61c-68f68acc4fd1": [ + { + "document_id": "844ab36b-9239-4d73-a61c-68f68acc4fd1", + "text": "Background\n\nGenetic, dietary and drug interventions can enhance longevity and suppress age-associated disease, such as cancer.Prominent genetic interventions that robustly extend longevity and healthspan in mammals include those that decrease growth hormone (GH) and insulin-like growth factor (IGF) signalling; for example, Ames dwarf mice live more than 50% longer than their wild-type siblings [1].These diminutive mice result from a point mutation in a gene (Prop1 df/df ) that drives development of the pituitary gland, so that mutant mice are deficient in specific hormones.The GH deficiency, in particular, has been shown to underlie their enhanced health span and extended lifespan.Ames mice are highly insulinsensitive, resistant to some stresses and the incidence of cancer is delayed [2][3][4].Dietary and drug interventions that extend lifespan include calorie restriction (CR) and the mTOR inhibitor rapamycin [5].Like the Ames dwarf mutation, CR and rapamycin also suppress and/ or delay the incidence of cancer [5][6][7].A detailed understanding of how these interventions exert their beneficial effects is essential to develop strategies to promote healthy aging in humans [8].Currently, these interventions are thought to exert their effects by related and interconnected effects on some or all of the following: genome stability, the epigenome, telomere attrition and/or function, protein quality control, mitochondrial function, nutrient sensing, cellular senescence, stem cell exhaustion, cellular stress responses and altered intercellular communication [9].Of note, the effects of longevity promoting interventions on the epigenome, a key determinant of cell phenotype, are poorly understood." + } + ], + "8a8bea99-d3b9-4109-88e4-ad459dcd7173": [ + { + "document_id": "8a8bea99-d3b9-4109-88e4-ad459dcd7173", + "text": "\n\nThe target of rapamycin (TOR) signaling pathway has also emerged as a major regulator of lifespan.TOR is a highly conserved kinase that transduces signals from nutrients to regulate cell size, cell growth, and metabolism (Martin & Hall, 2005).Genetic studies in yeast Saccharomyces cerevisiae have shown that reduced levels of nutrients, namely amino acids and sugars, can extend yeast lifespan through regulation of the TOR signaling pathway (Kaeberlein et al ., 2005;Powers et al ., 2006).In Drosophila , recent studies have shown that amino acid restriction, rather than 'calorie restriction', extends lifespan (Min & Tatar, 2006).In C. elegans , either inactivation of CeTOR/let-363 by RNAi, or mutations in Raptor/daf-15 , encoding a regulatory subunit of CeTOR, leads to lifespan extension (Vellai et al ., 2003;Jia et al ., 2004)." + } + ], + "a95e6806-06d3-4775-8287-fda4cf6ac42f": [ + { + "document_id": "a95e6806-06d3-4775-8287-fda4cf6ac42f", + "text": "\n\nAs mentioned above, a number of genes regulating longevity also control growth and development.Some of these, such as the insulin/IGF1/GH pathway, have been suggested to play a role in the mechanisms of CR (Fig. 1).An emerging critical player is the target of rapamycin (TOR) signaling pathway, which involves both nutrient sensing and regulation of growth.Several genes in the TOR pathway, and the TOR gene itself, regulate longevity in flies (Kapahi et al., 2004) and both longevity and dauer diapause in worms (Jia et al., 2004).Strikingly, not only have genetic manipulations of the TOR gene extended lifespan in yeast and worms (Stanfel et al., 2009) but also feeding rapamycin (which inhibits TOR and is also known as sirolimus) to middle-aged mice significantly (9 -14%) increased lifespan (Harrison et al., 2009).Whether rapamycin is extending lifespan by delaying of aging or by affecting a specific disease, such as cancer, remains unclear.More recent studies show that starting rapamycin administration earlier in life does AGING GENES AS TARGETS FOR DRUG DISCOVERY not result in a significantly greater increase in lifespan (10 -18%) than that obtained in middle-aged mice (Miller et al., 2011)." + } + ], + "b1ffece8-f805-4d99-8e3b-402df309f1ed": [ + { + "document_id": "b1ffece8-f805-4d99-8e3b-402df309f1ed", + "text": "\n\nReplacement of the C/ebpα gene with C/ebpβ increases lifespan by 20% [35,36], and may alter the rate of aging [37], indicating that altering the isoform expression of these genes can affect lifespan.Moreover, the life-extending drug rapamycin may affect isoform ratios of C/ebpβ.Rapamycin has been shown to increase lifespan via the suppression of Mtor [38] which in turn controls the isoform ratios of C/ebpβ [39].Therefore, we speculate that rapamycin may in part exert its life extending effect through C/ebpβ." + } + ], + "c1df5fa6-1d3b-4085-9248-683c9666faa5": [ + { + "document_id": "c1df5fa6-1d3b-4085-9248-683c9666faa5", + "text": "\n\nThe genome-wide RNAi study conducted by the Ruvkun lab, authored by Hamilton et al. [88], identified a total of 89 additional aging genes with disparate functions including cell structure, cell surface proteins, cell signaling, cellular metabolism, and protein turnover.Of the 66 genes with previously known functions, 17 corresponded to various aspects of carbon metabolism, including citric acid cycle enzymes and subunits of complexes I, IV, and V of the ETC.Researchers also speculated that protein translation might play a role in lifespan regulation, based on the identification of iff-1 (T05G5.10),a gene that has homology to the translation initiation factor eIF5A.Other hits from this screen included two genes containing PH domains known to interact with phosphatidylinositol lipids, multiple G protein-coupled receptors, protein processing and degradation genes such as proteases and ubiquitin ligases/hydrolases, and chromatin modifying factors." + } + ], + "c89f6c23-d5ac-4352-9b82-2ba559b20c0b": [ + { + "document_id": "c89f6c23-d5ac-4352-9b82-2ba559b20c0b", + "text": "\n\nHow cellular processes that regulate aging impact genome stability also remain unclear.Compelling evidence now exists that in all eukaryotes, aging is regulated by conserved insulin/insulin-like growth factor (I-(IFG-1)) pathways and growth-signaling pathways regulated by the target of rapamycin (TOR) family of kinases (4).In general, experimental manipulations that upregulate these pathways promote aging, and manipulations that downregulate these pathways-including mutational inactivation or caloric restriction-extend life span and mitigate age-related pathologies.Downregulation of these pathways often leads to a reduction in oxidative stress and oxidative damage to DNA and other cellular constituents.For the most part, however, the relationship between aging and changes in oxidative damage downstream of alterations in growth-signaling pathways remains correlative rather than causal." + } + ], + "e397ac93-f115-4cee-8b87-59137a017985": [ + { + "document_id": "e397ac93-f115-4cee-8b87-59137a017985", + "text": "\n\nThe potential of interventional approaches targeted at aging has yet to be realized in part because aging is a complicated multisystem process that has remained enigmatic.However, research over the last two decades has led to significant excitement.One of the most striking findings is that it is possible to administer a clinically approved drug, rapamycin, to mice at 20 months of age and extend both their life span and health span (Harrison et al., 2009).Surprisingly, much of the recent success of aging research can be traced back to one of its simplest model organisms: yeast.Two of the major pathways studied in the context of aging and age-related disease are the sirtuin pathway and the TOR signaling pathway, and yeast was pivotal in their discovery." + } + ] + }, + "data_source": [], + "document_id": "32AFC12B71BD0478376834592459857C", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "rapamycin", + "mTOR", + "aging", + "lifespan", + "healthspan", + "cellular&bioenergetics", + "metabolic&effects", + "insulin&resistance", + "autophagy", + "TOR&signaling&pathway" + ], + "metadata": [], + "question": "what is the influence of rapamycin and aging on the in vivo physiological and metabolic states and cellular bioenergetic, and how do these relate to the rapamycin-induced health and lifespan modification for that genometype?", + "subquestions": null, + "task_id": "32AFC12B71BD0478376834592459857C", + "usage": { + "chatgpt": 8516, + "gpt-4": 4792, + "gpt-4-turbo-preview": 3850 + }, + "user_id": 2 + }, + "document_id": "32AFC12B71BD0478376834592459857C", + "task_id": "32AFC12B71BD0478376834592459857C" +} diff --git a/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_10.json b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_10.json new file mode 100644 index 0000000..2fd4921 --- /dev/null +++ b/gnqa/paper1_eval/src/data/responses/aging/other/aging_resp_10.json @@ -0,0 +1,144 @@ +{ + "created_at": "2024-04-02T13:14:53.880936Z", + "data": { + "amplify": false, + "answer": "The genotypes related to Alzheimer's and dementia that have commonalities with those for aging include a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11. Other genes associated with Alzheimer's include APP, PSEN1, PSEN2, and APOE. These genes are also associated with early-onset Alzheimer's disease. The APOE gene is the strongest genetic risk factor for later onset Alzheimer's. The heritability of late-onset Alzheimer's disease (LOAD) is estimated to be ~60-80%, suggesting a large proportion of individual differences in LOAD risk is driven by genetics.", + "context": { + "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7": [ + { + "document_id": "03a4f57c-3a11-4d3d-a1e9-6d0d8bdb7cb7", + "text": "\n\nWe briefly comment on rare mutations that shorten life span through the early onset of diseases that are increasingly common during aging in the general population, e.g., familial forms of Alzheimer, breast cancer, coronary artery disease, type II diabetes, etc.The later onset forms of these diseases are associated with causes of death at later ages.A major question is what role the more common allelic variants of these same genes have in \"normal aging\".Although examination of this huge emerging topic goes beyond the present discussion, we may consider the example of Werner's syndrome, a rare autosomal recessive that causes adult onset progeria with a high incidence of cancer and atherosclerosis (70).The absence of Alzheimer-type dementia in Werner's syndrome illustrates the \"segmental\" nature of this and other progerias (70).Thus, heritable shortening of life span should not be considered as a simple acceleration of general aging processes.The Werner's lesion maps to a defective gene encoding a helicase and exonuclease, which also has several polymorphisms.In Japan, 1367Arg was associated with a lower risk of myocardial infarction (70), although it was not associated with longevity in Finland (14).In general, we know little of the genetic factors involved in frailty and morbidity at later ages, which are important to the geneenvironment interactions implied in the major longevity increase seen during the twentieth century." + } + ], + "0af83a97-18ef-47f4-9f0c-872633ca3414": [ + { + "document_id": "0af83a97-18ef-47f4-9f0c-872633ca3414", + "text": "\n\nIndicative diseases associated with the candidate aging genes" + } + ], + "213afab9-b2fb-40ed-abb7-d80853a0fbf3": [ + { + "document_id": "213afab9-b2fb-40ed-abb7-d80853a0fbf3", + "text": "D\n\nementia has an age-and sex-standardized prevalence of ~7.1% in Europeans 1 , with Alzheimer's disease (AD) being the most common form of dementia (50-70% of cases) 2 .AD is pathologically characterized by the presence of amyloid-beta plaques and tau neurofibrillary tangles in the brain 3 .Most patients are diagnosed with AD after the age of 65, termed late-onset AD (LOAD), while only 1% of AD cases have an early onset (before the age of 65) 3 .On the basis of twin studies, the heritability of LOAD is estimated to be ~60-80% (refs. 4,5 ), suggesting that a large proportion of individual differences in LOAD risk is driven by genetics.The heritability of LOAD is spread across many genetic variants; however, Zhang et al. 6 suggested that LOAD is more of an oligogenic than a polygenic disorder due to the large effects of APOE variants.Zhang et al. 6 and Holland et al. 7 predicted there to be ~100-10,000 causal variants contributing to LOAD; however, only a fraction have been identified.Increasing the sample size of genome-wide association studies (GWAS) will improve the statistical power to identify the missing causal variants and may highlight additional disease mechanisms.In combination with increasing the number of samples, it is beneficial to use different approaches to identify rare and private variation to help identify additional causal variants and increase understanding of disease mechanisms; however, we deem this to be out of the scope of the current analysis." + }, + { + "document_id": "213afab9-b2fb-40ed-abb7-d80853a0fbf3", + "text": "\nDementia has an age-and sex-standardized prevalence of ~7.1% in Europeans 1 , with Alzheimer's disease (AD) being the most common form of dementia (50-70% of cases) 2 .AD is pathologically characterized by the presence of amyloid-beta plaques and tau neurofibrillary tangles in the brain 3 .Most patients are diagnosed with AD after the age of 65, termed late-onset AD (LOAD), while only 1% of AD cases have an early onset (before the age of 65) 3 .On the basis of twin studies, the heritability of LOAD is estimated to be ~60-80% (refs. 4,5 ), suggesting that a large proportion of individual differences in LOAD risk is driven by genetics.The heritability of LOAD is spread across many genetic variants; however, Zhang et al. 6 suggested that LOAD is more of an oligogenic than a polygenic disorder due to the large effects of APOE variants.Zhang et al. 6 and Holland et al. 7 predicted there to be ~100-10,000 causal variants contributing to LOAD; however, only a fraction have been identified.Increasing the sample size of genome-wide association studies (GWAS) will improve the statistical power to identify the missing causal variants and may highlight additional disease mechanisms.In combination with increasing the number of samples, it is beneficial to use different approaches to identify rare and private variation to help identify additional causal variants and increase understanding of disease mechanisms; however, we deem this to be out of the scope of the current analysis.The largest previous GWAS of LOAD, identified 29 risk loci from 71,880 (46,613 proxy) cases and 383,378 (318,246 proxy) controls 8 .Our current study expands this to include 90,338 (46,613 proxy) cases and 1,036,225 (318,246 proxy) controls.The recruitment of LOAD cases can be difficult due to the late age of onset, so proxy cases can allow for the inclusion of younger individuals by estimating their risk of LOAD using parental status.Proxy cases and controls were defined on the basis of known parental LOAD status weighted by parental age (Supplementary Information).In the current study, we identified 38 loci, including seven loci that have not been reported previously.Functional follow-up analyses implicated tissues, cell types and genes of interest through tissue and cell type enrichment, colocalization and statistical fine-mapping.This study highlights microglia, immune cells and protein catabolism as relevant to LOAD, while identifying previously unidentified genes of potential interest. ResultsGenome-wide inferences.We performed meta-analysis on data from 13 cohorts, totaling 1,126,563 individuals (Supplementary" + } + ], + "38f806a9-f265-4854-b86b-38cf56b57dd8": [ + { + "document_id": "38f806a9-f265-4854-b86b-38cf56b57dd8", + "text": "Introduction\n\nAlzheimer's disease (AD) is a complex disorder and is the most common form of dementia [1].After age, family history is the single greatest risk factor for AD.AD can be classified into early and late onset forms.Mutations in three genes: PSEN1/2 and APP are known to cause early onset AD in an autosomal dominant manner [2,3].The majority of AD cases, however, are late onset (LOAD) and the APOE e4 allele is the strongest known genetic risk factor.Many additional genetic polymorphisms have been identified, though with substantially lower risk estimates [1,4,5,6,7,8,9,10].LOAD appears to be inherited and/or sporadic and there is evidence of a maternal inheritance pattern [11].Current estimates suggest that more than 20% of inherited LOAD cases are maternally inherited [12]." + } + ], + "3f41e709-4cf1-472b-b12b-804c6ebb07c9": [ + { + "document_id": "3f41e709-4cf1-472b-b12b-804c6ebb07c9", + "text": "INTRODUCTION\n\nMany common noninfectious diseases exhibit a more severe clinical presentation in older individuals.These diseases often exhibit complex etiology and can affect different tissues and cell types, with a wide spectrum of clinical outcomes.Prominent aging-associated neurodegenerative diseases are Alzheimer's disease (AD), Parkinson's disease (PD), and age-related macular degeneration (AMD), all of which can severely compromise the quality of life and have serious repercussions on both the individual and society at large.These late-onset diseases generally result from the interplay between multiple genetic susceptibility factors and environmental components.Sequencing of the human genome, cataloging of millions of single nucleotide polymorphisms (SNPs) together with the development of a map of common haplotypes, and technological innovations in genotyping are among the major milestones that are facilitating exploration of the genetic basis of common diseases (1,7,50).In the field of AMD genetics, these advances have led to the identification of several genetic susceptibility factors and enabled us to start dissecting the relationship between environmental risk factors and the genetic constitution of each individual (66,118,148).As a result, new opportunities are emerging for improved understanding of disease pathogenesis that may lead to better management and treatment of AMD.Clinical aspects of AMD are discussed only briefly (for a more in-depth discussion, see Reference 79)." + }, + { + "document_id": "3f41e709-4cf1-472b-b12b-804c6ebb07c9", + "text": "\nAging-associated neurodegenerative diseases significantly influence the quality of life of affected individuals.Genetic approaches, combined with genomic technology, have provided powerful insights into common late-onset diseases, such as age-related macular degeneration (AMD).Here, we discuss current findings on the genetics of AMD to highlight areas of rapid progress and new challenges.We also attempt to integrate available genetic and biochemical data with cellular pathways involved in aging to formulate an integrated model of AMD pathogenesis." + }, + { + "document_id": "3f41e709-4cf1-472b-b12b-804c6ebb07c9", + "text": "\n\nAging-associated neurodegenerative diseases significantly influence the quality of life of affected individuals.Genetic approaches, combined with genomic technology, have provided powerful insights into common late-onset diseases, such as age-related macular degeneration (AMD).Here, we discuss current findings on the genetics of AMD to highlight areas of rapid progress and new challenges.We also attempt to integrate available genetic and biochemical data with cellular pathways involved in aging to formulate an integrated model of AMD pathogenesis." + } + ], + "4c2f8dcb-02a1-4968-a117-bdf505cad02f": [ + { + "document_id": "4c2f8dcb-02a1-4968-a117-bdf505cad02f", + "text": "Genetics of Alzheimer Disease: Early-Onset AD\n\nIn the early to mid-1990s, genetic studies of AD focused on extended families with high burden of disease (two or more cases among first-degree relatives), and used linkage analysis of highly polymorphic genetic markers called short tandem repeats (STRs, or microsattelites) in order to identify genomic regions co-transmitting with disease in affected family members.This strategy, followed by \"fine mapping\"-the positional cloning of candidate genes-was used to identify genes and genetic variants contributing to AD risk.The first three genes known to cause AD were identified among families with multiple early-onset cases (age-at-onset <60 years): APP, encoding amyloid precursor protein [Goate et al., 1991], and PS1 and PS2, encoding presenilins I and II respectively [Levy-Lahad et al., 1995;Rogaev et al., 1995;Sherrington et al., 1995], each transmitting disease-causing variants in the predicted autosomal-dominant fashion." + }, + { + "document_id": "4c2f8dcb-02a1-4968-a117-bdf505cad02f", + "text": "\nAlzheimer's disease (AD) (MIM: 104300) is a highly heritable disease with great complexity in its genetic contributors, and represents the most common form of dementia.With the gradual aging of the world's population, leading to increased prevalence of AD, and the substantial cost of care for those afflicted, identifying the genetic causes of disease represents a critical effort in identifying therapeutic targets.Here we provide a comprehensive review of genomic studies of AD, from the earliest linkage studies identifying monogenic contributors to early-onset forms of AD to the genome-wide and rare variant association studies of recent years that are being used to characterize the mosaic of genetic contributors to late-onset AD (LOAD), and which have identified approximately $20 genes with common variants contributing to LOAD risk.In addition, we explore studies employing alternative approaches to identify genetic contributors to AD, including studies of AD-related phenotypes and multi-variant association studies such as pathway analyses.Finally, we introduce studies of next-generation sequencing, which have recently helped identify multiple lowfrequency and rare variant contributors to AD, and discuss ongoing efforts with next-generation sequencing studies to develop statistically well-powered and comprehensive genomic studies of AD.Through this review, we help uncover the many insights the genetics of AD have provided into the pathways and pathophysiology of AD." + }, + { + "document_id": "4c2f8dcb-02a1-4968-a117-bdf505cad02f", + "text": "\n\nAlzheimer's disease (AD) (MIM: 104300) is a highly heritable disease with great complexity in its genetic contributors, and represents the most common form of dementia.With the gradual aging of the world's population, leading to increased prevalence of AD, and the substantial cost of care for those afflicted, identifying the genetic causes of disease represents a critical effort in identifying therapeutic targets.Here we provide a comprehensive review of genomic studies of AD, from the earliest linkage studies identifying monogenic contributors to early-onset forms of AD to the genome-wide and rare variant association studies of recent years that are being used to characterize the mosaic of genetic contributors to late-onset AD (LOAD), and which have identified approximately $20 genes with common variants contributing to LOAD risk.In addition, we explore studies employing alternative approaches to identify genetic contributors to AD, including studies of AD-related phenotypes and multi-variant association studies such as pathway analyses.Finally, we introduce studies of next-generation sequencing, which have recently helped identify multiple lowfrequency and rare variant contributors to AD, and discuss ongoing efforts with next-generation sequencing studies to develop statistically well-powered and comprehensive genomic studies of AD.Through this review, we help uncover the many insights the genetics of AD have provided into the pathways and pathophysiology of AD." + } + ], + "6d98da1a-9964-4be7-bb67-47f829dcd2cf": [ + { + "document_id": "6d98da1a-9964-4be7-bb67-47f829dcd2cf", + "text": "Indeed, as\nage increases, there is an exponential increase in the incidence of\nAD, with a corresponding effect on healthcare costs and quality of\nlife. AD is a complex disease involving several genetic and environmental components (Hardy, 1997; Munoz & Feldman, 2000), and\n15% of patients have a genetic predisposition. Almost 100 candidate\ngenes are currently known to be involved in the development of AD,\nand only 4 (APP, PSEN1, PSEN2, APOE) in humans have been\nproven to play a direct role in AD pathogenesis (Thomas & Fenech,\n2007)." + } + ], + "70b52a1e-834b-43c0-9e6a-3010bc3a06ae": [ + { + "document_id": "70b52a1e-834b-43c0-9e6a-3010bc3a06ae", + "text": "T\n\nhe genetics of Alzheimer disease (AD) to date support an age-dependent dichotomous model whereby earlier age of disease onset (Ͻ60 years) is explained by 3 fully penetrant genes (APP [NCBI Entrez gene 351], PSEN1 [NCBI Entrez gene 5663], and PSEN2 [NCBI Entrez gene 5664]), whereas later age of disease onset (Ն65 years) representing most cases of AD has yet to be explained by a purely genetic model.The APOE gene (NCBI Entrez gene 348) is the strongest genetic risk factor for later onset, although it is neither sufficient nor necessary to explain all occurrences of disease.Numerous putative genetic risk alleles and genetic variants have been reported.Although all have relevance to biological mechanisms that may be associated with AD pathogenesis, they await replication in large representative populations.Genome-wide association studies have emerged as an increasingly effective tool for identifying genetic contributions to complex diseases and represent the next frontier for furthering our understanding of the underlying etiologic, biological, and pathologic mechanisms associated with chronic complex disorders.There have already been success stories for diseases such as macular degeneration and diabetes mellitus.Whether this will hold true for a genetically complex and heterogeneous disease such as AD is not known, although early reports are encouraging.This review considers recent publications from studies that have successfully applied genome-wide association methods to investigations of AD by taking advantage of the currently available high-throughput arrays, bioinformatics, and software advances.The inherent strengths, limitations, and challenges associated with study design issues in the context of AD are presented herein." + }, + { + "document_id": "70b52a1e-834b-43c0-9e6a-3010bc3a06ae", + "text": "\n\nArch Neurol.2008;65(3): 329-334 Alzheimer disease (AD) is the most common cause of dementia and the most prevalent neurodegenerative disorder associated with aging. 1 Alzheimer disease is a heterogeneous disorder with a complex etiology owing to genetic and environmental influences as causal or risk modifiers.The neuropathologic hallmarks of disease are extracellular amyloid plaques and intracellular neurofibrillary tangles of hyperphosphorylated tau protein. 2 Only 10% of AD cases occurring before 60 years of age (early-onset AD) are due to rare, fully penetrant (autosomal dominant) mutations in 3 genes: A precursor protein (APP) on chromosome 21, 3 presenilin 1 (PSEN1) on chromosome 14, 4 and presenilin 2 (PSEN2) on chromosome 1. 5,6In contrast, most cases of AD are later in onset (Ն 65 years of age) (late-onset AD), are nonfamilial, and are likely the result of highly prevalent genetic variants with low penetrance. 7To date, the only genetic risk factor for lateonset AD remains the apolipoprotein E gene (APOE), specifically the ε4 allele, which is moderately penetrant, accounting for up to 50% of cases. 8owever, a robust literature reports numerous putative genetic risk alleles and promising genetic variants.Recent reports from individual studies reveal significant associations with the sortilin-related receptor (SORL1 [NCBI Entrez gene 6653]) 9,10 and glycine-rich protein 2-associated binding protein 2 (GAB2 [NCBI Entrez gene 9846]) 11 on chromosome 11; death-associated protein kinase 1 (DAPK1 [NCBI Entrez gene 1612]), 12 ubiquilin 1 (UBQLN1 [NCBI Entrez gene 299798]), 13 and adenosine triphosphate-binding cassette transporter 1, subfamily A (ABCA1 [NCBI Entrez gene 19]), on chromosome 9 14 ; and low-density lipoprotein receptor-related protein 6 (LRP6 [NCBI Entrez gene 4040]) on chromosome 12. 15 All of these putative variants still lack replication in large representative populations but have relevance to neuropathologic mechanisms and pathways that may be associated with AD pathogenesis ( A large meta-analysis from the AlzGene database 16 17 All are associated with relevant biological mechanisms and pathways but await replication to further elucidate their utility as significant markers for AD." + } + ], + "7fee50dc-7172-4574-a3e7-4961060a655b": [ + { + "document_id": "7fee50dc-7172-4574-a3e7-4961060a655b", + "text": "Background\n\nAlzheimer's disease (AD) is the most common neurodegenerative disorder and the leading cause of dementia in the elderly [1].Diagnosis of AD is based on the presence of neurofibrillary tangles and amyloid plaques [2], and symptoms typically include memory loss and impaired cognitive ability.Although the pathological hallmarks associated with dementia-related symptoms in AD appear largely similar between both the early-onset and late-onset forms of the disease, their underlying etiologies contrast [3].Whereas early-onset AD is a familial autosomal dominant disorder caused by rare, highly penetrant mutations in one of a small set of genes (APP, PSEN1, and PSEN2), the more common late-onset form of the disease (accounting for 90-95 % of cases) occurs sporadically, and risk is determined by complex underlying mechanisms [3][4][5][6].Estimates based on twin concordance rates suggest heritability of late-onset AD is as high as 70 %, implicating major roles for genetic as well as non-genetic factors [6].Indeed, through candidate gene studies, as well as more recent genome-wide association studies (GWASs) and whole-exome sequencing, both common and rare variants associated with the late-onset form of AD have been identified [7][8][9][10][11].Collectively, however, common GWAS variants account for only a modest proportion (~30 %) of the underlying variance in disease susceptibility [12].Several environmental factors are also thought to play a role [5,6], yet exactly how these contribute to risk, onset, and progression remains poorly defined." + } + ], + "8275b075-735b-44dc-b549-32ee94dec32e": [ + { + "document_id": "8275b075-735b-44dc-b549-32ee94dec32e", + "text": "\nAlzheimer's disease is the most common type of dementia, and it is characterized by a decline in memory or other thinking skills.The greatest risk factor for Alzheimer's disease is advanced age.A recent genome-wide study identified a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11 is probably responsible for the association.The association of a protective haplotype with a 10-year delay in the onset of Alzheimer's disease and the identification of a CCL11 variant with possible functional roles in this association might allow the future development of immunomodulators with the potential to halve disease incidence." + }, + { + "document_id": "8275b075-735b-44dc-b549-32ee94dec32e", + "text": "\n\nAlzheimer's disease is the most common type of dementia, and it is characterized by a decline in memory or other thinking skills.The greatest risk factor for Alzheimer's disease is advanced age.A recent genome-wide study identified a locus on chromosome 17 associated with the age at onset, and a specific variant in CCL11 is probably responsible for the association.The association of a protective haplotype with a 10-year delay in the onset of Alzheimer's disease and the identification of a CCL11 variant with possible functional roles in this association might allow the future development of immunomodulators with the potential to halve disease incidence." + } + ], + "8881b5b0-fd7a-400d-9dd2-d4c3f9b012b4": [ + { + "document_id": "8881b5b0-fd7a-400d-9dd2-d4c3f9b012b4", + "text": "INTRODUCTION\n\nAlzheimer's disease (AD) is a common debilitating disorder with a prevalence that rises steeply with age from below 1% at 65 years to as high as 40% after the age of 90 [Bachman et al., 1992].Genes are known to play a role in the development of AD.Twin studies show heritabilities of around 60% [Bergem et al., 1997;Gatz et al., 1997].Indeed, variation in four genes has already been shown to cause rare forms of early-onset AD [the Amyloid Precursor Protein Gene (APP); Goate et al., 1991; Presenilin 1 (PS1); Sherrington et al., 1995; Presenilin 2 (PS2); Levy Lahad et al., 1995, Rogaev et al., 1995] or increase the general risk of disease development [Apolipoprotein E (APOE), Corder et al., 1993].As well as increasing disease susceptibility, APOE e4 alleles are associated with reduced age at onset (AAO) and appear to show their strongest effect below 70 years [Farrer et al., 1997].There is also evidence from both twin [Pedersen et al., 2001] and family studies [Tunstall et al., 2000;Li et al., 2002] that AAO in AD is heritable.Daw et al. [2000] have estimated that in addition to APOE, there are at least four loci with similar effect sizes, which contribute to AAO in AD." + } + ], + "8b03aabf-8965-42c9-a054-44592bd98e86": [ + { + "document_id": "8b03aabf-8965-42c9-a054-44592bd98e86", + "text": "Introduction\n\nAlzheimer's disease (AD), a devastating neurodegenerative disease, is the most common form of dementia among the elderly.Genetically, AD is a complex and multifactorial disease with the possible involvement of multiple genes.The rare early-onset form of the disease usually follows an autosomal-dominant inheritance pattern and to date three genes have been identified: amyloid precursor protein (APP) and presenilin 1 and 2 (PSEN1 and PSEN2).The common late-onset form of the disease is much more complex than the early-onset form and until recently the apolipoprotein E (APOE) gene was the only major genetic factor accounting for 20-29% of the risk for late-onset AD. 1,2 Recent large genome-wide association studies (GWAS) have identi-fied nine additional genes for late-onset AD, including CR1, BIN1, CLU (a.k.a.4][5][6][7] There is high heritability for AD risk (up to 80%), 8 but the total risk attributable to all confirmed loci is about 50%, indicating the presence of additional risk genes for late-onset AD." + } + ], + "c59757a9-deea-491e-a93c-3dfdb3d217f8": [ + { + "document_id": "c59757a9-deea-491e-a93c-3dfdb3d217f8", + "text": "\n\nNE OF EVERY 5 PERSONS AGED 65 years is predicted to develop Alzheimer disease (AD) in their lifetime, and genetic variants may play an important part in the development of the disease. 1 The apparent substantial heritability of late-onset AD 2 is inadequately explained by genetic variation within the well-replicated genes (apolipoprotein E [APOE; RefSeq NG_007084], presenilin-1 [PSEN1; RefSeq NG_007386], presenilin-2 [PSEN2; RefSeq NG_007381], and amyloid beta precursor protein [APP;" + } + ] + }, + "data_source": [], + "document_id": "6AD8F2432A4CDFAA63C74712C1165B0E", + "engine": "gpt-4", + "first_load": false, + "focus": "api", + "keywords": [ + "APOE&e4", + "Alzheimers&disease", + "genome-wide&association&studies", + "amyloid-beta", + "tau&neurofibrillary&tangles", + "PSEN1", + "PSEN2", + "APP", + "late-onset&AD", + "early-onset&AD" + ], + "metadata": [], + "question": "Describe the genotypes related to alzheimers and dementia which have commonalities with those for aging?", + "subquestions": null, + "task_id": "6AD8F2432A4CDFAA63C74712C1165B0E", + "usage": { + "chatgpt": 10216, + "gpt-4": 5973, + "gpt-4-turbo-preview": 5048 + }, + "user_id": 2 + }, + "document_id": "6AD8F2432A4CDFAA63C74712C1165B0E", + "task_id": "6AD8F2432A4CDFAA63C74712C1165B0E" +} |