path: root/gnqa/data/study1/datasets/gpt4o/dataset_citizenscientist_aging_4.json

{
  "question": [
    "Are there any known lifestyle interventions that can positively impact genes related to aging?",
    "What is the 'epigenetic clock,' and how is it used in aging research?",
    "How do researchers use model organisms like yeast or worms to study human aging?",
    "Are there any promising anti-aging therapies being developed based on genetic research?",
    "How do caloric restriction and diet impact the genetics of aging?"
  ],
  "answer": [
    "Yes, there are several lifestyle interventions that can positively impact genes related to aging. These include mindful changes in behavior, environmental exposure, food/supplement use, weight loss, and regular exercise. A Mediterranean diet containing fruits, whole grains, fiber, antioxidants, soy protein, and healthy fats is also suggested to reduce DNA damage and protect telomeres, potentially increasing longevity.",
    "The 'epigenetic clock' is a predictive tool that uses DNA methylation patterns at specific sites, known as CpG sites, to estimate biological age. It is used in aging research to predict the age of unknown samples, calculate",
    "Researchers use model organisms like yeast or worms to study human aging due to their short lifespan and well-characterized genetic structures. These organisms allow for genome-wide studies and genetic manipulation to identify genes and pathways that influence lifespan. The findings can then be translated into mammalian models. The assumption is that if a gene or pathway modulates longevity in these simple organisms, it might have a similar function in more complex organisms like humans. This approach aids in the identification of potential therapeutic targets for age-associated diseases in humans.",
    "Yes, there are promising anti-aging therapies being developed based on genetic research. These include nutritional supplements that target genes/pathways involved in aging, drugs that mimic the life-extension effects of caloric restriction, and multitarget drugs and combinatorial therapies developed through network approaches. Additionally, genomic methods are being used to identify biochemical pathways for increasing lifespan, which can then be targeted for pharmaceutical development.",
    "Caloric restriction (CR) has been shown to extend lifespan and reduce the incidence of age-related diseases and tumors in mammals. It also delays the onset of many features of aging, including age-related diseases. CR increases the response to oxidative stress and reduces the shortening of telomeres in chromosomes, which directly intervenes in the repair of DNA damage. It also impacts metabolism, particularly the insulin/insulin-like growth factor 1 (IGF-1) pathways. Many genes and pathways associated with longevity and CR are part of nutrient-sensing pathways that also regulate growth and development. Therefore, understanding these pathways could lead to potential therapeutic applications for age-related diseases."
  ],
  "contexts": [
    [
      "\t\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.\t\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.",
      "\t\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].",
      "\t\nStudies of the basic biology of aging have identified several genetic and pharmacological interventions that appear to modulate the rate of aging in laboratory model organisms, but a barrier to further progress has been the challenge of moving beyond these laboratory discoveries to impact health and quality of life for people.The domestic dog, Canis familiaris, offers a unique opportunity for surmounting this barrier in the near future.In particular, companion dogs share our environment and play an important role in improving the quality of life for millions of people.Here, we present a rationale for increasing the role of companion dogs as an animal model for both basic and clinical geroscience and describe complementary approaches and ongoing projects aimed at achieving this goal.",
      "\t\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.",
      "\t\n\nsmall number of genes or interventions are known to increase life span in different model organisms.A selection of these are shown here.\t\n\nThe most direct method to address how well the features that determine longevity have been conserved is to identify genes or interventions that function similarly to modulate life span in different organisms.Components of insulin/IGF-1like signaling pathway, the sirtuin family of protein deacetylases, and the nutrient-responsive TOR kinase, among others, have been found to have this property (Table 1).Until recently, however, the genetic analysis of longevity was largely limited to mutagenesis screens for secondary phenotypes (such as stress resistance) or targeted studies of specific *Address correspondence to this author at the Department of Pathology, University of Washington, Seattle, WA 98195, USA; Tel: 206-543-4849; Fax: 206-543-3644; E-mail: kaeber@u.washington.edugenes, based on prior knowledge.While many important insights were gained from such studies, they, by necessity, self-selected for mutants with specific properties that are (at best) secondarily related to longevity.Thus, it remains unclear to what degree the pathways regulating longevity are evolutionarily conserved and whether the known longevity genes represent most of the important players or only a small fraction.\t\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.\t\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.",
      "\tIntroduction\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).",
      "\tIV. 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.\t\n\nEven if sirtuins and resveratrol do not live up to their expectations, this research is pioneering in terms of genome-environment interactions and nutritional manipulations of aging.These studies also show the path from basic discovery on the biology of aging to potential antiaging and pharmacological interventions and can therefore be applied to other genes and pathways.The lessons learned from the pitfalls of SIRT1 and resveratrol research can also help others to translate basic research on the biology of aging to the clinic, such as avoiding the use of short-lived rodent strains (e.g., by using unhealthy diets), which may lead to findings that only apply to a subset of individuals.\t\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.",
      "\tINTRODUCTION\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.\t\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.\t\n\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.",
      "\tRelevance to nurse practitioner practice\n\nCurrently, there is no cure for genetic variants associated with rapid aging, but novel agents that may slow down the aging process are being tested.The authors of this article advocate individual participation in association studies of aging and pharmacologic risk mitigation or reversal of symptoms for those with known genetic disease risk.Direct to consumer epigenetic biological aging tests and telomere length tests are available; but they are not approved by the Food and Drug Administration.Health care providers may want to consider the simple but key clinical and personal changes, suggested above, to enhance DNA health, wellness, and longevity.Simple mindful changes in behavior, environmental exposure, food/supplement use, weight loss, and regular exercise can reduce adduct exposure damage and impact telomere length, potentially increasing longevity.A Mediterranean diet containing fruits and whole grains along with fiber, antioxidants, soy protein, and healthy fats (from avocados, fish, flax, and walnuts) is suggested to reduce DNA adducts and protect telomeres.In light of our current pandemic, focus on population health, and restrictions to health care access, especially in rural communities, health care providers could incorporate these lifestyle and dietary principles in telehealth visits with patients to reduce disease risk and optimize healthy aging.",
      "\t[PubMed: 18208581]\n3. de Magalhes JP, Wuttke D, Wood SH, Plank M & Vora C Genome-environment interactions that\nmodulate aging: Powerful targets for drug discovery. Pharmacol. Rev. 64, 88101 (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, 106118 (2015). [PubMed: 25815989]\n6.",
      "\t\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.",
      "\t\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."
    ],
    [
      "\t\n\nThe first generation of epigenetic aging clocks used penalized regression models to predict chronological age on the basis of DNA methylation data, e.g., the widely used clocks from Hannum (2013) and Horvath (2013) apply to blood and 51 human tissues/ cell types, respectively [12][13][14].A derivative of the Horvath clock, intrinsic epigenetic age acceleration (IEAA) has since been developed, conditioning out (i.e., removing) estimates of blood cell composition.An increasing literature supports the view that IEAA relates to properties of hematopoietic stem cells [2,8,15].The second generation of epigenetic clocks move beyond estimating chronological age by incorporating information on morbidity and mortality risk (e.g., smoking, plasma protein levels, white blood cell counts), and chronological age.Two such predictors, termed PhenoAge (a DNAm predictor trained on a measure that itself was trained on mortality, using 42 clinical measures and age as input features) and GrimAge (trained on mortality, including a DNAm measure of smoking as a constituent part), outperform both Hannum and Horvath clocks in predicting mortality and are associated with various measures of morbidity and lifestyle factors [16,17].DNAm GrimAge outperforms PhenoAge and the first generation of epigenetic clocks when it comes to predicting time to death [8,18,19].\t\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.",
      "\tDiscussion\n\nWe developed precise epigenetic clocks (ABEC and eABEC) using blood-based DNAm data from EPIC.Our epigenetic clocks showed a more precise chronological age prediction than existing blood-based epigenetic clocks (e.g., the Hannum Blood-based clock and Horvath Skin & Blood clock; Fig. 5).The reason for the higher precision is more likely due to the large training set (n = 2227, Table 1) and the wide age-span of the samples (19 to 88 years for the training set of eABEC, Table 1), which is consistent with the findings by Zhang and colleagues [34].Compared to eABEC, both Hannum Blood-  [3,19].Other clocks (the Horvath Pan-tissue clock and Levine PhenoAge clock) may not be directly comparable to eABEC for chronological age prediction.For instance, the Horvath Pan-tissue clock was designed to measure epigenetic aging not only in blood but in multiple tissues [20], and the Levine Pheno-Age was designed to predict phenotypic age (estimated using 10 clinical biomarkers, e.g., albumin, creatinine, serum glucose, and seven others) based on DNAm [16].",
      "\tAn Epigenetic Clock\n\nThe aging transcriptome could be used to gauge the physiological age of worms, and in that way serve as an epigenetic clock revealing how much of life span has been spent and how much remains (23).Middle-aged worms show an aging transcriptome half-way between the aging expression profiles of young and old worms.This provides an independent way to assess the age of an animal independent of its life span.This is important as there are at least 2 explanations to account for increased life span due to a longevity mutation.One is that the mutation slows down the process of aging so that worms die at the same physiological age, but that it takes worms longer to reach old age.According to this possibility, the aging transcriptome of a longevity mutant at 2 weeks might resemble the aging transcriptome of wild-type worms at 1 week of age.Another is that the longevity mutant allows the worm to survive damage accumulation in old age, so that the worms age at a normal rate but they avoid death until succumbing at a later time.For instance, improved health care increases life span by enabling people to avoid disease and live longer, not by aging slower.In this scenario, the rate of aging in the longevity mutant and wild-type worms at 2 weeks could be similar, but with higher survivability in the longevity mutant due to an ability to better withstand damage accumulation.",
      "\tEpigenetic Clock\n\nChronological age is the number of years a person has lived, and biological or physiological age refers to a measure of how well your body functions compared to your chronological age.Biological age is influenced by multiple factors (genes, lifestyle, behavior, environment, among others) and correlates with mortality and health status.The epigenetic clock is one potentially reliable predictor of biological age.\t\n\nA recent study conducted in the Dunedin cohort [73] combined measurements of telomere lengths, epigenetic clocks and composite biomarkers and compared them to clinically relevant outcomes, such as health status, physical function, cognitive decline, and personal signs of ageing.The 71-cytosine-phosphate-guanine epigenetic clock and biomarker composites were consistently related to these outcomes.In another study, neural networks were applied to predict an age by using measurements from necessary blood tests, such as albumin, glucose, alkaline phosphatase, urea, and erythrocytes [74].",
      "\tThe changing ticking rate of the epigenetic clock\n\nThe linear combination of the 353 clock CpGs (resulting from the regression coefficients) varies greatly across ages as can be seen from Figure 6B,C.The red calibration curve (formula in Additional file 2) reveals a logarithmic dependence until adulthood that slows to a linear dependence later in life (Figure 6B).I interpret the rate of change (of this red curve) as the ticking rate of the epigenetic clock.Using this terminology, I find that organismal growth (and concomitant cell division) leads to a high ticking rate that slows down to a constant ticking rate (linear dependence) after adulthood.",
      "\tBackground\n\nRecently, a great deal of work has been performed in an effort to understand the nature of aging, the mechanisms that drive the process, and the biomarkers that may be predictive of, or affected by, age.In this effort, a seminal manuscript was published in 2013 which described the ability to use DNA methylation signatures in somatic tissues to predict an individual's chronological age [1].In this work, Dr. Horvath demonstrated that the epigenetic mechanisms that reflect the aging process are tightly conserved between individual tissues and across multiple species.Remarkably, these patterns are sufficiently consistent to enable accurate age prediction with Horvath's age calculator despite the significant contrast in epigenetic profiles between various somatic tissues.",
      "\tRelationship to mortality prediction\n\nAlthough the epigenetic clock method was only published in 2013, there is already a rich body of literature that shows that it relates to biological age.Using four human cohort studies, we previously demonstrated that both the Horvath and Hannum epigenetic clocks are predictive of all-cause mortality [23].Published results in Marioni et al. [23] show that DNAm age adjusted for blood cell counts (i.e.IEAA) is prognostic of mortality in four cohort studies.We recently expanded our original analysis by analyzing 13 different cohorts (including three racial/ethnic groups) and by evaluating the prognostic utility of both IEAA and EEAA.All considered measures of epigenetic age acceleration were predictive of age at death in univariate Cox models (p AgeAccel = 1.9  10 -11 , p IEAA = 8.2  10 -9 , p EEAA = 7.5  10 -43 ) and multivariate Cox models adjusting for risk factors and pre-existing disease status (p AgeAccel = 5.4  10 -5 , p IEAA = 5.0  10 -4 , p EEAA = 3.4  10 -19 ) where the latter adjusted for chronological age, body mass index, education, alcohol, smoking pack years, recreational physical activity, and prior history of disease (diabetes, cancer, hypertension).These results will be published elsewhere.Further, the offspring of centenarians age more slowly than age matched controls according to Age Accel and IEAA [26] which strongly suggests that these measures relate to heritable components of biological age.Two independent research groups have shown that epigenetic age acceleration predicts mortality [24,25].\t\n\nWe addressed this concern in multiple ways.First, we re-analyzed the WHI data by removing the 47 CpGs (out of 353 epigenetic clock CpGs) from the analysis.The epigenetic clock software imputes the 47 missing CpGs using a constant value (the mean value observed in the original training set).Using the resulting modified epigenetic clock, we validate our findings of racial/ethnic differences in terms of IEAA and EEAA (Additional file 8A-C).However, this type of robustness analysis is limited because the removal of a subset of DNA methylation probes, potentially influenced by proximal genetic variation, is not as good a control as directly having matched genetic data.Second, we used a completely independent epigenetic biomarker based on a published signature of age-related CpGs from Teschendorff et al. [13].Again, these results corroborate our findings (Additional file 8D, E).Third, we validated our findings using the original blood-based aging measure by Hannum [19] (Additional file 8F, G).Fourth, we highlight that both the Horvath and Hannum age estimators were developed based on training data from mixed populations.The training data underlying the Horvath clock involved four racial/ethnic groups (mainly Caucasians, Hispanics, African Americans, and to a lesser extent East Asians).The Hannum clock was trained on Caucasians and Hispanics.While race/ethnicity can lead to a significant offset between DNAm age and chronological age (which is interpreted as age acceleration), these two variables are highly correlated in all racial/ethnic groups.\t\n\nThe following evidence shows that the epigenetic clock captures aspects of biological age.First, the epigenetic age of blood has been found to be predictive of all-cause mortality even after adjusting for chronological age and a variety of known risk factors [23][24][25].Second, the blood of the offspring of Italian semi-supercentenarians (i.e.participants who reached an age of at least 105 years) has a lower epigenetic age than that of age-matched controls [26].Third, the epigenetic age of blood relates to frailty [27] and cognitive/physical fitness in the elderly [28].The utility of the epigenetic clock method has been demonstrated in applications surrounding obesity [29], Down's syndrome [30], HIV infection [31], Parkinson's disease [32], Alzheimer's disease-related neuropathologies [33], lung cancer [34], and lifetime stress [35].Here, we apply the epigenetic clock to explore relationships between epigenetic age and race/ethnicity, sex, risk factors of coronary heart disease (CHD), and the CHD outcome itself.",
      "\t\n\nConclusions: This study indicates that the epigenetic clock can be improved by increasing the training sample size and that its association with mortality attenuates with increased prediction of chronological age.",
      "\tBackground:\n\nThe Horvath epigenetic clock is widely used.It predicts age quite well from 353 CpG sites in the DNA methylation profile in unknown samples and has been used to calculate \"age acceleration\" in various tissues and environments.\t\nBackground:The Horvath epigenetic clock is widely used.It predicts age quite well from 353 CpG sites in the DNA methylation profile in unknown samples and has been used to calculate \"age acceleration\" in various tissues and environments.Results: The model systematically underestimates age in tissues from older people.This is seen in all examined tissues but most strongly in the cerebellum and is consistently observed in multiple datasets.Age acceleration is thus agedependent, and this can lead to spurious associations.The current literature includes examples of association tests with age acceleration calculated in a wide variety of ways. Conclusions:The concept of an epigenetic clock is compelling, but caution should be taken in interpreting associations with age acceleration.Association tests of age acceleration should include age as a covariate.\tDiscussion\n\nThe Horvath epigenetic clock [8] has been of practical use in predicting the age of unknown samples and as a quality check in epigenetic research.Additional widely used age predictors specific for blood were published by Hannum [6] and Levine [42] (phenotype-based).Here we analyze the Horvath model, but the methods and many of the conclusions may be more widely applicable, in particular the Hannum clock model shows a similar underestimation of ages in elderly subjects.\t\n\nIn addition to age prediction, the Horvath [8] paper also featured the idea of \"age acceleration\" in which discrepancies between DNA methylation (DNAm) age and chronological age might tell us something about the biological aging status of the organism.A number of positive association findings with age association, particularly mortality [43], make it compelling to think of the epigenetic clock as an index of an underlying aging program that adapts to health and environment.In light of the methodological variety though, we are concerned that the different epigenetic clocks, and the variety of age acceleration methods to choose from, lay a trap of potentially hidden multiple testing, as the temptation will be to survey the available methods for interesting results.\tConclusions:\n\nThe concept of an epigenetic clock is compelling, but caution should be taken in interpreting associations with age acceleration.Association tests of age acceleration should include age as a covariate.",
      "\tEpigenetic clocks\n\nFour epigenetic clocks were studied: the blood clock developed by Hannum et al. (Hannum Bld) [2], the multi-tissue clock developed by Horvath (Horvath MT) [3], the skin/ blood clock developed by Horvath et al. (Horvath Skn/Bld) [4], and the blood/saliva clock developed by Zhang et al. (Zhang Bld/Slv) [5].These clocks are described in Table 1.Together, the four epigenetic clocks comprised 1147 unique CpGs.One CpG from Horvath Skn/Bld (cg14614643) did not pass QC in our DNAm data and was therefore excluded from our analyses (i.e., 1146 CpGs were included).The four epigenetic clocks were used to predict chronological age in all 3132 samples for which methylome data were available.To this end, the coefficients of all clock CpGs were downloaded (available in their respective publications [2][3][4][5]).Beta-values of the clock CpGs were used as input for all clocks.For Horvath MT and Horvath Skn/Bld, predicted ages were transformed according to the authors' instructions [3,4].For Zhang Bld/Slv, DNAm values were normalized according to the authors' instructions, so that all samples had a mean of 0 and a standard deviation of 1 across all 450K CpGs [5].\tEpigenetic clocks accurately predict chronological age and show high similarity\n\nOur analyses were performed on whole blood samples from 3132 unrelated individuals, aged 18 to 87, originating from 6 Dutch cohorts (Table 2), for which both DNAm data and gene expression data were obtained, measured by Illumina 450K arrays and RNAseq, respectively.Only samples for which both DNAm and gene expression data passed QC were analyzed.First, we applied 4 epigenetic clocks (Table 1) to the DNAm data to predict age.All clocks accurately predicted age in our data.The Pearson correlation (r) between chronological age and predicted age was greater than 0.90 for all clocks, but there were differences in the prediction errors (Fig. 1A).Hannum Bld and Horvath MT showed the highest age prediction error (mean absolute error (MAE) = 4.5 years), followed by Horvath Skn/Bld (MAE = 3.1 years), and the prediction error was lowest for Zhang Bld/Slv (MAE = 2.7 years).We found that the errors in age prediction of the epigenetic clocks were highly correlated between clocks, with the pairwise correlation coefficients ranging from 0.57 to 0.79 (Fig. 1B).Thus, a person whose predicted age exceeds their chronological age according to one clock was likely to have a similar deviation according to another clock.However, this was not the case for extreme differences between predicted and chronological age, which were generally not reproduced between clocks (Additional file 1: Fig. S1A-B).For example, of the individuals for whom the prediction error of Hannum Bld was 10 years or higher, 32% had a prediction error above 10 years according to Horvath MT, and only 4% according to Zhang Bld/Slv (Additional file 1: Fig. S1A-B, top row).However, the individuals marked as extreme by Zhang Bld/Slv were more consistent with the other clocks, with up to 91% overlap (Additional file 1: Fig. S1A-B, bottom row).These findings indicate that extreme deviations between chronological and predicted age should be interpreted with caution.\tConclusions\n\nThe ability of epigenetic clocks to predict chronological age involves their ability to detect changes in proportions of naive and activated immune blood cells.This finding may contribute to the interpretation of associations between clock-derived measures and age-related health outcomes."
    ],
    [
      "\t\nYeast is a useful model organism to study the genetic and biochemical mechanisms of aging.Genomic studies of aging in yeast have been limited, however, by traditional methodologies that require a large investment of labor and resources.In this chapter, we describe a newly-developed method for quantitatively measuring the chronological life span of each strain contained in the yeast ORF deletion collection.Our approach involves determining population survival by monitoring outgrowth kinetics using a Bioscreen C MBR shaker/incubator/plate reader.This method has accuracy comparable to traditional assays, while allowing for higher throughput and decreased variability in measurement.\t\n\nYeast is a useful model organism to study the genetic and biochemical mechanisms of aging.Genomic studies of aging in yeast have been limited, however, by traditional methodologies that require a large investment of labor and resources.In this chapter, we describe a newly-developed method for quantitatively measuring the chronological life span of each strain contained in the yeast ORF deletion collection.Our approach involves determining population survival by monitoring outgrowth kinetics using a Bioscreen C MBR shaker/incubator/plate reader.This method has accuracy comparable to traditional assays, while allowing for higher throughput and decreased variability in measurement.",
      "\t\nThe genetic analysis of life span has only begun in mammals, invertebrates, such as Caenorhabditis elegans and Drosophila, and yeast.Even at this primitive stage of the genetic analysis of aging, the physiological observations that rate of metabolism is intimately tied to life span is supported.In many examples from mice to worms to flies to yeast, genetic variants that affect life span also modify metabolism.Insulin signaling regulates life span coordinately with reproduction, metabolism, and free radical protective gene regulation in C. elegans.This may be related to the findings that caloric restriction also regulates mammalian aging, perhaps via the modulation of insulin-like signaling pathways.The nervous system has been implicated as a key tissue where insulin-like signaling and free radical protective pathways regulate life span in C. elegans and Drosophila.Genes that determine the life span could act in neuroendocrine cells in diverse animals.The involvement of insulin-like hormones suggests that the plasticity in life spans evident in animal phylogeny may be due to variation in the timing of release of hormones that control vitality and mortality as well as variation in the response to those hormones.Pedigree analysis of human aging may reveal variations in the orthologs of the insulin pathway genes and coupled pathways that regulate invertebrate aging.Thus, genetic approaches may identify a set of circuits that was established in ancestral metazoans to regulate their longevity.",
      "\tIntroduction\n\nThe budding yeast Saccharomyces cerevisiae has been used as a model of cellular aging for more than 6 decades (Fabrizio and Longo 2007;Jazwinski 2005;Kaeberlein et al. 2007;Steinkraus et al. 2008).S. cerevisiae has several features that make it useful as a model organism for aging research, including short life span, well-characterized genetic and molecular methods, low relative cost, cell type homogeneity, and a vast organismal information base.These advantages have facilitated unbiased screens for genes that influence life span in yeast, as well as candidate gene approaches.Several dozen genetic determinants of yeast longevity have been identified from these studies, at least some of which appear to play a conserved role in the aging of multicellular eukaryotes.\t\n\nSince these early morphology-based studies, yeast replicative aging has become a prominent model for aging genetics and has been instrumental in the discovery and characterization of several of the best studied genetic pathways involved in life span determination.These pathways include dietary restriction (DR), sirtuins, TOR signaling, and mitochondrial metabolism (Table 12.1).\t\nIn the past several decades the budding yeast Saccharomyces cerevisiae has emerged as a prominent model for aging research.The creation of a single-gene deletion collection covering the majority of open reading frames in the yeast genome and advances in genomic technologies have opened yeast research to genome-scale screens for a variety of phenotypes.A number of screens have been performed looking for genes that modify secondary age-associated phenotypes such as stress resistance or growth rate.More recently, moderate-throughput methods for measuring replicative life span and high-throughput methods for measuring chronological life span have allowed for the first unbiased screens aimed at directly identifying genes involved in determining yeast longevity.In this chapter we discuss large-scale life span studies performed in yeast and their implications for research related to the basic biology of aging.",
      "\t\n\nThe use of humans in aging studies is complicated due to several factors, including ethical, environmental, and social issues, and even economic reasons, and more importantly, due to the human long natural life span.The human aging process takes decades to develop, making it virtually impossible to perform longitudinal studies by following subjects throughout their lives.Thus, the most widely employed models of aging are short-lived organisms, including yeast, roundworm, fruit fly, and mice.Indeed, large-scale genetic screenings have identified numerous genes and drugs that significantly lengthen life span in these organisms; however, the biological relevance of such longevity genes to human aging remains not fully established [3].\tIntroduction\n\nResearch into the underlying mechanisms of organismal ageing has advanced at a tremendous rate over the past decade.Studying the ageing process presents a significant challenge as it is a systemic phenomenon that affects numerous organs and tissue systems in humans.Due to the complex nature of the ageing process, it has been most extensively modelled using short-lived non-vertebrate systems such as nematode worms (C.elegans), yeast (C.cerevisiae) and flies (D. melanogaster), as well as longer-lived vertebrate models, such as the mouse (M.musculus) and zebrafish (D. rerio) [1].Importantly, research using these model organisms alongside both traditional and novel genetic manipulation techniques has delineated nine hallmarks of ageing that are common across various species, including humans [2].Tremendous effort is now being expended into understanding the relationship between these different hallmarks and how their interactions impact on the ageing process.This has created a constant necessity for studying multiple interactions between complex genetic pathways, sometimes under the influence of fluctuating factors, such as epigenetic mechanisms, and especially in vertebrate models where traditional genetic engineering techniques are less efficient or involve higher costs due to longer lifespans (the maximal lifespan of mice is around 3-4 years and 5 years for zebrafish).It has therefore become of great interest for the ageing research community to develop new in vivo and in vitro genetically engineered models capable of addressing complex research questions in a time-cost efficient manner.",
      "\tCONCLUSION\n\nOur understanding of the basic mechanisms of aging have benefited greatly from the use of simple model systems such as yeast and worms.The development of technologies that allow direct analysis of longevity on a genome-wide scale in these organisms has provided a wealth of new data regarding the genes and pathways that modulate longevity.Some of these genes and pathways are specific to each organism; however, others appear to be evolutionarily conserved.Future efforts will move toward translating the data from genomic longevity studies in yeast and worms into mammalian models.Any gene that functions similarly to modulate longevity and disease in yeast, worms, and mice will be an outstanding candidate for therapeutic intervention targeting age-associated diseases in people.\t\n\nGenomic comparisons of longevity across species also provide an opportunity to identify novel factors that modulate aging and age-associated disease in humans.The evolutionary distance between yeast and worms is approximately equivalent to the evolutionary distance between worm and humans.Therefore, if an ortholog pair has maintained a conserved longevity determining function between yeast and worms, it is reasonable to speculate that the function will also be retained in mammals.At lease one effort is underway to directly test this assumption (http://www.pathology.washington.edu/research/bioage/ellison/).A consortium of laboratories at the University of Washington is utilizing the data from the genome-wide yeast and worm longevity screens described above to identify candidate genes for longevity studies as gene knock-outs in mice [1].A CRE-based conditional knock-out system is being employed for these studies, to allow either complete knock-out of a particular gene or tissue specific (or post-development) gene deletion.Along with longevity, a select group of potential agingrelated biomarkers will be assayed for each of these mouse models.In addition, it should be possible to assay several of these mouse lines for resistance to specific age-associated diseases, such as diabetes and neurological disorders, by crossing them into the appropriate transgenic disease background.\t\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.\t\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.\t\n\nWhen considering the use of simple eukaryotes to study aging and age-related disease, it is pertinent to ask whether, and to what degree, the aging process is evolutionarily conserved.Does a yeast cell age by the same mechanism(s) as a mouse?Is the longevity of a nematode determined in the same way as that of a person?The complete answers to these questions remain largely unknown; however, discoveries made over the last several years have unequivocally demonstrated that at least some of the factors regulating longevity are shared between yeast, worms, flies, and mice.The degree to which these pathways will be relevant to human longevity and age-associated disease is an important unanswered question.",
      "\t\n\nMany of the genes and gene networks that modulate aging are conserved across animal phyla.For this reason, the highly tractable model systems Drosophila and Caenorhabditis have provided fundamental advances in our understanding of the genetic control of cellular processes that affect aging.There is a growing realization that increasing the evolutionary breadth in animal systems used in aging studies will lead to discovery of effects and mechanisms that are more likely to be robust and reveal fundamental principles of aging.The use of diverse models may also reveal previously unknown genetic factors involved in healthy aging in humans.The lineages leading to Drosophila melanogaster and Caenorhabditis elegans have each undergone significant genome reduction, and these standard model systems lack many vertebrate gene homologs that are present in other invertebrates [2][3][4][5][6][7][8][9].In addition, arthropods and nematodes are more closely related to each other than originally thought [10,11], limiting the evolutionary range in comparative studies of aging [12] and thus the degree to which conclusions can be reliably generalized from these models to humans.",
      "\t\n\nIt seems that organisms from yeast to mammals have evolved genetic programs to cope with periods of starvation that can also postpone aging and age-related diseases, but how can we take advantage of those mechanisms to improve human health?Because assaying the longevity effects of CR in humans is practically impossible, studying its molecular mechanisms in lower life forms could be beneficial to humans through the identification of candidate genes, pathways and molecular mechanisms.Although CR will not be suitable for everyone, targeting its mechanisms and developing CR mimetics may lead to drug development for a number of age-related and metabolic diseases.",
      "\tINTRODUCTION\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.",
      "\tINTRODUCTION A Brief History of Longevity Genetics Research in C. elegans\n\nProgress in aging research has identified genetic and environmental factors that regulate longevity across species [1][2][3].The nematode worm Caenorhabditis elegans has become an invaluable model system for investigating the molecular mechanisms of aging and longevity, offering the advantages of its relatively low cost, short lifespan, and conservation of key nutrient and stress-responsive signaling pathways in mammals.",
      "\t\n\nIn addition to these advanced tools, new studies in emerging aging models, such as eusocial insects, and in yet-uncharacterized models will provide additional opportunities for insight into key epigenetic mechanisms in aging.In the case of the Indian jumping ant Harpegnathos saltator, a worker can replace a queen in the colony, resulting in a change in longevity, acquisition of reproductive function, and loss of worker behavior, all of which can ultimately be reversed.The epigenetic mechanisms that underlie this transition are of great interest, including characterization and manipulation of epigenetic patterning during development, which lead to key behavioral differences in these organisms (Simola et al., 2016).Particularly long lifespans have been observed in several types of deepwater fishes, various crustaceans, bow head whales, several turtles, and naked mole rats (relative to other rodents) among others.While some may be unfeasible for creation of laboratory models, tissue and cellular studies of these or similar organisms may prove to be insightful.In addition, short-lived model organisms such as yeast, worms, and killifish are useful for quick lifespan estimations (Table 1).Together with the technological advances highlighted above, new experimental avenues and models in aging research will provide key insight into the epigenetic pathways that underlie longevity and aging and will likely identify factors and pathways that can be targeted to improve health and lifespan in humans.",
      "\t\n\nSaccharomyces cerevisiae has directly or indirectly contributed to the identification of arguably more mammalian genes that affect aging than any other model organism.Aging in yeast is assayed primarily by measurement of replicative or chronological life span.Here, we review the genes and mechanisms implicated in these two aging model systems and key remaining issues that need to be addressed for their optimization.Because of its well-characterized genome that is remarkably amenable to genetic manipulation and highthroughput screening procedures, S. cerevisiae will continue to serve as a leading model organism for studying pathways relevant to human aging and disease.",
      "\t\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."
    ],
    [
      "\t\n\nKnowledge of genetic interrelationship between the biomarkers of aging may lead to the discovery of a downstream common pathway that summarizes aging processes; the list of biomarkers should be as comprehensive as possible via incorporating other well-known systems involved in aging in addition to the musculoskeletal system.Further development of the pleiotropy-based approaches will be useful for other studies of multiple related phenotypes which employ genome-wide associations to decipher genetics in the absence of disease endophenotypes, which is the case of human aging.With the advent of these approaches, new candidate genes may emerge for further pursuit.In its turn, discovery of the \"phenome of aging\" may translate into innovative diagnostic and therapeutic interventions to improve the overall health of older men and women.",
      "\t\n\nFig. 4. Functional genomics technologies promise to go deeply into the understanding and the development of therapeutic strategies for sarcopenia.",
      "\tRejuvenation without Dedifferentiation\n\nRecent studies have begun to test the potential of different interventions to restore youthfulness to aged cells or tissues.",
      "\tWhat does this study add?  Combining genomics with in vitro human skin cell cultures is a promising approach for the identification of new antiageing and antidiscoloration compounds.\tWhat's already known about this subject?  Genomics data from the study of skin biopsies has identified new biomarkers for targeting skin ageing and discoloration for therapeutic intervention. In vitro human skin cell cultures are routinely used for the rapid evaluation of cosmetic compounds.",
      "\tImplications and Interventions for Antiaging Medicine\n\nOne of the aims of this work is to make others aware that age-related changes and pathologies can derive from early-onset developmental mechanisms, as supported by recent results (1, 2).Hopefully, researchers and clinicians will try to understand age-related pathologies by looking at the physiology and genetics of normal developmental processes.Assuming a link between development and aging also has major implications for how experiments are designed and interpreted in gerontology.If we see aging as triggered by development, rather than a mere accumulation of damage, then to study aging it is necessary to understand the life span as a whole and not merely its last segment.Herein, we offer a few ideas about how this can be achieved, including suggestions for experiments.",
      "\t\n\nKnowledge of genetic and molecular pathways related to aging and its modulation can also be translated into predictions on health effects of dietary components (Mu ller and Kersten, 2003).Therefore, in addition to pharmaceuticals, another marketplace for basic aging research involves supplements, which avoids the need for clinical trials.Indeed, companies are now focusing on nutritional supplements that target genes/pathways involved in aging.One example is Genescient (http://www.genescient.com/), a biotechnology company; its strategy involves choosing supplements that affect pathways that may be important in long-lived flies as assayed from gene expression analyses (Rose et al., 2010).\t\n\nWe now know of hundreds of genes that regulate aging in model organisms, dozens associated with longevity in humans, and hundreds differentially expressed with age.This vast amount of information yields increased power for personalized and stratified medicine, for identifying biomarkers of aging, and for drug development to extend lifespan and ameliorate age-related diseases.Overall, it gives us a blueprint (albeit still imperfect) of how aging is controlled that we can use to potentially manipulate the basic aging process, whatever its underlying molecular mechanisms may be.Moreover, our knowledge of nutrient-sensing pathways that mediate the effects of CR has greatly increased in recent years, opening new opportunities for drug discovery and ultimately for perhaps developing an antiaging pill that retards aging with minimal side effects.\t\nAging is the major biomedical challenge of this century.The percentage of elderly people, and consequently the incidence of age-related diseases such as heart disease, cancer, and neurodegenerative diseases, is projected to increase considerably in the coming decades.Findings from model organisms have revealed that aging is a surprisingly plastic process that can be manipulated by both genetic and environmental factors.Here we review a broad range of findings in model organisms, from environmental to genetic manipulations of aging, with a focus on those with underlying gene-environment interactions with potential for drug discovery and development.One well-studied dietary manipulation of aging is caloric restriction, which consists of restricting the food intake of organisms without triggering malnutrition and has been shown to retard aging in model organ-isms.Caloric restriction is already being used as a paradigm for developing compounds that mimic its life-extension effects and might therefore have therapeutic value.The potential for further advances in this field is immense; hundreds of genes in several pathways have recently emerged as regulators of aging and caloric restriction in model organisms.Some of these genes, such as IGF1R and FOXO3, have also been associated with human longevity in genetic association studies.The parallel emergence of network approaches offers prospects to develop multitarget drugs and combinatorial therapies.Understanding how the environment modulates aging-related genes may lead to human applications and disease therapies through diet, lifestyle, or pharmacological interventions.Unlocking the capacity to manipulate human aging would result in unprecedented health benefits.\t\n\nCurrent progress in genomics, high-throughput methods, informatics, and systems biology should help to develop network approaches that test target combinations resulting in the emerging paradigm of network pharmacology (Keith et al., 2005;Hopkins, 2008).Systematic drug-design strategies directed against multiple targets hold much promise in the field of aging (Csermely et al., 2005), although challenges remain in developing accurate computer models of relevant pathways and suitable in vitro and in vivo models for testing.In the same vein, progress in personalized medicine and in predicting individual responses (e.g., using SNPs) to the environment (including diet, lifestyle, and drugs), will be key to maximizing environmental interventions that improve health and counteract aging.Therefore, network approaches to both aging and pharmacology are promising future avenues (Simko et al., 2009).\t\n\nAging is the major biomedical challenge of this century.The percentage of elderly people, and consequently the incidence of age-related diseases such as heart disease, cancer, and neurodegenerative diseases, is projected to increase considerably in the coming decades.Findings from model organisms have revealed that aging is a surprisingly plastic process that can be manipulated by both genetic and environmental factors.Here we review a broad range of findings in model organisms, from environmental to genetic manipulations of aging, with a focus on those with underlying gene-environment interactions with potential for drug discovery and development.One well-studied dietary manipulation of aging is caloric restriction, which consists of restricting the food intake of organisms without triggering malnutrition and has been shown to retard aging in model organ-isms.Caloric restriction is already being used as a paradigm for developing compounds that mimic its life-extension effects and might therefore have therapeutic value.The potential for further advances in this field is immense; hundreds of genes in several pathways have recently emerged as regulators of aging and caloric restriction in model organisms.Some of these genes, such as IGF1R and FOXO3, have also been associated with human longevity in genetic association studies.The parallel emergence of network approaches offers prospects to develop multitarget drugs and combinatorial therapies.Understanding how the environment modulates aging-related genes may lead to human applications and disease therapies through diet, lifestyle, or pharmacological interventions.Unlocking the capacity to manipulate human aging would result in unprecedented health benefits.\t\n\nIn conclusion, we now know of many target genes that either individually or collectively could be used for screening molecules (nutritional compounds and drugs) that may modulate aging.Even if proving that a particular diet or drug can delay aging is not feasible from a scientific and regulatory perspective, there is a huge potential to identify molecules that ameliorate age-related diseases and/or dysfunction.This represents a tremendous opportunity for companies working in nutrition and pharmacology in a field on an upward trajectory.\t\n\nMarred by decades of \"quackery\" (including grafting testicles from young animals into men), the science of aging has come a long way in gaining respectability (Stipp, 2010).Already more than 20 companies worldwide are focusing specifically on the aging process (http://whoswho.senescence.info/corp.php), in addition to \"big pharma,\" with agingoriented research and development projects.Although this number is modest, it shows the growing potential of a field that is bound to increase.In 2008, GlaxoSmithKline purchased Sirtris for $720 million (Sipp, 2008), a huge amount for a company with no clinical data; presumably the purchase was based on the extraordinary potential suggested by a compound capable of delaying aging.Even though questions have been raised about their efficiency, resveratrol and other drugs targeting SIRT1 showcase how a gene initially identified as a regulator of aging in yeast can be used as a pharmaceutical target for multiple human diseases.It demonstrates confidence in the field and in the idea that aging is not immutable.The recent problems raised concerning SIRT1 and resveratrol research also serve as a cautionary tale of the hurdles in translation of laboratory discoveries to the clinic.\tVI. Concluding remarks\n\nAging is the major driving factor of disease in the 21st century.Manipulation of aging-related genes by diet, lifestyle, and pharmaceuticals could dramatically improve human health and could be used to develop drugs against age-related diseases such as cancer, heart disease, type 2 diabetes, obesity, and neurodegenerative diseases.The hundreds of aging-related genes and genes related to CR already identified offer enormous opportunities for target discovery (Fig. 2).Although agingrelated genes cannot be modified in humans, understanding how these can be manipulated by diet or pharmaceuticals can have a profound impact on health.In other words, work on the genetics of aging allows the identification of novel genomic targets for drug development, opening the door for aging pharmacogenomics.\tC. Translation to Extend Human Healthspan\n\nAlthough a number of genes and even a few drugs have emerged as candidates for targeting the aging process pharmacologically, several problems are associated with translation to human aging.In principle, human clinical trials on aging cannot be performed.One major problem is that aging cannot be quantified, and even a trial running for several years would struggle to identify endpoints.Lifespan or survival could be quantified, as well as health biomarkers such as low blood pressure, insulin sensitivity, inflammatory markers, glucose metabolism, etc., but these may or may not reflect alterations in the aging process.\t\n\nOverall, demonstrating that a particular intervention is affecting human aging, as done in model organisms, is virtually impossible.Interventions, including drugs, emerging from basic research on aging will probably target specific age-related pathological conditions and/or dysfunction.Subsequent studies of health biomarkers and multiple age-related diseases may reveal broader effects.Success in animal models or short-term human studies may be sufficient to convince potential patients of the usefulness of particular dietary supplements or approaches, as exemplified by those voluntarily undergoing CR (http://www.crsociety.org/),which can serve as basis for further studies (Soare et al., 2011).",
      "\tConcluding Remarks\n\nGenome instability plays a significant role in the progression of aging and protecting our aging genomes is therefore of fundamental importance for healthy aging.A major issue for the development of interventions targeting aging is the long trial time and difficulty in determining positive outcomes (see Outstanding Questions).Premature-aging diseases could represent an interesting group of disorders where aging interventions could be tested and outcomes could be determined at a much lower cost and potentially in less time.Here, treatments such as rapamycin, dietary interventions, sirtuin-activating compounds, metformin, NAD precursors, and senolytics could be more diligently tested in DNA repair disorders.A large number of therapies are emerging that may directly or indirectly lead to less DNA damage and the vast ongoing research across the globe will undoubtedly eventually be able to target this for the benefit of humankind.In sum, the future is bright.",
      "\t\n\nAging is a multifold process affected by many genes and thus many biochemical pathways.This conclusion is underscored by the failure to find simple central controls for the aging process during the 20th Century.This situation poses a fundamental challenge to anti-aging medicine: how to develop effective therapies for a genomically complex pathology.We propose such a strategy.As a first step, we recommend the use of model systems in which significant genetic intervention is not proscribed or impractical.Second, we propose that work with such model systems begin with selected lines that have genetic enhancements that allow increased lifespan.Third, genomic methods should be used to identify a number of biochemical pathways for increasing lifespan.Fourth, biochemical pathways that have been identified in model systems would then be available for pharmaceutical development, first in rodents, eventually in a clinical human population.This may seem to be a cumbersome R&D strategy, but starting with human populations or inadequately pre-screened compounds would be unlikely to succeed because of the complexity of the aging problem.\t\nAging is a multifold process affected by many genes and thus many biochemical pathways.This conclusion is underscored by the failure to find simple central controls for the aging process during the 20th Century.This situation poses a fundamental challenge to anti-aging medicine: how to develop effective therapies for a genomically complex pathology.We propose such a strategy.As a first step, we recommend the use of model systems in which significant genetic intervention is not proscribed or impractical.Second, we propose that work with such model systems begin with selected lines that have genetic enhancements that allow increased lifespan.Third, genomic methods should be used to identify a number of biochemical pathways for increasing lifespan.Fourth, biochemical pathways that have been identified in model systems would then be available for pharmaceutical development, first in rodents, eventually in a clinical human population.This may seem to be a cumbersome R&D strategy, but starting with human populations or inadequately pre-screened compounds would be unlikely to succeed because of the complexity of the aging problem.",
      "\tIntegrating genomics and biomarker research\n\nOnce the use of established biomarkers of biological age is standardized, the biomarker information can be integrated into studies aimed at finding causal determinants of aging and longevity.An example of an integrated approach to identify lifespan regulating loci is represented by testing whether genetic variants associated with potential biomarkers also associate with longevity.To date, GWAS have identified many genetic variants that associate with age-associated traits, such as leukocyte telomere length and features from glycome and metabolome profiles [84][85][86].The joint effect of the majority of these variants on aging and longevity still needs to be determined.One study identified a haplotype in the TERT gene that was associated with increased telomere length and longevity, which indicates that genetic variants associated with telomere length regulation might also play a role in longevity [87]."
    ],
    [
      "\t\nThe genetic analysis of life span has only begun in mammals, invertebrates, such as Caenorhabditis elegans and Drosophila, and yeast.Even at this primitive stage of the genetic analysis of aging, the physiological observations that rate of metabolism is intimately tied to life span is supported.In many examples from mice to worms to flies to yeast, genetic variants that affect life span also modify metabolism.Insulin signaling regulates life span coordinately with reproduction, metabolism, and free radical protective gene regulation in C. elegans.This may be related to the findings that caloric restriction also regulates mammalian aging, perhaps via the modulation of insulin-like signaling pathways.The nervous system has been implicated as a key tissue where insulin-like signaling and free radical protective pathways regulate life span in C. elegans and Drosophila.Genes that determine the life span could act in neuroendocrine cells in diverse animals.The involvement of insulin-like hormones suggests that the plasticity in life spans evident in animal phylogeny may be due to variation in the timing of release of hormones that control vitality and mortality as well as variation in the response to those hormones.Pedigree analysis of human aging may reveal variations in the orthologs of the insulin pathway genes and coupled pathways that regulate invertebrate aging.Thus, genetic approaches may identify a set of circuits that was established in ancestral metazoans to regulate their longevity.",
      "\tConclusions\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.",
      "\t\n\nHistorically, the effects of CR have been viewed as being associated with the aging process [1][2][3].This standpoint argues that effects of CR extend beyond any one disease process (e.g., tumorigenesis), but that CR has multiplex effects on a range of physiological systems, ultimately amounting to an inhibitory effect on the progression of aging.The association between CR and aging, however, remains poorly understood, largely because the aging process itself remains poorly defined [17].While an uncontroversial definition of aging may not be developed anytime soon, it should be possible to add rigor to the concept by generating quantitative models of aging that are operationally useful.In this regard, whole-genome microarray datasets would seem especially valuable [18], and can be used to generate models that test, quantitatively, the assertion that CR acts to oppose the progression of aging [11].Conclusions generated from previous investigations conflict regarding the association between the effects of CR and aging.On the one hand, an early investigation revealed that age-associated expression patterns in muscle were \"either completely or partially prevented by caloric restriction\" [19], and this conclusion was supported in subsequent studies [14,20,21].Other investigations, however, have yielded different conclusions.For instance, effects of CR were entirely unrelated to those of aging in muscle tissue from Rhesus monkeys [22], and in one aptly designed experiment examining mouse cardiac tissue, only 79 of 1075 age-responsive genes (7.3%) were significantly altered by CR [23].Clearly, experimental design and statistical methodology are two important considerations for evaluating this diverse set of results.Many studies, for instance, have not evaluated whether the observed overlap between CR and aging effects is larger than expected by chance alone.This statistical evaluation would not be straight-forward in many cases, since experiments involved a shared control treatment that was used to evaluate the effects of both aging and CR (e.g., a young control treatment, an old control treatment, and an old CR treatment).Given this design, the effect of CR is not estimated independently of the effect of aging, and some correspondence between CR and aging effects would be expected by chance [12].\t\n\nThe association between CR and aging was next examined at the global scale, among all genes, and also with respect to each of the four most well-studied tissue types (liver, heart, muscle and central nervous system) (Figure 8).In liver, there was a slight, positive association between the effects of CR and aging (r = 0.04) (Figure 8A).This association was significant (P < 6.72  10 -12 ), although given the large number of genes involved in the comparison, this Relationship between caloric restriction and aging in liver, heart, muscle and the central nervous system Figure 8 Relationship between caloric restriction and aging in liver, heart, muscle and the central nervous system.The association between CR and aging was evaluated for the (A) liver, (B) heart, (C) muscle and (D) central nervous system (hippocampus + cortex).The CR effect is positive for genes up regulated by CR and negative for genes down regulated by CR (see Figure 7 legend).Likewise, the age effect is positive for genes up regulated with age and negative for genes down regulated with age (see Figure 7 legend).The abundance of genes in relation to the CR and age effect is reflected by the color intensity, with deep blue colors corresponding to regions with the largest number of genes.The dashed red line is based upon a least-squares regression fit that quantifies the overall relationship between the CR and aging effects.In each panel, the estimated Pearson correlation is shown in the upper-right, and the percentage values (green font) indicate the fraction of genes that belong to each quadrant.The effects of CR and aging were computed in each organ system based upon p-values generated by combining results from at least 3 independent experiments.In liver, CR and aging effects are based upon 9 and 7 experiments, respectively.In heart, CR and aging effects are based upon 5 and 10 experiments, respectively.For muscle and central nervous system, CR and aging effects are each based upon 3 -6 experiments.For each organ, distinct sets of data were used to estimate the CR and aging effects, such that CR and aging effects are a priori independent.significance test was not too informative.In the heart, muscle and central nervous system, the expected negative association between CR and aging did emerge, albeit weakly, with the estimated correlation coefficient less than or equal to -0.10 in each case.The strongest association was found in heart (Figure 8B), in which age-related expression patterns were weakly opposed by CR (r = -0.096;P = 2.20  10 -16 ).In muscle and central nervous system (Figures 8C and 8D), the association between CR and aging was again weak (r < -0.048), and non-significant in the case of muscle (P = 0.054), despite the large number of genes upon which the association was based.With respect to central nervous system, a large fraction of genes (56.6%) were both increased by CR and decreased with age (i.e., within the lower-right quadrant of Figure 8D), although very few genes (8.9%) were decreased by CR and increased with age (i.e., within the upper-left quadrant of Figure 8D).",
      "\t\n\nThen we have those pharmaceutical strategies that are www.impactaging.combased on emulating the pathways implicated in the response of lifespan to dietary restriction, particularly sirtuin-targeting agents like resveratrol [e.g.25].Again, like hormone manipulation, these pathways are heavily bound up with the regulation of reproduction, making the curtailment of the cost of reproduction the most likely mechanism by which the beneficial effects of emulating dietary restriction are achieved [cf. 26].This is a strategy in which longevity is increased by metabolic refrigeration, pseudo-hibernation, or curtailing functions [11].From the standpoint of evolutionary biology, this is, again, not an extension of the period of adaptation.It is instead trading one set of adaptations off against another.Most people do not regard curtailing their metabolism, cognition, affective stability or reproductive functions as a useful approach to the problem of aging.Nonetheless, some are willing to trade-off some of their adaptive functions for an increased lifespan, and for them this \"anti-aging\" strategy will have its attractions.",
      "\tMetabolism\n\nStudies show that calorie restriction is the most consistent means to prolong life expectancy and health across several experimental models [55], ranging from yeasts to primates.It not only increases life expectancy, but it also delays the onset of many features and hallmarks of ageing, including age-related diseases.Transcriptional profiles are currently being applied and investigated.One of them is a caloric restriction (CR), which increases the response to oxidative stress and reduces the shortening of telomeres in chromosomes; this has a direct intervention in the repair of DNA damage.Data from human trials (such as CALERIE, Biosphere-2 and CRON) indicate that moderate CR accompanied by adequate nutrition has positive effects on health and dramatically reduces the multiple metabolic factors involved in the pathogenesis of disease chronicles, including type 2 diabetes, heart and cerebrovascular diseases, and cancer [56].",
      "\t\n\nOn the other hand, the beneficial effects of caloric restriction are associated with alterations in metabolism, particularly the insulin/insulin-like growth factor 1 (IGF-1) pathways, which could reflect an evolution mechanism to ensure survival of a species during period of food shortage [3].Many genetic manipulations affecting nutrient-sensing pathways including the insulin and mTOR (mammalian target of rapamycin) pathways mimic the effect of caloric restriction on lifespan in yeast, worm, flies and mice and support this hypothesis [3].This review will firstly discuss in general terms how trace elements affect ageing and then use Selenium (Se) as an example to illustrate how trace elements influence the ageing process.Furthermore, the review will also illustrate how the so-called \"Omics technologies\" can be used to unravel the modes of action of trace elements and to identify biomarkers to define the optimal intake for health at the molecular level.\t\n\nEvidence is building up showing that caloric restriction, without malnutrition, extends lifespan in species ranging from yeast to non-human primates [3], but it appears, on the contrary, that inadequate/sub-optimal intake of micronutrients contribute to the development of chronic diseases.In his \"Triage theory\", B. Ames suggested that this could reflect the need for an organism to re-allocate micronutrients according to triage priorities to favour short-term survival over long-term wellbeing [4,5].The consequences of this re-allocation may remain unnoticed in the day-to-day experience but are likely to show up late in life as cancers, Alzheimer's disease, Parkinson's disease, diabetes and cardiovascular diseases.",
      "\t\n\nCaloric restriction (CR) is the only intervention shown to extend lifespan in mammals (5).It is also the most effective means known of reducing cancer incidence and increasing the mean age of onset of age-related diseases and tumors (6).Our studies made use of an experimental design that allowed us to clearly distinguish the effects of diet from those of age on genome-wide expression patterns.Another distinctive aspect of the study allowed us to resolve changes in gene expression induced directly by CR from those that arise over time as a consequence of the interaction between CR and aging.",
      "\tGenDR-genomics of DR\n\nDR, of which caloric restriction is the most widely studied regimen, is the most robust non-genetic intervention shown to extend lifespan in a multitude of species, from yeast to mammals (12,14).However, the exact mechanisms of how DR extends lifespan remain unknown.To decipher the mechanisms of DR in a systematic fashion, we established GenDR (http://genomics.senescence.info/diet/), the first database of DR-associated genes.Because GenDR and related analysis of DR networks have been recently described elsewhere (15), they will only be briefly described herein.To create GenDR, we compiled from the literature a list of DR-essential genes from model organisms.DR-essential genes were defined as those which, if genetically modified, interfere with DR-mediated lifespan extension and, ideally, do not affect the lifespan of animals on an ad libitum diet (or at least do not appear to be merely causing disease).A subset of these genes act as genetic DR mimetics, as their manipulation leads to an increased lifespan for ad libitum fed animals, which is not further extended by DR.One such example is the growth hormone receptor gene in mice (16), in fact the only mouse gene currently in GenDR.In GenDR, the respective homologues of DR-essential genes are included for all the common model organisms, as well as for humans (15).A complementary data set in GenDR is a list of genes consistently differentially expressed in mammals under DR.In a recent meta-analysis, a common signature of genes differentially expressed in DR across different mammalian species, strains, tissues and experiments was derived.This signature provides a set of genes that are most robustly responding to DR (17).",
      "\t\n\nBackground: Dietary restriction (DR), a reduction in food intake without malnutrition, increases most aspects of health during aging and extends lifespan in diverse species, including rodents.However, the mechanisms by which DR interacts with the aging process to improve health in old age are poorly understood.DNA methylation could play an important role in mediating the effects of DR because it is sensitive to the effects of nutrition and can affect gene expression memory over time.",
      "\tIV. 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.\t\n\nBy far the most widely studied dietary manipulation of aging is caloric restriction (CR), also called dietary restriction.CR consists of restricting the food intake of organisms normally fed ad libitum without triggering malnutrition and is the only dietary intervention shown, to date, to increase longevity and modulate the process of aging in several model organisms (Bishop and Guarente, 2007;Fontana et al., 2010;Spindler, 2010).Even in mammals, such as mice and rats, CR can extend longevity by up to 50%, delay physiological aging, and postpone or diminish the morbidity of most age-related diseases (Masoro, 2005).Ongoing studies in rhesus monkeys suggest that CR can lower the incidence of aging-related deaths in primates (Colman et al., 2009).",
      "\tGenDR--a database of dietary restriction-related genes\n\nDietary restriction (DR) delays the ageing process and extends lifespan in a multitude of species from yeast to mammals (22).However, the exact mechanisms of how DR extends lifespan are still unknown.As previously described (23), GenDR (http://genomics.senescence.info/diet/) is a database of DR-related genes.Herein, the use and function of GenDR will be briefly outlined along with updates since the 2013 HAGR paper (3).",
      "\tINTRODUCTION\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.",
      "\tIn comparison, caloric\nrestriction, intermittent fasting, or a ketogenic diet generally improve lifespan and health\n811 These dietary effects are not solely dependent on patterns of caloric intake, but are\nmodulated by dietary macro- and micronutrient composition, the amount of time spent in\ndifferent metabolic states, age of onset, periodicity of access to food, sex, and of greatest\nimportance to us in this studydifferences in genometype (strain) and gene-by-dietary\ninteractions 12,13. While the effects of differences in dietary composition and caloric restriction on lifespan\nhave been studied extensively, key results remain controversial 1416.",
      "\tNutrition, phenotype and longevity\n\nNo issue so 'vividly' illustrates the power of diet to alter health as the consistent observation of the effect of caloric restriction (CR) on longevity.To date, neither drug, gene nor environmental intervention have been successfully demonstrated to prolong longevity in animals; however, the simple reduction of food calories can increase life span by 30-40% across a number of model organisms, including yeast, Drosophilia, Caenorhabditis elegans, rodents and monkeys [5][6][7].This effect of CR raises one of the most intriguing questions facing life scientists today.Despite the demonstrated positive age-related benefits of a reduction in energy intake -including decreased insulin resistance [8], increased production of glucocorticoids [9] and increased production of heat-shock proteins [10] -the mechanisms by which CR contributes to increased longevity remain unknown.How CR leads to longer life span cannot be attributed to any single factor without considering the simultaneous effects of the others.CR could alter multiple age-related processes, from energy metabolism to oxidative stress and DNA repair.Unravelling the multiparametric links of CR and aging led to the seminal genomic experiment for nutrition: the gene expression analysis of young and old tissues in normal and CR animals [11   ] is a pioneering example of the use of DNA arrays to explore the effects of CR and aging on gene expression in mouse skeletal muscle.The experiment is compelling for its simplicity and its implications, that is, the gene expression profiles for a clear phenotypic difference were compared (young versus old versus CR old mice).The power of the technique was evident by the discovery of a wide range of affected genes, including those involved in protein and energy metabolism, biosynthesis (e.g. of fatty acids), and macromolecular damage, implying immediately that the effects of aging and CR are broad, yet interrelated.More detailed experiments are now being pursued around the world following the identification of the genes that are altered during aging and protected by CR.The publication of this experiment also followed the now routine approach of supplying the raw database through an accessible internet site.",
      "\t\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.",
      "\tWe present a metabolic model in which the anti-aging effects of DR\nare consistent with the ability to efficiently utilize dietary resources. NIH-PA Author Manuscript\n\nKeywords\naging; food restriction; lifespan; fertility; metabolic efficiency; quantitative trait loci; genetic\nmapping; physiology\n\nIntroduction\nDietary restriction (DR) extends lifespan and slows aging across a variety of taxa and also\ncauses myriad other physiological changes (Weindruch and Walford, 1998; Mobbs et al. ,\n2007).",
      "\t\n\nIn summary, we postulate that due to metabolic changes and the subsequent increase in stress response, physiological processes evoked by starvation show strong correlation with anti-aging processes (Table 2).These observations suggest that starvation may evoke the same stress response reaction as caloric restriction, which is the only treatment known to prolong lifespan in all organisms tested to date.The difference is that starvation results in a much stronger or prolonged induction.This accentuated response during starvation may facilitate the experimental identification of basic molecular mechanisms linking nutrition and health using a nutrigenomic approach."
    ]
  ],
  "task_id": [
    {
      "task_id": "4BA9088EE51B4FEB12D723A3AA9E1585"
    },
    {
      "task_id": "1883A6F02376841E63AE392D386836D7"
    },
    {
      "task_id": "CC48E25A01F0D10FF57B5E5B9EC8F179"
    },
    {
      "task_id": "5E49F30B15AA7350275E583CF98E70BF"
    },
    {
      "task_id": "94989E7D750B3FB87B9E5DA1F47DFD1F"
    }
  ]
}