{
  "question": [
    "How do age-dependent changes in the immune system, such as immunosenescence, contribute to increased susceptibility to diseases?",
    "How do advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging?",
    "What recent discoveries have been made regarding the impact of systemic factors, such as circulating microvesicles, on aging phenotypes?",
    "How do changes in the gut microbiome composition correlate with aging and longevity?",
    "What are the key molecular mechanisms through which caloric restriction exerts its lifespan-extending effects across different species?"
  ],
  "answer": [
    "Immunosenescence, the age-related decline in immune efficacy, affects both the innate and adaptive immune systems. This decline compromises the body's defense against pathogens, leading to increased susceptibility to infections and diseases. Additionally, aging is associated with 'inflamm-aging,' a state of chronic sterile inflammation that can promote age-related diseases and functional decline. Changes in the immune system also include a decrease in the expression of genes associated with key immune functions, such as phagocytosis in macrophages, which can lead to dysfunction of innate immune cells. Furthermore, the accumulation of apoptosis-resistant cells in the elderly can lead to dysfunctional immune responses.",
    "Advancements in machine learning and artificial intelligence aid in the identification of biomarkers for biological aging by integrating and analyzing large and diverse datasets generated from genomic, functional, phenotypic, and lifestyle data. These technologies can predict age, forecast potential diseases in aging, and contribute to personalized medical treatment. Machine learning algorithms can also be used to assess the importance of specific factors in aging, predict future outcomes, and identify potential biomarkers for age-related diseases. Furthermore, they can help in the interpretation of complex omics data sets, which contain vast measurements of potential candidate markers.",
    "Recent discoveries have shown that systemic factors, including circulating microvesicles, play a significant role in aging phenotypes. For instance, age-related alterations in vasoprotective endocrine factors, such as growth hormone, IGF-1, and estrogens, have been found to regulate multiple aspects of vascular aging processes. Studies using heterochronic parabiosis in mice have demonstrated the impact of circulating factors on aging phenotypes. Additionally, there is initial evidence that antigeronic factors present in young mice can rejuvenate microvascular network architecture in aged mice. However, the exact nature of these antigeronic circulating factors remains unknown. Progeronic circulating factors, which increase with age and impair tissue homeostasis, have also been identified. Further studies are needed to identify additional progeronic and antigeronic factors and their impact on aging.",
    "Changes in the gut microbiome composition correlate with aging and longevity in several ways. Studies on centenarians and supercentenarians have shown that the microbiota adapts to the physiological changes of the long aging process, promoting health and survival. The concentration of certain bacteria, like Bacteroidetes, increases with age, while others like Actinobacteria decrease. Age-related decrease in microbiota diversity can lead to larger populations of certain microbial species, potentially increasing the chances for the evolution of novel, potentially pathogenic strains. These changes can contribute to increased frailty and development of diseases during the late stages of life. However, a healthy microbiota, characterized by the presence of bacterial compounds like Christensenellaceae, Akkermansia, and Bifidobacterium, has been linked to longevity.",
    "The key molecular mechanisms through which caloric restriction extends lifespan across different species include signaling through the insulin-like growth factor pathway, chromatin regulation by sir2, and oxidative damage. Caloric restriction also increases the response to oxidative stress and reduces the shortening of telomeres in chromosomes, which directly intervenes in the repair of DNA damage. Additionally, it affects nutrient-sensing pathways such as insulin/insulin-like growth factor (IGF-1) signalling and target of rapamycin (TOR) signalling."
  ],
  "contexts": [
    [
      "\t\n\nOn the other hand, a direct relationship exists between physiological aging and increasing incidence of chronic inflammatory diseases.In its acute form, inflammation acts as a protective mechanism in response to pathogen invasion or tissue damage and helps to restore physiological integrity and function.However, in its chronic form, inflammation can exert detrimental effects on the cellular as well as the organismic level.Chronically inflamed tissue is characterized by infiltration of immune cells, neovascularization, fibrosis, and often tissue damage and necrosis [3].The innate immune system, especially the mononuclear phagocyte system, is the most important mediator of chronic inflammation.Monocytes originate from the myeloid hematopoietic cell lineage in bone marrow.In the blood stream, monocytes are recruited by specific stimuli into different tissues, where they differentiate into phagocytic Oxidative Medicine and Cellular Longevity macrophages.Macrophages participate in the killing of invading microorganisms and emerging tumor cells through the production of reactive oxygen or nitrogen species (ROS and RNS).In addition, macrophages secrete cytokines, which play a key role in the regulation of multiple immune functions, especially inflammatory responses [3].During aging, the continuous pressure on the immune system caused by repeated antigen stimulation, such as infections, food antigens, allergens, and self antigens, leads to an increase in activated cells and secretion of proinflammatory cytokines, such as TNF [4].These circulating proinflammatory factors may keep the immune system in a state of chronic lowlevel activation, a phenomenon described as \"inflammaging\" [5,6].Eventually, this causes \"immunosenescence,\" that is, an age-related decline in the capacity of adaptive immunity, consisting of more specific responses carried out by B and T cells [7].Thus, with advanced age, the immune system undergoes a gradual remodeling in the attempt to reestablish a new balance that assures survival, however, favoring the development of chronic inflammatory conditions [5,6,8,9].",
      "\tThe Neuroimmune System Upon Aging\n\nThe age-associated synaptic dysfunction can also be a consequence of alterations in astrocytes and microglia, as the aging process has also been described as inflammaging, a status of chronic inflammation that contributes to the pathogenesis of neurodegenerative diseases [174].Recent data further suggest an important role of the immune system in regulating the progression of brain aging and neurodegenerative disease.This can be seen as a cause-or-consequence dilemma: do immune and inflammatory pathways become hyperactivated with age and promote degeneration or, instead, immune responses fail to cope with age-related stress and may contribute to disease [175]?",
      "\tAging is one of the inevitably dominant risk associated with many diseases. Several biological factors contribute to this etiology which include loss of telomeres, stem cells activity and metabolism, escalation of environmental and biological stress, dysfunctioning of various micro-and macromolecules, and cell cycle and weakening of immune system (Franceschi et al., 2018).In case of cellular and molecular damage before elderly age, injury is healed to maintain the hemostasis.Nonetheless, with aging, repair mechanism is slowed or completely halted, leading to number of pathologies (Cortopassi, Gurung, & Pinto-Plata, 2017).",
      "\t\n\nimmunity can become hyperactivated, exacerbating the age-related damage caused by innate immune responses [33].The risk of collateral damage by the adaptive immune system also potentially increases with age via autoimmunity factors, but this is believed to be counteracted by a parallel rise in self-protective mechanisms [42].Overall, the collateral damage inflicted by the innate immune system over the course of a long life is likely to be greater than that caused by adaptive immunity.\t\n\nThe damage caused by the ageing adaptive and innate immune systems gives us insights into how these different arms of the immune system may influence longevity.In general, adaptive immune function diminishes with age, whereas innate immune function is maintained [34,[43][44][45][46]. Whilst this may initially suggest that the innate immune system withstands the test of time better than the adaptive immune system, a chronic stimulation of innate immunity underpins this pattern [35].Innate immune cells become increasingly proinflammatory with age [46,47] and trained",
      "\t\n\nThe increased expression of genes involved in immune response and inflammation observed in the colon of the 21-month-old mice points to an affected immune system in this part of the intestine of aging mice.This observation is in agreement with the fact that changes in the immune system are one of the hallmarks of the aging body.Immunosenescence is the functional decline of the adaptive immune system brought on by natural aging whereby protection against infection by pathogens and the effectiveness of vaccination decline [45,46].The second aging-induced change in the immune system is called inflammaging which is characterized by a lowgrade chronic inflammation process that contributes to the pathogenesis of many age-related diseases [47][48][49].A large variety of cells with a defense function are present especially in the lamina propria and the submucosa of the intestine accomplishing immune protection via the innate as well as by the adaptive immune response.Interestingly, our microarray and Q-PCR data clearly show that activity of both branches of the immune system is enhanced in response to aging exclusively in the colon but not in the small intestine of old mice.Expression levels of well-established pro-inflammatory cytokines like IFN, TNF, IL6 and IL1 turned out to be extremely low in the colon of both old and young mice and below the threshold of our microarray analysis.These low expression levels are probably due to the fact that these cytokines are predominantly produced by immune cells in the mucosa which is a rather low percentage of cells in relation to all cells present in the intestinal tissue.Q-PCR analysis confirmed the very low basal expression levels of these pro-inflammatory cytokines, yet a weak but significant induction of IFN TNF and IL-1 in the colon of aging mice was observed.This result suggests that low-grade inflammation might be present in the colon of the aging mice in our study, although it should be noted that no altered expression of a number of established inflammation markers like Tolllike receptors (TLRs), C-type lectin receptors (CLRs) and retinoic acid-inducible receptors (RLRs) [50] was detectable.",
      "\tIntroduction\n\nAgeing of the immune system (immunosenescence) contributes to the increased susceptibility of the elderly to infectious disease and to the poor outcome of vaccination.Defence against pathogens is compromised mainly because of changes in adaptive immunity mediated by T and B lymphocytes; however, all components of the immune system are affected (Fig 1).Dissecting the crucial alterations responsible for dysfunctional immunity in old age will facilitate the development of rational interventions to reconstitute appropriate immune function.Given the increasing proportion of elderly people in most countries and their disproportionate consumption of health-care resources, this issue is rapidly gaining in importance.The meeting, which was dedicated solely to studies of immunosenescence, filled two days with the 'A to Z' of immunity, covering topics ranging from development to senescence, innate immunity to adaptive immunity, and genes to environments, in organisms ranging from mice to monkeys and humans.Understanding and eventually modulating immune dysfunction in the elderly now beckons.\tClinical implications of immunosenescence\n\nAs mentioned above, complications from acute infectious are likely to be more severe in the elderly owing to impaired innate immunity.However, questions remain concerning 'normal, healthy' ageing and the important clinical issue of responses to vaccinations in old age.In a mouse model of the highly relevant human pathogen influenza, the virus is cleared from the lungs more slowly in old animals, correlating with a delayed and decreased peak of cytotoxic T-cell production (D.Murasko, Philadelphia, PA, USA).Therefore, cellular responses are crucial for controlling the virus, but do not function adequately in old animals.Although there is an accumulation of memory cells (the clonal expansion referred to above), they are not solely responsible for this decrease in the virus-specific response.Both memory and naive T cells in old, but not young, mice are resistant to apoptosis, and do not 'make space' for new responses.In the mouse model, cell-transfer experiments showed that both the old environment and the old cells contributed to the problem-young cells did not deplete when transferred to an old environment and old cells did not deplete when transferred to a young environment.The factors inducing apoptosis resistance have not yet been identified; however, it is clearly important to do so and to search for them in humans.\tConclusions\n\nAll components of the immune system are altered as ageing proceeds (Fig 1 ); however, the T-cell and B-cell compartments seem to be particularly susceptible.The most severe clinical impact is probably a result of the loss of diversity in the TCR and B-cell-receptor repertoire, owing to the accumulation of dysfunctional cells, and decreased thymic and bone-marrow output.Several interventions discussed at the meeting could conceivably contribute to the restoration of appropriate immune function in the near future.\tLymphocyte development and ageing\n\nThe cells of the immune system turn over rapidly and therefore need constant replacement from the pool of haematopoietic stem cells (HSCs).If the HSCs themselves aged, it would compromise all downstream events that depend on their integrity, including production of immune cells and subsequent immune responsiveness (Rando, 2006).Evidence for age-associated alterations in the ability of HSCs to reconstitute the haematopoietic system of an animal derives from findings of increased self-renewal with age, resulting in an expansion of the HSC pool size even when transplanted into young animals (D.Rossi, Stanford, CA, USA).However, purified HSCs from old mice showed less activity on a per-cell basis and tended to generate more myeloid cells-for example, macrophages-than lymphocytes.Expression profiling of young and old HSCs revealed that genes mediating lymphoid fate and function were systematically downregulated, whereas myeloid-specification genes were upregulated, with age.The concerted nature of these changes suggests epigenetic involvement as a mechanism that contributes to HSC functional decline with age.There is also a gradual decline in the ability of murine HSCs to progress through the various stages of B-cell-differentiation (K.Dorshkind, Los Angeles, CA, USA).This reflects, in part, the microenvironmental changes involving altered production of interleukin 7 (IL-7) by stromal cells as they age (M.Cancro, Philadelphia, PA).B cells must also compete for the cytokine BLys (or B-cell activating factor (BAFF)), the receptor levels of which determine survival.Declining B-cell production in aged animals results in selective accumulation of marginal zone and memory B cells at the expense of the follicular pool of B cells.The follicular pool is responsible for producing protective immune responses to newly encountered pathogens, such as influenza H5N1.Loss of the declining stem-cell function, and the resultant decline of the follicular B-cell compartment, leads to enhanced infectious disease-related morbidity with ageing (J.Cambier, Denver, CO, USA).Hence, age affects both HSCs and the environment that determines their fate.\tInnate immunity\n\nSo, what are the age-associated changes that can be directly measured in macrophages, dendritic cells, neutrophils, natural killer (NK) cells and so on?These might be at least as important, if not more so, than the changes to adaptive immunity discussed above (Solana et al, 2006).The number and proliferation of a particular subset of 'natural' T cells with NK-cell and regulatory functions, bearing invariant V14J18 receptors (iNKT cells), is decreased in the elderly; however, whether these changes have any clinical impact is not yet known (R. Solana, Crdoba, Spain).Neutrophils from old people retain normal chemotaxis and superoxide-generation capacity, but are compromised in phagocytosis in the healthy elderly and more so in the traumatized elderly ( J. Lord, Birmingham, UK); these findings have important implications for infection in the elderly.Trauma, in the form of burn injury in mice, resulted in the death of old animals from infections that young animals were able to resist.This susceptibility of old mice correlated with higher levels of pro-inflammatory IL-6 and decreased T-cell function, and could be in part reversed by oestrogen treatment (E.J. Kovacs, Maywood, IL, USA).Dendritic cells-the essential bridge between innate and adaptive immunity-are similar in young and old people in terms of their response to cytokines (although those from the elderly secrete more IL-6 and tumour necrosis factor- (TNF)), surface phenotypes and morphology, whereas chemotaxis and, as with neutrophils, phagocytosis are impaired (S.Gupta, Irvine, CA, USA).Gene arrays indicate only a small number of differences between young and old dendritic cells, far fewer than in T cells.Nonetheless, functional impairment in antigen presentation was found, such that dendritic cells from young or old people stimulated naive CD8 cells equally well, but those from the elderly failed to stimulate CD4 cells appropriately.\t\n\nApoptosis-resistant cells that accumulate in old mice and humans-and fill the 'immunological space'-might be dysfunctional in several ways.In young mice, the number of T cells staining with soluble major histocompatibility complex (MHC)-peptide multimers carrying influenza epitopes was similar to the number of cells producing the antiviral and pro-inflammatory cytokine interferon- (IFN) on antigen stimulation.However, in old mice, the number of tetramer-positive cells exceeded the number of IFNproducers, indicating that some cells bearing antigen-specific receptors failed to respond appropriately to receptor ligation (H.Ertl, Philadelphia, PA, USA).This is similar to the situation in elderly humans, who have been found to accumulate large clonal expansions, primarily-and for unknown reasons-of cytomegalovirus (CMV)-specific CD8 cells (Pawelec et al, 2005).In the mice, this lack of reactivity was not due to poor antigen presentation by dendritic cells (Ertl).The reason for poor reactivity remains unknown; however, responses could be restored, in part, by vaccination using an adenovirus vector AdC68 that naturally infects chimpanzees rather than mice, as a way of improving immunizations by modifying the vaccine product.This might also be possible in humans by using better adjuvants for vaccination (E.Nagy, Vienna, Austria).Deciphering the mechanisms by which adjuvants enhance responses in order to design 'elderly-specific' vaccines will become increasingly important.This applies not only to infectious diseases but also possibly to vaccinating against cancer, as illustrated by differences in responses to anticancer immunizations in young and old mice.In a breast cancer model, preventive vaccination using DNA encoding certain cancer antigens was successful in protecting 90% of the young mice, but only 60% of the old mice, from developing metastases.This correlated with lower levels of IFN and IL-2 in old mice (C.Gravekamp, San Francisco, CA, USA).The production of IL-6, which is a potential inhibitor of vaccine-induced T-cell responses, was high in both young and old mice.Increasing IFN and IL-2, and depressing IL-6 production in the elderly, would therefore seem to be desirable.",
      "\tAging and variability among immune cells\n\nHow and why the immune system becomes less effective with age are not well understood.Martinez-Jimenez et al. performed single-cell sequencing of CD4+ T cells in old and young mice of two species.In young mice, the gene expression program of early immune activation was tightly regulated and conserved between species.However, as mice aged, the expression of genes involved in pathways responding to immune cell stimulation was not as robust and exhibited increased cell-to-cell variability.",
      "\t\nThe aging population is at a higher risk for age-related diseases and infections.This observation could be due to immunosenescence: the decline in immune efficacy of both the innate and the adaptive immune systems.Age-related immune decline also links to the concept of 'inf lamm-aging,' whereby aging is accompanied by sterile chronic inf lammation.Along with a decline in immune function, aging is accompanied by a widespread of 'omics' remodeling.Transcriptional landscape changes linked to key pathways of immune function have been identified across studies, such as macrophages having decreased expression of genes associated to phagocytosis, a major function of macrophages.Therefore, a key mechanism underlying innate immune cell dysfunction during aging may stem from dysregulation of youthful genomic networks.In this review, we discuss both molecular and cellular phenotypes of innate immune cells that contribute to age-related inf lammation.\t\n\nThe aging population is at a higher risk for age-related diseases and infections.This observation could be due to immunosenescence: the decline in immune efficacy of both the innate and the adaptive immune systems.Age-related immune decline also links to the concept of 'inf lamm-aging,' whereby aging is accompanied by sterile chronic inf lammation.Along with a decline in immune function, aging is accompanied by a widespread of 'omics' remodeling.Transcriptional landscape changes linked to key pathways of immune function have been identified across studies, such as macrophages having decreased expression of genes associated to phagocytosis, a major function of macrophages.Therefore, a key mechanism underlying innate immune cell dysfunction during aging may stem from dysregulation of youthful genomic networks.In this review, we discuss both molecular and cellular phenotypes of innate immune cells that contribute to age-related inf lammation.\tIntroduction\n\nThe human population is aging, which has led to the rise in prevalence of many so-called age-related diseases.Not only is the aging population much more susceptible to age-related diseases, they are also more susceptible to infections.For example, elderly individuals are at a higher risk of developing severe COVID-19 or complications from influenza infections [1,2].This increased chance of infection can be due to the decline of the function of the immune system, a phenomenon called 'immunosenescence' [3].Age-related changes in the function of the immune system are also accompanied by a chronic sterile inflammation, a mechanism dubbed 'inflamm-aging,' which is thought to promote age-related disease and functional decline [4].Inflamm-aging is associated with many different factors, most typically encompassing increases in pro-inflammatory cytokines tumor necrosis factor alpha [TNFa], interleukin 1 beta [IL1b] and interleukin 6 [IL6] [5].Although these cytokines may directly contribute to increased systemic inflammation.Age-related increase in genomic instability may itself also drive aspects of inflammaging.Indeed, re-activation of LINE-1 transposable elements during aging and in senescent cells has been proposed to drive an interferon response, thus contributing to sterile inflammation [20][21][22].In addition, chronic DNAdamage signaling itself, for instance in aged lymphocytes, may also render them more activation-prone through innate receptors even in the absence of infection [23].\t Immune decline is a hallmark of aging.  Aging associates with a state of chronic sterile inflammation.\t Aging associates with a state of chronic sterile inflammation.  Innate immune cells undergo widespread molecular and functional remodeling with aging.\t\n\nIn this review, we will focus on how innate immune cells act as key contributors to age-related inflammation (Figure 1).We will discuss both molecular and cellular phenotypes which have been described in the aging innate immune system, and how they could relate to the phenomenon of inflamm-aging and immunosenescence.\t\n\nImportantly, a key mechanism underlying innate immune cell dysfunction during aging may stem from dysregulation of youthful genomic networks.Indeed, aging is accompanied by widespread remodeling of transcriptional landscapes across tissues and cell types (reviewed in [33]).In addition, age-related inflammatory signatures at the transcriptional levels have been observed across species and tissues, suggesting that such 'omic' remodeling is a conserved aging response [34,35]."
    ],
    [
      "\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\nBiomarkers of aging can be used to assess the health of individuals and to study aging and age-related diseases.We generate a large dataset of genome-wide RNA-seq profiles of human dermal fibroblasts from 133 people aged 1 to 94 years old to test whether signatures of aging are encoded within the transcriptome.We develop an ensemble machine learning method that predicts age to a median error of 4 years, outperforming previous methods used to predict age.The ensemble was further validated by testing it on ten progeria patients, and our method is the only one that predicts accelerated aging in these patients.",
      "\t\n\nIt should be mentioned that although the objectives of those researchers sound encouraging and ambitious, the search for biomarkers of ageing for their application in the improvement of human health, and prevention of diseases related to ageing, will only increase the generation of data.The great part of the search for biomarkers has been as a result of the extensive studies of human cohorts, resulting in genomic, functional, phenotypic, and lifestyle data of the individuals studied (Table 13.1).Thus, due to the generation of these data and technological advances, possibly in the future, artificial intelligence programs will be able to reliably forecast the life of an individual, as well as the possible diseases that he may suffer in ageing; so these advances and discoveries will allow us to achieve a \"personalized medical treatment\" as a result of to the integration of biomarkers of ageing.\tMeg3\n\nDecrease in cell senescence [85] (continued) number of biomarkers that are candidates to determine human ageing.However, these biomarkers have considerable variability among different individuals because the ageing process has an intrinsic multicausal nature.So, a multisystemic integration of biomarkers to determine biological age is still reliably found.Currently, thanks to the different analyses performed using new technologies and new knowledge on the molecular basis, there are leading to the discovery of many Biomarkers classified according to their type and their modulation in ageing novel molecular markers.Some of these technologies are the omics techniques, such as metabolomics, proteomics or genomics, also induces data generation, offering an overview of new biomarkers of ageing.However, it remains to be clarified which markers can be an accurate, reliable predictor of ageing.Among the various studies carried out to solve these questions, the MARK-AGE study was a project supported by the European Commission.The main objective of this project was to carry out a population study of approximately 3200 subjects to identify a set of ageing biomarkers, which together with correctly established parameters, would measure the biology of an individual, compared to the result that would only have using a biomarker individually [72].\tIntegration of Biomarkers of Ageing\n\nBiomarkers of ageing allow estimating the biological age of an organism (Table 13.1) while providing information on their health status.Different studies are looking for the integrated use of multiple biomarkers, in order to make the estimation of health status more accurate.As we could see throughout this chapter, there are a large\t\n\nTo make significant progress in aging research, we urgently need molecular biomarkers for aging studies, particularly in humans.This chapter focuses on the inflammatory state, the markers of oxidative stress, and the hormonal profile which are the main functions that impact the development of aging and can be influenced by the gene and environmental variables in which human beings develop.",
      "\tDiscussion\n\nMachine-learning can be applied as a systems biology approach, integrating multiple classes of biometric data to assess the importance of specific factors, while also predicting future outcomes.Whereas conventional assessments of disease identification exist, more detailed genomic and epigenomic testing is likely to reveal a comprehensive, systemic valuation of an etiology.To-date, studies have applied machine-learning algorithms in examining the physiological, biochemical, and/or genetic components of disease onset or progression [51].The advantage of our current study is through the assimilation of patient-matched data across a variety of critically impacted systems, providing an archetype for developing novel, descriptive, diagnostic measures through machine-learning algorithms that are specific for each disease type.By individually representing our datasets in Figs. 2, 3 and 4, we were able to reach more conclusive data in Fig. 5 by choosing the most predictive features for our final model.For the first time, a multi-omics, machine-learning approach was used to assess the progression and development of type 2 diabetes mellitus in a patient population, identifying potential biomarkers for cardiovascular risk and revealing the fundamental role of genetics in the pathology.\t\n\nIn the current study, machine-learning was used as a predictive tool to integrate cardiac physiological, biochemical, genomic, and epigenomic biomarker data in a patient-matched fashion and enable determination of type 2 diabetic status.In 50 patients, machine-learning algorithms revealed the interconnectedness between diabetic classification, mitochondrial function, and methylation status.Our study highlights how novel biomarkers can be used to augment existing diagnostic standards as well as provide new, and more precise, methods for identifying the development and severity of type 2 diabetes mellitus in potentially at-risk populations, such as those with prediabetes.While we examine physiological, biochemical, and molecular datasets using machine-learning algorithms, our goal was to understand which features possessed the best predictive accuracies and if these specific features could be used alone, or in conjunction, with HbA1c.The purpose for the inclusion of models that do not rise above 50% predictive accuracy was to contrast them against those models that do rise above 50% in the absence of HbA1c, to determine which biomarkers are the best overall predictors.\t\n\nThe quantity and diversity of omics-based approaches continues to expand.Convenience and increasingly inexpensive options for biometric-based valuations incite a growing demand for the incorporation and meaningful explanation of large and diverse patient datasets.The methodology outlined in this manuscript can serve as an archetype for the development and implementation of machine-learning to other disciplines seeking to evaluate disease progression.By using various health outcomes datasets, we were able to identify, and combine, the most prominent biomarkers into an accurate predictive algorithm engineered around 50 patients.While we have identified specific genetic features that are highly predictive in 50 patients, as a much larger patient population is applied to this model, the prioritization of other features is likely to occur, enhancing the diagnostic potential for the individual diabetic or prediabetic patient.Indeed, this is the advantage of using machine-learning models, in that they continue to learn and develop more accurate predictions as the number of features and sampled population grows.\tMolecular pathogenesis and machine-learning\n\nWhile clinical practice has recently experienced a surge in deep learning applications used for non-invasive imaging [52], implementing machine-learning algorithms to the fundamental biochemistry and cellular and molecular processes of the body is now only blossoming.Onset and progression of type 2 diabetes has been traditionally measured through blood glucose levels, but, the multifaceted aspects of the disease could create variability in prognosis between vastly different demographic and ethnic groups.Owusu Adjah et al. [14] recently identified BMI as a risk factor for determining ethnic group disposition to type 2 diabetes mellitus.Specifically, the relationship between BMI and increased incidence of diabetes mellitus is non-linear; some groups, such as South Asian populations, were more disposed to developing the disease even at lower BMIs.While the current Fig. 6 Overview of machine-learning pipeline implementing biological variables across a spectrum of gathered information.From the patient population undergoing coronary artery bypass graft surgery (CABG), physiological parameters (demographics, health reports, etc.) and atrial tissue were used for subsequent analyses.From cardiac tissue genomic (mitochondrial DNA), epigenomic (TFAM promoter CpG methylation), and biochemical (nuclear and mitochondrial function) were assessed.Cumulatively, the biological data was processed through tree ensembles in SHAP and validated through CART analysis with tenfold cross validation.Using these machine-learning algorithms, graphical depictions and biomarker feature importance are able to be derived, allowing for prediction of the onset and progression of diabetes.Ultimately, by using biological data at the genomic and epigenomic level, it allows for precision medicine approaches and more personalized diagnostics and prognostics.TFAM: transcription factor A, mitochondrial; mtDNA: mitochondrial DNA; CpG: cytosine nucleotide followed by a guanine nucleotide; CART: Classification and Regression Trees; SHAP: SHapley Additive exPlanations manuscript examines cardiovascular tissue, other less invasive approaches have been used to apply machinelearning algorithms.By retrieving blood from the basilica vein, circulating biomarkers were examined for their role in predicting early recurrence of atrial fibrillation following cryoballoon ablation [53].Support vector machines confirmed that decreased levels of creatine-kinase (CK-MB) and Troponin T (TnT) were associated with increased early recurrence of atrial fibrillation following cryoballoon ablation.Additionally, a unique, non-invasive approach for potentially diagnosing type 2 diabetes in patients was performed through the examination of toenails.Carter et al. [54], through a variety of machine learning algorithms, focused on 22 elements, including aluminum, cesium, nickel, vanadium, and zinc, and was able to get an AUC of 0.90 when predicting diabetic status using a random forest model.Similar to parts of the aims of this study, other groups have attempted to use machine learning to separate diabetic and non-diabetic patients without the inclusion of blood glucose or HbA1c [55].In a testing set of 13,700 patients from the Luzhou, China region, random forest machine-learning algorithms provided a 0.7225 accuracy when predicting diabetic status from physical examination data in the absence of blood glucose [55].Also using a random forest model, Tang et al. [56] revealed how CpG island methylation data, combined with microRNA expression profiles, can be instrumental in cancer pathogenesis; implementing this two-feature selection process, they were able to identify the best tissue specific features, ultimately allowing for the identification of the originating tissue where tumor progression began.In a similar fashion, the machine-learning algorithm HeteSim [57], which examines heterogeneous datasets and calculates their relatedness, was employed in ascribing how gene profiles can be related to phenotypic outcomes, specifically in the validation and prediction of genes classified within major diseases [58].",
      "\tWhat do chemical biomarkers tell us about aging? Aging is not a homogeneous process\tThe nature of chemical biomarkers of aging\n\nCentral to the study of chemical theories of biogerontology is the definition of biomarkers of the aging process, chemical 'handles' that can be used to assess the progress of aging and the effectiveness of anti-aging strategies.As it turns out, most of the age-biomarkers measured today are products of non-enzymatic chemistry.Living organisms are complex mixtures of reactive chemicals, including dietary components, metabolic intermediates, side-products of metabolism, xenobiotics, drugs, etc.Reactions between the constituents of this mixture occur at random throughout the body, but evidence of the role of cumulative non-enzymatic chemistry in aging is most apparent in long-lived proteins, such as lens crystallins and tissue collagens.The increase in post-synthetic chemical modifications of crystallins with age results in an agedependent increase in brown color and fluorescence of lens proteins.These chemical modifications are associated with aggregation, crosslinking and insolubilization of lens proteins, leading gradually to the development of cataracts (Hoenders and Bloemendal 1983;Harding et al. 1989).Similar changes occur in collagens (Bailey et al. 1998) leading to decreased elasticity of the extracellular matrix, resulting, for example, in the age-dependent stiffening of tendons  Dilysine crosslink",
      "\t\n\nPeople of the same chronological age have different aging states, which can be monitored using various biomarkers (Belsky et al. 2015).These markers are usually measurable indicators of a particular outcome or source of aging, such as phenotypical measures like frailty and molecular measures like DNA methylation dynamics (Schumacher et al. 2021;Lpez-Otn et al. 2023).Although informative, they are not always quantitatively predictive of an individual's true biological age, nor are they easy to obtain.The advancement of high-throughput screening platforms and extensive longitudinal studies has greatly facilitated the search for new noninvasive and quantitative biomarkers of aging.For instance, highthroughput sequencing allows unbiased multiomics profiling of DNA, RNA, and epigenetic changes during aging, providing a comprehensive view of senescence at tissue and single-cell levels (Solovev et al. 2020;Aging Atlas Consortium 2021).These omics data sets contain vast and noisy measurements of potential candidate markers and, consequently, require carefully designed computational models to identify and extract predictive signals from the data.However, construction of such models is often highly degenerate, yielding little overlap of identified biomarkers between studies and thus making results difficult to interpret (Thompson et al. 2018;Galkin et al. 2020).\t\n\nMost of the existing omics-based aging clocks have been constructed using data from bulk tissues, which neglect the variations in cell compositions and cell-to-cell aging heterogeneity.To gain a more detailed and nuanced view of cell type-specific molecular changes during aging, several studies have applied machine-learning models to single-cell transcriptomics and DNA methylation data (Trapp et al. 2021;Buckley et al. 2023).Despite their success in predicting chronological age within specific training contexts, these clocks are constrained by their applicability to a limited number of cell types and tissues.Their generalizability to other cell types and disease data, particularly in cases with ambiguous cell type identities, remains uncertain.Additionally, problems like data sparsity and batch effects are more pronounced in single-cell omics data, further complicating the identification of consensus aging markers and the interpretation of model results.Furthermore, as chronological age is often the only available measure of biological age, it becomes critical to determine whether the features learned from single-cell omics data can capture other dimensions of biological aging.",
      "\t\n\nEach of these criteria deserves some amplification.A biomarker validation program would start with a list of candidate biomarkers, each known to be age-sensitive (by cross-sectional and/or longitudinal analyses) in adults.By hypothesis, some of these traits would reflect interindividual differences in the aging process, but each would also be sensitive to genetic and nongenetic factors that also vary among individuals, statistical \"noise\" that would interfere with the extraction of the \"signal\" attributable to aging itself.A correlation between age-sensitive immune parametersfor example, T-cell proliferation and T-cell cytokine production-would be relatively unhelpful in evaluating each of these parameters as potential biomarkers of aging, because the two assays are closely related and likely to be influenced by many factors unrelated to aging (e.g., recent infection, vaccination history, polymorphisms in immune system genes).However, a correlation between T-cell proliferation and, for example, muscle strength, or reflex speed, or lens protein cross-linking, or age at menopause, would be difficult to attribute to any obvious metabolic or pathophysiological mechanism other than linkage to some fundamental aging rate that might by hypothesis retard or accelerate changes in a wide range of age-sensitive traits.",
      "\tMultiomics technology\n\nThe broad diversity of omics biomarkers that have been used to assess biological responses provides new opportunities to understand the impact of the environment on the risk of age-related diseases.For example, the multiomics analysis and integration method produces a priority list of multiple sets of biomarkers, which together reflect the molecular responses of the exposome.Each of these data warrants integration into a biomarker panel to aid physicians in developing age-related disease diagnoses and prognoses [78].",
      "\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).\t\n\nTo facilitate target gene prioritization, a number of additional approaches may be employed.For example, in silico studies of transcriptional regulation can allow the identification of upstream regulators (for review, see de Magalha es et al., 2010).Furthermore, an emerging approach to study the complex interactions between the multiple components of biological systems is network biology (Baraba si et al., 2011).Given the complexity of aging, network approaches may be particularly suited to identify crucial regulators of its modulation by the environment.For instance, knowing the protein-protein interaction network of candidate proteins allows the identification of hubs, proteins with a large number of interactions, which tend to be more biologically relevant (Fig. 3).Together with other biological (e.g., kinases and receptors are often seen as promising drug targets), medical, and strategic considerations already used for target selection in drug discovery (for review, see Knowles and Gromo, 2003), the integrated knowledge of aging-related pathways can help identify suitable targets for drug discovery.In addition, the advent of largescale databases of compounds and drugs, such as Drug-Bank (Wishart et al., 2008), STITCH (Kuhn et al., 2008), and the Connectivity Map (Lamb et al., 2006), paves the way to cross-linking longevity/CR-associated genes with drug databases to identify candidate molecules for effects on aging.\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.",
      "\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]."
    ],
    [
      "Several studies have shown\nthat the systemic milieu regulates stem cell decline during aging. Liang et al. showed\nthat HSCs have a reduced ability to home to the bone marrow and spleen after\ntransplantation into old versus young recipients (Liang et al. , 2005). Further experiments\ndemonstrated that the muscle stem cell niche adversely effects stem cell function as\nevidenced by the restoration of old stem cell regenerative potential upon exposure to a\nyoung systemic microenvironment (Conboy et al. , 2005; Conboy and Rando, 2005).\tHowever, studies do indicate that aged tissues have a diminished capacity to return to a\nhomeostatic state after exposure to stress or injury, therefore indicating a defect in stem\ncell function during the aging process. Since the HSC population provides an ideal\nmodel to study stem cell aging, it is necessary to elucidate the mechanisms of\nhematopoietic aging and expand the findings to other tissues and organ systems. Theories of Aging and Age Related Epigenomic Changes\nThere are two major theories of organismal aging: evolutionary and damage\nbased.\tHSCs as a Model for Stem Cell Aging\nWhen studying aging it is important to choose an appropriate model system. For\ninstance, cells (such as skin and blood) that undergo continuous turnover are removed\nfrom circulation long before they have time to feel the effects of aging, and certainly long\nbefore they could exert an effect on tissue function. The predominant substrates for\naging, thus it seems, would be long-lived cells in the organism, namely tissue specific\nstem cells, since this population is exposed to both intrinsic and extrinsic effectors of\naging throughout the lifespan of an individual.\tWith\nthis in mind, it has been hypothesized that the aging or functional failure of tissuespecific stem cells, which fulfill this job, may limit tissue repair and renewal, therefore\ncontributing to overall organismal aging (Krtolica, 2005; Van Zant and Liang, 2003). Because of the unprecedented experimental model systems that are available for the\nexploration of HSCs, stem cell aging research in the field of hematology has been the\nsubject of extensive studies. Indeed, the hematopoietic system has served as an important\nmodel for advancing our understanding of stem cell biology and its association with\naging.",
      "The several lines of evidence support the hypothesis that essential metabolic pathways interconnected with environmental factors and genetic background are involved in the appearance of different markers of cellular senescence.They have emerged as potential regulators of cellular senescence, particularly through those pathways involved in the maintenance and repair of stem cells and progenitor cells: mitochondrial integrity, mitotic competence, and eradication of senescent cells.The complexity of events that are under the control of the genetic programs induced in response to environmental challenges creates the need for further studies that must be performed to unravel the biological roles of the highly dynamic aging process through different tissues and different stages of cell life.The increasing research across different species has allowed the identification of conserved processes associated with the biology of aging.However, it is essential to consider that information from lower organisms cannot be generalized, since worms do not develop age-associated diseases such as osteoporosis, arthritis, or Alzheimer's disease.",
      "There is growing evidence that noncell-autonomous mechanisms play a critical role in orchestrating vascular aging processes (Figure 1).Aging-induced alterations in vasoprotective endocrine factors are of particular importance.Such changes include an age-related decline in circulating levels of growth hormone, 215 IGF-1, 216 and estrogens, all of which regulate multiple aspects of endothelium-dependent vasodilation, 217 autoregulation of blood flow, 218 vascular structural remodeling, atherogenesis, 219 and angiogenic processes. 220he impact of circulating factors on aging phenotypes was also demonstrated by studies using mice with heterochronic parabiosis, which involves surgically connecting the circulatory system of a young and an aged mouse. 221erebromicrovascular density typically declines with advanced age, 222 and there is initial evidence that circulating antigeronic factors (which reverse/prevent development of aging phenotypes) present in young mice can rejuvenate microvascular network architecture in aged heterochronic parabionts. 221he antigeronic circulating factors present in young mice are currently unknown, and the previously proposed role for GDF11 (growth differentiation factor 11) 221 remains controversial.Future studies should identify additional antigeronic factors that might be targeted by interventions to extend vascular health span.Progeronic circulating factors increase with age and impair tissue homeostasis in young animals.There is initial evidence that mediators secreted by senescent cells (eg, inflammatory cytokines, such as TNF- 35 ) may serve as progeronic circulating factors.Further studies are warranted to identify additional progeronic proteins and determine their impact on atherogenesis, endothelial function, blood-brain barrier integrity, and microvascular function in aging.\t\n\nAdditional evidence to support a central role of antigeronic circulating factors governing vascular aging processes is derived from studies on caloric restriction-a dietary regimen, which improves health and slow the aging process in evolutionarily distant organisms. 223Caloric restriction was shown to promote a youthful endothelial phenotype by upregulating and activating eNOS in aged animals [223][224][225] and perhaps humans. 226 critical role of antigeronic circulating factors in vasculoprotective phenotypic responses induced by caloric restriction was first indicated by the observations that in vitro treatment of cultured aged endothelial cells with sera derived from caloric restricted animals mimics phenotypic effects observed in vivo during caloric restriction, promoting anti-inflammatory and proangiogenic effects. 42,227Treatment with sera derived from caloric restricted animals upregulates SIRT1 228 ; however, the exact nature of the circulating factor responsible for this effect remains elusive. ][231] Human studies are needed to identify novel progeronic and antigeronic circulating factors and their cofactors, activators, or inhibitors/antagonists and to seek associations with vascular aging phenotypes.Future studies should also identify cellular origins of circulating progeronic and antigeronic factors that impact vascular aging and characterize pathological conditions that alter their levels in circulation with aging.Further, mechanistic studies describing the cellular effects of progeronic and antigeronic circulating factors in the vascular wall are warranted.",
      "Mitochondrial-derived peptides (MDPs) in aging-related phenotypes",
      "Background: Aging is believed to have a close association with cardiovascular diseases, resulting in various pathological alterations in blood vessels, including vascular cell phenotypic shifts.In aging vessels, the microRNA(miRNA)mediated mechanism regulating the vascular smooth muscle cell (VSMC) phenotype remains unclarified.MiRNA microarray was used to compare the expressions of miRNAs in VSMCs from old rats (oVSMCs) and young rats (yVSMCs).Quantitative reverse transcription real-time PCR (qRT-PCR) and small RNA transfection were used to explore the miR-542-3p expression in oVSMCs and yVSMCs in vitro.Calcification induction of yVSMCs was conducted by the treatment of -glycerophosphate (-GP).Alizarin red staining was used to detect calcium deposition.Western blot and qRT-PCR were used to investigate the expression of the smooth muscle markers, smooth muscle 22 (SM22) and calponin, and the osteogenic markers, osteopontin (OPN), and runt-related transcription factor 2 (Runx2).Lentivirus was used to overexpress miR-542-3p and bone morphogenetic protein 7 (BMP7) in yVMSCs.Luciferase reporter assay was conducted to identify the target of miR-542-3p.Results: Compared with yVSMCs, 28 downregulated and 34 upregulated miRNAs were identified in oVSMCs.It was confirmed by qRT-PCR that oVSMC expressed four times lower miR-542-3p than yVSMCs.Overexpressing miR-542-3p in yVSMCs suppressed the osteogenic differentiation induced by -GP.Moreover, miR-542-3p targets BMP7 and overexpressing BMP7 in miR-542-3p-expressing yVSMCs reverses miR-542-3p's inhibition of osteogenic differentiation.Conclusions: miR-542-3p regulates osteogenic differentiation of VSMCs through targeting BMP7, suggesting that the downregulation of miR-542-3p in oVSMCs plays a crucial role in osteogenic transition in the aging rat.\t\n\nBackground: Aging is believed to have a close association with cardiovascular diseases, resulting in various pathological alterations in blood vessels, including vascular cell phenotypic shifts.In aging vessels, the microRNA(miRNA)mediated mechanism regulating the vascular smooth muscle cell (VSMC) phenotype remains unclarified.MiRNA microarray was used to compare the expressions of miRNAs in VSMCs from old rats (oVSMCs) and young rats (yVSMCs).Quantitative reverse transcription real-time PCR (qRT-PCR) and small RNA transfection were used to explore the miR-542-3p expression in oVSMCs and yVSMCs in vitro.Calcification induction of yVSMCs was conducted by the treatment of -glycerophosphate (-GP).Alizarin red staining was used to detect calcium deposition.Western blot and qRT-PCR were used to investigate the expression of the smooth muscle markers, smooth muscle 22 (SM22) and calponin, and the osteogenic markers, osteopontin (OPN), and runt-related transcription factor 2 (Runx2).Lentivirus was used to overexpress miR-542-3p and bone morphogenetic protein 7 (BMP7) in yVMSCs.Luciferase reporter assay was conducted to identify the target of miR-542-3p.Results: Compared with yVSMCs, 28 downregulated and 34 upregulated miRNAs were identified in oVSMCs.It was confirmed by qRT-PCR that oVSMC expressed four times lower miR-542-3p than yVSMCs.Overexpressing miR-542-3p in yVSMCs suppressed the osteogenic differentiation induced by -GP.Moreover, miR-542-3p targets BMP7 and overexpressing BMP7 in miR-542-3p-expressing yVSMCs reverses miR-542-3p's inhibition of osteogenic differentiation.",
      "The major question is whether replicative senescence does play a role in human aging.Several studies have shown an inverse relationship between donor age and the replicative life span in vitro for fibroblasts or MSC [13,44,45].This effect is usually relatively small with a high variation between different donor samples [12,46].At least some of the variability was attributed to differences in donor health status, conditions for the biopsy and the initial CFU-F frequency in the bone marrow sample [47].Furthermore, the pace of senescence might be affected by the culture conditions [19,48].In MSC preparations used in this study we did not discern any age-associated effects on replicative senescence.If the number of cumulative population doublings was not significantly affected by aging it is all the more surprising, that there was a significant association between age-induced gene expression changes and replicative senescence.These results indicate that the molecular sequels of aging in vivo and replicative senescence in vitro are based on similar mechanisms.",
      "Finally, we asked whether additional cellular components of the immune system also show increased transcriptional variability upon aging.",
      "Systemic aging has been more formally proposed as the hormonal\n\n3\ncontrol of aging, where changes in humoral factors with age can cause system-wide\nchanges in the homeostatic condition (Wise, Krajnak et al. 1996). Support for this idea\nhas gained traction from studies of mice expressing a mutant form of the KLOTHO gene\nencoding a protein hormone that leads to phenotypic changes characteristic of accelerated\naging (Kuro-o, Matsumura et al. 1997). Conversely, when the wild-type KLOTHO gene\nis overexpressed in mice it leads to a modest yet significant increase in both male and\nfemale lifespan (Kurosu, Yamamoto et al. 2005).\tStudies of invertebrate systems such as C. elegans and D. melanogaster\nhave yielded keen insight into stem cell biology and mechanisms of aging, but it has\npredominantly been the study of the mammalian hematopoietic system that has led to the\ncurrent understanding of the physiology of hematopoiesis. The utilization of mouse\ngenetics has only recently been fully realized as a tool as it was this mammalian model\nthat yielded the breakthrough discoveries of Till and McCulloch (Till and McCulloch\n1961).",
      "Our results indicate that cell identity influences multiple aspects of aging, highlighting the importance of aging studies at the single-cell level.However, it remains difficult to identify which age-related changes are causal and link molecular changes at the level of individual cell types to physiological aging phenotypes, like reduced glomerular filtration rate or decreased pulmonary regeneration.Future single-cell studies may focus on collecting additional time points and phenotypes throughout the aging process, allowing for time series-based causal inference methods (Granger 1969;Bar-Joseph et al. 2012;Finkle et al. 2018;Qiu et al. 2018;Lu et al. 2019) to reveal the relationships between the molecular players of aging.Functional challenges, such as the differentiation of stem cells during regeneration or the stimulation of immune cells during infection, would also help dissect how transcriptional aging magnitudes and differential gene expression influence tissue function.Single-cell measurements collected during functional challenges may also reveal the dynamics of perturbation and subsequent return to homeostasis necessary to evaluate \"resilience\" in a given cell type (Kirkland et al. 2016;Hadley et al. 2017).\t\n\nAt both the molecular and functional level, a host of aging phenotypes and associated mechanisms have been revealed in individual cell types (Shaw et al. 2010;Chakkalakal et al. 2012;Keyes et al. 2013;Liu et al. 2013;Flach et al. 2014;Blau et al. 2015;Brack and Muoz-Cnoves 2016;Keyes and Fuchs 2018).Although some of these studies present unique features of aging within individual cell identities, it is difficult to compare them systematically because of differences in experimental conditions and assay methodology.Using traditional molecular biology assays, it is difficult to measure high-dimensional molecular phenotypes across multiple cell identities, making large-scale comparisons of aging phenotypes across cell identities intractable.The recent development of single-cell RNA-sequencing (scRNA-seq) has ameliorated this limitation, allowing for measurement of transcriptional features across all prevalent cell identities in a tissue in a single experiment.\t\nAging is a pleiotropic process affecting many aspects of mammalian physiology.Mammals are composed of distinct cell type identities and tissue environments, but the influence of these cell identities and environments on the trajectory of aging in individual cells remains unclear.Here, we performed single-cell RNA-seq on >50,000 individual cells across three tissues in young and old mice to allow for direct comparison of aging phenotypes across cell types.We found transcriptional features of aging common across many cell types, as well as features of aging unique to each type.Leveraging matrix factorization and optimal transport methods, we found that both cell identities and tissue environments exert influence on the trajectory and magnitude of aging, with cell identity influence predominating.These results suggest that aging manifests with unique directionality and magnitude across the diverse cell identities in mammals.\t\n\nAging is a pleiotropic process affecting many aspects of mammalian physiology.Mammals are composed of distinct cell type identities and tissue environments, but the influence of these cell identities and environments on the trajectory of aging in individual cells remains unclear.Here, we performed single-cell RNA-seq on >50,000 individual cells across three tissues in young and old mice to allow for direct comparison of aging phenotypes across cell types.We found transcriptional features of aging common across many cell types, as well as features of aging unique to each type.Leveraging matrix factorization and optimal transport methods, we found that both cell identities and tissue environments exert influence on the trajectory and magnitude of aging, with cell identity influence predominating.These results suggest that aging manifests with unique directionality and magnitude across the diverse cell identities in mammals.",
      "Discussion Consequences of disease as well as age exert profound influences upon cells including alteration of gene expression, metabolism, functional competency, replicative potential, and more [10,18].Certain features of aged cells are exacerbated or mitigated by environmental conditions in host tissues such as oxidative stress, nutrient status, inflammatory / cytokine production, and pathological changes [5,7,40,42].Many of these conditions can be recapitulated in cell culture studies with treatments that mimic the aged tissue environment [6,37].Studies using established cell lines to study biological consequences of aging are of limited value for extrapolation to the complex in vivo mileau.In situ studies have provided significant insight regarding adaptations and distinct features of aged cells [9,46,55], but whether the characteristic phenotypic state of aged cells is retained following isolation and culture expansion is poorly understood.Moreover, conditions of culture expansion inherently favor cells with the highest proliferative and survival potential.Thus, it is unclear to what extent culture expansion allows hallmarks of aging to persist when harvesting cellular samples from aged tissue and subjecting them to multiple passages after initial isolation.",
      "Concluding remarks and future perspectives\n\nAging research has rapidly expanded over the past two decades, with studies ranging from lifespan-extending  [68,69,71].However, when their effect on cell death and senescence leads to stem cell loss and tissue degeneration, they might contribute to aging [66,67]."
    ],
    [
      "\t\n\nHowever, the simplest solution to restoring pathological disturbances in the composition of the gut microbiota may be a change in dietary habits.Diet has been shown to strongly affect the composition of the microbiome (73).When obese humans were put either on a fatrestricted or carbohydrate-restricted low-calorie diet, an increase in the abundance of Bacteroidetes and a decrease in Firmicutes was reported (12).In another study, diet-induced weight loss versus weight-stabilization interventions in obese humans increased intestinal microbial gene richness and was associated with a reduced systemic inflammation (74).These data corroborate with another controlled diet intervention study in 98 human subjects showing that certain dominant gut microbial communities, or \"enterotypes,\" correlated with specific kinds of diets (73).For example, Bacteroides was associated with a protein-rich diet, whereas Prevotella correlated with a fiber-rich diet; moreover, gut microbiota composition could be altered within 24 h whereas enterotype remained stable during the 10 days of the study.Based on this rapid and dramatic plasticity of intestinal microbiota composition, there is a specific need to determine intestinal microbiota composition in a standardized way (e.g., sequencing several fecal samples per person over a specific time point while taking dietary intake and medication use into account).",
      "\t\n\nWe next performed partial correlation analysis to investigate whether exercise-induced compositional changes in microbiota were associated with improvements in clinical parameters independent of body weight, fat mass, and visceral fat.We found that after adjustment for body weight and adiposity, associations between alterations of microbial species and improvements in insulin sensitivity-related indexes and a cluster of other metabolic features remained significant (Figure 3).At the community level, alteration in the gut microbiota was significantly associated with the percentage reduction of HOMA-IR (p < 0.01, ADONIS).Among the 19 species significantly correlated with the improvements of glucose homeostasis and insulin sensitivity, Ruminococcus gnavus, Alistipes shahii, Streptococcus mitis group, Eubacterium hallii, and Escherichia coli showed the strongest associations (Figure 3).Consistently, most of these species were also found to be differentially altered between responders and non-responders (Figure 2E).Taken together, the above findings imply that distinct changes of these species may underlie the difference in the improvement of glycemic homeostasis in response to a standard exercise regimen.",
      "\t\n\nOn the other hand, studies on centenarians and supercentenarians have evidenced the adaptation of the microbiota to the physiological changes of the long aging process.It has been demonstrated that the microbiota on this population maintains the health and promotes the survival.Additionally, a relationship between a healthy microbiota and longevity had been proposed [44].A possible pathway is an immunological and metabolic regulation linked to the increase of bacterial compounds like Christensenellaceae, Akkermansia, and Bifidobacterium [44,45].\t\n\nFigure 9.1 depicts a visual representation of the gut microbiota composition throughout the lifespan.Variations between individuals and within an individual throughout the lifespan can be seen.In this respect, it can be said that the concentration of Bacteroidetes grows as an individual does, from 12.6% for newborns to 57% for older adults.Conversely, Actinobacteria composition reduces with age until it reaches 0.4%, and the Firmicutes, Proteobacteria, and other microbial are maintained relatively stable throughout life in healthy adults and decay at old age [20][21][22].\t\n\nThe human holobiont (commensal microbes and their multicellular eukaryotic host) constitutes a highly integrated system, which undergoes dynamic changes through time as it integrates and responds to signals from the environment.Microbiome research and aging is flourishing as we better understand the bidirectional interactions, and its evolution with a life-course perspective for the gut microbiota undergoes dynamic changes during host aging.Changes in host intestinal cell Foreword vii composition and architecture occurring during aging are matched by a decrease in the microbiota taxonomic diversity.Age-related decrease in taxonomic diversity leads to larger population size for a few age-associated microbial species, increasing the chances for the evolution of novel potentially pathogenic microbial strains, which have been related both to neurodegeneration and frailty.This knowledge positions the microbiome as a promising element for translational research.\t\n\nAll the information given by the aging research allows knowing that the microbial composition has an essential role in the establishment of cellular and tissue homeostasis.Additionally, it is known that age-dependent changes in the microbial composition can contribute to increasing of frailty and development of diseases during the late stages of life [42,43].\t\n\nAlthough the causes that lead to changes in the composition and function of the microbiota during aging are still unknown, the evidence has established that the local microbiome plays an essential role in human health.\t\n\nTherefore, research in the field has demonstrated that aging is a potential modifier of the composition and function of the human microbiome.Figure 9.3 shows the local composition of the microbiome in an average older adult.It can be seen that Bacteroidetes and Firmicutes species are the most prevalent in this age.\tMicrobiome Research and Aging: A Clinical Perspective\n\nAging is characterized by the accumulation of damage at the molecular level (DNA and proteins) and dysfunction of the organelles [31][32][33].In addition to senescent cells and compositional changes in the extracellular compartment, these changes are determinants of the organic and systemic decline [34][35][36].The microbiota reacts dynamically to these environment changes by altering the metabolic function and composition of individual bacterial species.\tConclusions\n\nDuring the last years, significant advances in the field of microbiome and aging research have been carried out; new approaches for its study have allowed the understanding of the genomic nature of the microbiota.In this regard, the introduction of metagenomics had increased knowledge of the genes that potentially allow microbes to influence their hosts in unexpected ways.Thanks to these advances, it is well known that microbiota constitutes an essential determinant of the health and longevity of humans.\t\n\nFig. 9.1 Gut microbiota throughout lifespan\tMicrobiome and Age-Related Neurodegenerative Diseases\n\nDifferent microorganisms such as bacteria, fungi, archaea, and viruses compose the human intestinal microbiota that represents, in physiologic conditions, a perfect commensalism association with their host [51,52].In general, the human intestinal microbiota is shaped by the healthy microbiota (bacteria that normally colonize the intestine) and opportunistic bacteria (which are the agents responsible for infections).Among the billions of symbiotic microorganisms that compose the intestinal microbiome, four bacteria phyla are mainly reported in adults, i.e.Firmicutes (~51%), Bacteroidetes (~48%), Proteobacteria, and Actinobacteria, (1%) [53].Lactobacteria species stand out among the normal microbiome (Lactobacillus rhamnosus, Lactobacillus acidophilus, and Lactobacillus plantarum), Bifidobacterium (B.bifidum), Enterococci, Propionobacteria, and Peptostreptococci.In the same way, opportunistic bacteria include the Bacteriodes spp.Bacilli, Clostridia, Enterobacteria, Actinobacteria, Peptococci, Staphylococci, and Streptococcus [54].Several factors, such as diet, hygiene, antibiotic exposure, and modify the intestinal microbiota [55,56].Interestingly, age also contributes significantly to the microbiome modification; in fact a recent publication highlights the vital role that represents the host aging in the microbial evolution since as the host get aged the organism experiments molecular and functional changes that induce shifts to the microbial niche [57], nevertheless, for detailed information about changes in microbiome during aging, please refer to the Chap.9 in this book.In the following paragraphs, we discuss the recent data about the relationship between the pathogenesis of the two most prevalent ND and the microbiome, which represents a new field of research.\t\n\nDiet can be a potent gut microbiome modifier.For this reason, numerous studies have been conducted to demonstrate the impact of specific diet components on the diversity of the gut microbiota [8].The results of many of these studies have proved that probiotics and prebiotics consumption are a feasible alternative, especially for specific population groups such as older adults [59].\t\n\nMany areas of opportunity can be mentioned.However, modulation of the microbiome by extrinsic factors can be a way to apply the actual knowledge in the clinical setting.Nowadays, it is possible to ensure that lifestyle and diet play a significant role in determining the microbiome.In this respect, novel therapies, as fecal transplantation adds to the traditional dietary interventions, both demonstrated to be a potential therapeutic approach for the aging population.\t\n\nIt is well known that aging is a risk factor for neurodegeneration and dementia [58]; nevertheless, recent studies support the idea that gut microbiota may have an effect on the brain and the behaviour of patients, since the evidence suggests that some metabolites secreted by the intestinal microbiota can affect in a certain way, the cognitive capacity of patients diagnosed with ND [59][60][61][62][63].This hypothesis is not entirely new since several decades ago, the concept that bidirectional communication between the CNS and the intestinal organs plays a role in emotional regulation [64,65].Four decades later, the hypothesis that the brain has a regulation of the gastrointestinal tract arose and with the help of the murine model, the existence of the brain-gut axis was reported [66].This axis is carried out through the neuroendocrine and neuroimmune system, working together with the sympathetic and parasympathetic arms of the autonomic nervous system and the enteric nervous system.",
      "\t\n\nChanges in the gut microbiota in terms of composition and functionality during the process of aging have previously been reported [19,20,51] and it has been postulated that these changes might contribute to the development of immunosenescence and inflammaging [18,52].To establish whether the enhanced expression of genes playing a role in the immune system are due to modifications in the microbiota we measured the total number of all bacteria and of the two most prominent phyla colonizing the colon, Bacteriodetes and Firmicutes, in the luminal content of the colon.We did not observe aging-related changes.More advanced techniques like pyrosequencing are required to determine whether total number of bacteria and changes in the composition of the microbiota might play a causal role in the observed changed expression of immune-related genes in the colon of our aging mice.Although it is difficult to assess the physiological consequences of the enhanced expression of genes involved in inflammation and immune response, it seems most likely that this effect is important for the health status of the aging colon.",
      "\tSignatures of aging in gut\n\nFor gut or the digestive system, six clusters of age-associated genes had significant enrichment of functional annotations (Fig. 2C; Supplemental Table 10).Aging in gut was found to be associated with down-regulation of genes (Clusters 1, 2, 3, and 4) participating in oxidative phosphorylation, aromatic compound metabolism, muscle contraction, amino sugar metabolism, regulation of apoptosis, and vesicle transport.Aging was also associated with up-regulation of genes (Clusters 5 and 6) involved in regulating various physiological processes, amino acid metabolism, and regulation of transport.These results suggest that metabolic pathways, especially nutrient intake and energy production, are primarily affected during aging of gut, which are the fundamental function of the digestive system.",
      "\t\n\nSequencing of bacteria species within our gut, collectively labeled the gut microbiome, explains individual differences in the metabolism of consumed food with potential associations with body weight (Karlsson, Tremaroli, Nielsen, & Backhed, 2013).Gut permeability to bacteria is further associated with obesity and obesity-related inflammation (Teixeira et al., 2012).Over time, these mechanisms will more fully be integrated into the overarching models of obesity.",
      "\tThe microbiome and weight change\n\nThe human microbiome may play a significant role in the etiology of obesity in both humans and animal models (64).Hosted in the gastrointestinal tract, the gut microbiome is part of a large endocrine organ that regulates not only nutrient sensing and metabolism but also satiety and energy homeostasis.The millions of microorganisms comprising the complex intestinal \"superorganism\" perform a number of functions for host health, including food processing, breakdown and metabolism of indigestible nutrients, pathogen displacement, synthesis of vitamins, and regulation of body weight (65).They play such an important role that we now know that microbiota disruptions in early life can have long-lasting effects on body weight in adulthood (66).The host bacterial composition has been shown to adapt in response to dietary factors and in response to weight loss.Diet or surgically induced weight loss promote alterations in the gut that can impact the efficacy of the treatment strategies (67,68).Specific bacterial species can have influences by themselves.For example, the archaeon Methanobrevibacter smithii, has an enhanced ability to metabolize dietary substrates or end products of the metabolism of other bacteria, thereby increasing host energy intake and weight gain (69).",
      "\tThis microbial\ncommunity is established early in life, influenced by maternal and environment factors and\nable to impact the health of the host [2]. For example, early studies provided evidence that\ndiet plays an important role in the composition of gastrointestinal microbiota. Specifically,\ntransition to a low-fat diet in overweight humans led to a gut microbial composition similar\nto that of healthy controls [3, 4]. Also, gnotobiotic animals displayed substantial weight gains\nfollowing exposure to a complex gastrointestinal microbiota from overweight individuals\n[5, 6]."
    ],
    [
      "\tCONCLUSIONS\n\nOur purpose in this review is to outline the prospects of unifying mechanism in the genetics of aging.In case after case, from mice to worms to flies to yeast, genetic variants that modify metabolism also modify life span.These effects, collectively, are as general as that of caloric restriction, which also increases longevity and resistance to stress in many situations.The evolutionary theory of aging proposes that the life span is indirectly selected on the basis of the reproductive schedule.In turn, the reproductive schedule is coordinated by neural and endocrine mechanisms in multicellular organisms.Therefore, to consider that genes determining the life span could be expressed in neuronal and endocrine cells in diverse animals is no longer far-fetched.Consistent with this hypothesis are experiments in Drosophila and C. elegans in which life span was manipulated by the expression of genes in specific neurons.Genetic approaches may, thus, be able to identify a set of circuits that regulate longevity that were established in ancestral metazoans.",
      "\tGenetic Programs\n\nAs stated above, the universality of aging phenotypes within a species argues for an underlying genetic program.The redistribution of the Sir complex from telomeres to the nucleolus in yeast is a specific molecular While the effects of these hormones on specific orthan rats fed ad libitum, with a consequent decline in the incidence of hepatocellular carcinoma (Muskhelishvili gans are apparent, their relation to the aging process itself, if any, is not yet clear.To our knowledge, there has occurred in the past 200 years.However, slowing the aging process may increase vitality and quality of has been no animal study in which hormone supplementation extended maximum life span.However, the recent life over the entire life span of individuals.In this regard, it is noteworthy that calorically restricted rodents have findings in C. elegans provide a basis to believe that humoral factors may turn out to play an important role an extended life span that is relatively free of disease.For society, the implications of slowing the aging pro-in at least some aspects of human aging.cess are more complex.Of course, in an increasingly overpopulated world, it would be important to offset Perspective any significant effects on longevity with a compensatory Recent advances in the study of aging indicate that this reduction in birth rates.In fact, in many industrialized process is amenable to molecular analysis and may be countries, the current birth rate is sufficiently low to relatively simple.The potential of single gene mutations afford zero or negative growth.Most importantly, if the to greatly extend life span in model systems suggests slowing of aging is associated with improved health and that relatively few limiting cellular or organismal proproductivity of long-lived individuals, there may be a cesses control the rate of aging, at least in these species.",
      "\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\nLimitations of translating the results of preclinical studies should be recognized.An important recent example is caloric restriction. 239Although caloric restriction confers significant life span extension and cardiovascular protection in laboratory rodents 5,18,42,97,223,240,241 and in certain cohorts of nonhuman primates, 227,242 its protective effects in nonhuman primates in other studies 243 and in patients with multiple cardiovascular risk factors are less evident. 244Additionally, in cross-sectional studies, the older groups may represent a selected long-lived subset of the younger population.There are existing longitudinal studies in humans (eg, InCHIANTI study) and nonhuman primates, and important information related to mechanisms of vascular aging could be derived from add-on studies to these existing cohorts.",
      "\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\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.\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).",
      "\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\n\nThe 'hormesis' hypothesis of aging is based on the observation that caloric restriction or chronic low-level exposure to any of these stresses induces cross-resistance to other stresses at the same time that it extends life span (41).Hormesis effects on aging are observed in many eukaryotes in addition to budding yeast.Although the mechanistic details of these effects remain unclear, we have argued that they include a general response to environmental stresses that blocks entry into S phase under environmentally stressful conditions that are suboptimal for replicating DNA, thus protecting cells from replication stress (30).",
      "\tINTRODUCTION\n\nMore than 70 years ago, McCay and his colleagues demonstrated that a reduction in total food intake after weaning significantly increased both mean and maximum life spans of laboratory rats (1).Over the last seven decades, numerous laboratories have successfully repeated McCay's findings using various strains of rats and mice as well as non-mammalian species, such as fish and flies (2)(3)(4)(5)(6).Thus, food restriction has been established as a powerful experimental tool, and the anti-aging action of food restriction has become one of the most active areas of research in the realm of biogerontology (6).While life span extension by food restriction appears to be due to alterations in aging processes, the underlying mechanism(s) by which food restriction exerts its anti-aging effects remain elusive.Identification of important antiaging and anti-tumor targets of food restriction and elucidating the molecular mechanisms by which food restriction exerts its beneficial effects could eventually provide targets for intervention in humans.",
      "\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.\tThis again indicates that that weight gain\naccounts for only 45% of the change in lifespan. Author Manuscript\n\nOur findings can be compared to strain variation and GXE effects in response to dietary\nrestriction. Dietary restriction without malnutrition is regarded as having an almost universal\nbenefit on longevity 5355. One exception is a pair of studies on the impact of moderately\nintense restrictiona 40% reduction in caloric intakeacross a large family of LXS strains\nof mice (n of up 44 strains with 1020 replicates per strain) 17,19.",
      "\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.",
      "\t\n\nA key question still unresolved is to what extent the mechanisms of aging are conserved between species with vastly different lifespans.Some studies suggest that similar mechanisms are involved in aging in many species.For example, caloric restriction extends lifespan in yeast, worms, flies, mice, and primates (Weindruch 2003).Additionally, signaling through the insulin-like growth factor pathway, chromatin regulation by sir2, and oxidative damage have each been shown to affect lifespan in diverse model organisms (Tissenbaum and Guarente 2002).Other studies emphasize that changes occurring at the end of life are unlikely to be evolutionarily conserved (Kirkwood and Austad 2000).In the wild, very few animals (including humans) survive to their maximal biological lifespan.Thus, the changes in physiology that occur in very old animals have minimal effects on the fitness of individuals, and are unlikely to be evolutionarily conserved.Therefore, aging is likely to be species-specific, and studies of old age in model organisms are unlikely to be relevant to humans.",
      "\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": "99D2FBCC88055B11FD6F2181CECBFB9C"
    },
    {
      "task_id": "1FBEC3B0E217E49DDB92D2633A8C8A7E"
    },
    {
      "task_id": "2F1DB635DB8670F444D7835B65E10E06"
    },
    {
      "task_id": "F1D310BC7601CB79594B450C7874881E"
    },
    {
      "task_id": "C56D0E0F1C9234ECD5058B1BAC023499"
    }
  ]
}