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UNSOLVED MYSTERY Why Is Aging Conserved and What Can We Do about It? Jason N. Pitt, Matt Kaeberlein* Department of Pathology, University of Washington, Seattle, Washington, United States of America * firstname.lastname@example.org Abstract The field of aging research has progressed rapidly over the past few decades. Genetic mod- ulators of aging rate that are conserved over a broad evolutionary distance have now been identified. Several physiological and environmental interventions have also been shown to influence the rate of aging in organisms ranging from yeast to mammals. Here we briefly re- view these conserved pathways and interventions and highlight some key unsolved chal- lenges that remain. Although the molecular mechanisms by which these modifiers of aging act are only partially understood, interventions to slow aging are nearing clinical application, and it is likely that we will begin to reap the benefits of aging research prior to solving all of the mysteries that the biology of aging has to offer. Introduction Aging is something everyone can relate to. From grandparents, to parents, and ultimately our own bodies, we are intimately familiar with the declines in form and function that accompany old age. Yet, we don’t all appear to age at the same rate. Many individuals are healthy and active well into their 70s, 80s, or even 90s, while others will suffer from chronic disease and disability by the time they reach their 40s or 50s. Those of us that have companion animals also observe that different animal species or even subspecies, as in the case of dog breeds, age at profoundly different rates (Box 1). Defining the factors that influence individual rates of aging is a major focus of aging research. From a biomedical perspective, it is critically important to gain a better understanding of the mechanisms that drive biological aging, as age is the single greatest risk factor for the lead- ing causes of death in developed nations . The fact that aging influences so many different conditions is particularly curious (Fig 1). What is it about aging that creates an environment within our cells, tissues, and organs that is permissive for all of these seemingly disparate path- ological states? In order to understand the biological mechanisms of aging, scientists have turned to labora- tory model organisms such as rats and mice, fruit flies, nematodes, and even yeast. While some have questioned the utility of these systems as models for human aging, it is now clear that sim- ilar pathways and processes affect longevity in each of these species. These studies have resulted PLOS Biology | DOI:10.1371/journal.pbio.1002131 April 29, 2015 1 / 11 OPENACCESS Citation: Pitt JN, Kaeberlein M (2015) Why Is Aging Conserved and What Can We Do about It? PLoS Biol 13(4): e1002131. doi:10.1371/journal.pbio.1002131 Published: April 29, 2015 Copyright: © 2015 Pitt, Kaeberlein. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: JNP was supported by National Institutes of Health training grant T32AG000057 and by National Institutes of Health grant AG038518 to MK. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. Abbreviations: AMPK, 5' adenosine monophosphate-activated protein kinase; CR, caloric (or calorie) restriction; DR, dietary restriction; IGF-1, insulin-like growth factor 1; ILS, insulin-like signaling; mTOR, mechanistic target of rapamycin; NAD, nicotinamide adenine dinucleotide; NIA, National Institute on Aging; SOD, superoxide dismutase in the identification of interventions that slow aging in taxa spanning broad evolutionary dis- tances. Although it is still unknown whether these interventions will slow human aging, the po- tential impact on human health, if they do, is enormous. Box 1. Variation in Aging Rates across and within Species Despite many decades of study, the mechanisms that underlie the dramatic differences in life span across species still remain unknown. Even among the most common laboratory models used in aging research, there is a large range of aging rates: Caenorhabditis ele- gans grow old and die within a matter of 3 weeks, while mice and rats take about 50 times as long to age. Humans, of course, can live on average about 80 years in developed countries, with a documented maximum life span of 122 years . It is worth noting that these differences in species life span dwarf the magnitude of effects currently achievable from longevity-promoting interventions in the lab, which generally increase life span by 30%–50%. In an effort to understand the mechanisms underlying species differences in aging rate, efforts have been made to identify phenotypes that correlate with interspecies lon- gevity, and among the strongest is body size: larger animals tend to live longer than smaller animals when comparing across species . The molecular mechanisms underly- ing the correlation between body size and longevity across species remain unknown, al- though evolutionary arguments can be invoked to explain this relationship. For example, smaller species tend to be prone to higher rates of predation in the wild, and, thus, there is selective pressure to reproduce early in life and, perhaps, age quickly. Metabolic rate has also been suggested to underlie this relationship, as large animals tend to have a lower metabolic rate when normalized for body size. This is somewhat controversial, however, as the methods used to measure and normalize for metabolic rate are not wide- ly accepted or agreed upon. Not surprisingly, there are numerous outliers in such correlational analyses, and ef- forts are being made to attempt to understand interspecies aging rates by studying these outliers. One well-known example is the naked mole rate, which is similar in size and closely related evolutionarily to other rodents but lives about ten times as long and may never get cancer . Another example is certain species of clams that have exceptional longevity exceeding 300 years . Other species of particular interest include those possi- bly exhibiting negligible senescence, such as the hydra, some plants, and certain species of turtles and rockfish . Interestingly, the relationship between body size and longevity becomes inverted when comparing individuals within the same species: smaller individuals tend, on aver- age, to live longer. Domestic dogs offer a particularly good example of this: small breeds such as the Chihuahua tend to live about twice as long, on average, as larger breeds such as the Great Dane. For dogs, one of the largest predictors of body size is the level of insu- lin-like growth factor 1 (IGF-1) activity , which has also emerged as a conserved lon- gevity pathway in laboratory models (see Table 1). Although less pronounced in people than in dogs, the relationship between smaller body size and increased life expectancy also exists in humans, and one recent study suggests that rates of age-related diseases in- cluding cancer and diabetes are lower in humans with deficiency in growth hormone sig- naling . PLOS Biology | DOI:10.1371/journal.pbio.1002131 April 29, 2015 2 / 11 Fig 1. Common pathologies of aging. Incidence of disparate pathological conditions all showing a strong correlation with advanced human age. Many of these same conditions are also seen in aged dogs. doi:10.1371/journal.pbio.1002131.g001 Table 1. Some conserved pathways and interventions of aging. Interventions Yeast Worms Flies Mice Humans Environmental Dietary Restriction ✓ ✓ ✓ ✓** Lower Temperature ✓ ✓ ✓ ✓ ? Low Oxygen ? ✓✓?? Genetic Insulin/ILS ✓ ✓ ✓ ✓** mTOR/Rapamycin ✓ ✓ ✓ ✓** AMPK/Metformin ✓ ✓ ✓ ✓** Sirtuins/NAD ✓ ✓ ✓ ✓** SOD/Catalase ✓ ✓ ✓ ✓ ? **There is indirect evidence that each of these is associated with health span or longevity in humans. For speci ﬁ c examples, see references [8–49] and S1 Text. Abbreviations: AMPK, 50 adenosine monophosphate-activated protein kinase; ILS, insulin-like signaling; mTOR, mechanistic target of rapamycin; NAD, nicotinamide adenine dinucleotide; SOD, superoxide dismutase. doi:10.1371/journal.pbio.1002131.t001 PLOS Biology | DOI:10.1371/journal.pbio.1002131 April 29, 2015 3 / 11 Evidence That Aging Mechanisms Are Conserved and Some Potential Explanations There can no longer be any reasonable debate over whether shared environmental and genetic modifiers of longevity exist; several have been identified, and more will almost certainly be found (Table 1). This has been best studied in the context of dietary restriction (DR, also often referred to as caloric or calorie restriction, CR), which refers to a reduction in nutrient avail- ability in the absence of malnutrition. A variety of different DR interventions have been shown to extend life span in a diverse array of organisms, including numerous studies in yeast, nema- todes, fruit flies, mice, and rats, and one study in rhesus monkeys [51,52]. These observations have been strengthened by the discovery that genetic or pharmacological manipulation of key nutrient response pathways can have similar effects on longevity. Most notably, the insulin-like signaling (ILS)/ mechanistic target of rapamycin (mTOR) network has been found to play a central role in controlling life span in yeast, nematodes, fruit flies, and mice [53,54]. ILS and mTOR are both inhibited in response to DR and are thought to mediate many of the beneficial effects of DR on health and longevity. What remains less certain is whether these longevity fac- tors, and the pathways they act within, are truly modulating the rate of biological aging and, if so, whether they do so by similar or divergent mechanisms in different organisms. In general, the known conserved modifiers of longevity tend to mediate the relationship be- tween fundamental environmental and physiological cues (i.e., temperature, nutrient status, and oxygen availability) and the regulation of growth and reproduction. One school of thought holds that this relationship results from the ability of organisms to forgo reproduction and in- vest in somatic maintenance during times of adversity . In other words, based on the quality of the environment, the organism has evolved to make the appropriate choice between allocat- ing its limited resources toward reproducing rapidly, and hence aging more quickly, versus de- laying reproduction and allocating resources toward maintaining the soma, thereby aging more slowly. Lack of sufficient nutrients or other forms of environmental stress would thus tend to favor reduced signaling through growth promoting pathways, delayed reproduction, and longer life span. The idea that there is a direct trade-off between reproduction itself and longevity has been weakened by examples of long-lived mutants in both C. elegans and Drosophila that un- couple fecundity from life span extension [56,57]. At the same time, there is growing evidence that the sensing of the environmental cues is perhaps as important as the environmental compo- sition itself . This suggests that conserved sensory mechanisms coordinate both reproduc- tion and longevity but that reproduction and longevity are distinct and that it is the investment in somatic maintenance, in response to stress, that results in greater longevity (Fig 2). An alternative, but not mutually exclusive, model is that aging results from the continued expression of key developmental processes essential for growth and reproduction . This “hyperfunction model” posits that although such growth-promoting pathways are beneficial— indeed necessary—early in life, their unregulated continuous activity becomes detrimental late in life and leads to many of the pathological conditions associated with old age . Although the hyperfunction model has also been referred to as quasi-programmed aging, it is important to understand that, unlike programmed aging, this model does not postulate that natural selec- tion has evolved a genetically encoded aging program . True programmed aging, referring to an evolutionarily selected program of senescence, appears to be essentially nonexistent in an- imals . Instead, longevity pathways are likely to have been selected to optimally regulate de- velopment, growth, and reproduction, with the resulting effects on aging a secondary consequence that is largely invisible to natural selection. Based on this, we can speculate that conserved modifiers of aging exist because the sensory and signaling pathways that coordinate development, growth, reproduction, and stress PLOS Biology | DOI:10.1371/journal.pbio.1002131 April 29, 2015 4 / 11 resistance in response to environmental cues are themselves highly conserved. Whether the downstream mechanisms by which they affect life span are similarly conserved across species remains an open question that is discussed in greater detail below. What Are the Conserved Mechanisms of Aging? Although conserved longevity pathways clearly exist, it has been challenging to identify their primary molecular mechanisms of action or even to definitively determine whether they direct- ly modulate the rate of aging. This is true, in part, because there are no generally accepted mo- lecular markers of aging rate in any organism (Box 2). In mammals, several phenotypes are known to correlate with chronological age, and a handful have been suggested to have some predictive power for future life expectancy; however, none have been demonstrated convinc- ingly in prospective studies. One example is telomere length, which is often discussed as an “aging clock,” the idea being that our telomeres shorten as we get older and that this contrib- utes to declines in tissue function. Despite the popularity of the concept, it remains unclear whether telomere shortening actually causes a majority of the pathological consequences of aging, and there is no consensus in the field on this question [63,64]. Similar arguments have been made for age-associated changes in hormonal levels, deregulation of gene expression, post-translational modification of circulating proteins, activation of transposable elements, Fig 2. Environmental signals that modulate growth and reproduction also modulate aging. Organisms have evolved to grow and reproduce rapidly when environmental conditions, including temperature, oxygen levels, and food availability, are within optimal ranges. When these parameters are suboptimal but not lethal, organisms tend to become stress resistant and, perhaps as a by-product, longer lived. Conserved sensory pathways mediate this relationship, although there is also evidence that signals from the germ line can affect stress resistance and longevity and vice versa. doi:10.1371/journal.pbio.1002131.g002 PLOS Biology | DOI:10.1371/journal.pbio.1002131 April 29, 2015 5 / 11 Box 2. Biomarkers of Age and Aging Rate The identification of biomarkers of aging or biomarkers of aging rate has been a major goal of aging research for several decades. When considering biomarkers of aging, it is useful to differentiate between three important but different types of biomarkers: those that report on chronological age, those that report on biological age, and those that re- port on the rate of aging. Biomarkers of chronological age are intuitively straightforward: they provide quantitative measures of how old an individual is in units of time. Biomark- ers of biological age, in contrast, provide quantitative measures of how old an individual is in units of the physiological progression from young to old. This is, unfortunately, dif- ficult to define precisely, as we are still figuring out exactly what it means to be physiolog- ically young and old. Biomarkers of aging rate provide quantitative measures of how quickly an organism is aging biologically. In other words, biological age can be consid- ered as a function of chronological age multiplied by aging rate. The development of bio- markers of biological age or aging rate has the potential to greatly facilitate the development, validation, and translation of therapies to slow aging. In addition, such bio- markers could also be used to evaluate environmental or genetic factors that may acceler- ate the rate of aging, such as exposure to environmental toxins or unhealthy lifestyle choices. From 1988–1998, the National Institute on Aging (NIA) sponsored a Biomarkers Ini- tiative that included a series of workshops and a request for applications . The results of this effort are now regarded as largely disappointing because the initiative resulted in no bona fide biomarkers of aging. This may be, in part, due to the fact that the technolo- gy was not available at the time to deeply probe molecular features of aging systems, and the only well-characterized intervention to slow aging at that time was dietary restriction. Advances since then have yielded numerous different ways to slow aging and extend lon- gevity in model systems (see Table 1 for examples), and multiple studies have been aimed at identifying physiological, gene expression, proteomic, and metabolomic signa- tures that may be useful as biomarkers of aging rate . One commonly discussed biomarker of biological age is telomere length. Telomeres shorten with age in most human tissues, and a prominent model of aging proposes that telomere shortening is the primary driver of age-associated cellular dysfunction and se- nescence. Shorter telomeres in blood cells have been associated with chronological age, disease, all-cause mortality, and environmental factors such as stress and poor diet; how- ever, conflicting data from epidemiological studies and limitations to methodologies ap- plied to measure telomere length have limited the utility of telomere length as a measure of biological age . More recently, epigenetic changes to DNA in the form of methylation at specific sites have also emerged as a potential biomarker of aging. DNA methylation patterns appear to be predictive for chronological age across a variety of tissues, and one recent report claims that DNA methylation in blood can be used to predict all-cause mortality, even after controlling for other risk factors . Additional studies will be needed to confirm this report; however, if such measures are sensitive enough, they may provide a useful ap- proach to quantifying biological age and, perhaps, to testing the effects of interventions aimed at slowing aging rate. PLOS Biology | DOI:10.1371/journal.pbio.1002131 April 29, 2015 6 / 11 changes in chromatin structure, stem cell exhaustion, and accumulation of somatic or mito- chondrial DNA mutations [65,66], but to date none have been documented to provide quanti- tative measures of aging rate. If we define aging less specifically, to encompass the declines in cellular, organ, tissue and organismal function over time, then there is some evidence that the rate of aging can be slowed. For example, both DR and inhibition of mTOR with the drug rapamycin have been reported to delay the onset of multiple age-related phenotypes in mice, in addition to extending life span [52,70]. This includes reduced incidence of age-associated cancers, as well as improvements in age-related declines in cardiac, immune, kidney, liver, and cognitive function. In the case of DR, similar reductions in age-associated diseases have also been seen in rhesus monkeys . However, not all phenotypes of aging are delayed in any of these cases, preventing a definitive answer to this question. At a molecular level, conserved longevity pathways regulate numerous cellular processes that may contribute to their effects on health span and life span. In particular, a reduction in global mRNA translation, enhanced protein homeostasis, and improvements in mitochondrial func- tion with age are associated with DR, reduced ILS, and inhibition of mTOR . In mammals, these longevity-associated cellular changes are also associated with systemic changes, including a decrease in inflammatory processes that are likely to contribute to improved health [72,73]. The degree to which all of these changes, both individually and in concert, play a causal role in longevity and health span remains an area of active investigation, which may be informed by ap- proaches that focus on conserved genomic, proteomic, and metabolomic features of aging. Toward a Solution In addition to gaining an understanding of the molecular mechanisms of aging, a primary goal of aging research is to identify interventions that will slow aging in people. Advanced age is the primary risk factor for the majority of diseases in developed nations, and there are enormous so- cial and financial pressures associated with demographic shifts toward more elderly populations. Interventions that expand the period of healthy life and reduce the period of chronic disease and disability (referred to as “compression of morbidity”) offer the potential to alleviate these pressures while simultaneously increasing individual productivity and quality of life [74,75]. In practical terms, it may not be necessary to understand in detail why aging is conserved in order to do something about it. In several cases, components of the insulin signaling/ mTOR network, as well as the sirtuins, have been shown to be associated with longevity and age-asso- ciated disease risk in people [33,76]. While it remains unclear how difficult it will be to develop interventions to improve healthy aging in humans, there is reason for optimism that this may not be far off. Drugs that target these pathways, including some already shown to increase life span and health span in rodents, are beginning to be tested for effects on age-associated pheno- types or disease in humans. Rapamycin, for example, has recently been shown to partially re- verse age-associated declines in immune response to influenza vaccine in elderly humans , as had been seen previously in aged mice . Unfortunately, because of the glacial pace of human aging when compared to common ani- mal models, it will likely take several decades to determine whether rapamycin or other such compounds generally improve age-associated outcomes in people. We have recently proposed that companion animals, in particular mid- to large-size dogs, are an exceptionally good choice for bridging this gap through a midlife intervention trial with rapamycin (www.dogagingproject. com). Although there are some challenges, such as breed-specific susceptibilities to certain dis- eases, companion dogs offer many advantages for such a study: they share our environment, they suffer from many of the same age-related degenerative disorders that humans do and benefit PLOS Biology | DOI:10.1371/journal.pbio.1002131 April 29, 2015 7 / 11 from the same medical treatments , and it will be possible to obtain initial indications of effi- cacy as early as 6 months after the start of treatment (i.e., improvement in cardiac function), with survival outcomes available in a few years. Furthermore, the highly advanced state of veterinary medicine ensures that such a study can be done safely and that outcomes can be assessed accu- rately. Given that there are more than 70 million companion dogs in the United States alone, the impact of a successful intervention would be enormous and unprecedented, even if healthy life span is extended by only a year or two. Demonstrating the efficacy of rapamycin in dogs would not only greatly enhance the quality of life for companion animals and their owners but also pro- vide invaluable information for trials seeking to promote healthy aging in people. Supporting Information S1Text. Additionalreferences anddetailsof the experiments summarized in Table 1. (DOCX) Acknowledgments We thank Daniel Promislow for critically reading this manuscript. References 1. Coles LS (2004) Demography of human supercentenarians. J Gerontol A Biol Sci Med Sci 59: B579–586. PMID: 15215268 2. Promislow DE (1993) On size and survival: progress and pitfalls in the allometry of life span. J Gerontol 48: B115–123. PMID: 8315214 3. Buffenstein R (2008) Negligible senescence in the longest living rodent, the naked mole-rat: insights from a successfully aging species. J Comp Physiol B 178: 439–445. doi: 10.1007/s00360-007-0237-5 PMID: 18180931 4. Philipp EE, Abele D (2010) Masters of longevity: lessons from long-lived bivalves—a mini-review. Ger- ontology 56: 55–65. doi: 10.1159/000221004 PMID: 19468199 5. Finch CE (2009) Update on slow aging and negligible senescence—a mini-review. Gerontology 55: 307–313. doi: 10.1159/000215589 PMID: 19439974 6. Sutter NB, Bustamante CD, Chase K, Gray MM, Zhao K, et al. (2007) A single IGF1 allele is a major de- terminant of small size in dogs. Science 316: 112–115. PMID: 17412960 7. Guevara-Aguirre J, Balasubramanian P, Guevara-Aguirre M, Wei M, Madia F, et al. (2011) Growth hor- mone receptor deficiency is associated with a major reduction in pro-aging signaling, cancer, and dia- betes in humans. Sci Transl Med 3: 70ra13. doi: 10.1126/scitranslmed.3001845 PMID: 21325617 8. Lin SJ, Defossez PA, Guarente L (2000) Requirement of NAD and SIR2 for life-span extension by calo- rie restriction in Saccharomyces cerevisiae. Science 289: 2126–2128. PMID: 11000115 9. Smith ED, Kaeberlein TL, Lydum BT, Sager J, Welton KL, et al. (2008) Age- and calorie-independent life span extension from dietary restriction by bacterial deprivation in Caenorhabditis elegans. BMC Dev Biol 8: 49. doi: 10.1186/1471-213X-8-49 PMID: 18457595 10. Weindruch R, Walford RL, Fligiel S, Guthrie D (1986) The retardation of aging in mice by dietary restric- tion: longevity, cancer, immunity and lifetime energy intake. J Nutr 116: 641–654. PMID: 3958810 11. Mair W, Goymer P, Pletcher SD, Partridge L (2003) Demography of dietary restriction and death in Dro- sophila. Science 301: 1731–1733. PMID: 14500985 12. Holloszy JO, Fontana L (2007) Caloric restriction in humans. Exp Gerontol 42: 709–712. PMID: 17482403 13. MacLean M, Harris N, Piper PW (2001) Chronological lifespan of stationary phase yeast cells; a model for investigating the factors that might influence the ageing of postmitotic tissues in higher organisms. Yeast 18: 499–509. PMID: 11284006 14. Leiser SF, Begun A, Kaeberlein M (2011) HIF-1 modulates longevity and healthspan in a temperature- dependent manner. Aging Cell 10: 318–326. doi: 10.1111/j.1474-9726.2011.00672.x PMID: 21241450 15. Sestini EA, Carlson JC, Allsopp R (1991) The effects of ambient temperature on life span, lipid peroxi- dation, superoxide dismutase, and phospholipase A2 activity in Drosophila melanogaster. Exp Geron- tol 26: 385–395. PMID: 1936197 PLOS Biology | DOI:10.1371/journal.pbio.1002131 April 29, 2015 8 / 11 16. Conti B, Sanchez-Alavez M, Winsky-Sommerer R, Morale MC, Lucero J, et al. (2006) Transgenic mice with a reduced core body temperature have an increased life span. Science 314: 825–828. PMID: 17082459 17. Leiser SF, Fletcher M, Begun A, Kaeberlein M (2013) Life-span extension from hypoxia in Caenorhab- ditis elegans requires both HIF-1 and DAF-16 and is antagonized by SKN-1. J Gerontol A Biol Sci Med Sci 68: 1135–1144. doi: 10.1093/gerona/glt016 PMID: 23419779 18. Rascon B, Harrison JF (2010) Lifespan and oxidative stress show a non-linear response to atmospheric oxygen in Drosophila. J Exp Biol 213: 3441–3448. doi: 10.1242/jeb.044867 PMID: 20889824 19. Fabrizio P, Pozza F, Pletcher SD, Gendron CM, Longo VD (2001) Regulation of longevity and stress re- sistance by Sch9 in yeast. Science 292: 288–290. PMID: 11292860 20. Kenyon C, Chang J, Gensch E, Rudner A, Tabtiang R (1993) A C. elegans mutant that lives twice as long as wild type. Nature 366: 461–464. PMID: 8247153 21. Clancy DJ, Gems D, Harshman LG, Oldham S, Stocker H, et al. (2001) Extension of life-span by loss of CHICO, a Drosophila insulin receptor substrate protein. Science 292: 104–106. PMID: 11292874 22. Anisimov VN, Bartke A (2013) The key role of growth hormone-insulin-IGF-1 signaling in aging and can- cer. Crit Rev Oncol Hematol 87: 201–223. doi: 10.1016/j.critrevonc.2013.01.005 PMID: 23434537 23. Suh Y, Atzmon G, Cho MO, Hwang D, Liu B, et al. (2008) Functionally significant insulin-like growth fac- tor I receptor mutations in centenarians. Proc Natl Acad Sci U S A 105: 3438–3442. doi: 10.1073/pnas. 0705467105 PMID: 18316725 24. Willcox BJ, Donlon TA, He Q, Chen R, Grove JS, et al. (2008) FOXO3A genotype is strongly associated with human longevity. Proceedings of the National Academy of Sciences of the United States of Amer- ica 105: 13987–13992. doi: 10.1073/pnas.0801030105 PMID: 18765803 25. Kaeberlein M, Powers RW 3rd, Steffen KK, Westman EA, Hu D, et al. (2005) Regulation of yeast repli- cative life span by TOR and Sch9 in response to nutrients. Science 310: 1193–1196. PMID: 16293764 26. Powers RW 3rd, Kaeberlein M, Caldwell SD, Kennedy BK, Fields S (2006) Extension of chronological life span in yeast by decreased TOR pathway signaling. Genes Dev 20: 174–184. PMID: 16418483 27. Vellai T, Takacs-Vellai K, Zhang Y, Kovacs AL, Orosz L, et al. (2003) Genetics: influence of TOR kinase on lifespan in C. elegans. Nature 426: 620. PMID: 14668851 28. Robida-Stubbs S, Glover-Cutter K, Lamming DW, Mizunuma M, Narasimhan SD, et al. (2012) TOR sig- naling and rapamycin influence longevity by regulating SKN-1/Nrf and DAF-16/FoxO. Cell metabolism 15: 713–724. doi: 10.1016/j.cmet.2012.04.007 PMID: 22560223 29. Kapahi P, Zid BM, Harper T, Koslover D, Sapin V, et al. (2004) Regulation of lifespan in Drosophila by modulation of genes in the TOR signaling pathway. Curr Biol 14: 885–890. PMID: 15186745 30. Bjedov I, Toivonen JM, Kerr F, Slack C, Jacobson J, et al. (2010) Mechanisms of life span extension by rapamycin in the fruit fly Drosophila melanogaster. Cell Metab 11: 35–46. doi: 10.1016/j.cmet.2009.11. 010 PMID: 20074526 31. Harrison DE, Strong R, Sharp ZD, Nelson JF, Astle CM, et al. (2009) Rapamycin fed late in life extends lifespan in genetically heterogeneous mice. Nature 460: 392–395. doi: 10.1038/nature08221 PMID: 19587680 32. Wu JJ, Liu J, Chen EB, Wang JJ, Cao L, et al. (2013) Increased mammalian lifespan and a segmental and tissue-specific slowing of aging after genetic reduction of mTOR expression. Cell Rep 4: 913–920. doi: 10.1016/j.celrep.2013.07.030 PMID: 23994476 33. Passtoors WM, Beekman M, Deelen J, van der Breggen R, Maier AB, et al. (2013) Gene expression analysis of mTOR pathway: association with human longevity. Aging Cell 12: 24–31. doi: 10.1111/ acel.12015 PMID: 23061800 34. Mannick JB, Del Giudice G, Lattanzi M, Valiante NM, Praestgaard J, et al. (2014) mTOR inhibition im- proves immune function in the elderly. Sci Transl Med 6: 268ra179. doi: 10.1126/scitranslmed. 3009892 PMID: 25540326 35. Lu JY, Lin YY, Sheu JC, Wu JT, Lee FJ, et al. (2011) Acetylation of yeast AMPK controls intrinsic aging independently of caloric restriction. Cell 146: 969–979. doi: 10.1016/j.cell.2011.07.044 PMID: 21906795 36. Apfeld J, O'Connor G, McDonagh T, DiStefano PS, Curtis R (2004) The AMP-activated protein kinase AAK-2 links energy levels and insulin-like signals to lifespan in C. elegans. Genes Dev 18: 3004–3009. PMID: 15574588 37. Slack C, Foley A, Partridge L (2012) Activation of AMPK by the putative dietary restriction mimetic met- formin is insufficient to extend lifespan in Drosophila. PLoS ONE 7: e47699. doi: 10.1371/journal.pone. 0047699 PMID: 23077661 PLOS Biology | DOI:10.1371/journal.pbio.1002131 April 29, 2015 9 / 11 38. Martin-Montalvo A, Mercken EM, Mitchell SJ, Palacios HH, Mote PL, et al. (2013) Metformin improves healthspan and lifespan in mice. Nat Commun 4: 2192. doi: 10.1038/ncomms3192 PMID: 23900241 39. Bannister CA, Holden SE, Jenkins-Jones S, Morgan CL, Halcox JP, et al. (2014) Can people with type 2 diabetes live longer than those without? A comparison of mortality in people initiated with metformin or sulphonylurea monotherapy and matched, non-diabetic controls. Diabetes Obes Metab 16: 1165–1173. doi: 10.1111/dom.12354 PMID: 25041462 40. Onken B, Driscoll M (2010) Metformin induces a dietary restriction-like state and the oxidative stress re- sponse to extend C. elegans Healthspan via AMPK, LKB1, and SKN-1. PloS ONE 5: e8758. doi: 10. 1371/journal.pone.0008758 PMID: 20090912 41. Satoh A, Brace CS, Rensing N, Cliften P, Wozniak DF, et al. (2013) Sirt1 extends life span and delays aging in mice through the regulation of Nk2 homeobox 1 in the DMH and LH. Cell Metab 18: 416–430. doi: 10.1016/j.cmet.2013.07.013 PMID: 24011076 42. Kanfi Y, Naiman S, Amir G, Peshti V, Zinman G, et al. (2012) The sirtuin SIRT6 regulates lifespan in male mice. Nature 483: 218–221. doi: 10.1038/nature10815 PMID: 22367546 43. Rogina B, Helfand SL (2004) Sir2 mediates longevity in the fly through a pathway related to calorie re- striction. Proc Natl Acad Sci U S A 101: 15998–16003. PMID: 15520384 44. Tissenbaum HA, Guarente L (2001) Increased dosage of a sir-2 gene extends lifespan in Caenorhabdi- tis elegans. Nature 410: 227–230. PMID: 11242085 45. Kaeberlein M, McVey M, Guarente L (1999) The SIR2/3/4 complex and SIR2 alone promote longevity in Saccharomyces cerevisiae by two different mechanisms. Genes Dev 13: 2570–2580. PMID: 10521401 46. Bellizzi D, Rose G, Cavalcante P, Covello G, Dato S, et al. (2005) A novel VNTR enhancer within the SIRT3 gene, a human homologue of SIR2, is associated with survival at oldest ages. Genomics 85: 258–263. PMID: 15676284 47. Schriner SE, Linford NJ, Martin GM, Treuting P, Ogburn CE, et al. (2005) Extension of murine life span by overexpression of catalase targeted to mitochondria. Science 308: 1909–1911. PMID: 15879174 48. Orr WC, Sohal RS (1994) Extension of life-span by overexpression of superoxide dismutase and cata- lase in Drosophila melanogaster. Science 263: 1128–1130. PMID: 8108730 49. Melov S, Ravenscroft J, Malik S, Gill MS, Walker DW, et al. (2000) Extension of life-span with superox- ide dismutase/catalase mimetics. Science 289: 1567–1569. PMID: 10968795 50. Kaeberlein M (2013) Longevity and aging. F1000Prime Rep 5: 5. doi: 10.12703/P5-5 PMID: 23513177 51. Omodei D, Fontana L (2011) Calorie restriction and prevention of age-associated chronic disease. FEBS letters 585: 1537–1542. doi: 10.1016/j.febslet.2011.03.015 PMID: 21402069 52. Anderson RM, Weindruch R (2012) The caloric restriction paradigm: implications for healthy human aging. Am J Hum Biol 24: 101–106. doi: 10.1002/ajhb.22243 PMID: 22290875 53. Johnson SC, Rabinovitch PS, Kaeberlein M (2013) mTOR is a key modulator of ageing and age-related disease. Nature 493: 338–345. doi: 10.1038/nature11861 PMID: 23325216 54. Kenyon CJ (2010) The genetics of ageing. Nature 464: 504–512. doi: 10.1038/nature08980 PMID: 20336132 55. Kirkwood TB (2005) Understanding the odd science of aging. Cell 120: 437–447. PMID: 15734677 56. Flatt T (2011) Survival costs of reproduction in Drosophila. Exp Gerontol 46: 369–375. doi: 10.1016/j. exger.2010.10.008 PMID: 20970491 57. Giannakou ME, Goss M, Jacobson J, Vinti G, Leevers SJ, et al. (2007) Dynamics of the action of dFOXO on adult mortality in Drosophila. Aging Cell 6: 429–438. PMID: 17465980 58. Linford NJ, Kuo TH, Chan TP, Pletcher SD (2011) Sensory perception and aging in model systems: from the outside in. Annu Rev Cell Dev Biol 27: 759–785. doi: 10.1146/annurev-cellbio-092910- 154240 PMID: 21756108 59. Blagosklonny MV (2006) Aging and immortality: quasi-programmed senescence and its pharmacologic inhibition. Cell cycle 5: 2087–2102. PMID: 17012837 60. Gems D, Partridge L (2013) Genetics of longevity in model organisms: debates and paradigm shifts. Annu Rev Physiol 75: 621–644. doi: 10.1146/annurev-physiol-030212-183712 PMID: 23190075 61. Blagosklonny MV (2013) Aging is not programmed: genetic pseudo-program is a shadow of develop- mental growth. Cell Cycle 12: 3736–3742. doi: 10.4161/cc.27188 PMID: 24240128 62. Kirkwood TB, Melov S (2011) On the programmed/non-programmed nature of ageing within the life his- tory. Curr Biol 21: R701–707. doi: 10.1016/j.cub.2011.07.020 PMID: 21959160 63. Mather KA, Jorm AF, Parslow RA, Christensen H (2011) Is telomere length a biomarker of aging? A re- view. J Gerontol A Biol Sci Med Sci 66: 202–213. doi: 10.1093/gerona/glq180 PMID: 21030466 PLOS Biology | DOI:10.1371/journal.pbio.1002131 April 29, 2015 10 / 11 64. Boonekamp JJ, Simons MJ, Hemerik L, Verhulst S (2013) Telomere length behaves as biomarker of somatic redundancy rather than biological age. Aging Cell 12: 330–332. doi: 10.1111/acel.12050 PMID: 23346961 65. Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G (2013) The hallmarks of aging. Cell 153: 1194–1217. doi: 10.1016/j.cell.2013.05.039 PMID: 23746838 66. Butler RN, Sprott R, Warner H, Bland J, Feuers R, et al. (2004) Biomarkers of aging: from primitive or- ganisms to humans. J Gerontol A Biol Sci Med Sci 59: B560–567. PMID: 15215265 67. Hou L, Huang J, Green CD, Boyd-Kirkup J, Zhang W, et al. (2012) Systems biology in aging: linking the old and the young. Curr Genomics 13: 558–565. doi: 10.2174/138920212803251418 PMID: 23633915 68. Sanders JL, Newman AB (2013) Telomere Length in Epidemiology: A Biomarker of Aging, Age-Related Disease, Both, or Neither? Epidemiol Rev. doi: 10.1093/epirev/mxt011 PMID: 24363355 69. Marioni RE, Shah S, McRae AF, Chen BH, Colicino E, et al. (2015) DNA methylation age of blood pre- dicts all-cause mortality in later life. Genome Biol 16: 25. PMID: 25633388 70. Johnson SC, Martin GM, Rabinovitch PS, Kaeberlein M (2013) Preserving youth: does rapamycin deliv- er? Sci Transl Med 5: 211fs240. doi: 10.1126/scitranslmed.3007316 PMID: 24225941 71. Colman RJ, Beasley TM, Kemnitz JW, Johnson SC, Weindruch R, et al. (2014) Caloric restriction re- duces age-related and all-cause mortality in rhesus monkeys. Nat Commun 5: 3557. doi: 10.1038/ ncomms4557 PMID: 24691430 72. Licastro F, Candore G, Lio D, Porcellini E, Colonna-Romano G, et al. (2005) Innate immunity and in- flammation in ageing: a key for understanding age-related diseases. Immun Ageing 2: 8. PMID: 15904534 73. Chung HY, Cesari M, Anton S, Marzetti E, Giovannini S, et al. (2009) Molecular inflammation: underpin- nings of aging and age-related diseases. Ageing Res Rev 8: 18–30. doi: 10.1016/j.arr.2008.07.002 PMID: 18692159 74. Fries JF, Bruce B, Chakravarty E (2011) Compression of morbidity 1980–2011: a focused review of par- adigms and progress. J Aging Res 2011: 261702. doi: 10.4061/2011/261702 PMID: 21876805 75. Goldman DP, Cutler D, Rowe JW, Michaud PC, Sullivan J, et al. (2013) Substantial health and econom- ic returns from delayed aging may warrant a new focus for medical research. Health Aff (Millwood) 32: 1698–1705. doi: 10.1377/hlthaff.2013.0052 PMID: 24101058 76. Holzenberger M (2011) Igf-I signaling and effects on longevity. Nestle Nutr Workshop Ser Pediatr Pro- gram 68: 237–245; discussion 246–239. doi: 10.1159/000325914 PMID: 22044904 77. Chen C, Liu Y, Zheng P (2009) mTOR regulation and therapeutic rejuvenation of aging hematopoietic stem cells. Sci Signal 2: ra75. doi: 10.1126/scisignal.2000559 PMID: 19934433 78. Fleming JM, Creevy KE, Promislow DE (2011) Mortality in north american dogs from 1984 to 2004: an investigation into age-, size-, and breed-related causes of death. J Vet Intern Med 25: 187–198. doi: 10.1111/j.1939-1676.2011.0695.x PMID: 21352376 PLOS Biology | DOI:10.1371/journal.pbio.1002131 April 29, 2015 11 / 11 urev-cellbio-092910- 154240 PMID: 21756108 59. Blagosklonny MV (2006) Aging and immortality: quasi-programmed senescence and its pharmacologic inhibition. Cell cycle 5: 2087–2102. PMID: 17012837 60. Gems D, Partridge L (2013) Genetics of longevity in model organisms: debates and paradigm shifts. Annu Rev Physiol 75: 621–644. doi: 10.1146/annurev-physiol-030212-183712 PMID: 23190075 61. Blagosklonny MV (2013) Aging is not programmed: genetic pseudo-program is a shadow of develop- mental growth. Cell Cycle 12: 3736–3742. doi: 10.4161/cc.27188 PMID: 24240128 62. Kirkwood TB, Melov S (2011) On the programmed/non-programmed nature of ageing within the life his- tory. Curr Biol 21: R701–707. doi: 10.1016/j.cub.2011.07.020 PMID: 21959160 63. Mather KA, Jorm AF, Parslow RA, Christensen H (2011) Is telomere length a biomarker of aging? A re- view. J Gerontol A Biol Sci Med Sci 66: 202–213. doi: 10.1093/gerona/glq180 PMID: 21030466 PLOS Biology | DOI:10.1371/journal.pbio.1002131 April 29, 2015 10 / 11 64. Boonekamp JJ, Simons MJ, Hemerik L, Verhulst S (2013) Telomere length behaves as biomarker of somatic redundancy rather than biological age. Aging Cell 12: 330–332. doi: 10.1111/acel.12050 PMID: 23346