Physiological and comparative evidence fails to confirm an adaptive role for aging in evolution
Alan A. Cohen
Groupe de recherche PRIMUS Department of Family Medicine University of Sherbrooke 3001 12e Ave N Sherbrooke, QC, J1H 5N4 Canada Alan.Cohen@USherbrooke.ca 819-821-8000 x72590 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Abstract
The longstanding debate about whether aging may have evolved for some adaptive reason is generally considered to pit evolutionary theory against empirical observations consistent with aging as a programmed aspect of organismal biology, in particular conserved aging genes. Here I argue that the empirical evidence on aging mechanisms does not support a view of aging as a programmed phenomenon, but rather supports a view of aging as the dysregulation of complex networks that maintain organismal homeostasis. The appearance of programming is due largely to the inadvertent activation of existing pathways during the process of dysregulation. It is argued that aging differs markedly from known programmed biological phenomena such as apoptosis in that it is (a) very heterogeneous in how it proceeds, and (b) much slower than it would need to be. Furthermore, the taxonomic distribution of aging across species does not support any proposed adaptive theories of aging, which would predict that aging rate would vary on a finer taxonomic scale depending on factors such as population density. Thus, while there are problems with the longstanding non-adaptive paradigm, current evidence does not support the notion that aging is programmed or that it may have evolved for adaptive reasons.
Keywords: Adaptation, Aging, Comparative, Disposable soma, Physiological dysregulation, Programmed 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
Introduction
The question addressed in this special issue is whether aging is a failure or an attainment of natural selection, a question that has remained controversial despite numerous writings on the subject over many decades (1-13). From an evolutionary perspective, aging is paradoxical: why age, when aging and death reduce reproductive output, the currency of natural selection (14)? The pertinence of this question is heightened by two observations. First, most organisms develop from a single cell that contains a copy of the organism’s DNA that is replicated in each cell of the organism. Accordingly, each cell contains all the genetic material necessary to make an entire organism. Second, some organisms have apparently infinite regenerative capacities and appear not to age. Putting these observations together, it is not clear at first glance why any organism would age – why not evolve a way to regenerate any damaged tissues and keep reproducing (10)? In other words, why is it that some organisms lack the ability to repair damage and live indefinitely, despite apparently having all the genes necessary in each cell of their bodies?
There have been two general types of response to this question. The first and most widely accepted type of explanation is that there are limits on natural selection’s ability to counteract aging. The mutation accumulation, antagonistic pleiotropy, and disposable soma theories are based on the declining force of natural selection with age (“selection’s shadow”), proposing either (a) that selection is simply too weak to counteract other forces (such as mutation) that cause aging (2), or (b) that aging, while not desirable from an organism’s fitness perspective, is still better than the available alternatives, which presumably lower reproductive output (5, 6, 13). Note that in the second type of explanation, aging is selected for inadvertently due to 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57
trade-offs rather than actively; to simplify terminology, I consider all three of these theories “non-adaptive” explanations.
These three theories are compatible with each other, and together present a convincing portrait for how evolution could produce aging despite selective pressure against it. However, empirical support for the three theories has been somewhat patchy. There have been a number of studies clearly demonstrating the action of one mechanism or another (less often mutation accumulation), but such studies generally demonstrate a case where the underlying principle operates on a certain gene or trait (15-22). This is not the same as demonstrating that these processes determine aging as we know it in all or most of the species in which it exists. The absence of strong general empirical support for these theories is due to the difficulty of finding appropriate tests. Even the detection of 100 or 1000 examples of genes subject to antagonistic pleiotropy would not necessarily indicate that the cumulative effects of these genes explains the core of the aging process, and it is not immediately clear what kind of evidence could fully support the non-adaptive theories. Note that this is not the same as saying these theories are not testable – there are lots of ways the theories could be refuted, and even incomplete confirmatory evidence constitutes a test. Rather, the point here is that there is strong
theoretical support but only weak to moderate empirical support for the non-adaptive theories, raising the possibility that these theories are insufficient, incomplete, or (if an alternative theoretical framework could be found) wrong.
The alternative to the non-adaptive theories, obviously, is the “adaptive” theories, theories that suggest that aging is actively selected for (7, 14, 23-27). More precisely, such theories propose that aging is the result of natural selection favouring genetic changes that increase the 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79
aging rate, and that such selection occurs because faster aging is a beneficial phenotypic trait. This is much harder to justify from an evolutionary perspective: dying younger means
reproducing less, all else equal. However, there are a number of ways that evolution might favour traits that are harmful to the individuals that show them. Group selection occurs when some groups survive and reproduce better than others, thus favouring their members
evolutionarily (28-32). A trait that disadvantages an individual within its group but helps the group can sometimes still be selected for, provided the group is sufficiently small and stable in its composition, the benefit to the group is sufficiently large, and/or the harm to the individual is sufficiently small. Kin selection is similar, but the benefit accrues by aiding relatives who are likely to share an individual’s genetic material (33, 34). Traits/genes that sufficiently benefit many relatives will be selected for as long as those benefits, weighted by the degree of relatedness, outweigh the costs to the individual. Even higher levels of selection are also possible – on species, on lineages, or on ecosystems – but the criteria tend to be even more stringent. Several articles have proposed ways that forces such as group selection, kin selection, or population dynamics might produce aging (7, 14, 23-25), but this has not been widely
accepted among evolutionary biologists.
In parallel to theoretical arguments for how adaptive aging might possibly evolve, a stronger line of evidence comes from empirical studies that appear to show aging mechanisms that are more consistent with aging as a programmed phenomenon. In particular, two pieces evidence appear to support aging as a phenomenon “programmed” by evolution. First, the number of cell divisions possible is limited in most lineages, and is followed by cellular senescence (35). This process is sufficiently complex, involving shortening telomeres and various regulatory 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101
controls, that it is hard to see how it might have arisen inadvertently (36). Second, there are a number of genes that, when knocked out or down-regulated, produce longer lifespans (37). Many such genes, such as in insulin signaling pathways (38), are shared across diverse taxa including yeast (Saccharomyces cerevisiae), nematodes (Caenorhabditis elegans), fruit flies (Drosophila melanogaster), and mammals, implying that selection could easily create longer lifespans just by knocking out or down-regulating these genes. Why then does it not do so? Advocates of adaptive aging theories claim, with good reason, that such empirical evidence is not fully considered by advocates of the non-adaptive theories because it would force them to reconsider their assumptions (7, 9, 36). For example, the detection of “aging” genes was not greeted in the literature on the evolution of aging with much serious introspection, even though this appears to be relatively strong evidence for an aging program of some sort. That being said, in the absence of a strong theoretical justification for the adaptive theories, it is appropriate that the bar be somewhat high.
If it could be established that aging is programmed, this would go a long way toward showing that aging is adaptive – how else could a program arise? The converse is not necessarily true: adaptive aging could, in theory, arise through a mechanism other than a program. In particular, it is possible to imagine that selection on a non-aging species to favor aging might produce errors in regulation or accumulation of damage rather than a clear program, and that a clear program might only evolve at very long evolutionary time scales. In this sense the adaptive-non-adaptive and programmed-non-programmed dichotomies are not fully redundant (39). This possibility has been under-explored by advocates of adaptive aging; nonetheless, it is not the focus of this article.
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The topic of adaptive versus non-adaptive aging is broad enough that I cannot hope to tackle all of the relevant arguments and evidence on both sides in this article. I will focus primarily on two types of evidence I think have been largely ignored in the debate. First, and most
importantly, an accurate understanding of the biological mechanisms of aging is essential to assess the quality of the evidence. I will contend that the arguments for adaptive aging have relied too much on singling out individual mechanisms and discussing them outside their larger context; by considering this context, it is straightforward to arrive at a mechanistic
understanding that is coherent with the non-adaptive theories (perhaps slightly tweaked). Second, the adaptive hypotheses generally result in relatively straightforward predictions for the taxonomic distribution of aging. I will argue that the taxonomic distribution of aging is
inconsistent with the adaptive theories, but is also not explicitly predicted by the non-adaptive theories. There is thus a need for a framework that explains why some organisms age and some don’t, and why those that do age at the rates they do.
A comprehensive mechanistic framework to understand aging
In recent years, it has become increasingly apparent that no single mechanism is sufficient to explain aging (6, 40-42). Mechanisms such as oxidative damage, inflammation, and telomere shortening are all supported by substantial bodies of evidence, but are also contradicted as exclusive theories by other studies. For example, naked mole-rats are particularly long-lived rodents, living up to 28 years, and yet they sustain levels of oxidative damage much higher than other rodents (43). Inflammatory systems are not even present in some species that age, such as nematodes. Mice have very high levels of telomerase and thus minimal telomere shortening, 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145
but age much more quickly than most other mammals (44). The message is not that any of these theories is wrong, so much as that each alone is insufficient. The presence of multiple, interacting aging mechanisms is now accepted my most researchers.
Even beyond the presence of multiple mechanisms, there is increasing support for a more general theory of dysregulation as a unifying mechanism subsuming many of the other mechanisms (40, 45-49). (This would contrast, for example, with a theory based on simple cumulative effects of many mechanisms, such as is often considered under the disposable soma (6).) Under this theory, organisms maintain and adjust homeostasis via complex physiological regulatory networks (PRNs) (50). PRNs contain large numbers of interacting molecules and exhibit feedback loops, redundancy, system-level properties, and other characteristics of formally complex systems (51-53). The presence of complex PRNs capable of maintaining homeostasis and adjusted over evolutionary time is predicted by the general model of self-organizing systems and complex dynamics as outlined by Kauffman more than 20 years ago (52).
The existence of PRNs is not in doubt; what is not necessarily evident is that the functioning of PRNs breaks down over time and causes aging. Many geriatricians and others who see aging daily in a clinical context have longed believed some version of this theory, but it is hard to test. The structure of PRNs is not fully known; what would evidence of dysregulation look like? My research group has been actively tackling this question, and our recent findings provide strong support for the role of dysregulation in aging. Our method is based largely on a measure of dysregulation calculated via the statistical distance among a set of biomarkers (54). The statistical distance quantifies how aberrant any individual’s biomarker profile is relative to the population mean, in consideration of the correlation structure of the variables (55, 56). We have 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167
now made and confirmed the following predictions that would support (a) that statistical distance measures physiological dysregulation, and/or (b) that physiological dysregulation is related to aging:
1) Statistical distance increases with age within individuals (Fig. 1; 54, 57). Obviously, this is predicted if it is related to aging.
2) Statistical distance predicts health outcomes related to aging, after controlling for age (Fig. 1; 54, 57, 58, 59). This has been confirmed in humans for mortality, clinical frailty, number of chronic diseases, and presence of cardiovascular disease and diabetes (but not cancer). It has also been confirmed in a wild bird population using two
measures of body condition: a measure of foot inflammation, and maximum thermogenic capacity (60).
3) Statistical distance is not particularly sensitive to the combination of biomarkers chosen (54, 57, 58). (Corollary: the effects of individual markers on statistical distance are minor.) This is a critical prediction because the ability to detect a similar signal with very different combinations of markers strongly supports the idea that dysregulation is a system-level property of a network rather than a function of a small number of
molecules. It has now been broadly confirmed for 44 biomarkers from multiple
physiological systems in humans (58). In addition, it is supported by our analyses on wild birds, conducted with the only 11 markers available (a set quite different from in
humans) (60).
4) The strength of the signal increases as more biomarkers are included in the calculation of statistical distance (Fig. 1; 54, 58).
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5) These predictions hold in diverse human populations and in other species, implying a general principle (57, 59, 60).
6) A stronger signal is detected with the use of a reference population (the population used to calculate the means vector and covariance matrix) that is younger and healthier than the population as a whole (58). The principle of using statistical distance to measure dysregulation is based on the idea that the population mean vector is a reasonable approximation for a state of perfect health; using younger and healthier individuals should thus better approximate this state, and increase the force of the signal.
In addition, we have shown that dysregulation can be measured meaningfully within specific physiological systems, as well as globally (manuscript in preparation). This suggests a model in which sub-networks (i.e., physiological systems) become dysregulated within themselves, but with substantial feedback effects across sub-networks and perhaps also to key integrator molecules that coordinate sub-networks. Taken together, we feel this evidence shows
convincingly that regulatory networks break down during aging. What we have not yet shown is that the dysregulation is the fundamental cause of aging; we cannot exclude the possibility that dysregulation is a downstream effect of some single master aging process. However, there is also no particular evidence in favour of such a master process.
This mechanistic theory of aging-as-dysregulation can incorporate oxidative stress, inflammation, and telomere shortening. Free radical production is often beneficial to an organism, and is actively used in both signalling and the immune function (61). Harmful effects may thus result only when proper control of the system is lost. As can be seen with high-190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211
oxidative-damage naked mole-rats (43), there is likely to be substantial variation across species in the capacity to tolerate oxidative damage, and thus in the sensitivity to loss of control. Inflammation is likewise a normal and critical part of the immune system; pathological inflammation is generally recognized as an aberrant state of the system caused by various complex interactions with other systems, notably sugar and lipid metabolism (62, 63). Telomere shortening to the point where there are important phenotypic consequences might likewise be a result of improper telomerase regulation and interactions with other processes such as
oxidative damage (64). While it is not likely that all aging mechanisms can be subsumed under a dysregulation framework, the majority of the most important ones likely can.
If this dysregulation model is correct, it may explain not just the physiology of aging but also its evolution (50). The aging rate of a species (and even whether it has aging) is likely to be a result of PRN structure, with some taxa having structures that are inherently more stable – and thus less susceptible to dysregulation – than others. Key aspects of physiology, including aging rate, are likely adjusted via changes in PRN structure, as predicted by Kauffman for gene regulatory networks (52).
One of the first potential objections to a model of dysregulation is that a large number of genes, such as daf-16 in nematodes and Sir2 in yeast, induce aging via clear downstream effects that accelerate damage (37), including free radical damage, lack of macromolecule repair, etc. I would suggest that this interpretation is not quite correct: rather than induce aging, these genes regulate the rate of dysregulation. I propose that dysregulation is not completely avoidable in species that are subject to it, but that it can happen at different rates that can be affected by internal control mechanisms. The slowest rate may not always be optimal: there may often be 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233
trade-offs with other physiological or organismal processes, such as cancer protection, mounting an effective immune response, activating the reproductive system for breeding season, etc. Admittedly, little of this has yet been tested, but the hypothesis is consistent with current empirical evidence.
Implications of a dysregulation model of aging for adaptive aging theories
Obviously, by definition, dysregulation as an aging mechanism suggests that aging is not programmed. It suggests a specific physiological constraint that may be present in some taxa but not others. It is not fully subsumed within any of the three standard non-adaptive theories: the structure of an organism’s regulatory networks may be unstable, and thus subject the organism to aging regardless of selective pressure as implied by mutation accumulation, antagonistic pleiotropy, and the disposable soma. Furthermore, it has clear implications for a number of arguments made in favour of programmed aging.
“Dysregulation” versus “programmed” aging
The most general implication for adaptive theories is the need to be much more cautious in the use of the word “programmed.” Programmed and dysregulated physiological aging
processes are not necessarily mutually exclusive, but if both exist, the presence of a regulatory chain of molecules is not sufficient to establish that a process is programmed. The regulatory links are programmed, but the consequences of those links can vary depending on other
aspects of system state, and not every consequence of a regulatory chain can be assumed to be programmed. Perhaps the best-known example of this is the acute versus chronic stress
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responses in vertebrates (65). Acute stress as triggered by glucocorticoid hormones is clearly adaptive and programmed. Chronic stress is detrimental to health, though it is not necessarily clear whether a state of chronic stress may still be better than some hypothetical alternative. Regardless of the overall costs versus benefits of chronic stress, there are clearly some ways in which chronic stress has negative effects on phenotype, such as through increased risk of heart disease. Thus, the same “programmed” pathways have very different consequences when they are activated over different timescales. Is heart disease a “programmed” consequence of stress? Clearly it is not, even though the pathways themselves are programmed.
This more restrictive sense of “programmed” is particularly relevant for the role of telomere shortening in cellular senescence, cancer protection, and aging, as discussed at length by Milewski (36). A traditional non-adaptationist view of telomere shortening is that it evolved as a cancer protection mechanism, accompanied by the side-effect of aging. Milewski argues that many aspects of the pathways triggered by telomere shortening and cellular senescence are advantageous for aging but not for cancer prevention, such as inflammatory cytokines and epithelial growth factors secreted into the extracellular environment. However, if the regulatory links implicated in this process have other, normal functions, it is possible, even likely, that the negative “aging” consequences in this particular context are an inadvertent result of
dysregulation. This in turn means that the bar is relatively high to demonstrate programmed aging: it is not sufficient to show activation of a coherent pathway that results in aging, it must also be shown either that the pathway has no other function than to cause aging, or explicitly shown that the pathway evolved in order to cause aging.
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Dysregulation and hormesis
One prominent argument for adaptive aging is that there are hormetic effects of things like nutrition and exercise (9). That is, things that would seem from an evolutionary perspective like they could only hurt organisms – starvation and physical exertion – actually make organisms healthier and/or prolong lifespan when received in the correct dose. Thus caloric restriction extends the lifespan of most species it has been tested in (66), and moderate exercise is good for health. This would clearly appear to contradict the idea that energy is the limiting factor for lifespan, forcing trade-offs, as suggested most particularly by the disposable soma theory.
Once again, understanding aging as a process of dysregulation provides a clear, simple way to reconcile non-adaptive aging with these hormetic phenomena. An organism’s PRN is shaped by evolution to help the organism function in its imperfect environment (50). PRN structure needs to evolve as conditions change to ensure that organisms’ physiologies match their ecological contexts. This in turn implies trade-offs in PRN structure. A simple example is the Th1:Th2 balance of the immune system: changes in the balance affect which pathogens the immune system responds to most efficiently, with an inevitable loss of efficiency in response to the others (67). As the pathogen environment changes, PRN structure adapts, and compromises. Organismal physiology is thus not optimized for a hypothetical environment in which there is limitless food, no need to exercise, and few pathogens (such as in a laboratory, or in developed countries); it is optimized for an imperfect world, and paradoxically this may mean that the organism could actually be less well off under “perfect” conditions (1). In fact, we should expect that an environment that is further from the average ancestral conditions under which the PRN evolved should induce faster dysregulation. Lack of exercise and obesity-related metabolic 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299
disorders appear to be prime examples of this in modern humans: physiological conditions we were not adapted for perturbing our physiology, inducing dysregulation, and thus accelerating aging (or at least aspects of it).
However, caloric restriction cannot be explained this way. Caloric restriction implies levels of caloric intake that are below even those normally encountered by animals in the wild, and yet it extends lifespan. Under the dysregulation framework just presented, caloric restriction should imply greater dysregulation and faster aging than normal; clearly it does not. However, there is again a relatively straightforward way to reconcile this contradiction. While conditions of low caloric intake are not the norm for most species, temporal variability in food resources is nearly universal, and most species are at substantial risk of experiencing occasional famines. Caloric restriction appears to trigger a famine resistance mechanism: divert energy from reproductive function toward physiological maintenance in order to wait out the famine, then resume reproduction when conditions are good (6, 68).
Interestingly, this hypothesis also provides a clear answer to another main argument for adaptive aging: the presence of multiple genes that appear to accelerate aging, and are conserved across species. The key genes that have been studied in this context all regulate energy metabolism and growth in systems related to caloric restriction. It would appear these genes are upstream regulators of the control process for entering a wait-and-hold state when resources are scarce (69). They are conserved, much as the genes controlling eye development are conserved between arthropods and vertebrates despite an independent evolution of the eye itself (70). The downstream pathways that carry out the regulation of “reproduce” versus “survive a famine” may differ substantially between nematodes, fruit flies, and mammals, but 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321
the upstream regulators of the process are shared, what has been described as “public” versus “private” aging mechanisms (69).
One of the main barriers to this explanation of caloric restriction and aging genes is relatively limited evidence for a link between caloric restriction and fertility suppression (9). That is, inducing survive-another-day mechanisms via caloric restriction does not appear to also obligately trigger reproductive cessation. However, this appears to me an overly narrow interpretation of the hypothesis and its predictions. If “bad times” induce organisms to slow reproduction (and they clearly do for many species in the wild), there is no need for
reproduction and physiological maintenance to share a genetic trigger, as long as they are both triggered by the conditions. Because reproduction in the wild is quite different from
reproduction in captivity, implying a series of behaviours that can include searching for a mate, finding a reproductive site, etc., an observation that calorically restricted individuals reproduce in captivity would not necessarily contradict the overall hypothesis. For example, in dark-eyed juncos (Junco hyemalis), testosterone moderates a trade-off between reproduction and
longevity in males: giving supplements to males increases risk-taking behaviour, and with it both short-term reproductive success and mortality (71, 72). But neither the reproductive success nor the mortality is physiological; both are due to behavioural changes. We might thus imagine that famine in juncos would independently (a) cause a down-regulation of testosterone, and (b) trigger other pathways related to caloric restriction that would maximize the chance that the junco survives the famine. This would be fully consistent with the classical non-adaptive aging theories. Accordingly, it is quite hard to convincingly test the hypothesis that caloric restriction and conserved aging genes exist as famine survival mechanisms.
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Multi-factorial versus unifactorial explanations of aging
More generally than dysregulation, the need for multi-factorial explanations of aging is in and of itself a quite strong argument against adaptive theories of aging. If aging were truly programmed, we would expect a single control pathway (or perhaps several, but certainly not hundreds). If there were several mechanisms, we would expect that some are downstream of others, or that different pathways can be triggered in different ways, but only one pathway operates at a time. For example, if telomere shortening and related pathways did evolve specifically to cause aging, we would expect all aging mechanisms to be downstream effects of telomere shortening in one way or another. While it may not be possible to definitively rule out this possibility, there are many aging mechanisms that have no apparent links to telomeres. For example, stem cells gradually lose their ability to find appropriate niches in their cellular
environment, with substantial consequences for regenerative capacity (73).
In general, aging as we know it has two features that are not typical of other programmed biological phenomena such as apoptosis and development. These two features strongly support a model of aging as a multi-factorial, uncontrolled process.
(1) Aging is incredibly diverse. Even within a single species, each individual ages in a relatively unique way. There is not one human aging process, nor even ten. There are billions. This is because of the myriad ways the multiple different aging mechanisms can interact with each other. Aging is thus not a tightly controlled, regulated, uniform
process. Gavrilova et al (74) note that coefficients of variation for age at menopause and age at menarche are of a similar magnitude, and that coefficient of variation for age at 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365
death is about two-fold larger, suggesting that perhaps aging is not much more diverse than development. However, regardless of the precision of the timing, the
developmental process is clearly more controlled. All healthy children learn to walk around 1 year of age, learn to talk between ages 1.5-4 years, etc. Aging does not have clear windows like this: some individuals will show cognitive decline in their 60s, and others will never show it. Some will show macular degeneration, others will not. Some will show osteoporosis, others will not. I cannot think of any known programmed
process that is as diverse as this. Austad (1) makes the point that this diversity is present even in genetically and environmentally homogeneous strains of laboratory mice.
(2) Aging is gradual, starting in some ways even before birth, but showing important demographic effects in mid-life. Even by the mid-twenties, human mortality is increasing and female fertility is declining. While it is true that our ancestors had much shorter life expectancies, the typical numbers are misleading because they incorporate childhood mortality. Modern hunter-gatherers regularly survive into their 60s, 70s, and even 80s, suggesting that demographically important human aging occurs over two-thirds of the lifespan. Compare this to the 10 minutes apoptosis takes, relative to the lifespan of a cell (75). Given that aging can occur very rapidly, such as in semelparous species like Pacific Oncorhynchus salmon, it is not evident why most species that show aging show the process throughout their lives, if aging is programmed.
Similarly, the strongest apparent evidence for programmed aging – the conservation of aging genes – is tempered by a related observation: there is no evidence that any genetic changes 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387
could stop aging entirely. Even the longest observed genetic lifespan extension does not exceed about 50% of an organism’s lifespan. Though large from the perspective of an individual, this difference is miniscule compared to the 1000-plus-fold differences across taxa. This observation shows just how minor these genetic effects are compared to the totality of aging viewed
through a comparative lens. Again, this is a reflection of the multi-factorial nature of aging: if there were a single or even several key underlying programs causing aging, we would expect much larger effects of altering the control mechanisms for these programs, and in fact we would likely already have found a combination of mutations that could stop aging altogether.
The taxonomic distribution of aging in relation to its adaptive benefits
One rather under-discussed question in the debate over adaptive versus non-adaptive aging is which species age how, and how these patterns might confirm or refute different hypotheses. Aging is, obviously, incredibly diverse, with creatures such as nematodes living several days and others such as certain trees surviving for millennia(76, 77). It has long been known that a few species, including several turtle species, rock fish, sturgeon, and many trees, can live a very long time and appear not to age (76). Until recently, however, it was hard to have a good overall portrait of aging across the tree of life, due to the paucity of information on many branches. New research from the Max Planck Institute for Demographic Research is starting to change that, and we are finding that what were previously considered rare or exceptional species are quite typical (77). Although aging is universal or nearly so in birds and mammals, many other species show clear evidence of a lack of age-related mortality increases. Non-aging species are not rare exceptions to be explained, and may prove to be as numerous as species that age. The 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409
impression that aging is conserved across the tree of life is due largely to the use of C. elegans and D. melanogaster as model organisms of aging, but both were selected as model organisms precisely because they have short generation times, and are far from representative of
invertebrates (78). Many species that do age show patterns quite different than humans or these model organisms (77). For example, some species show negative senescence, with age-specific mortality decelerating rather than accelerating with age. A number of birds show a temporary decline in mortality during mid-life, for a W-shaped mortality curve.
Overall, combining the recent data with what has long been known, we can paint the
following picture of taxonomic diversity in aging. First, there is a strong phylogenetic signal both in whether aging exists and how it occurs, but the strength of the signal varies in different groups. Some closely related plant species show quite different aging patterns, whereas most mammals show quite similar patterns, if on different time scales. Within mammals, marsupials are similar to other marsupials, bats to other bats, primates to other primates, etc. (79). Second, within mammals and birds, there is a very strong relationship with body mass and metabolic rate (the two are tightly correlated and hard to tease apart) (80-82). Third, among species that age, there is a strong relationship between aging rate and the rate of age-independent morality (i.e., the lowest rate experienced at any age) (83). Fourth, at least in some birds, lifespan
appears not to be associated with degree of sociality one way or the other (84). Fifth, in many eusocial species (including a mammal, naked-mole rats) the queens can live an order of magnitude longer than the workers, despite having the same genetic code (85). Sixth, catastrophic aging such as is seen in salmon is taxonomically widespread, though rare in vertebrates (76). 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431
If aging were adaptive, what taxonomic distribution might we predict? This depends on why programmed aging would be selected for. Some hypotheses include a) group selection (now little favoured); b) kin selection (7); c) as a mechanism to avoid ecosystem collapse or stabilize population dynamics (14); d) as a mechanism to avoid transmission of infectious diseases (25); e) as a mechanism to avoid famine (14); and f) to increase evolvability (24). In most of these cases, some species would be predicted to have “better” conditions for the evolution of aging than others. For example, group selection is favoured when groups are small and the group succeeds or fails as a whole. Kin selection is favoured when kin are close (and thus able to profit from the ecological space opened by a death) but not in competition. Population dynamics would favour aging in space-limited species with fast potential population growth rates.
Infectious disease transmission and famine would suggest that aging would evolve faster when population densities are high.
In all cases, the strong taxonomic signal of aging presents a major problem. Variation in traits like group size, kin proximity, and population density vary at a fine taxonomic scale, often differing markedly among congeners. Variation in aging is mostly happening at a much higher taxonomic level. For example, among apes, orangutans and gibbons are relatively solitary and have low population densities, suggesting poor conditions for the evolution of aging.
Chimpanzees, bonobos, gorillas, and humans are highly social, with much better conditions for aging. Nonetheless, lifespan differences among these species are not remarkable (86), except for humans, who live much longer, the opposite of what is predicted. If aging were a program under simple genetic control, we would predict that gibbons and orangutans would live very long lives (and perhaps would not age at all). If apes were the only pertinent example, many 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453
specific arguments might be made to explain this. But the presence of a general, strong phylogenetic signal in aging, particularly among higher taxa, presents a major problem for existing adaptive theories of aging. Even a theory based on ecosystem collapse does not explain why lack of control on mammal and bird populations is more dangerous to ecosystems than lack of control of hydra, turtle, and bristlecone pine populations (87, 88).
One article often cited in favour of adaptive aging is Ricklefs’ (83) demonstration that there is substantial aging mortality in wild mammals and birds, and that there is more aging mortality in species with lower age-independent mortality rates. (A recent study has confirmed the
importance of senescence in wild populations (89), and Ricklefs’ result has been replicated in more species (90).) The presence of substantial aging-related mortality in the wild is taken as evidence that aging might have an adaptive function. But the converse is also true: in short-lived species, Ricklefs found that aging mortality was of minimal importance. In other words, the fastest aging species show little aging-related mortality in the wild. This observation appears to contradict adaptive explanations for the evolution of aging. If aging exists to further population control, short-lived, fast reproducing organisms such as mice and rabbits should either show a high proportion of senescent mortality in the wild (contrary to Ricklefs’ finding) or should show long lifespans in captivity because other mechanisms control population and aging is not necessary.
Other aspects of the taxonomic distribution of aging also are hard to justify in terms of adaptive aging. Why would large animals with low metabolic rates age more slowly? Why would age-independent mortality correlate positively with aging rate? Why would eusocial species have faster aging in their non-reproducing workers(39)? Population control in eusocial species 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475
would ultimately be a function of the reproductive individuals, and a much more efficient solution would be to produce a small number of immortal workers rather than to replace large numbers of short-lived workers. It should be noted that the classical non-adaptive theories are also difficult to justify in light of the taxonomic distribution of aging. In particular, the disposable soma theory cannot explain why many species with distinct somas and germ-lines show clear evidence of non-aging.
Discussion/conclusions
As argued by Mitteldorf, one of the main proponents of adaptive aging, the main evidence supporting adaptive aging is not the force of possible theoretical justifications, but rather the force of the empirical evidence that aging is programmed (14, 25). Shared “aging” genes across species, clear pathways involved in telomere shortening, and lack of predicted health benefits of eating more calories and exercising less: these all appear to argue for programmed aging. There are some creative theoretical justifications for adaptive aging, but they exist largely as a way to reconcile the empirical evidence with evolutionary theory and are likely to apply only under specific conditions.
My central thesis here is that this effort is unnecessary. Even viewed outside an evolutionary framework, our current knowledge of aging mechanisms better supports an argument for aging as a dysregulatory process, a disordered breakdown in function with time. The proposed
arguments for adaptive aging rest on a relatively limited number of specific observations that can easily be explained by (a) incorporating a multi-factorial dysregulation into our
understanding of aging, and (b) understanding caloric restriction and shared aging genes as the 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497
result of a conserved mechanism to adjust decisions to reproduce versus invest in maintenance and survival. More importantly, aging is, prima facie, quite different from other programmed biological processes such as development and apoptosis. It is too heterogeneous across individuals to be a program, and it takes too long in most species. (Development is also slow, but making an organism is difficult. Making it die is, biologically, a simple task.)
In addition to the lack of need for a theoretical justification for adaptive aging, the taxonomic distribution of which species age and how they age does not support any of the proposed adaptive aging theories, all of which focus on some justification for population control or long-term good of the species. Why would some species need more population control than others, and why would this need have such a strong phylogenetic signal? None of the known patterns of aging across species appears coherent with the existing adaptive aging theories.
I have argued clearly for a non-adaptive theory of aging, based on both physiological/ mechanistic data and on comparative data. However, the arguments for adaptive aging have played an important scientific role in stimulating debate. Much of the evidence I cite on
dysregulation and on the taxonomic distribution of aging is quite recent, and has had little time to circulate in the aging community. Advocates of non-adaptive aging have often too easily ignored evidence that challenged the classical framework. There are problems with the classical framework, particularly in terms of its ability to explain comparative data, and while I do not believe that the final answer will be an adaptive theory, some change is needed. Additionally, while unlikely, the possibility of non-programmed adaptive aging remains under-explored. After so much research, the fact that there is so little scientific consensus on almost any aspect of 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518
aging is breathtaking, and testament to the incredible complexity of the process. Who knows what we will all believe in 10 or 15 years?
Conflicts of Interest None.
Acknowledgments
AAC is a member of the FRQ-S-supported Centre de recherche sur le vieillissement and Centre de recherche du CHUS, and is a funded Research Scholar of the FRQ-S. This research was supported by NSERC Discovery Grant # 402079-2011.
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Figure Legend:
Figure 1: Changes in predictive power of DM (i.e., statistical distance of multiple biomarkers measured
by Mahalanobis distance) with increasing numbers of variables used in its calculation, redrawn from (57). Results are shown for the relationship of DM with age (a-b) and with mortality controlling for age (c-d).
Results are replicated in two data sets (InCHIANTI (91), (a) and (c), and WHAS, the Women’s Health and Aging Study (92), (b) and (d)). Each circle represents an analysis based on one of the 4095 combinatorial subsets of the 12 biomarkers in InCHIANTI or one of the 16,383 combinatorial subsets of the 14
biomarkers in WHAS, as appropriate. Both studies used: albumin, hemoglobin, hematocrit, RBCs, cholesterol, calcium, sodium, chloride, potassium, creatinine, BUN:creatinine ratio, and basophil count. WHAS also included direct bilirubin and osteocalcin. Color indicates p-value: black: p ≥ 0.1; blue: 0.05 ≤ p 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730
< 0.1; cyan: 0.01 ≤ p < 0.05; yellow-green: 0.001 ≤ p < 0.01; orange: 0.0001 ≤ p < 0.001; red: p < 0.0001. The line represents a linear regression of number of variables on relevant effect size. Effect size trend shows the results of a Pearson correlation analysis of variable number with relevant effect size. 731
732 733 734
Figure 1 735
