After witnessing the amazing diversity of aging phenotypes found in nature, one question is: are there any trends in the way animal age? In this essay, I briefly examine the factors affecting aging and longevity in animals.
Keywords: ageing, allometry of life, biogerontology, life span
Why does a mouse live less than 5 years while humans and whales can live over 100? Why do some species appear not to age? How did they evolve? In the end, why do different animals age at different paces? Many researchers have asked these questions (Austad, 1997a & 2005; Warner et al., 2002; de Magalhaes, 2003). So far, and even though this topic will be treated in other essays, the answer has eluded us. Nonetheless, there some general trends have been reported and some factors shown to correlate with maximum lifespan.
As mentioned before, quantifying aging is a difficult, controversial task. Quantifying the rate of aging for a given species can be done through the MRDT, but there are some caveats: MRDT calculations are available for only a fraction of species and, as shown before, are not perfect estimates of rate of aging. Consequently, most researchers use maximum lifespan (tmax) as an estimate of rate of aging. It has been argued that tmax represents the genetic potential for longevity of each species and is related to a species' rate of aging (Cutler, 1979; Allman et al., 1993; Finch and Pike, 1996). Even though there are potential problems in using tmax to estimate rate of aging, such as an impact of population sizes, tmax remains the best and most widely available way to quantify rate of aging.
One factor that correlates with maximum lifespan is body size. Clearly, the typical adult body mass for a species correlates with tmax (Fig. 1). In other words, larger animals live, on average, longer than smaller animals, as debated by a large number of authors (Calder, 1984; Schmidt-Nielsen, 1984; Promislow, 1993; Austad, 2005). The logarithmic relation between tmax and body mass (M), also called the allometry of lifespan, has been the subject of intense scrutiny. From Figure 1, we can obtain the equation: tmax = 5.58M0.146 with r2 = 0.340. The squared Pearson correlation coefficient (r) suggests that body mass explains 58% of the variation in tmax. Clearly, there are many exceptions to this correlation. One exception are bats, that live a lot longer than expected for their body size (Austad and Fischer, 1991; Austad, 2005). Even birds, when compared to mammals, live longer than expected for their body size.
Figure 1: Correlation between maximum lifespan (tmax) and typical adult body mass (M) using all species (n = 1,701) present in AnAge build 8. Plotted on a logarithmic scale.
At present, the simplest and most likely explanation for the allometry of lifespan is related to ecological constraints: smaller animals tend to be more prone to predation and thus are expected to have higher extrinsic mortality rates, a shorter tmax, and a faster aging process--as debated ahead in more detail. For example, the ability to fly gives most birds and bats the capacity to evade predators. Consequently, it seems that body mass is a determinant of ecological opportunities and habitat that impacts on mortality, which consequently influences the evolution of longevity and aging (Stearns, 1992). So far, there is no evidence to suggest some unknown physiological somehow affects aging in a way proportional to body mass.
Experimentally, this impact of body mass on tmax is relevant because it can bias comparative studies of aging (Promislow, 1993). Researchers trying to identify which factors are related to tmax must eliminate the effects of body mass from their calculations, which can be done with some fairly simple statistical calculations. In fact, body mass appears to correlate with many life history events besides maximum lifespan: gestation period, time to reproductive maturity, etc. Therefore, researchers studying whether a given factor correlates with tmax or not must play close attention to the impact of body size. As we will see ahead, this was not always done resulting in the incorrect interpretations of experimental results.
Brain mass also correlates with tmax, even independently of body mass. This is also true for primates (Allman et al., 1993). The way brain mass appears to be a better predictor of longevity than body mass is probably due to less variation in brain mass (Lindstedt and Calder, 1981). Therefore, even though it can be argued that this relationship shows the influence of the brain on longevity, it does not prove that the causes of aging are located in the brain. In fact, the size of other organs also correlates with tmax, in some cases more strongly than brain size (Austad and Fischer, 1992). Besides, ecological explanations are also possible: maybe animals with bigger brains are better at escaping predators for a number of reasons.
Another relationship long studied in gerontology is Kleiber's rule which relates the maximum lifespan with the metabolic rate (Kleiber, 1975; Gosden, 1996, pp. 103-110). It can be argued, for instance, that reptilians and amphibians live longer because they have decreased metabolic rates since they are cold-blooded animals. Similarly, if the metabolic rate, the rate at which reactions occur in cells, is higher in, for instance, mice than in humans then maybe that is why mice live less than humans (Prinzinger, 2005). (Kleiber's rule actually originates in a theory of aging called the "rate of living theory," which is discussed in more detail elsewhere.) Despite its intuitive nature, there is no evidence that metabolic rates influence aging in endotherms like birds and mammals. First of all, there are gross exceptions: once again, bats live longer than what would be expected for their metabolic rates. In addition, marsupials live less than eutherians and yet have lower body temperatures, which implies a lower metabolic rate, while birds show the opposite (Austad, 1997a, pp. 88-90). Another problem is related to body size. Metabolic rates are often estimated by measuring oxygen consumption at rest. Clearly, an elephant will breath in more oxygen than a mouse, so it is necessary to correct for body mass. Failure to do so will result in oxygen consumption being associated with tmax incorrectly--i.e., due to its relation to body mass which in turn correlates with tmax. When the effect of body mass is correctly eliminated from metabolic rates, which is a controversial topic in itself, metabolic rates do not appear to correlate with tmax. In fact, recent results suggest that metabolic rates are not associated with tmax in mammals or birds after correcting for the effects of body mass using the latest statistical methods (de Magalhaes et al., 2007). The exact methodology of these calculations can be attacked--e.g., because to the way metabolic rates are corrected for body mass or even the way tmax records are obtained. Nonetheless, there are no results in which metabolic rates are correctly adjusted for body mass that show a correlation between metabolic rates and maximum lifespan in mammals or birds. Kleiber's rule is thus mostly discarded now.
Still in the context of metabolic rates, a point of debate is whether hibernating species live longer than non-hibernating species, such as bats. So far the results are mixed, but some results suggest non-hibernating animals may live longer (see, for instance, Lyman et al., 1981; Brunet-Rossinni and Austad, 2004), which could suggest that a period of metabolic torpor could increase lifespan. On the other hand, it can be argued that spending a fraction of the year in hiding, where presumably mortality is low, contributes to a longer lifespan in hibernating animals.
Even though, as shown above, bigger species tend to be longer-lived than smaller ones, there are a number of cases in which smaller animals within a given species live longer in captivity. Examples include mice, horses, and dogs (Miller, 1999; Miller et al., 2002a). Interestingly, it has been argued that "little people" may also be longer lived (Krzisnik et al., 1999). So while on one hand bigger species tend to be long-lived, within a given species smaller animals tend to live longer if protected. The genetic reasons for this and implications for our understanding of aging are debated in detail in another essay. Lastly, it has been suggested, though the results are not conclusive, that growth rate correlates with demographic rate of aging (Ricklefs and Scheuerlein, 2001)--not MRDT but a similar parameter estimated from the Weibull model.
One last factor that correlates with tmax is development. For instance, independently of body mass, age at sexual maturity correlates with average and maximum adult lifespan in many taxa, including mammals (Charnov, 1993; Prothero, 1993). In other words, the longer it takes for a given mammal to reach sexual maturity, the longer it will live afterwards. There are some exceptions, however, such as the male Anthechinus. On another line of reasoning, each organism's body-plan is largely determined by its genetic program, and the body-plan certainly can have a powerful influence on longevity, as shown by aphagy in some insects or the semelparity of species like the salmon. Maybe species are influenced by development in different ways: the relation (adult phase)/(total lifespan) shows a wide variation, which is in accordance with the several aging phenotypes found in nature. So development and its consequential body-plan can influence aging to different degrees. The body-plan of mammals places indirect constraints on adult life but this could be regarded as a by-product and not an objective process. For evolutionary reasons, development can be timed similarly to aging. The relation between development and aging in mammals could also be indirect and minimal (Miller, 1999). The idea that development and aging are related is further developed in another essay.
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