While age-related changes in coping have been previously explicitly addressed in few studies of non-human animals, and never in field studies, there are indications that age-related changes frequently occur in the HPA-axis function of vertebrates. However, the patterns of aging in GC metabolism differ substantially across species, and also across contexts and sexes within species. Some of these findings are summarized in Table 2 to demonstrate the variability in outcomes from previous research. While almost every conceivable result is found on the effects of sex and age on GC metabolism across (and sometimes within) species, the one measure that seem to reflect moderately consistent directionality at aging is baseline GC, which is typically either unchanged or elevated at old age. The trend in negative feedback efficiency, on the other hand, is for unchanged or decreasing responsiveness to efficient down-regulation of GCs across studies. These findings combined seem to support the hypothesis that impaired negative feedback efficiency of the HPA-axis may act to chronically elevate baseline GC level [Jacobson and Sapolsky 1991]. For the rest of the measures detailed in Table 2 (influence of aging on the GC response to a stressor, sex differences in baseline and response GC as well as sex-specific effects of aging), the main conclusion is a remarkable absence of general patterns.
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Table 2: Evidence for the effects of sex and aging on GC-levels at baseline and during acute stress response in mammals and birds. + denotes a positive association, - a negative one, 0 no difference or non-significant effect and NA lack of data. M= males, F= females. Matrix: B=blood, F=feces, S=saliva, U=urine. References [Heuser et al. 1994] [Kudielka and Kirschbaum 2005] [Nicolson et al. 1997] [Otte et al. 2005] [Veldhuis et al. 2013] [Gust et al. 2000] 2002; Goncharova and Lapin 2004] [Goncharova and Lapin 2004] [Sapolsky and Altmann 1991] [Alberts et al. 2014] [Reul et al. 1991] [Rothuizen et al. 1993]
Setting Clinical Clinical, review Psychological stress Clinical, psychological stress, review Review Captive Captive Captive Wild Wild Captive Captive
Matrix B B, S S B & S NA B B B B F B B
Sex-specific aging NA M+(free GC), F+ (total GC) F- (response) F+ 0 / F+ / M+ NA (F only) NA (F only) NA (M only) 0 0 NA NA
Effect of aging Feedback # NA NA NA NA - - - - - NA 0 -
response + + - + 0 / - / + 0 0 / + NA NA NA + +
Baseline + NA + 0 NA + 0 0 + + + +
Sex differences response F+ NA 0 NA 0 / F+ / M+ NA (F only) NA (F only) NA (M only) 0 NA NA NA
Baseline F+ NA 0 0 0 / M+ NA (F only) NA (F only) NA (M only) 0 NA NA NA
Species Human (Homo sapiens) Rhesus monkey (Macaca mulatta) Hamadryas baboon (Papio hamadryas) Yellow baboon (Papio cynocephalus) Dog (Canis familiaris)
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Table 2. continued References [Touma et al. 2004] [Sapolsky 1992; Sapolsky et al. 1986] [Critchlow et al. 1963] [Kitay 1961] [Herman et al. 2001] [Brett et al. 1983] [Mizoguchi et al. 2009] [Kasckow et al. 2005] Fletcher et al. unpublished in [Boonstra et al. 2014]
Setting Captive Captive Captive Captive Captive Captive Captive Captive Wild
Matrix F B B B B B B B B?
Sex-specific aging M+ (baseline + begins earlier) 0 NA NA NA (Male only) F- (response - at old age) NA (males only) NA (males only) NA (males only)
Effect of aging Feedback# NA - NA NA NA NA + / - + -
response NA NA NA NA 0 / + 0 / - NA 0 NA
Baseline 0 / + + NA NA 0? 0 0 0 0
Sex differences response NA NA NA F+ NA (Male only) NA NA (Male only) NA (males only) NA
Baseline F+ NA F+ NA NA (Male only) NA NA (Male only) NA (males only) NA (males only)
Species Mouse (Mus musculus) Rat (Rattus norvegicus) Red-backed vole (Myodes rutilus)
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Table 2. continued References [Harris and Saltzman 2013] [Van Kampen and Fuchs 1998] [Wilcoxen et al. 2011] [Heidinger et al. 2006; Heidinger et al. 2008] [Goutte et al. 2010b] * Limited support from model selection for an age effect (based on graphics, negative trend at least in females), effects not reported # - = resistance to negative feedback at old age
Setting Captive Captive Wild Wild Wild
Matrix B U B B B
Sex-specific aging M- (response + at old age) NA (males only) 0 NA NA
Effect of aging Feedback# 0 / + (M) NA NA NA NA
response 0 NA - / + (general -, + in oldest age class) - 0 / + (+ in oldest animals)
Baseline 0 0 / + (+ until 200 days, 0 after) 0 0 0?*
Sex differences response 0 NA (males only) 0 0 M+
Baseline 0 NA (males only) 0 0 M+
Species California mouse (Peromyscus californicus) Tree shrew (Tupaia belangeri) Florida scrub-jay (Aphelocoma coerulescens) Common tern (Sterna hirundo) Snow petrel (Pagodroma nivea)
127 This variability might in part reflect the fact that few of the studies are directly comparable due to differences in the methodology used, parameters measured, demographic groups studied, the research setting (captivity, wild) and potential differences introduced by phylogeny or social/mating structure of the species studied. A major issue with quantifying GCs in blood has been that the potential significance of the dynamics of corticosteroid-binding globulins (CBG) [Breuner and Orchinik 2002; Breuner et al. 2013] was overlooked in older studies, rendering those results difficult to interpret. In blood, part of the GCs are bound to CBG and only the unbound fraction is thought to be biologically active, whereas fecal GC metabolites reflect the levels of active GCs in circulation [Breuner et al. 2013; Sheriff et al. 2010]. The measurement of total GCs estimates the cumulative concentration of free and bound GCs. This is potentially significant, because the fluctuation of CBGs may significantly influence the levels of active GCs in blood [Breuner and Orchinik 2002]. Excreted GC, as used in our study, might therefore be a useful addition to the methodology, when allostatic load to the system is of interest. However, we advocate thorough validation of these methods prior to using them for inference about individual state, since much is already known about the potential confounding factors influencing these measurements [Goymann 2012; Hämäläinen et al. 2014b;
Sheriff et al. 2011]. Overall, this variability in study designs makes it at present impossible to predict the directionality of effects and should alert the scientific community to the species- and context-specificity of HPA-axis aging.
Females (at least in the most studied organisms) tend to show more pronounced changes at advancing age compared to males (Table 2), probably because the sexes differ in their GC metabolism and the way that aging influences the HPA pathways [Kudielka and Kirschbaum 2005].
This in combination with life history differences may lead to the sex-specific patterns of aging observed in this study. The absence of age-effects in males could also be due to higher male mortality [Kraus et al. 2008], which eliminates males from the population before they show senescent declines.
Male mortality is elevated in the mating season [Hämäläinen et al. 2014a; Kraus et al. 2008], the time when they also experience higher GC levels. Due to the detrimental effects of chronic GC elevation on health [Glaser and Kiecolt-Glaser 2005], chronic stress may contribute to the selective disappearance of individuals [Pride 2005]. Due to the shorter life expectancy of males compared to females, the life history characteristics of males are likely shaped by the associated adaptive pressures, leading perhaps to higher reproductive investment by males in early life at the expense of survival.
GCs have been implicated as possible mediators of trade-offs between survival and reproductive effort due to their role in resource reallocation [Ricklefs and Wikelski 2002; Wingfield and Sapolsky 2003]. Because an individual’s future reproductive potential typically decreases with advancing age, it has been proposed that – in contrast to the coping hypothesis – aged individuals might exhibit lower fGCM compared to young animals during the mating season (perhaps via changes in adrenal capacity, [Heidinger et al. 2008]) to facilitate higher investment in reproduction (sensu CORT-trade-off hypothesis [Boonstra et al. 2001; Patterson et al. 2014; Wingfield and Sapolsky 2003]. The few studies that have thus far addressed this hypothesis have found limited support for it [Harris 2012;
Harris and Saltzman 2013; Heidinger et al. 2006; Heidinger et al. 2008]. Our study also fails to find evidence in favor of this idea, suggesting that gray mouse lemurs may not be able to compensate for
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their declining reproductive value by down-regulating their overall GC production at old age. Due to the paucity of studies, it is at this stage impossible to evaluate the causes of the discrepancies between the studies, but potential lines of future research might be the examination of differences between captive and wild studies, the influences of breeding status and annual fluctuations as well as different mating systems on the patterns of aging observed. Further study of these topics on various taxa may improve our understanding of the circumstances under which GC production is down- or up-regulated at old age.
Age-related physiological deterioration may expose aged individuals to extrinsic causes of mortality at a higher rate than their younger conspecifics, contributing to the increasing risk of mortality with advancing age observed in most species. However, individuals that live to old age in nature might be of high quality or have an HPA phenotype that promotes self-maintenance (perhaps at the expense of reproduction early in life). Therefore, the oldest surviving individuals may experience less pronounced senescent changes than would be expected without selective mortality of individuals with HPA activity that promotes early fitness at the expense of survival. Due to the potential significance of deteriorating physiological functioning at old age on survival probability, it is likely that the detectability of senescence is difficult in natural populations compared to captive conditions [Hämäläinen et al. 2014a] and might contribute to the relatively low number of studies on wild animals reporting significant age-related changes in HPA axis functioning (Table 2). Stressful conditions experienced early in life can also influence the phenotype later in life, and could influence long-term health and longevity via oxidative stress and telomere activity [Monaghan 2014], hence, the old individuals in our sample may be influenced by life events much prior to the start of our study. Longitudinal following of the same individuals at a longer time scale might help disentangle some of these effects.