Chronological age, biological age, and individual variation in the stress response in the European starling: a follow-up study

Introduction

The hypothalamic-pituitary-adrenal (HPA) axis is a highly conserved, integrated system in vertebrates that functions to prioritise immediate survival over non-essential activities in the face of acute threats. In birds, the strength of the HPA response generally declines with chronological age (Heidinger, Nisbet & Ketterson, 2006; Heidinger et al., 2010; Wilcoxen et al., 2011; Elliott et al., 2014; Lendvai, Giraudeau & Bo, 2015; López-Jiménez et al., 2017), possibly reflecting adaptive shifts in behavioural allocation as expected future lifespan reduces. However, individuals do not all age at the same rate: an individual’s biological age can be either older or younger than their chronological age (Belsky et al., 2015). Biological age is by definition a better predictor of future lifespan than chronological age is. Hence, we should expect markers of individual biological age to explain variation in the strength of the stress response that cannot be explained by chronological age alone. A possible reason that early-life conditions have often been observed to influence the functioning of the adult stress response may be that early-life conditions can speed up or slow down the biological ageing process, and consequently advance or retard age-related shifts in the functioning of the stress response system.

One potential marker of biological age is the attrition of telomeres, the DNA caps at the ends of linear chromosomes (Bize et al., 2009; Bauer et al., 2018). Short telomere length has been shown to predict shorter subsequent lifespan in a range of avian (Bize et al., 2009; Heidinger et al., 2012; Barrett et al., 2013; Salmón et al., 2017; Wilbourn et al., 2018) as well as non-avian (Boonekamp et al., 2013; Wilbourn et al., 2018) species. The rate of telomere attrition is much higher during the developmental period than in adulthood, and has been shown to be accelerated by early-life adversity (Boonekamp et al., 2014; Nettle et al., 2015, 2017). Thus, the extent of telomere shortening can potentially be used as a proxy for individual differences in biological age.

In a recent study of two cohorts of adult European starlings (Sturnus vulgaris, n = 20 and n = 31), Andrews et al. (2017) showed that developmental telomere attrition (DTA; the extent of shortening of erythrocyte telomeres over the course of development) explained variation in individuals’ stress responses. Specifically, individuals that were biologically older by this measure (all individuals were approximately the same chronological age) showed a lower peak level of corticosterone (CORT) in response to an acute capture-handling-restraint stressor, and also showed stronger recovery of CORT levels towards baseline between 15 and 30 min after the onset of the stressor. DTA was unrelated to baseline CORT. The birds studied by Andrews et al. (2017) had been subjected to experimental manipulations of early-life conditions: a manipulation of brood size for one cohort (Nettle et al., 2013), and of the focal individual’s size relative to its competitors in the other cohort (Nettle et al., 2015). These manipulations affected DTA, with the more adverse treatment (having more or larger competitors respectively for the two cohorts) leading to greater telomere shortening in early life. However, it was DTA, rather than the early-life treatments themselves, that significantly predicted stress response. This suggests that DTA captures both the adversity due to the experimental manipulation, and other sources of adversity, and also incorporates the fact that individuals are differentially affected by the same external conditions. Thus, DTA is a better marker of biological ageing than any of the individual environmental or genetic factors that may influence it. The cohorts of birds were made up of quartets of siblings, which allowed the researchers to examine potential familial effects. They found evidence consistent with modest familial effects on baseline and peak CORT, but not on the change in CORT between 15 and 30 min after the onset of the stressor.

In light of recent focus on reproducibility in bioscience (Fidler et al., 2017), the results of Andrews et al. (2017) require replication. Moreover, the CORT response was only measured at one age point (around one year of age on average). Thus, it is not clear whether associations between DTA and stress response appear before, or persist after, this stage of life. Longitudinal studies in birds have found varying results with respect to whether individuals are consistent in CORT measures from one year to the next (Cockrem & Silverin, 2002; Angelier et al., 2010; Ouyang, Hau & Bonier, 2011; Rensel & Schoech, 2011; Baugh et al., 2014; Lendvai, Giraudeau & Bo, 2015). Where individual consistency is found, it is generally in the CORT response to an acute stressor rather than baseline CORT (Cockrem & Silverin, 2002; Rensel & Schoech, 2011). Thus, it is important to establish whether there is individual consistency over time in the CORT parameters studied in Andrews et al. (2017), as well as whether the associations of these parameters with DTA are robust.

Here, we report a follow-up study conducted with a different cohort of birds. The quartets of siblings in the current cohort were hand-reared according to a two-by-two factorial design that varied early food amount and begging effort. The rationale for this design was to attempt to isolate the independent effects of these two factors, both of which vary with brood size in nature. Previous studies of early-life adversity in altricial birds have found that developing in a large brood has long-term phenotypic consequences (Nettle et al., 2013; Boonekamp et al., 2014), but have not been able to establish exactly why. We have reported the effects of the developmental treatments on DTA in these birds elsewhere (Nettle et al., 2017). Briefly, a restricted food amount, and a higher begging effort, both significantly accelerated DTA in these birds, and did so additively. Thus, the two developmental treatments can each be seen as accelerating biological ageing, as proxied by DTA.

In the present experiments, we measured the CORT response to an acute capture-restraint-handling stressor using the same protocol as Andrews et al. (2017), but did so twice, when the birds were juveniles of approximately 4 months of age (age point 1), and adults of approximately18 months of age (age point 2). Our main aim was to replicate the key findings of Andrews et al. (2017), namely that DTA is associated with peak CORT and change in CORT between 15 and 30 min, but not baseline CORT; that DTA is a better predictor of CORT parameters than the early-life conditions to which birds have been exposed; and that there are modest familial effects on baseline and peak CORT. In addition, our study, through having two age points, gave us the opportunity to ask a number of additional questions. First, it may be that the association between DTA and the strength of the stress response develops with chronological age. Our age point 2 was most similar to the ages of the birds in the study by Andrews et al. (2017). Thus, it is possible that the association between DTA and CORT variables will be observable at age point 2, but not yet in evidence at age point 1. Second, in their interpretation of their findings, Andrews et al. (2017) assumed that the strength of the stress response declines with chronological age in the starling, basing this assumption on published literature from other bird species. In our study, we were able to directly test this assumption within individuals. Third, our repeated-measure design allowed us to characterise individual consistency in CORT variables in the starling.

Our study suffered from a large limitation in respect of the analyses of within-individual change with chronological age, and individual consistency. Unfortunately, the second age point sample was not planned at the time the first was carried out. We therefore performed the laboratory assays on the blood samples from the first age point separately to the second, and with a small variation in the laboratory protocol. Although common standard samples were run on both occasions, this does limit the robustness of inferences we can make about within-individual change and consistency, since age point and laboratory batch are completely confounded. The findings in respect of within-individual change with age, and individual consistency, should therefore be interpreted with caution.

Materials and Methods

Results

Descriptive statistics

Descriptive statistics for CORT values at all time points and both age points are shown in Table 1. At both age points, the baseline blood sample gave the lowest CORT value for all birds (Fig. 1). Some birds’ CORT values were highest at 15 min and then declined by 30 min, giving ΔCORT values less than 0 (16/31 at age point 1, 16/30 at age point 2). The remainder continued to show an increase between 15 and 30 min.

Table 1:

Descriptive statistics (mean ± SD) for CORT variables (ng/ml) at age point 1 and age point 2.

Variable	Age 1 (127–134 days)	Age 2 (584–601 days)
Baseline CORT	2.21 ± 1.99	2.76 ± 1.53
CORT 15 min	19.58 ± 8.89	13.89 ± 6.23
CORT 30 min	18.44 ± 6.41	13.25 ± 5.27
Peak CORT	21.66 ± 8.50	15.48 ± 5.77
ΔCORT	−1.14 ± 7.00	−0.64 ± 5.01

DOI: 10.7717/peerj.5842/table-1

Note:

Peak CORT represents the higher of CORT 15 min and CORT 30 min; ΔCORT represents CORT 30 min minus CORT 15 min.

Individual consistency and familial effects

Our initial models contained only the random effects of bird nested within natal family. For baseline CORT and peak CORT, bird explained <1% of the variation in the initial models. This suggests negligible individual consistency in these CORT variables. This was confirmed by calculating intra-class correlation coefficients between age point 1 and age point 2 (baseline CORT 0.13, 95% CI [−0.24 to 0.46]; peak CORT −0.03, 95% CI [−0.38 to 0.33]). For ΔCORT, bird explained 35% of the variation, suggesting some degree of individual consistency. This was confirmed by the intra-class correlation coefficient, which was higher than for the other two CORT variables, though its 95% confidence interval still included zero (0.32, 95% CI [−0.04 to 0.61]).

Natal nest explained 33% of the variation in baseline CORT; 17% of the variation in peak CORT; and <1% of the variation in ΔCORT (Fig. 2).

Meta-analysis

The lack of significant effects involving ΔTL in the present study contrasts with the findings of Andrews et al. (2017). To evaluate the balance of evidence, we performed fixed-effects meta-analyses combining the data from the present study with the data from the earlier paper (Fig. 3). As the figure shows, the summary association between ΔTL and baseline CORT was not significantly different from zero, regardless of whether the age point 1 or age point 2 results were used to represent the 2014 cohort. The association between ΔTL and peak CORT after controlling for baseline CORT was significantly positive (that is, more telomere loss associated with a lower peak CORT concentration) if the age point 1 data were used to represent the 2014 cohort (β = 0.30, 95% CI [0.01–0.41], p = 0.04), but not significantly different from zero if the age point 2 data were used (β = 0.16, 95% CI [−0.04 to 0.36], p = 0.12). The summary association between ΔTL and ΔCORT after controlling for CORT at 15 min was significantly positive (that is, more telomere loss, more CORT return towards baseline between 15 and 30 min after onset of stressor). This was true using either the age point 1 (β = 0.30, 95% CI [0.11–0.49], p < 0.01) or age point 2 data (β = 0.30, 95% CI [0.11–0.49], p < 0.01).

Effects of developmental treatment

Table 3 summarises the models using the two developmental treatments (Amount and Effort), plus age point and their interactions, as the predictors. There were no significant main effects or interactions involving the developmental treatments for baseline CORT or peak CORT. For ΔCORT, there was a significant interaction between age point and the Effort treatment. Birds from the Hard groups showed more return of CORT towards baseline between 15 and 30 min (i.e. had more negative values of ΔCORT) than birds from the Easy groups at age point 1. However, at age point 2, the ΔCORT of the birds from the Hard groups had scarcely changed on average, whereas the Easy groups had showed substantially reduced ΔCORT with age (Fig. 4).

Table 3:

Summaries of statistical models testing for effects of developmental treatments on stress response (CORT) variables.

Outcome	Fixed predictors	B	s.e. (B)	LRT	p-value
Baseline CORT	Age point	0.48	0.38	1.61	0.20
	Amount	0.03	0.26	0.02	0.90
	Effort	−0.06	0.26	0.05	0.81
	Amount × Effort	0.11	0.19	0.33	0.57
	Amount × Age point	−0.13	0.37	0.11	0.74
	Effort × Age point	−0.02	0.38	0.00	0.97
Peak CORT	Baseline CORT	1.06	0.51	4.00	0.05*
	Age point	−6.69	1.59	15.43	<0.001*
	Amount	1.40	1.10	1.62	0.20
	Effort	1.17	1.10	1.13	0.29
	Amount × Effort	−0.41	0.78	0.27	0.60
	Amount × Age point	−1.34	1.57	0.73	0.39
	Effort × Age point	−2.18	1.57	1.90	0.17
ΔCORT	CORT 15 min	−0.58	0.07	38.71	<0.001*
	Age point	−2.77	1.03	6.58	0.01*
	Amount	−0.53	0.69	0.59	0.44
	Effort	1.37	0.67	3.99	0.05*
	Amount × Effort	0.24	0.48	0.24	0.62
	Amount × Age point	0.50	0.96	0.26	0.61
	Effort × Age point	−2.55	0.95	6.68	0.01*

DOI: 10.7717/peerj.5842/table-3

Notes:

All models contain random effects of bird nested within natal family.

LRT, likelihood ratio test.

* p < 0.05.

Discussion

The main aim of our experiment was to replicate the findings of Andrews et al.’s (2017) study in a different cohort of birds measured at two time points. We did not replicate the main patterns observed in the previous paper concerning DTA. The change in erythrocyte telomere length over development did not significantly predict any of the CORT parameters in the present cohort, in the whole dataset, or at either age point considered separately. Our failure to clearly replicate the patterns of the previous study does not support the conclusions of that paper. However, we should not necessarily infer that those conclusions were spurious, either. Meta-analysis of all the extant evidence supports a moderate association between DTA and ΔCORT (β = 0.30), with starlings that have experienced more DTA showing more rapid recovery of CORT towards baseline; and possibly between DTA and peak CORT, with starlings that have experienced greater DTA having a lower peak. The former conclusion is supported whether age point 1 or age point 2 is used to represent the present 2014 cohort of birds. The latter conclusion is only supported if the age point 1 data are used. Age point 2 (584–601 days) was more similar to the age at which the birds’ stress responses were measured in the previous study (2012 cohort, 208–432 days; 2013 cohort, 428–456 days). Our simulations showed that if associations between DTA and CORT variables do in fact exist, and have the strength estimated in Andrews et al. (2017), then we should not expect them to be statistically ‘significant’ (i.e. have p < 0.05) in every small-n sample considered individually. For example, if the estimated parameters in Andrews et al. (2017) are correct, then we should only expect a ‘significant’ finding about one experiment in three for peak CORT, and two in three for ΔCORT. (Our two age points constitute two separate experiments in the context of this statement).

We acknowledge the low power of our experiment for detecting associations between DTA and CORT parameters. However, there are strong logistical constraints involved in capturing, keeping, in our case hand-rearing, and sampling live wild animals. For example, to detect an association of β = 0.30 with the conventional 80% power requires over 80 birds, which is beyond the population size of our starling breeding colony or our ability to hand-rear nestlings under precisely controlled conditions. This means that modest sample sizes are difficult to avoid, especially in the early stages of exploration of certain questions. Given that we cannot rear very large cohorts of birds, we have to turn to sequential replication and cumulative meta-analysis, rather than individual-experiment p-values, as a way of ensuring robustness of conclusions.

A further limitation of both this study and Andrews et al. (2017) is that ‘biological age’ is proxied by a single marker, DTA. A better approach for capturing the biological age construct is to use a whole panel of biomarkers, each of which has been shown to predict subsequent lifespan (Levine, 2013; Belsky et al., 2015). Such a panel predicts morbidity and other phenotypic outcomes better than any single constituent biomarker, each of which might have quite weak predictive power. Thus, a stronger test of the hypothesis that individual differences in stress response among birds of the same chronological age are explained by differences in their biological ages would use a panel of multiple biomarkers of biological age, rather than the single one used here.

The three CORT parameters were not individually consistent across the two age points (only ΔCORT showed any suggestion of individual consistency across the two age points, and this was still fairly low). This must be interpreted cautiously given that the samples from the two age points were run in separate laboratory batches. It does however concur with some previous findings in the avian literature that individual consistency can be low (Ouyang, Hau & Bonier, 2011; Baugh et al., 2014; Lendvai, Giraudeau & Bo, 2015); and if consistency is found, is in the response to acute stress rather than baseline CORT (Cockrem & Silverin, 2002; Rensel & Schoech, 2011).

We found some evidence of familial effects. Specifically, natal nest explained a modest but non-zero proportion of the variation in baseline CORT and peak CORT, but a negligible amount of the variation in ΔCORT. The familial effect findings were extremely similar to those of Andrews et al. (2017). Since our birds were removed from the wild on day 5 post-hatching, and siblings were thereafter assigned to different developmental treatment groups, these familial effects must represent either genetics, maternal effects, or very early developmental influences.

We found significant effects of chronological age on both peak CORT (lower at age point 2) and ΔCORT (more return towards baseline at age point 2). Our design was more powerful for detecting these than it was for detecting associations with DTA, as the critical comparisons were within subjects. The findings should be interpreted cautiously, as chronological age was completely confounded with laboratory batch. Nonetheless, they may at least partly represent within-individual biological change with age. There were common control samples run in both batches; the average concentration for these standards at age point 2 was 99.84% and 78.15% of their age point 1 values, whereas peak CORT was 71.47% of its average age 1 value. Decline in the strength of the CORT response (but not baseline CORT) with chronological age is a very widely observed pattern in birds (Heidinger, Nisbet & Ketterson, 2006; Heidinger et al., 2010; Wilcoxen et al., 2011; Elliott et al., 2014; Lendvai, Giraudeau & Bo, 2015; López-Jiménez et al., 2017). Indeed, a chronological age-related decline in CORT response is one of the key assumptions on which Andrews et al. (2017) based their hypotheses, but which they did not directly test. Thus, we have provided some validating evidence for this part of their argument. The differences between age point 1 and age point 2 were strikingly large, and set the associations between DTA and CORT parameters in context. They suggest that any DTA-related differences in stress response amongst birds of the same age are small relative to the within-bird change as a bird develops from a juvenile into an adult.

We found some evidence that one of the developmental manipulations to which the birds were subjected may have affected their stress responses, in an age-dependent manner. Specifically, age point and begging Effort interacted to predict ΔCORT. At the first age point, birds from the Hard begging effort groups had more negative values of ΔCORT than those from the Easy groups. This is consistent with the idea that increased begging effort accelerates biological ageing, since ΔCORT values become more negative with chronological age. It is also consilient with the finding that Hard begging effort accelerated telomere shortening in early life in these birds (Nettle et al., 2017). However, at the second age point, the average ΔCORT had greatly reduced for the Easy birds, who showed the general age-related trend, whilst for the Hard birds it had remained almost unchanged compared to age point 1. Thus, the pattern cannot simply be described as begging effort increasing the rate of ageing of the stress response. Rather, increased begging effort produces in a juvenile bird the stress response that would be expected of an adult; and then that stress response remains the same into adulthood, whilst the stress responses of other birds are changing substantially. Thus, the shape of age-related pattern appears to be different for the Hard birds than for the Easy birds. We note that these developmental treatment findings were not predicted a priori; thus, we view these treatment-related findings as exploratory. However, other studies of these same birds have also found that the Hard Effort treatment leaves a lasting phenotypic legacy: Dunn et al. (2018) showed that the Hard birds maintain a lower body mass than the Easy birds through adulthood, and correspondingly employ different foraging strategies.

In Andrews et al. (2017), a major finding was that developmental conditions did not significantly predict stress parameters, whereas DTA did, the converse of what we see here. Given that developmental treatments were significantly related to DTA in all the cohorts of birds that we have studied, the fact that significant predictor of the strength of the stress response was sometimes DTA (Andrews et al., 2017), and sometimes the developmental treatment itself (here), may relate to limited statistical power, as discussed above.

Conclusions

In conclusion, we did not confirm any significant associations between biological age, as proxied by DTA, and the strength of the stress response, as measured by CORT response to an acute capture-restraint-handling stressor, in a cohort of hand-reared European starlings. However, the overall evidence is still consistent with biological age being associated with aspects of the stress response in starlings. Moreover, our data suggest there is a substantial decline in the strength of the stress response with chronological age in the starling, as the biological age hypothesis requires. They also suggest that early-life begging effort may affect the strength of the stress response in an age-dependent manner; and confirm earlier observations of familial effects on baseline and peak CORT.

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