Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Child mortality, hypothalamic-pituitary-adrenal axis activity and cellular aging in mothers

  • Cindy K. Barha,

    Current address: Faculty of Medicine, Department of Physical Therapy, University of British Columbia, Vancouver, British Columbia, Canada

    Affiliation Maternal and Child Health Laboratory, Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada

  • Katrina G. Salvante,

    Affiliations Maternal and Child Health Laboratory, Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada, Human Evolutionary Studies Program, Simon Fraser University, Burnaby, British Columbia, Canada

  • Courtney W. Hanna,

    Current address: The Babraham Institute, Babraham Research Campus, Cambridge, United Kingdom

    Affiliations Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada, Child and Family Research Institute, Vancouver, British Columbia, Canada

  • Samantha L. Wilson,

    Affiliations Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada, Child and Family Research Institute, Vancouver, British Columbia, Canada

  • Wendy P. Robinson,

    Affiliations Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada, Child and Family Research Institute, Vancouver, British Columbia, Canada

  • Rachel M. Altman,

    Affiliation Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada

  • Pablo A. Nepomnaschy

    pan2@sfu.ca

    Affiliations Maternal and Child Health Laboratory, Faculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada, Human Evolutionary Studies Program, Simon Fraser University, Burnaby, British Columbia, Canada, Child and Family Research Institute, Vancouver, British Columbia, Canada

Child mortality, hypothalamic-pituitary-adrenal axis activity and cellular aging in mothers

  • Cindy K. Barha, 
  • Katrina G. Salvante, 
  • Courtney W. Hanna, 
  • Samantha L. Wilson, 
  • Wendy P. Robinson, 
  • Rachel M. Altman, 
  • Pablo A. Nepomnaschy
PLOS
x

Abstract

Psychological challenges, including traumatic events, have been hypothesized to increase the age-related pace of biological aging. Here we test the hypothesis that psychological challenges can affect the pace of telomere attrition, a marker of cellular aging, using data from an ongoing longitudinal-cohort study of Kaqchikel Mayan women living in a population with a high frequency of child mortality, a traumatic life event. Specifically, we evaluate the associations between child mortality, maternal telomere length and the mothers’ hypothalamic-pituitary-adrenal axis (HPAA), or stress axis, activity. Child mortality data were collected in 2000 and 2013. HPAA activity was assessed by quantifying cortisol levels in first morning urinary specimens collected every other day for seven weeks in 2013. Telomere length (TL) was quantified using qPCR in 55 women from buccal specimens collected in 2013. Results: Shorter TL with increasing age was only observed in women who experienced child mortality (p = 0.015). Women with higher average basal cortisol (p = 0.007) and greater within-individual variation (standard deviation) in basal cortisol (p = 0.053) presented shorter TL. Non-parametric bootstrapping to estimate mediation effects suggests that HPAA activity mediates the effect of child mortality on TL. Our results are, thus, consistent with the hypothesis that traumatic events can influence cellular aging and that HPAA activity may play a mediatory role. Future large-scale longitudinal studies are necessary to confirm our results and further explore the role of the HPAA in cellular aging, as well as to advance our understanding of the underlying mechanisms involved.

Introduction

Several lines of evidence support the hypothesis that exposure to stressors, including psychosocial, energetic, and health challenges, can influence the pace of biological aging [14]. The biological mechanisms underlying this relationship, however, are still being investigated. Stress has been hypothesized to exert its influence via several mechanisms, including the acceleration of cellular aging through an increase in the pace of telomere length (TL) attrition [5].

Located at the ends of chromosomes, telomeres are repetitive nucleotide sequences that protect chromosomal DNA from degradation. DNA degradation can result from incomplete replication (end-replication problem) [6,7], as well as damage from oxidative stress [8]. Once telomeres shorten past a critical length, cells undergo senescence [9]. While TL in human peripheral blood cells has been reported to decline with chronological age, the pace of attrition does not appear to be constant across the lifespan, with greater attrition observed both early and later in life [8,10,11]. It has been proposed that exposure to stress can accelerate this typical pattern of TL attrition [5]. Whether the effects of stress on TL attrition vary with age is not currently known.

The hypothalamic-pituitary-adrenal axis (HPAA) has been proposed as a possible physiological pathway mediating the relationship between stressful life events and the rate of TL attrition [1217]. Stressors normally result in the activation of the HPAA which allows individuals to respond to the challenges at hand. Challenges stimulate the adrenal secretion of cortisol [18], resulting in the mobilization of metabolic energy to be used by the tissues involved in the response to the stressors at hand [19,20]. While adaptive, this process involves a biological cost. As metabolic resources are finite, energy mobilized to respond to a given challenge has to be reallocated away from regular metabolic tasks, such as somatic maintenance, including telomere maintenance and repair [2123]. Thus, traumatic or frequent stress is hypothesized to lead to greater telomere attrition and, consequently, accelerated biological aging. Numerous cross-sectional studies (for example see: [24,2528]) and a limited number of longitudinal studies [29,30] provide support for this hypothesis. A number of psychosocial stressors, including violence, financial hardship, divorce, unemployment, and caring for ill or elderly relatives, as well as other challenges, such as energetic stress, have consistently been associated with shorter telomeres (for review see: [5,31]). Yet, only a handful of studies have examined the role of HPAA activity as a modulator of TL in humans, and their results are equivocal [1217]. While some of these studies reported negative associations between markers of HPAA activity, such as cortisol, and TL [12,13,15,17], others have not found any significant associations [14,16]. Therefore, whether the HPAA is directly involved in mediating the effects of stress on the pace of cellular aging is still a matter of much debate. Some of these inconsistencies between studies may be explained by methodological differences. These differences include the use of different biological matrices to assess cortisol levels (i.e., urine, saliva, blood), as well as variation in the number of specimens and the frequency with which they were collected, all of which can affect a study’s results. Specifically, HPAA activity varies dramatically within individuals as a result of various confounding factors, such as circadian rhythms, food consumption and physical activity [32,33]. We have previously argued that the use of first morning urinary cortisol allows for a tighter control of these confounders than cortisol quantification in other matrices such as saliva and blood [34]. Additionally, basal HPAA activity and responsivity varies markedly within and between individuals [35,36]. Thus, the single cortisol measurement frequently used in cross-sectional studies is not adequate to accurately assess HPAA activity [35]. Along the same lines, accurately assessing telomere attrition requires multiple TL measurements across time, which cross-sectional studies lack. Therefore, longitudinal cohort studies that include the repeated collection of biological specimens from participants can provide critical information regarding the role of HPAA activity as a mediator of the link between stress and TL attrition.

Taking into account these methodological issues, here we take advantage of an ongoing longitudinal-cohort study of Kaqchikel Mayan women to test the hypothesis that maternal exposure to child mortality, a traumatic life event, across a 13-year period is associated with age-related declines in TL. We also investigate whether HPAA activity plays a role in this relationship. We chose this stressor because child mortality is particularly high in this Mayan population (approximately 50% of women in this cohort experienced the death of one or more children). Consistent with the hypothesis that traumatic life events play a role in cellular aging, we predict that women who experienced the traumatic life event of child mortality will present shorter TL, and that this link will be significantly associated with the activity of their HPAA.

Materials and methods

Study population and participants

The analyses are based on a cohort of 107 Kaqchikel Mayan women initially recruited in the year 2000 for the Society, Environment and Reproduction (SER) study [37,38]. SER is a naturalistic, longitudinal study focused on the relationship between “naturally occurring” stress and women’s reproductive function. All participants are Kaqchikel Maya with at least 5 generations of traceable ancestors, which reduces genetic variability within our sample. Variation in terms of lifestyle (i.e., diet, physical activity, education, and socio-economic status) is lower in our population than that usually found among women living in urban communities of industrialized populations. This homogeneity reduces the potential confounding effects of cultural and lifestyle factors. Women in our study population did not smoke. This is particularly important, as previous studies have shown a negative influence of cigarette smoking on telomere length [26].

Of the 107 SER participants originally recruited in the year 2000, 94 volunteered to participate in the current study and provided urinary biospecimens and a buccal epithelial cell sample, as well as a bioelectrical impedance measurement (to gauge percent body fat [39]) in the year 2013. Of the 94 women, 19 were excluded from our sample because critical demographic information was missing for 2013 (e.g., no information on parity or child mortality) [40]. These exclusions resulted in a sample size of 75 women. For the present study, women who were pregnant or in postpartum amenorrhea in 2013 (n = 20) were further excluded from analyses because HPAA activity has been shown to differ between these reproductive stages and cycling women [4143]. This resulted in a final sample of 55 women. The average age of the 55 participants in 2013 was 39.8 ± 5.8 years (average ± sample SD; range: 29–53 years) (Table 1).

thumbnail
Table 1. Characteristics of the study population.

Values are average ± sample SD (range).

https://doi.org/10.1371/journal.pone.0177869.t001

Procedures

Ethics.

Data collection in the year 2000 was approved by the University of Michigan’s Institutional Review Board. Data collection in 2013 and data analyses were approved by Simon Fraser University’s Ethics Review Board and the University of British Columbia’s Clinical Ethics Review Board. Informed consent was obtained orally from illiterate individuals and in writing from literate ones. In all cases the consent document was read in Kaqchikel Mayan, the local language, by a female research assistant to each prospective participant and signed by the participants with a cross, finger print or name initials, according to their individual preferences. The University of Michigan’s Institutional Review Board approved the 2000 consent procedure and study protocols, and Simon Fraser University’s Ethics Review Board approved the ones used in 2013. The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

Assessment of exposure to child mortality.

Child mortality was assessed via comparison of demographic interviews administered in 2000 and 2013. Trained local, bilingual female research assistants administered the interview in Kaqchikel (one of two local languages), and recorded all of the answers by hand. This interview included questions about age, number of children born to a woman, family income and child mortality. Child mortality included all post-natal deaths, but not miscarriages or stillbirths as we did not have complete information regarding these two types of child mortality. Of the children who died, most did so after 6 months of age, mainly from infection-related illnesses, which induced diarrhea, vomiting and fever. Based on the demographic information, we created a binomial variable to identify women who experienced the death of at least one child between 2000 and 2013 (n = 25) and those who did not (n = 30). Two of the women in our sample, one in each group, were post-menopausal.

Telomere length assessment.

TL was measured in cells recovered from buccal epithelial specimens collected in 2013. A buccal specimen was collected from each participant using an SK-1 Isohelix Buccal Swab (Cat. No: SK-1S) by rubbing the swab firmly against the inside of the right cheek for 1 minute. Swabs were then stored with an Isohelix Dri-Capsule (Cat. No: SGC-50), which is designed to ensure long-term buccal DNA stability at room temperature for over three years [44,45]. Buccal samples were stored at room temperature in a dark, dry cabinet for approximately 2 months, then transported at room temperature from the field site to our laboratory where they were similarly stored for 6 months until analysis.

Genomic DNA was extracted from buccal samples using the DDK DNA Isolation Kit (Isohelix Ltd, Kent, UK). DNA quality was checked by running extracted genomic DNA from a subsample of buccal samples for 1 hour on a 0.8% agarose gel with a Hind III ladder. DNA from buccal samples was found to be of sufficient quality, with high molecular weight DNA observed in all tested samples. DNA samples were quantitated using the NanoDrop 2000 UV-Vis Spectrophotometer (Thermo Scientific, Wilmington, USA) and diluted to 10ng/uL in 10mM Tris, 0.1mM EDTA buffer. Quantitative PCR (qPCR) was used to measure average relative TL (T/S ratio) as previously described [46]. Briefly, quantification of the amplification of telomeric repeats relative to a single-copy gene, 36B4, was performed using SYBR green. The 36B4 gene, which encodes acidic ribosomal phosphoprotein PO, is located on chromosome 12. The telomere primers were designed to allow for amplification of the smallest possible amplicon (76bp). Consequently, the qPCR amplification (CT value) and the amount of primer binding sites in the genomic DNA template were proportional [47]. The primer sequences (written 5′→3′) were: tel 1, GGTTTTTGAGGGTGAGGGTGAGGGTGAGGGTGAGGGT; tel 2, TCCCGACTATCCCTATCCCTATCCCTATCCCTATCCCTA; 36B4u, CAGCAAGTGGGAAGGTGTAATCC; 36B4d, CCCATTCTATCATCAACGGGTACAA. Standard curves and control samples were generated from whole blood DNA [46].

Sample identifiers were blinded to the experimenter and, thus, sample groups were randomly distributed among the runs. During preparation, samples were distributed in a non-replicated order across each plate, with each sample replicate always run on an independent plate. To monitor reproducibility, samples were run in triplicate within each 96-well plate, with at least two independent replicates. For the current study samples, 28 sets of 96-well T and S plates were completed. Across the 28 sets of plates, the average R2 was 0.995 (± 0.003). CT values were excluded if the standard deviation among triplicates was greater than 0.20. Furthermore, if the coefficient of variation between the two independent replicates was greater than 0.15, two additional independent replicates were performed. Independent replicates were then averaged to generate an average relative TL for each sample (T/S ratio), the ratio of the number of telomere repeat copies (T) to the number of 36B4 gene copies (S). As the efficiencies of the T plates (77.8% ± 6.4%) were consistently lower than those of the corresponding S plates (85.6% ± 3.4%), we used the Pfaffl method [48] to calculate T/S ratios, which takes into account this variation in plate efficiency. The average intra-plate coefficient of variation for CT was 0.33% (±0.07%) and for T/S values was 6.28% (±0.97%). The average inter-plate coefficient of variation was 5.23% (±3.56%).

T/S ratio and the average TL of a given sample have previously been shown to be proportional [47]. While the ‘gold standard’ for TL measurement is Southern blot of the terminal restriction fragment (TRF), the qPCR-based method has several advantages, including the ability to use smaller amounts of DNA and its high reproducibility, which have led to a recent increase in its use [4951]. The reproducibility of our qPCR methodology was previously validated by blind comparison of measurements obtained using qPCR and the flow fluorescence in situ hybridization (flow-FISH) technique (r = 0.96; [46]).

Assessment of hypothalamic-pituitary-adrenal axis activity.

Basal HPAA activity was determined by quantifying free unconjugated cortisol levels in first morning urinary (FMU) biospecimens collected every other day over a period of seven weeks in 2013. FMU cortisol provides an integrative measure of basal HPAA activity and is less likely to be affected by diurnal confounders than cortisol quantified in other matrices such as serum and saliva [34,52]. Participants provided, on average, 19 ± 1.90 FMU samples each (average ± sample SD; range: 13–22). This sampling collection protocol permitted us to determine an average level of FMU cortisol for each individual and monitor day-to-day variation in urinary free cortisol levels within- and among-individuals [35]. Upon waking, participants collected their first urinary voids into sterile, inert plastic containers provided by our research team the night before the collection day. Within 4 hours of being produced, specimens were aliquoted into 2 ml cryovials, and stored at -10°C in the field. Within six months of their collection, samples were shipped on dry ice to our laboratory and archived at -80°C until analysis. FMU cortisol was quantified using a previously validated enzyme immunoassay (EIA) array (Quansys Biosciences, Logan, UT) [53], with a lower limit of detection of 0.5ng/ml. All samples were run in duplicate and intra- and inter-assay coefficients of variation were 6.7% and 12.0%, respectively. The cortisol concentration of each sample was corrected for specific gravity using refractometry to adjust for variation in hydration state [54,55].

Statistical analyses.

We fit a linear multivariate regression model using JMP (version 12, SAS Institute) to describe buccal TL as a function of the predictor variables. Specifically, we included child mortality, age, the interaction between child mortality and age, average FMU cortisol and intra-woman standard deviation (SD) of daily FMU cortisol (markers of HPAA activity) as predictors. Whereas average FMU cortisol is reflective of basal HPAA activity, SD cortisol reflects within-woman variability in HPAA activity among days. Higher SD in FMU cortisol may indicate that an individual has a more sensitive or reactive HPAA, or possibly, that an individual has been exposed to more challenges (e.g., psychosocial stress, infection, energetic stress, etc.). All FMU cortisol concentrations were log-10 transformed to normalize data distribution.

Previously we showed that increased parity is associated with reduced telomere shortening in women in our study population [40]. To account for this effect of parity, we included total number of children born to a woman as a covariate in our model. We also included percent body fat as a covariate in the model to account for mothers’ body composition as a potential confounding factor. Percent body fat was not a significant covariate (p > 0.424). As excluding percent body fat from the model did not change the estimates or significance for the other parameters, we excluded it to avoid over-fitting [56]. Thus, our final linear multivariate regression model was: buccal TL ~ age + child mortality group + age*child mortality group + average FMU cortisol + within-woman SD in FMU cortisol, with number of children born to a woman entered as a covariate. This final model included our effects of interest while adjusting for known, available confounders. The significance level was set at 0.05.

To evaluate whether HPAA activity (average FMU cortisol and within-woman SD in FMU cortisol) mediated the associations between 1) child mortality variable and buccal TL and 2) the child mortality x age interaction and buccal TL, we used a non-parametric, bias-corrected and accelerated (BCa) bootstrapping procedure in R (Version 3.3.1, 2016 R Development Core Team, Vienna, Austria) to estimate the mediation effect [5760]. The bootstrapping procedure has been recommended for regression-based mediation because mediation effect estimates are generally not normally distributed unless sample size is very large [61]. Briefly, for each of 10,000 bootstrapped samples, two models were fit: the first including all of the variables in our final linear multivariate regression model (Model 1, Fig 1), and the second excluding average FMU cortisol and within-woman SD in FMU cortisol (Model 2, Fig 1). The differences between the estimated main effects of child mortality group in the two models and between the estimated effects of the child mortality group by age interaction in the two models were computed. These differences, if significantly different from 0, would suggest a mediatory role for HPAA in the associations between buccal TL and experiencing child mortality and between buccal TL and the interaction between experiencing child mortality and age [59].

thumbnail
Fig 1. Mediation model.

The two models compared to examine whether HPAA activity mediates the main effect of child mortality group and the interaction effect of child mortality and age on telomere length, including total number of children as a covariate (dotted lines).

https://doi.org/10.1371/journal.pone.0177869.g001

Results

The average age of the 55 participants in 2013 was 39.8 ± 5.8 years (average ± sample SD; range: 29–53 years) (Table 1). Women in the group who experienced child mortality were, on average 3 years older than those in the group who did not experience this life event (p = 0.040) (Table 1). Average log-10 transformed FMU cortisol was 1.32 ± 0.18 across seven weeks in 2013, and the average within-woman standard deviation (SD) of daily log-10 transformed FMU cortisol across seven weeks in 2013 was 0.22 ± 0.07 (Table 1). The 55 participants had an average of 5.6 ± 2.1 children (Table 1). Average and SD FMUC cortisol and total number of children were not significantly different between the two groups (all p’s > 0.05).

Our final linear multivariate regression model explained 26% of the variation in buccal TL (p = 0.018) (Table 2). The relationship between maternal age and buccal TL was significantly associated with the experience of losing a child (age x child mortality interaction: p = 0.025). In women who did not experience child mortality, buccal TL was not significantly associated with age (B = 0.004, SE = 0.011, p = 0.716; Fig 2A), after adjusting for total number of children. In contrast, in women who experienced child mortality, buccal TL was negatively associated with age (B = -0.039, SE = 0.015, p = 0.015; Fig 2B), after adjusting for total number of children. Average FMU cortisol was negatively associated with buccal TL (B = -0.773, SE = 0.276, p = 0.007), after adjusting for total number of children. A similar negative trend was observed for SD of FMU cortisol (B = -1.327, SE = 0.667, p = 0.053). In other words, women with higher and more variable FMU cortisol levels exhibited shorter buccal TL (Fig 3A and 3B, respectively).

thumbnail
Fig 2. Relationships between buccal telomere length, child mortality and maternal age.

a) Increasing chronological age was significantly associated with shorter buccal telomere lengths in women who experienced the death of one or more children (slope = -0.039, p = 0.015), b) but not in women who had not experienced child mortality (slope = 0.004, p = 0.716), after adjusting for all other predictors and covariates. The lines in each graph represent the estimated relationship between buccal telomere length and age for a woman with average values for all of the other predictors and covariates in the model.

https://doi.org/10.1371/journal.pone.0177869.g002

thumbnail
Fig 3. Relationships between buccal telomere length and HPAA activity.

Regardless of child mortality exposure, shorter buccal telomere lengths were also a) significantly associated with higher first morning urinary cortisol levels (slope = -0.773, p = 0.007) and b) marginally associated with higher within-woman variation (standard deviation) in first morning urinary cortisol levels (slope = -1.327, p = 0.053), after adjusting for all other predictors and covariates. The lines in each graph represent the estimated relationship between buccal telomere length and average first morning urinary cortisol and within-woman standard deviation in first morning urinary cortisol levels, respectively, for a woman with average values for all of the other predictors and covariates in the model.

https://doi.org/10.1371/journal.pone.0177869.g003

thumbnail
Table 2. The relationships between buccal telomere length and child mortality, age, and basal HPAA activity, adjusted for number of children born to each woman.

Final linear multivariate regression model: buccal TL ~ age + child mortality group + age:child mortality group + average FMU cortisol + within-woman SD in FMU cortisol + number of children born to a woman (covariate) (R2 = 0.26, p = 0.018).

https://doi.org/10.1371/journal.pone.0177869.t002

In evaluating whether HPAA activity (average and SD FMU cortisol) mediated the associations between the child mortality and buccal TL, we found that 95% of the bootstrap estimated differences in the main effect of child mortality fell below 0, while 95% of the bootstrap estimated differences in the child mortality by age interaction effect fell above 0. Therefore, the estimates for both the main effect of experiencing child mortality and the child mortality by age interaction differed significantly between the model that included the HPAA activity variables and the model that excluded these variables, suggesting that HPAA activity may act as a mediator of the effect of child mortality on TL.

Discussion

The rate of age-associated decline in TL is often assumed to be relatively consistent among individuals across time [62]. Nonetheless, exposure to various insults, including social, environmental and physiological challenges, have been hypothesized to accelerate this process [5,24,25,27,28,31,6366]. The evidence supporting this hypothesis, however, has been mixed, with some studies supporting this association [12,13,15,17], while other studies have not [14,16]. Furthermore, the mechanisms involved in the putative effects of stress on the pace of cellular aging are still being investigated. Our results show a link between experiencing a traumatic challenge–child death–and maternal age-related TL loss, which is consistent with the original hypothesis regarding the effects of stress on the pace of cellular aging. Our analyses are consistent with the prediction that HPAA activity is involved in the mechanism mediating the association between stress and cellular aging.

Interestingly, the age-associated decline in TL was only observed in women who experienced child mortality. The lack of an obvious association between age and TL in those who did not experience this traumatic event may be explained by the limited age range of the women in our study (29 to 53 at follow-up in 2013). While TL is generally expected to decrease with age (for review see: [67,68]), within the age range of the women in our sample, this decline was anticipated to be minimal. Indeed, in human peripheral blood cells, TL attrition follows a biphasic pattern, accelerated in early childhood and in old age, with a possible period of relative quiescence during adulthood [8]. The age range of the women in our sample during the period that we observed them (average age in 2000 = 25.37 years; average age in 2013 = 38.37 years) falls within the period of hypothesized relative quiescence. This overlap may explain why the women who did not suffer child mortality did not exhibit a statistically significant age-associated TL decline. The maintenance of TL during this period of adulthood in women may be explained by protective factors associated with women’s reproductive physiology and pregnancy that may mitigate TL shortening. Ovarian cycling is accompanied by regular increases in circulating levels of estradiol. Estradiol is known to promote telomerase activity and reduces oxidative stress levels [6972], two factors that can protect TL. Pregnancy is accompanied by even higher and more prolonged increases in circulating estradiol levels. Thus, parity may exert additional protective effects on TL. Furthermore, pregnancy has been argued to confer protection against the age-related decline in the regenerative capacity of various maternal tissues [73]. These effects have been observed in a variety of tissues, including the liver, central nervous system, and heart, in a number of vertebrate species, ranging from mice to humans [73]. Several mechanisms have been proposed as potential mediators of these effects including the parabiotic-like effects of microchimeric fetal cells on the regenerative capacity of maternal tissues, as well as those of pregnancy-related maternal hormones, like estradiol and prolactin [73]. In mice models, for example, elevated prolactin induces an increase in neural precursor cells leading to an increase in newly generated oligodendrocytes, which are involved in the remyelination of axons in experimentally demyelinated animals [73]. These protective effects associated with reproduction, could be linked to, and perhaps help explain, the quiescence period in TL attrition in women during their reproductive years, particularly in high fertility populations such as the one we study. Consistent with this hypothesis, in our study population the total number of children born to a woman is positively correlated with TL [40]. The existence and extent of the protective effects of pregnancy on aging and the period of relative quiescence in TL attrition during women’s reproductive years is still being investigated and debated. Future research is needed to further examine these effects as well as any other factors that may affect the pace of TL attrition in women across different life stages (e.g., pre-puberty vs. adulthood vs. post-menopause).

The negative association we observed between child mortality and age-related variation in maternal TL during a woman’s reproductive years is consistent with the hypothesis that the putative protective effects of maternity on aging [40,73] can be overcome by the stress elicited by exposure to traumatic events. Our results parallel those of other studies showing that traumatic events are linked to the pace of telomere attrition across the human lifespan. Shalev et al. [30], for example, showed that exposure to violent events during childhood exacerbated telomere attrition in children between the ages of five and ten. In post-menopausal women, major life stressors, such as loss of household, major financial difficulties or death of a family member or friend, were associated with greater telomere attrition over a one-year period [29]. Our results extend these findings, showing that traumatic events are associated with shorter TL even in the life stage during which TL seems to be the most stable.

A role for the HPAA?

To our knowledge, this is the first study to use a longitudinal design to evaluate the role of HPAA activity as a potential mechanism through which traumatic life events may affect the age-associated variation in TL. Consistent with our prediction, our results showed that women with higher FMU cortisol levels presented shorter TL. In terms of mediating mechanisms, there is evidence suggesting that exposure to high circulating cortisol leads to increased oxidative stress [74], which preferentially damages the telomere region of the chromosome [64,75]. Additionally, previous work has shown that in vitro exposure of human T lymphocytes to cortisol can reduce the activity of telomerase [76], an enzyme that protects telomeres from shortening by adding back telomeric DNA [77]. Consistent with these observations, Epel and colleagues found that caregivers, who have greater levels of perceived stress, showed reduced telomerase activity at baseline and in reaction to an acute stressor compared to a non-caregiver group [78]. Similarly, higher fasting morning serum cortisol levels have been reported to be associated with reduced telomerase activity in a randomized mindfulness intervention study [79]. Another potential mechanism through which cortisol may influence telomere attrition involves increased division and proliferation of cells in tissues with high levels of cell turnover [8082]. Rapid cell turnover can lead to increased TL shortening through the end-replication problem [6,7]. Our observation that HPAA activity was associated with TL may have been facilitated by our use of buccal epithelial cells to assess TL, because these cells have a rapid turnover rate. Together, our results and those of previous studies discussed above are consistent with the hypothesis that high levels of HPAA activation may influence cellular aging via oxidative stress and telomerase pathways and increased cell turnover.

Of note, we observed a trend towards a negative correlation between intra- individual variation (SD) in basal FMU cortisol and TL while accounting for variation in total number of children. The functional significance of intra-individual variability in HPAA activity has not received due attention in the literature. This variability has been argued to provide a measure of how often the HPAA is activated (i.e., the level of physiologic stress to which an individual is exposed) and may also reflect past exposure to stress [35,83]. Thus, the observed trend suggesting a negative link between within-individual basal cortisol SD and TL is also consistent with the hypothesis that life challenges may affect cellular aging through changes in HPAA activity.

The estimated mediating effects of average and SD FMU cortisol on the association between experiencing child mortality and TL were not large. This may be due to a variety of factors, including our relatively small sample size and our choice of HPAA activity measurement. In this study, we evaluated HPAA activity using FMU cortisol, an integrated measure of cortisol secretion across the night. Alternative HPAA measures, including the pattern of cortisol reactivity to and recovery from a stress challenge or diurnal cortisol profiles may provide more information as to the role of HPAA activity in cellular aging. Future studies that evaluate other aspects of HPAA activity and their potential to mediate the relationships between stressful life events and cellular aging are needed.

Limitations

Inherent to all observational studies, we cannot rule out the possibility that the relationship observed between child mortality and TL in mothers could be explained by unmeasured factors not considered in our analyses. Socioeconomic status could potentially influence both maternal TL and child mortality risk [84]. At the time of the study, however, our rural, indigenous study population was socioeconomically quite homogenous compared to industrialized communities. Thus, it is unlikely that this variable alone could explain our results.

Mothers who experienced child mortality were, on average, 3 years older than those who did not. This difference is to be expected as the probability of losing a child should be directly proportional to a woman’s age (in this population the more time goes by, the more children a woman is likely to have and the greater the chances that one of her children may die). Thus, even though the age ranges were the same between the two groups, the group of women who experienced child mortality had fewer younger women than the group of women who didn’t. It could be argued that the link we observed between child mortality and TL may be attributed to this difference in average age. However, as age was included in our model as a predictor variable, the effect of age is accounted for in our analyses.

Maternal body condition could also potentially confound the relationship between child mortality and TL. However, when percent body fat was included in our initial model as a covariate to account for body condition, it was not associated with TL, nor did its exclusion from the model change the estimates or significance of any of the other model parameters. Other measures of body condition that were not measured, however, could still act as potential confounding factors. Further, as some of our qPCR reaction efficiencies were slightly lower than other studies that used qPCR to quantify TL [85,86], the results should be taken with caution.

We were not able to determine the specific date at which the traumatic event (child death) occurred for each woman, limiting our ability to assess the temporal influence of stress on TL shortening. This may be of particular interest when considering previous work suggesting that the time between exposure to stressful life events and TL measurement may have an impact on the strength of the association between the two variables [26]. More recent events may also have stronger effects on basal HPAA activity than past events. Future studies should also endeavour to assess the temporal nature of traumatic stressful events as well as HPAA activity closer to the time of the event. Despite these limitations, the present study offers novel information that contributes to our understanding of the role that life events may have on cellular aging. Larger epidemiological studies of women of different ethnicities will be necessary to replicate our analyses and confirm our results.

Conclusions

Our results are consistent with the hypothesis that traumatic life events can affect the pace of cellular aging in women of reproductive age, and suggest that maternal HPAA activity may mediate this association. Additional large-scale longitudinal studies are necessary to confirm and further explore the effects of other traumatic life events on cellular aging and the role of the HPAA. These studies should incorporate the collection of multiple biological markers of stress physiology and cellular aging to further elucidate the biological mechanisms involved.

Acknowledgments

We would like to thank our participants and their families and the members of our Guatemalan research team, including Luseadra McKerracher and Nazanin Mohtadi, for their assistance with data collection. We would also like to thank Drs. Nestor Carrillo-Poton, Constantino Isaac Sanchez Montoya and Mayron Martinez, as well as the rest of the personnel of the Guatemalan Ministry of Health for permits and logistical collaboration. We would also like to thank the anonymous reviewers who helped us improve the quality of this manuscript.

Author Contributions

  1. Conceptualization: CKB KGS CWH WPR PAN.
  2. Data curation: CKB KGS CWH SLW WPR RMA PAN.
  3. Formal analysis: CKB KGS CWH SLW WPR RMA PAN.
  4. Funding acquisition: CKB CWH SLW WPR RMA PAN.
  5. Investigation: CKB KGS CWH SLW WPR RMA PAN.
  6. Methodology: CKB KGS CWH SLW WPR RMA PAN.
  7. Project administration: CKB KGS PAN.
  8. Resources: CKB KGS CWH SLW WPR RMA PAN.
  9. Software: RMA.
  10. Supervision: CKB KGS CWH SLW WPR RMA PAN.
  11. Validation: CKB KGS CWH SLW WPR RMA PAN.
  12. Visualization: CKB KGS CWH SLW WPR RMA PAN.
  13. Writing – original draft: CKB KGS CWH SLW WPR RMA PAN.
  14. Writing – review & editing: CKB KGS CWH SLW WPR RMA PAN.

References

  1. 1. Miller MW, Sadeh N (2014) Traumatic stress, oxidative stress and post-traumatic stress disorder: neurodegeneration and the accelerated-aging hypothesis. Mol Psychiatry 19: 1156–1162. pmid:25245500
  2. 2. Epel ES, Lithgow GJ (2014) Stress biology and aging mechanisms: toward understanding the deep connection between adaptation to stress and longevity. J Gerontol A Biol Sci Med Sci 69 Suppl 1: S10–16.
  3. 3. Juster RP, McEwen BS, Lupien SJ (2010) Allostatic load biomarkers of chronic stress and impact on health and cognition. Neurosci Biobehav Rev 35: 2–16. pmid:19822172
  4. 4. Pardon MC, Rattray I (2008) What do we know about the long-term consequences of stress on ageing and the progression of age-related neurodegenerative disorders? Neurosci Biobehav Rev 32: 1103–1120. pmid:18436304
  5. 5. Epel ES (2009) Psychological and metabolic stress: a recipe for accelerated cellular aging? Hormones (Athens) 8: 7–22.
  6. 6. Olovnikov AM (1973) A theory of marginotomy. The incomplete copying of template margin in enzymic synthesis of polynucleotides and biological significance of the phenomenon. J Theor Biol 41: 181–190. pmid:4754905
  7. 7. Watson JD (1972) Origin of concatemeric T7 DNA. Nat New Biol 239: 197–201. pmid:4507727
  8. 8. Aubert G, Lansdorp PM (2008) Telomeres and aging. Physiol Rev 88: 557–579. pmid:18391173
  9. 9. Armanios M, Blackburn EH (2012) The telomere syndromes. Nat Rev Genet 13: 693–704. pmid:22965356
  10. 10. Aubert G, Baerlocher GM, Vulto I, Poon SS, Lansdorp PM (2012) Collapse of telomere homeostasis in hematopoietic cells caused by heterozygous mutations in telomerase genes. PLoS Genet 8: e1002696. pmid:22661914
  11. 11. Frenck RW Jr., Blackburn EH, Shannon KM (1998) The rate of telomere sequence loss in human leukocytes varies with age. Proc Natl Acad Sci U S A 95: 5607–5610. pmid:9576930
  12. 12. Epel ES, Lin J, Wilhelm FH, Wolkowitz OM, Cawthon R, et al. (2006) Cell aging in relation to stress arousal and cardiovascular disease risk factors. Psychoneuroendocrinology 31: 277–287. pmid:16298085
  13. 13. Kroenke CH, Epel E, Adler N, Bush NR, Obradovic J, et al. (2011) Autonomic and adrenocortical reactivity and buccal cell telomere length in kindergarten children. Psychosom Med 73: 533–540. pmid:21873585
  14. 14. Parks CG, Miller DB, McCanlies EC, Cawthon RM, Andrew ME, et al. (2009) Telomere length, current perceived stress, and urinary stress hormones in women. Cancer Epidemiol Biomarkers Prev 18: 551–560. pmid:19190150
  15. 15. Revesz D, Verhoeven JE, Milaneschi Y, de Geus EJ, Wolkowitz OM, et al. (2014) Dysregulated physiological stress systems and accelerated cellular aging. Neurobiol Aging 35: 1422–1430. pmid:24439483
  16. 16. Savolainen K, Eriksson JG, Kajantie E, Lahti J, Raikkonen K (2015) Telomere length and hypothalamic-pituitary-adrenal axis response to stress in elderly adults. Psychoneuroendocrinology 53: 179–184. pmid:25622010
  17. 17. Tomiyama AJ, O'Donovan A, Lin J, Puterman E, Lazaro A, et al. (2012) Does cellular aging relate to patterns of allostasis? An examination of basal and stress reactive HPA axis activity and telomere length. Physiol Behav 106: 40–45. pmid:22138440
  18. 18. Uhart M, Chong RY, Oswald L, Lin PI, Wand GS (2006) Gender differences in hypothalamic-pituitary-adrenal (HPA) axis reactivity. Psychoneuroendocrinology 31: 642–652. pmid:16616815
  19. 19. Miller DB, O'Callaghan JP (2002) Neuroendocrine aspects of the response to stress. Metabolism 51: 5–10.
  20. 20. Padgett DA, Glaser R (2003) How stress influences the immune response. Trends Immunol 24: 444–448. pmid:12909458
  21. 21. Ellison PT, Valeggia CR (2003) C-peptide levels and the duration of lactational amenorrhea. Fertil Steril 80: 1279–1280. pmid:14607590
  22. 22. Jasienska G (2001) Why energy expenditure causes reproductive suppression in women: an evolutionary and bioenergetic perspective. In: Ellison PT, editor. Reproductive ecology and human evolution. New York: Aldine de Gruyter. pp. 59–84.
  23. 23. Stearns SC (1992) The evolution of life histories. Oxford, UK: Oxford University Press.
  24. 24. Humphreys J, Epel ES, Cooper BA, Lin J, Blackburn EH, et al. (2012) Telomere shortening in formerly abused and never abused women. Biol Res Nurs 14: 115–123. pmid:21385798
  25. 25. Malan S, Hemmings S, Kidd M, Martin L, Seedat S (2011) Investigation of telomere length and psychological stress in rape victims. Depress Anxiety 28: 1081–1085. pmid:22065550
  26. 26. Verhoeven JE, van Oppen P, Puterman E, Elzinga B, Penninx BW (2015) The Association of Early and Recent Psychosocial Life Stress With Leukocyte Telomere Length. Psychosom Med.
  27. 27. Damjanovic AK, Yang Y, Glaser R, Kiecolt-Glaser JK, Nguyen H, et al. (2007) Accelerated telomere erosion is associated with a declining immune function of caregivers of Alzheimer's disease patients. J Immunol 179: 4249–4254. pmid:17785865
  28. 28. Epel ES, Blackburn EH, Lin J, Dhabhar FS, Adler NE, et al. (2004) Accelerated telomere shortening in response to life stress. Proc Natl Acad Sci U S A 101: 17312–17315. pmid:15574496
  29. 29. Puterman E, Lin J, Krauss J, Blackburn EH, Epel ES (2015) Determinants of telomere attrition over 1 year in healthy older women: stress and health behaviors matter. Mol Psychiatry 20: 529–535. pmid:25070535
  30. 30. Shalev I, Moffitt TE, Sugden K, Williams B, Houts RM, et al. (2013) Exposure to violence during childhood is associated with telomere erosion from 5 to 10 years of age: a longitudinal study. Mol Psychiatry 18: 576–581. pmid:22525489
  31. 31. Shalev I, Entringer S, Wadhwa PD, Wolkowitz OM, Puterman E, et al. (2013) Stress and telomere biology: a lifespan perspective. Psychoneuroendocrinology 38: 1835–1842. pmid:23639252
  32. 32. Kudielka BM, Hellhammer DH, Wust S (2009) Why do we respond so differently? Reviewing determinants of human salivary cortisol responses to challenge. Psychoneuroendocrinology 34: 2–18. pmid:19041187
  33. 33. Young EA, Abelson J, Lightman SL (2004) Cortisol pulsatility and its role in stress regulation and health. Front Neuroendocrinol 25: 69–76. pmid:15571755
  34. 34. Sarkar PL, Zeng L, Chen Y, Salvante KG, Nepomnaschy PA (2013) A longitudinal evaluation of the relationship between first morning urinary and salivary cortisol. Am J Hum Biol 25: 351–358. pmid:23564709
  35. 35. Nepomnaschy PA, Lee TC, Zeng L, Dean CB (2012) Who is stressed? Comparing cortisol levels between individuals. Am J Hum Biol 24: 515–525. pmid:22434611
  36. 36. Kirschbaum C, Prussner JC, Stone AA, Federenko I, Gaab J, et al. (1995) Persistent high cortisol responses to repeated psychological stress in a subpopulation of healthy men. Psychosom Med 57: 468–474. pmid:8552738
  37. 37. Nepomnaschy PA, Welch K, McConnell D, Strassmann BI, England BG (2004) Stress and female reproductive function: a study of daily variations in cortisol, gonadotrophins, and gonadal steroids in a rural Mayan population. Am J Hum Biol 16: 523–532. pmid:15368600
  38. 38. Nepomnaschy PA, Welch KB, McConnell DS, Low BS, Strassmann BI, et al. (2006) Cortisol levels and very early pregnancy loss in humans. Proc Natl Acad Sci U S A 103: 3938–3942. pmid:16495411
  39. 39. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, et al. (2004) Bioelectrical impedance analysis—part I: review of principles and methods. Clin Nutr 23: 1226–1243. pmid:15380917
  40. 40. Barha CK, Hanna CW, Salvante KG, Wilson SL, Robinson WP, et al. (2016) Number of Children and Telomere Length in Women: A Prospective, Longitudinal Evaluation. PLoS One 11: e0146424. pmid:26731744
  41. 41. Smith R, Owens PC, Brinsmead MW, Singh B, Hall C (1987) The nonsuppressibility of plasma cortisol persists after pregnancy. Horm Metab Res 19: 41–42. pmid:3557282
  42. 42. Lopes LM, Francisco RP, Galletta MA, Bronstein MD (2015) Determination of nighttime salivary cortisol during pregnancy: comparison with values in non-pregnancy and Cushing's disease. Pituitary.
  43. 43. Abrao AL, Leal SC, Falcao DP (2014) Salivary and serum cortisol levels, salivary alpha-amylase and unstimulated whole saliva flow rate in pregnant and non-pregnant. Rev Bras Ginecol Obstet 36: 72–78. pmid:24676015
  44. 44. DNA stability on Isohelix SK1 buccal swabs stored with Isohelix Dri-Capsules is maintained over a 3+ year period. Application Note: SGC/SK1-03. Cell Projects.
  45. 45. Preliminary DNA stability data from studies using silica gel capsules with Isohelix DNA SK-1 buccal swabs. Application Note: SGC/SK1-01. Cell Projects
  46. 46. Hanna CW, Bretherick KL, Gair JL, Fluker MR, Stephenson MD, et al. (2009) Telomere length and reproductive aging. Hum Reprod 24: 1206–1211. pmid:19202142
  47. 47. Cawthon RM (2002) Telomere measurement by quantitative PCR. Nucleic Acids Res 30: e47. pmid:12000852
  48. 48. Pfaffl MW (2001) A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res 29: e45. pmid:11328886
  49. 49. Aubert G, Hills M, Lansdorp PM (2012) Telomere length measurement-caveats and a critical assessment of the available technologies and tools. Mutat Res 730: 59–67. pmid:21663926
  50. 50. Martin-Ruiz CM, Baird D, Roger L, Boukamp P, Krunic D, et al. (2015) Reproducibility of telomere length assessment: an international collaborative study. Int J Epidemiol 44: 1673–1683. pmid:25239152
  51. 51. Aviv A, Hunt SC, Lin J, Cao X, Kimura M, et al. (2011) Impartial comparative analysis of measurement of leukocyte telomere length/DNA content by Southern blots and qPCR. Nucleic Acids Res 39: e134. pmid:21824912
  52. 52. Fibiger W, Singer G, Miller AJ, Armstrong S, Datar M (1984) Cortisol and catecholamines changes as functions of time-of-day and self-reported mood. Neurosci Biobehav Rev 8: 523–530. pmid:6514254
  53. 53. Salvante KG, Brindle E, McConnell D, O'Connor K, Nepomnaschy PA (2012) Validation of a new multiplex assay against individual immunoassays for the quantification of reproductive, stress, and energetic metabolism biomarkers in urine specimens. Am J Hum Biol 24: 81–86. pmid:22121074
  54. 54. Miller RC, Brindle E, Holman DJ, Shofer J, Klein NA, et al. (2004) Comparison of specific gravity and creatinine for normalizing urinary reproductive hormone concentrations. Clin Chem 50: 924–932. pmid:15105350
  55. 55. White BC, Jamison KM, Grieb C, Lally D, Luckett C, et al. (2010) Specific gravity and creatinine as corrections for variation in urine concentration in humans, gorillas, and woolly monkeys. Am J Primatol 72: 1082–1091. pmid:20648576
  56. 56. Babyak MA (2004) What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med 66: 411–421. pmid:15184705
  57. 57. Preacher KJ, Hayes AF (2008) Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods 40: 879–891. pmid:18697684
  58. 58. Baron RM, Kenny DA (1986) The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol 51: 1173–1182. pmid:3806354
  59. 59. Mackinnon DP, Dwyer JH (1993) Estimating mediated effects in prevention studies. Evaluation Review 17: 144–158.
  60. 60. Efron B (1987) Better bootstrap confidence intervals. Journal of the American Statistical Association 82: 171–185.
  61. 61. Hayes AF (2009) Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs 76: 408–420.
  62. 62. Benetos A, Kark JD, Susser E, Kimura M, Sinnreich R, et al. (2013) Tracking and fixed ranking of leukocyte telomere length across the adult life course. Aging Cell 12: 615–621. pmid:23601089
  63. 63. Puterman E, Epel E (2012) An intricate dance: Life experience, multisystem resiliency, and rate of telomere decline throughout the lifespan. Soc Personal Psychol Compass 6: 807–825. pmid:23162608
  64. 64. von Zglinicki T (2002) Oxidative stress shortens telomeres. Trends Biochem Sci 27: 339–344. pmid:12114022
  65. 65. Carrero JJ, Stenvinkel P, Fellstrom B, Qureshi AR, Lamb K, et al. (2008) Telomere attrition is associated with inflammation, low fetuin-A levels and high mortality in prevalent haemodialysis patients. J Intern Med 263: 302–312. pmid:18070000
  66. 66. Demissie S, Levy D, Benjamin EJ, Cupples LA, Gardner JP, et al. (2006) Insulin resistance, oxidative stress, hypertension, and leukocyte telomere length in men from the Framingham Heart Study. Aging Cell 5: 325–330. pmid:16913878
  67. 67. Muezzinler A, Zaineddin AK, Brenner H (2013) A systematic review of leukocyte telomere length and age in adults. Ageing Res Rev 12: 509–519. pmid:23333817
  68. 68. Eisenberg DT (2011) An evolutionary review of human telomere biology: the thrifty telomere hypothesis and notes on potential adaptive paternal effects. Am J Hum Biol 23: 149–167. pmid:21319244
  69. 69. Behl C, Widmann M, Trapp T, Holsboer F (1995) 17-beta estradiol protects neurons from oxidative stress-induced cell death in vitro. Biochem Biophys Res Commun 216: 473–482. pmid:7488136
  70. 70. Itagaki T, Shimizu I, Cheng X, Yuan Y, Oshio A, et al. (2005) Opposing effects of oestradiol and progesterone on intracellular pathways and activation processes in the oxidative stress induced activation of cultured rat hepatic stellate cells. Gut 54: 1782–1789. pmid:16284289
  71. 71. Barrett EL, Richardson DS (2011) Sex differences in telomeres and lifespan. Aging Cell 10: 913–921. pmid:21902801
  72. 72. Kyo S, Takakura M, Kanaya T, Zhuo W, Fujimoto K, et al. (1999) Estrogen activates telomerase. Cancer Res 59: 5917–5921. pmid:10606235
  73. 73. Falick Michaeli T, Bergman Y, Gielchinsky Y (2015) Rejuvenating effect of pregnancy on the mother. Fertil Steril 103: 1125–1128. pmid:25813291
  74. 74. Aulinas A, Ramirez MJ, Barahona MJ, Mato E, Bell O, et al. (2013) Telomeres and endocrine dysfunction of the adrenal and GH/IGF-1 axes. Clin Endocrinol (Oxf) 79: 751–759.
  75. 75. Houben JM, Moonen HJ, van Schooten FJ, Hageman GJ (2008) Telomere length assessment: biomarker of chronic oxidative stress? Free Radic Biol Med 44: 235–246. pmid:18021748
  76. 76. Choi J, Fauce SR, Effros RB (2008) Reduced telomerase activity in human T lymphocytes exposed to cortisol. Brain Behav Immun 22: 600–605. pmid:18222063
  77. 77. Chan SR, Blackburn EH (2004) Telomeres and telomerase. Philos Trans R Soc Lond B Biol Sci 359: 109–121. pmid:15065663
  78. 78. Epel ES, Lin J, Dhabhar FS, Wolkowitz OM, Puterman E, et al. (2010) Dynamics of telomerase activity in response to acute psychological stress. Brain Behav Immun 24: 531–539. pmid:20018236
  79. 79. Daubenmier J, Lin J, Blackburn E, Hecht FM, Kristeller J, et al. (2012) Changes in stress, eating, and metabolic factors are related to changes in telomerase activity in a randomized mindfulness intervention pilot study. Psychoneuroendocrinology 37: 917–928. pmid:22169588
  80. 80. van der Pompe G, Bernards N, Kavelaars A, Heijnen C (2001) An exploratory study into the effect of exhausting bicycle exercise on endocrine and immune responses in post-menopausal women: relationships between vigour and plasma cortisol concentrations and lymphocyte proliferation following exercise. Int J Sports Med 22: 447–453. pmid:11531039
  81. 81. Bernabe DG, Tamae AC, Biasoli ER, Oliveira SH (2011) Stress hormones increase cell proliferation and regulates interleukin-6 secretion in human oral squamous cell carcinoma cells. Brain Behav Immun 25: 574–583. pmid:21187140
  82. 82. Anacker C, Cattaneo A, Luoni A, Musaelyan K, Zunszain PA, et al. (2013) Glucocorticoid-related molecular signaling pathways regulating hippocampal neurogenesis. Neuropsychopharmacology 38: 872–883. pmid:23303060
  83. 83. Williams TD (2008) Individual variation in endocrine systems: moving beyond the 'tyranny of the Golden Mean'. Philos Trans R Soc Lond B Biol Sci 363: 1687–1698. pmid:18048295
  84. 84. Cohen S, Janicki-Deverts D, Turner RB, Marsland AL, Casselbrant ML, et al. (2013) Childhood socioeconomic status, telomere length, and susceptibility to upper respiratory infection. Brain Behav Immun 34: 31–38. pmid:23845919
  85. 85. Carroll JE, Esquivel S, Goldberg A, Seeman TE, Effros RB, et al. (2016) Insomnia and Telomere Length in Older Adults. Sleep 39: 559–564. pmid:26715231
  86. 86. Raymond AR, Norton GR, Woodiwiss AJ, Brooksbank RL (2015) Impact of gender and menopausal status on relationships between biological aging, as indexed by telomere length, and aortic stiffness. Am J Hypertens 28: 623–630. pmid:25413841