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Psychological and biological resilience modulates the effects of stress on epigenetic aging


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Our society is experiencing more stress than ever before, leading to both negative psychiatric and physical outcomes. Chronic stress is linked to negative long-term health consequences, raising the possibility that stress is related to accelerated aging. In this study, we examine whether resilience factors affect stress-associated biological age acceleration. Recently developed “epigenetic clocks” such as GrimAge have shown utility in predicting biological age and mortality. Here, we assessed the impact of cumulative stress, stress physiology, and resilience on accelerated aging in a community sample ( N = 444). Cumulative stress was associated with accelerated GrimAge ( P = 0.0388) and stress-related physiologic measures of adrenal sensitivity (Cortisol/ACTH ratio) and insulin resistance (HOMA). After controlling for demographic and behavioral factors, HOMA correlated with accelerated GrimAge ( P = 0.0186). Remarkably, psychological resilience factors of emotion regulation and self-control moderated these relationships. Emotion regulation moderated the association between stress and aging ( P = 8.82e−4) such that with worse emotion regulation, there was greater stress-related age acceleration, while stronger emotion regulation prevented any significant effect of stress on GrimAge. Self-control moderated the relationship between stress and insulin resistance ( P = 0.00732), with high self-control blunting this relationship. In the final model, in those with poor emotion regulation, cumulative stress continued to predict additional GrimAge Acceleration even while accounting for demographic, physiologic, and behavioral covariates. These results demonstrate that cumulative stress is associated with epigenetic aging in a healthy population, and these associations are modified by biobehavioral resilience factors.
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Psychological and biological resilience modulates the effects of
stress on epigenetic aging
Zachary M. Harvanek
, Nia Fogelman
and Rajita Sinha
© The Author(s) 2021
Our society is experiencing more stress than ever before, leading to both negative psychiatric and physical outcomes. Chronic
stress is linked to negative long-term health consequences, raising the possibility that stress is related to accelerated aging. In this
study, we examine whether resilience factors affect stress-associated biological age acceleration. Recently developed epigenetic
clockssuch as GrimAge have shown utility in predicting biological age and mortality. Here, we assessed the impact of cumulative
stress, stress physiology, and resilience on accelerated aging in a community sample (N=444). Cumulative stress was associated
with accelerated GrimAge (P=0.0388) and stress-related physiologic measures of adrenal sensitivity (Cortisol/ACTH ratio) and
insulin resistance (HOMA). After controlling for demographic and behavioral factors, HOMA correlated with accelerated GrimAge (P
=0.0186). Remarkably, psychological resilience factors of emotion regulation and self-control moderated these relationships.
Emotion regulation moderated the association between stress and aging (P=8.82e4) such that with worse emotion regulation,
there was greater stress-related age acceleration, while stronger emotion regulation prevented any signicant effect of stress on
GrimAge. Self-control moderated the relationship between stress and insulin resistance (P=0.00732), with high self-control
blunting this relationship. In the nal model, in those with poor emotion regulation, cumulative stress continued to predict
additional GrimAge Acceleration even while accounting for demographic, physiologic, and behavioral covariates. These results
demonstrate that cumulative stress is associated with epigenetic aging in a healthy population, and these associations are modied
by biobehavioral resilience factors.
Translational Psychiatry (2021) 11:601 ;
Cumulative stress can have adverse psychiatric and physical
effects, increasing risk for cardiometabolic diseases, mood
disorders, post-traumatic stress disorder and addiction [111].
There are several potential psychological and biological
mechanisms through which these effects may occur. For
example, stress may reduce psychological resilience measures
such as emotion regulation and self-control that are known to
protect against psychiatric and physical health outcomes
[1,1214]. Notably, emotional stress exposure decreases
cognitive and emotion regulation abilities [1518], and this
effect may be modulated by cortisol [15]. Furthermore, stress
decreases self-control abilities [1921] and impacts the like-
lihood of individuals engaging in healthy behaviors such as
exercise or maintaining a healthy diet, and is associated with
unhealthy behaviors such as smoking, alcohol, and drug use
[2225]. Recent evidence also suggests that stress effects on
metabolic health may be affected by BMI-related changes in
insulin resistance and other gut hormones [26,27]. Indeed,
stressseffectsonphysiologyresulting in alterations in neuro-
hormonal signaling pathways as well as increased inamma-
tion are well documented [26,2830]. Both stress and these
physiologic changes may increase the risk of multiple physical
and psychiatric illnesses, which in turn increase morbidity and
mortality risk. This has often been described as an increased
allostatic load, and notably many of these processes, such as
metabolic and cardiovascular dysfunction, have been asso-
ciated with human aging [31]. For example, insulin signaling
might be linked to aging and aging-related diseases in humans
[32], with recent data on metformin (a treatment for insulin
resistance) suggesting it may be useful as an anti-aging drug
There is growing evidence that cumulative stress may
adversely impact health via accelerating the cellular aging
process. For example, stress shortens telomere length and alters
telomerase activity, and this interaction is modied by
behavioral and psychological resilience factors [3437]. How-
ever, recent studies have demonstrated mixed results on
whether characteristics that contribute to resilience improve
or worsen the impact of stress on health [3847]. These data
suggest that resiliency factors may modulate the relationship
between chronic stress and aging, but to our knowledge this
has not been tested in a healthy community sample. While
there are many aspects of resilience, including cultural/societal,
environmental, and personal which can decrease the negative
consequences of stressors on individuals, herein we will focus
Received: 28 June 2021 Revised: 31 October 2021 Accepted: 10 November 2021
Department of Psychiatry, Yale University, New Haven, CT, USA.
Yale Stress Center, Yale University, New Haven, CT, USA.
Department of Psychiatry, Connecticut Veteran
Healthcare System, West Haven, CT, USA.
Department of Neuroscience, Yale University, New Haven, CT, USA.
Child Study Center, Yale University, New Haven, CT, USA.
Translational Psychiatry
Content courtesy of Springer Nature, terms of use apply. Rights reserved
on personal-level, psychological skills, including self-control
and emotion regulation.
Recently developed DNA methylation-based epigenetic
clocksappear to provide a more accurate measure of
biological age than telomere length [4851]. These clocks are
built from a set of DNA methylation markers that correlate with
chronologic age and serve as molecular estimators of biological
age in cells, tissues, and individuals [52]. Epigenetic clocks have
asignicantly higher predictive value than previously used
measures such as telomere length for frailty, [53] mortality risk
[54,55], hazard ratios [56], and chronologic age [57]. The
development of these biological aging markers promises to not
only aid in identifying individuals at higher risk for aging-
related illnesses, but potentially also developing interventions
to prevent accelerated aging.
Previous studies (reviewed by Palma-Gudiel et al [58]) have
utilized epigenetic clocks to demonstrate associations between
trauma, early life adversity, or low socioeconomic status and
accelerated epigenetic aging. Studies have often been focused
upon selected populations, such military veterans [45], indivi-
duals with signicant trauma histories [59], or specic cohorts at
higher risk [6062]. Notably, these studies did not exclude, and
often explicitly included, individuals with signicant mental and
physical illnesses, including PTSD, MDD, and other disabilities
[59,63]. These studies also primarily utilized epigenetic clocks
trained upon chronologic age. However, a recently developed
epigenetic clock, GrimAge, was trained using biomarkers of
mortality and indicators of health, and has superior performance
in predicting health outcomes when compared with other
epigenetic clocks [51,64].
We utilized GrimAge Acceleration (GAA,dened as the
residual of the regression of GrimAge to chronologic age, with a
positive number indicating biological age greater than chron-
ologic age) to conduct a cross-sectional study to answer three
questions. First, is cumulative stress related to epigenetic
markers of biological aging in a healthy young-to-middle-
aged community population? Second, if stress is associated
with epigenetic aging, does stress-related physiology contri-
bute to stress-associated biological aging? And nally, how do
psychological factors that contribute to resilience modulate
these relationships? Based on previous research, we hypothe-
sized that cumulative stress will be positively associated with
GrimAge Acceleration (GAA), that stress effects on GrimAge will
be related to changes in the hypothalamic-pituitary-adrenal
axis (HPA) and insulin sensitivity, and that strong emotion
regulation as measured by the Difculties in Emotion Regula-
tion Scale (DERS, [65]) and high self-control as measured by the
Self Control Scale-Brief (SCS-B, [66]) will moderate the relation-
ships between stress, physiology, and accelerated aging (See
Fig. 1for a model summarizing our hypotheses).
Cohort recruitment
The participant cohort included 444 community adults between the ages
of 1850 in the greater New Haven, CT area who volunteered to participate
in a study examining the role of stress and self-control at the Yale Stress
Center as previously described [67]. Briey, participants were recruited via
advertisements online, in local newspapers, and at a community center
between 2008 and 2012. Participants were excluded if they had a
substance use disorder (not including nicotine) as assessed via the
Structured Clinical Interview for Diagnostic and Statistical Manual of
Mental Disorders, 4th Edition (SCID-I for DSM-IVTR), were pregnant, had a
chronic medical condition (e.g, hypertension, diabetes, hypothyroidism), or
were unable to read English at or above the 6th grade level. Participants
were also excluded if they had a concussion with loss of consciousness
greater than 30 minutes, another head injury such as documented
traumatic brain injury or another injury with documented lasting decits,
or were using any prescribed medications for any psychiatric or medical
disorders. Breathalyzer and urine toxicology screens were conducted at
each appointment to ensure the participants were drug-free. Of a total of
1000 potential participants who underwent initial screening for eligibility,
epigenetic data combined with physiologic and behavioral data were
available on 444, who comprised the current sample. All participants
provided written and verbal informed consent to participate, and the
research protocol was reviewed and approved by the Yale IRB.
Initial assessment and measurement of physiologic
All eligible subjects met with a research assistant for two intake sessions to
complete a physical health review with the Cornell Medical Index (CMI,
[68]), structured clinical interview for diagnoses (SCID) of DSM-IVTR
psychiatric illnesses, cumulative stress interview, self-report assessments
and a separate morning biochemical evaluation after fasting overnight.
The structured clinical interview was performed by mastersor doctoral
level clinical research staff. Fasting insulin and glucose were obtained and
Cortisol was assessed at four time-points, spaced 15 min apart beginning
at 7:30 AM after overnight fasting and collected while participants were in
a quiet and comfortable laboratory setting at the Yale Stress Center.
Participants were nancially compensated for participating in the study.
Psychological measures
Cumulative stress was assessed using the Cumulative Adversity Inventory
(CAI, [69]), a 140-item multifaceted interview-based assessment of life
events and subjective stress through which trained interviewers asked
participants about specic stressful events that occurred during their
lifetime, which comprised the subscales of major life events, life trauma
events and recent life events. For purposes of scoring, a yesto the
specic stressful event occurring led to a 1and a sum of all the yes
endorsements comprised the subscale score for these events subscale. The
nal subscale of chronic stress was the participants own sense of feeling
overwhelmed and unable to manage the events for the other subscales
listed. This was rated on a not true,somewhat true,orvery truescale,
with assigned scores of 0, 1, and 2, respectively. The nal score is a sum of
these values for the chronic stress subscale. The CAI-total score was a sum
of each of the subscale score with a higher score indicating a higher overall
level of lifetime cumulative stress. The CAI has been demonstrated to have
excellent overall reliability as reported in previous research [12,26,7072].
Accelerated Aging
Fig. 1 Model of relationships between cumulative stress, resilience, physiology, and aging. We hypothesize that stress is positively
associated with accelerated biological aging, which we measure via GrimAge Acceleration (GAA), and that this relationship will be mediated
by stress-related physiologic changes such as insulin and HPA signaling. We also hypothesize that strong psychological resilience factors will
be protective against the negative consequences of stress on aging. Note that these relationships are predictive, not causative, as this study is
cross-sectional and thus directionality of relationships cannot be conclusively examined.
Z.M. Harvanek et al.
Translational Psychiatry (2021) 11:601
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In our population for this study, the alpha reliability is 0.86. It has been
previously shown to predict cumulative stress related brain volume
reductions and sensitized stress functional responses as well as prediction
of physical, metabolic and behavioral responses [26,7072].
Emotion regulation was assessed using the Difculties with Emotion
Regulation Scale (DERS, [65]), which is a 41-item trait-level measure that
assesses across domains of lack of emotional awareness, goals, clarity,
strategies, acceptance, and impulse control in managing emotions. Higher
scores on the DERS correspond to lower ability to regulate emotion. Alpha
reliability has been reported to be >0.90 for the total score, and 0.80 for
the sub-scores [65]. In this population, the alpha reliability is 0.92.
Self-control was assessed using the Self-Control Survey-Brief (SCS-B,
[66]), which is a 13-item scale that assesses overall self-control. A higher
score on the SCS-B suggests a stronger level of self-control. There are no
sub-scores provided by the SCS-B, and the overall SCS-B has been reported
to have an alpha reliability >0.80 [66]. The alpha reliability in this study
is 0.85.
The Cornell Medical Index (CMI) was used to assess for participants
current health. It is a 195-question interview that captures both physical
and psychological health symptoms, and has been validated as an
indicator for current general health in many studies [68,73,74]. A higher
score on the CMI suggests more symptoms and worse overall health. The
alpha reliability of the total CMI is 0.94. The psychological subscore has an
alpha reliability of 0.92, and the biological subscore has a reliability of 0.90.
Cronbach alpha reliabilities for each of the scales described above were
obtained using the alpha function in the R psych package [75].
DNA methylation and epigenetic clock analysis
DNA for epigenetic analysis was collected from whole blood samples as
previously described [67]. Briey, all samples were proled using Illumina
Innium HumanMethylation450 Beadchips, which covers 96% of CpG
islands and 99% of RefSeq genes. Quality control on these data are as
previously published [67]. They are described in brief below:
Probe QC: To ensure high-quality data, we set a more stringent threshold
of P<10
. Intensity values showing P>10
were set as zero.
Additionally, we removed 11,648 probes on sex chromosomes and
36,535 probes within 10 base pairs of single-nucleotide polymorphisms.
Finally, a total of 47,791 probes were removed and the remaining 437,722
probes were used for further analysis.
Sample QC: Using a detection Pvalue < 10
, one sample showing a call
rate < 98% was excluded from analysis. Five samples showing sex
discrepancy between the methylation predicted sex and self-reported sex
were also excluded from analysis.
Data processing and normalization: Data processing and normalization
were performed using the recently published protocol (Lehne et al., 2015).
We rst perform background correction and within-array normalization to
the original green/red channel intensity data using the preprocessIllumina
function in the minR package. The processed data were transformed to
M/U methylation categories. Next, we separately performed between-
array-normalization with the quantile method using the normalizeBetwee-
nArrays function in the limma R package (version 3.26.2) after dividing the
data matrix into 6 independent parts: Type I M Green, Type I M Red, Type I
U Red, Type I U Green, Type II Red, Type II Green. The normalized data were
merged and the beta value at each CpG site was determined.
After obtaining beta values, epigenetic clock analysis was performed as
described in Lu et al. using the New Methylation Age Calculator at https:// [51]. Data were normalized as per their
protocol, and the advanced analysis option was used. We focus on
GrimAge acceleration (GAA), which is dened as the residuals of a linear
correlation of GrimAge to chronologic age. No effects of array batch on
GAA were observed (Supplementary Fig. 1).
The analyses herein were performed without accounting for individual
variations in cell types. The Houseman method was used to determine cell
type proportion [76], and the inclusion of cell fractions as covariates in a
linear model does not impact the primary conclusions of this paper (see
Supplementary material).
Statistical analysis
Data organization and analysis were conducted using R 3.6.3 [77]andRStudio.
Linear regressions were rst implemented to examineunivariateassociations
between independent and dependent variables. Multivariable linear regres-
sions adjust for demographic (sex, race, years of education, marital status,
income) and behavioral (smoking, alcohol use, and BMI) covariates unless
otherwise stated. These covariates were selected due to prior work
demonstrating a relationship to epigenetic aging. Chronologic age is
incorporated into the model as part of the calculation of GAA (the residual
of GrimAge regressed upon chronologic age). There was no signicant
correlation between chronologic age and GAA. Analyses of the relationship
between CAI, GAA, psychological and physiologic variables were performed in
both the univariate unadjusted model and the multivariate adjusted model
accounting for demographic and behavioral measures, but except when the
conclusions differ, statistical values in the text represent the multivariate
models for simplicity. CAI, DERS, and SCS were mean-centered to address
issues of collinearity (particularly regarding individual regression coefcients)
when assessing for moderation.
All tests were two-tailed with alpha set at 0.05. Statistical signicance in
both standard linear regressions and moderation analyses were assessed
from tvalues. R
reported on plots represent the simple relationship
between the stated variables, while adjusted R
values in the text represent
the model. Partial η
values represent the effect size for the specic
variable, with a value >=0.01 typically indicating a small effect, >=0.06 a
medium effect, and >=0.14 a large effect [78]. Wilcoxon signed-rank test
was used to compare data between sexes. Mediation analysis was
performed to determine if stress impacts GAA via behavioral and
physiologic factors. Simple mediation effects were calculated via R using
10,000 simulations without bootstrapping using the mediation package
[79]. Mediation was considered signicant if the proportion mediated was
greater than 0 with an alpha of 0.05. Serial mediation was calculated via R
using the Lavaan package [71], with an indirect effect dened as the
product of the coefcients of the effect of stress on BMI, of BMI on HOMA,
and of HOMA on GAA. Assessment of the individual variablesattributable
GrimAge acceleration as well as condence intervals were calculated using
the Emmeans package using unadjusted pairwise comparisons.
Demographics and clinical characteristics
As shown in Table 1, study participants were healthy and without
evidence of medical or psychiatric diseases. The majority were
non-smokers (79.6%), social drinkers with low risky alcohol intake
screening scores (72.7% of participants have Alcohol Use
Disorders Identication Test (AUDIT) < 8, and 91.7% < 15), and
were not obese (74.5% of participants have a BMI < 30, 89.2% <
35). Both physical and psychological symptoms assessed on the
Cornell Medical Index (CMI, [68]) were low, with 86% of
participants scoring below the typical screening threshold of 30.
Cumulative stress predicts accelerated biological aging as
measured by GrimAge
As expected, there was a high association between individuals
chronologic age and GrimAge (Age: t=51.4, P<2e16, adjusted
=0.856, Fig. 2A). This relationship is not altered by inclusion of
the covariates of smoking, alcohol use, BMI, race, sex, income, and
years of education (Age: t=49.1, P<2e16, partial η
model (GrimAge ~ Age +covariates) adjusted R
=0.912), and
this relationship remained signicant accounting for cellular
fractions (Supplementary Table 1). Also, using a univariate linear
regression, greater cumulative stress as measured by the total
Cumulative Adversity Index (CAI) score signicantly predicted
higher GAA (CAI: t=4.82 P=2.00e6, η
=0.050, adjusted R
0.0478, Fig. 2B). While there were signicant differences in GAA
based on sex (P=1.33e7), both males (CAI: P=3.35e4,
adjusted R
=0.0586) and females (CAI: P=3.12e5, adjusted
=0.0652) demonstrated similar correlations between stress and
GAA. Further analysis showed these results are consistent across
CAI subscales, as well as with the Childhood Trauma Questionnaire
and several of its subscales (Supplementary Table 2).
After accounting for the covariates of smoking, alcohol use, BMI,
race, sex, income, and years of education, the relationship
between GAA and CAI remains signicant (CAI: t=2.073, P=
0.0388, partial η
=0.010; model (GAA ~ CAI-total +covariates):
adjusted R
=0.3869); individual covariate effects shown in
Supplementary Table 3). When considered as potential mediators
of the relationship between stress and GAA, BMI (proportion
Z.M. Harvanek et al.
Translational Psychiatry (2021) 11:601
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mediated =0.288, P=0.0042) and smoking (proportion mediated
=0.443, P=0.0030), but not alcohol use (proportion mediated =
0.001, P=0.931), show partial mediating effects (Supplementary
Table 4).
Consistent with the underlying assumption that GAA is related
to measures of health, GAA also predicted psychological and
physical health symptoms as measured by the CMI (Supplemen-
tary Fig. 2A; total CMI: t=3.449, P=6.18e4, adjusted R
Stress-related physiology is associated with GrimAge
Given the known relationship between cumulative stress and
physiology, we assessed the relationship between the stress-
related physiologic factors of insulin resistance and HPA-axis
signaling and GAA. We found that higher HOMA (a measure of
insulin resistance) signicantly predicted GAA (Fig. 2C, HOMA: t=
2.362, P=0.0186, partial η
=0.013; model (GAA ~ HOMA +
Covariates): adjusted R
We then assessed whether cortisol/ACTH ratio changes
impacted GAA. Indeed, low cortisol/ACTH ratio, a measure of
adrenal sensitivity, was associated with GAA in a simple univariate
model, (Fig. 2D, Cort/ACTH ratio: t=4.78, P=2.39e6, η
0.049, adjusted R
=0.0470), though this becomes non-signicant
when accounting for covariates (Cort/ACTH ratio: t=0.721, P=
0.471, partial η
=0.001; model (GAA ~ Cort/ACTH +Covariates):
adjusted R
=0.3816). We also nd a signicant association
between stress and Cortisol/ACTH ratio (Supplementary Fig. 2B,
CAI: t=2.146 P=0.0324; model (Cort/ACTH ratio ~ CAI +
covariates): adjusted R
Emotion regulation moderates the relationship between
stress and GrimAge acceleration directly
We then asked whether the relationship between cumulative
stress and epigenetic aging was modulated by characteristics that
contribute to an individuals psychological resilience. We hypothe-
sized that strong emotion regulation abilities would be protective
against stress-related accelerated aging. We found that emotion
regulation as assessed by the Difculties in Emotion Regulation
Scale (DERS, [65]) signicantly moderated the relationship
between GAA and CAI (Fig. 3A, CAI:DERS: F=11.22, P=8.82e4,
partial η
=0.025; model (GAA ~ CAI X DERS +covariates):
adjusted R
=0.4004), such that poor emotion regulation sig-
nicantly increased the effects of CAI on GAA. There was not a
signicant difference between males and females in emotion
regulation (P=0.0949).
Self-control moderates the association between stress and
insulin resistance, which is associated with GrimAge
We next assessed whether psychological resilience in the form of
self-control (as measured via the SCS-B, [66]) alters the association
between cumulative stress and GAA. We found higher self-control
is protective against the effects of stress on GAA before
accounting for covariates, but the interaction became non-
signicant when covariates were accounted for (Fig. 3B, CAI:SCS:
F=2.303, P=0.130, partial η
=0.005; model (GAA ~ CAI X SCS +
Covariates: adjusted R
Given the potential interplay between self-control, insulin
resistance, and stress, we next asked whether self-control
moderated the relationship between stress and HOMA. We
observed that, even when covariates are accounted for, self-
control moderates the positive relationship between stress and
HOMA, with stronger self-control blunting their relationship (Fig.
3C, CAI:SCS: F =7.263, P=0.00732, partial η
=0.017; model
(HOMA ~ CAI X SCS +Covariates: adjusted R
=0.2871). Notably,
self-control does not moderate the relationship between CAI and
BMI (CAI:SCS: F=0.679, P=0.41). Self-control did not signicantly
differ between males and females (P=0.0550).
Exploratory mediation analyses suggest stress inuences
GrimAge via BMI and HOMA
While our ability to draw causative inferences are limited by the
cross-sectional nature of our data, we used mediation analyses to
explore potential relationships between weight, insulin resistance,
and GAA. We hypothesized that the effects of BMI on GAA may be
mediated through insulin resistance. Indeed, mediation analysis
suggested that a signicant portion of the effect of BMI on GAA
may be mediated through HOMA (Supplementary Fig. 3A,
proportion mediated =0.247, P=0.02). Given these ndings,
we next asked whether BMI and insulin resistance act sequentially
to mediate the effects of stress on GAA. We identied a signicant
indirect effect, suggesting that stress may affect GAA through
Table 1. Demographics of community population.
Category Frequency
5th% to 95th% Stdev
Female 55.2%
Male 44.8%
No 79.6%
Yes 20.4%
White 73%
Black 18%
Other 9%
Married 16.2%
Yes 70.4%
No 29.6%
5.94 0 19 5.97
26.96 2037.8 5.377
Days smoking past
4 weeks
4.0 028 9.30
Days drinking past
4 weeks
6.3 020 6.95
CAI-total score
19.8 641 10.41
69.9 43108 19.73
45.6 3160 8.66
28.6 1947 8.74
Years of Education
15.4 1220 2.47
Employment Income
$1,010.59 0$3500 $1,421.33
10.2 130 9.1
5.3 020 6.7
15.5 246 14.48
Cortisol/ACTH (AUC)
0.30384 0.09880.741 0.21344
3.169 1.05 7.13 1.9697
Demographics and average statistics for the test population
AUDIT alcohol use disorder identication test, BMI body mass index, CAI
cumulative adversity index, DERS difculty with emotion regulation scale,
SCS self-control scale, HOMA homeostatic model assessment of insulin
Z.M. Harvanek et al.
Translational Psychiatry (2021) 11:601
Content courtesy of Springer Nature, terms of use apply. Rights reserved
increased BMI and elevated insulin resistance (Supplementary Fig.
3B, indirect effect =0.003; P=0.030), though there continues to
be a signicant direct effect of stress on GAA as well (direct effect
=0.034, P=0.009).
Cumulative stress and estimated change in GrimAge
Finally, we sought to identify the comparative contributions of our
signicant variables to GAA. To do this, we constructed a linear
regression model using all demographic covariates (sex, race,
marital status, education, income), behavioral covariates (smoking,
alcohol, BMI), physiologic factors (HOMA, Cortisol/ACTH ratio), and
psychological factors. In this model, we continue to see a
signicant interaction between stress and emotion regulation in
relation to GAA (CAI:DERS t=3.424, P=0.000677, partial η
0.027; model (GAA ~ CAI-total X DERS +HOMA +Cort/ACTH ratio
+SCS +Covariates): adjusted R
=0.4056). Notably in this model,
HOMA (t=2.308, P=0.0215, partial η
=0.012), BMI (t=2.641, P
=0.00857, partial η
=0.016), and smoking (t=10.47, P<2e16,
partial η
=0.204) also demonstrate signicant effects on GAA.
The impact of the cortisol/ACTH ratio on GAA is not signicant
(t=0.668, P=0.504, partial η
=0.001), and its removal from
the model does not impact any of the above conclusions.
Using this nal linear model, we estimated the changes in
GrimAge for each signicant variable (Table 2) using estimated
marginal means [80]. When comparing the effects of high stress
(CAI-total: 75th percentile) versus low stress (CAI-total: 25th
percentile) in those with poor emotion regulation (DERS: 75th
percentile), stress was associated with half a year of aging
independent of all other covariates and physiologic factors.
However, when emotion regulation was strong (DERS: 25th
percentile), stress did not independently predict GAA. Again
comparing 75th versus 25th percentiles, BMI independently was
related to an increase of 0.46 years of GrimAge, and HOMA for ¼
of a year. We also identied daily smoking (3.8 years), male sex (1.2
years), self-identifying as Black (1 year), and never having married
(0.71 years) as covariates that signicantly predicted accelerated
GrimAge. When accounting for cellular fractions we see similar
results regarding the relationships between stress, emotion
regulation, and GAA. However, when accounting for cellular
fractions, the associations between GAA and both HOMA and
marital status become non-signicant (Supplementary Table 5).
Prior literature [51] suggests that GrimAge predicts the hazard
ratio exponentially (HR =1.1
). Thus, each additional year of
GAA would be expected to increase the relative risk of death by
approximately 10%.
In this study, we report novel ndings that cumulative stress is
associated with accelerated epigenetic aging in a healthy, young-
to-middle-aged community sample, even after adjusting for sex,
race, BMI, smoking, alcohol use, income, marital status, and
education. Epigenetic aging was measured by GrimAge, a marker
which has previously been associated with increased morbidity
and mortality and correlates with physical and psychological
health symptoms in our study. The relationship between stress
and age acceleration is most prominent in those with poor
emotion regulation and was related to behavioral factors such as
smoking and BMI. Both stress and GAA were associated with
changes in insulin resistance, which was moderated via self-
control. These results suggest a relationship between stress,
physiology, and accelerated aging that is moderated by emotion
regulation and self-control. Overall, these ndings point to
multiple potentially modiable biobehavioral targets of
20 30 40 50
Chronologic Age (Years)
GrimAge (Years)
CAI - Total Score
GrimAge Acceleration (Years)
R² = 0.857*** R² = 0.048***
P < 0.05
P < 0.01
P < 0.001
0.0 0.5 1.0
Cortisol/ACTH Ratio
GrimAge Acceleration (Years)
R² = 0.049***
GrimAge Acceleration (Years)
R² = 0.071***
Fig. 2 GrimAge and GrimAge acceleration correlate with cumulative stress and physiologic stress pathways. A Chronologic age
signicantly predicts GrimAge (P<2e16). BCumulative stress total as measured by the CAI (CAI-Total) signicantly predicts GAA before (P=
2.00e6) and after accounting for covariates. CHigher insulin resistance (as measured by HOMA) shows a signicant positive correlation with
GAA before (P=1.11e8) and after accounting for covariates. DThe Cortisol/ACTH ratio is negatively correlated with GAA before accounting
for covariates (P=2.39e6), but not afterward. Pand R
values in the gure represent simple univariate models (Y ~ X). In the main text,
models are adjusted for covariates as stated.
Z.M. Harvanek et al.
Translational Psychiatry (2021) 11:601
Content courtesy of Springer Nature, terms of use apply. Rights reserved
intervention that may reduce or prevent the deleterious effects of
stress on aging and long-term health outcomes.
This study included a generally healthy, young-to-middle-aged
community population, yet we still identied a signicant
relationship between cumulative stress and age acceleration.
The population was taking no prescription medications for any
medical conditions, nor were they suffering from current mental
illnesses, including major depressive disorder or generalized
CAI - Total Score
GrimAge Acceleration (Years)
Emotion regulation
unadjusted: F = 15.5***
w/ covariates: F = 11.2***
P < 0.05
P < 0.01
P < 0.001
Interaction term
0 204060
CAI - Total Score
GrimAge Acceleration (Years)
unadjusted: F = 9.44**
w/ covariates: F = 2.30
CAI - Total Score
unadjusted: F = 6.44*
w/ covariates: F = 7.26**
Interaction term
Interaction term
Fig. 3 Psychological resilience factors moderate the effects of cumulative stress on GrimAge Acceleration and physiologic stress
pathways. A Individuals with stronger emotion regulation (as measured by lower DERS scores) suffer less GAA at high stress than individuals
with poor emotion regulation before (GAA ~ CAI X DERS P=9.51e5; GAA ~ CAI X DERS +Covariates: P=8.82e4) and after accounting for
covariates. For panel A, goodrepresents the slope at the 25th percentile of DERS, fairat the 50th percentile, and poorthe 75th percentile.
BBetter self-control (as measured by higher B-SCS scores) is protective against the effects of stress on GAA before accounting for covariates
(GAA ~ CAI X SCS P=0.00226; GAA ~ CAI X SCS +Covariates: P=0.130), but not after including them in the model. CStronger self-control
moderates the relationship between stress and insulin resistance before (HOMA ~ CAI X SCS P=0.0115; HOMA ~ CAI X SCS +Covariates P=
0.00732) and after accounting for covariates. For panels (B) and (C), goodrepresents the slope at the 75th percentile of B-SCS, fairat the
50th percentile, and poorthe 25th percentile.
Table 2. Estimated change in GrimAge with signicant variables in nal model.
Independent variables Comparison Attributable GrimAge acceleration
Conf int (5% - 95%) pvalue
Stress (poor emotion reg.) CAI: 25th% vs 75th%; DERS at 75th
0.48 0.164 to 0.794 0.003
Stress (good emotion reg.) CAI: 25th% vs 75th%; DERS at 25th
0.04 0.426 to 0.341 0.8276
HOMA 25th% vs 75th% 0.27 0.041 to 0.506 0.0215
BMI 25th% vs 75th% 0.46 0.118 to 0.803 0.0086
Smoking none vs daily 3.79 3.08 to 4.5 <0.0001
Race White vs Black 1.04 0.421 to 1.655 0.001
Sex Female vs male 1.2 0.726 to 1.68 <0.0001
Marital status Married vs never married 0.71 0.073 to 1.345 0.0291
Analysis of estimated change in GrimAge (dependent variable) for different independent variables in the nal model. Comparisons for continuous variables
are made between 25th percentile and 75th percentile, with a positive value signifying higher GrimAge in the 75th percentile. Categorical variables are
compared as listed, with a positive value signifying higher GrimAge in the 2nd listed group.
BMI body mass index, CAI cumulative adversity index, DERS difculty with emotion regulation scale, HOMA homeostatic model assessment of insulin resistance
Z.M. Harvanek et al.
Translational Psychiatry (2021) 11:601
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anxiety disorder. The study includes individuals with obesity, as
well as a small number of individuals with risky drinking levels as
determined by the AUDIT scores. The frequency of these
individuals in the sample is generally in line with those in a
community population, and thus we included alcohol use and BMI
as covariates to account for the impact of these variables on the
results. Prior work has demonstrated that GrimAge better predicts
mortality than other epigenetic clocks, and GrimAge predicts
lifespan more accurately than self-reporting smoking history,
demonstrating that GrimAge is a biologically meaningful and
potentially clinically useful biomarker for health [51,64]. Our
ndings are consistent with recent work showing that those with
signicant trauma histories [59,81] or with diagnoses of mental
illnesses, such as Bipolar disorder or MDD, may experience
accelerated aging as measured by epigenetic clocks [57,8184].
In particular, this study builds on previous ndings by Zannas et al
that demonstrated a relationship between trauma and epigenetic
aging using the Horvath clock. However, to the best of our
knowledge this is the rst study to investigate the impact of
cumulative stress on epigenetic aging in a healthy community
sample without signicant physical or mental illness. Also it is the
rst to our knowledge to identify factors that contribute to
psychological resilience as potential modulators of such an effect.
This opens the possibility that the distinction between the effects
of stress on pathologic and non-pathologic samples may be along
a continuum. It would be interesting to examine resilience
characteristics in the population studied by Zannas et al to
determine if there is a limit to the protective effects of
psychological resilience. Thus, preventive interventions that
decrease stress and improve resilience may be useful for
maintaining long-term mental and physical health.
The relationship between stress and epigenetic aging appears to
be modulated via specic psychological traits, including emotion
regulation and self-control. Those with better emotion regulation
and higher levels of self-control were observed to have less age
acceleration even at similar levels of stress. Indeed, based on their
GAA, our estimates indicate that the relationship between stress
and GrimAge is as powerful as BMI, but only for those with poor
emotion regulation. As these are skills that may be developed
through specic psychological interventions [85], these results
raise the possibility that building emotion regulation skills could
result in improvements in epigenetic aging, morbidity, and
mortality [86] for these populations. As this is a cross-sectional
study, we are not able to address whether these relationships are
causal. These novel cross-sectional ndings provide support for
potential future research that may assess whether such an
intervention could positively impact epigenetic aging and other
indices of long-term health outcomes. Other studies could also
examine different aspects of resilience, such as cultural or
environmental factors that contribute to resilience to determine
if they also are protective against the effects of stress on epigenetic
age acceleration. Future studies could also explore other
physiologic mechanisms through which psychological resilience
may inuence epigenetic aging. Based on prior work, inammation
could be particularly important for this relationship. In particular,
prior studies have found C-reactive protein [87] and IL-6 [88]tobe
related to emotion regulation and measures of health. The work by
Gianaros et al suggests that neurologic activity of the dorsal
anterior cingulate cortex may be involved as well.
The relationship between cumulative stress, epigenetic aging,
and insulin resistance is of particular note given the prominence
of insulin signaling in aging-related pathways [89,90], as well as
current trials investigating metformin as a potential anti-aging
drug [33]. In association with this body of work, our study suggests
insulin resistance as at least one factor through which stress is
associated with accelerated aging, even in a healthy population
not suffering from diabetes. As this study is limited by its cross-
sectional nature, any causal hypotheses regarding interactions
between stress, BMI, insulin resistance, and aging will require
longitudinal data to draw specic inferences beyond correlative
relationships. Longitudinal studies would also enable prospective
assessments of stress, which may be less subject to recall bias
based on their current context. This study also identies the
cortisol/ACTH ratio as a potential point of connection between
stress and epigenetic aging. However, this measure is somewhat
limited in that it reects an acute measure of the HPA axis, and
this relationship becomes non-signicant with the inclusion of our
covariates. Future studies could utilize other, longer-term mea-
sures of HPA axis function such as hair cortisol to better
characterize the relationship between stress, epigenetic aging,
and the HPA axis.
Nonetheless, this study is the rst to identify a clear relationship
between cumulative stress and GrimAge acceleration in a healthy
population, which suggests stress may play a role in accelerated
aging even prior to the onset of chronic diseases. Notably, this
relationship was strongly moderated by resilience factors, includ-
ing self-control and emotion regulation. We also identied
smoking, BMI, insulin signaling, and potentially HPA signaling as
mediators of this response. However, even when accounting for all
these factors as well as demographic covariates such as race,
cumulative stress continues to demonstrate a signicant impact
on GAA, suggesting other mechanisms relating stress to aging not
identied herein are also present.
R scripts utilized for data analysis are available by contacting the authors directly.
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The authors would like to acknowledge the Yale Center of Genome Analysis for DNA
methylation proling. Funding for this study is from NIH Common Fund UL1-
DE019586 (R.S.), PL1-DA24859 (R.S.), R01-AA013892 (R.S.), NIH R01DA047063 (K.X.),
NIH T32MH019961 (Z.M.H.), NIH R25MH071584 (Z.M.H.). These data were presented
at the SOBP virtual conference in April 2021 as a poster.
Z.M.H., K.X., and R.S. conceptualized the project. Z.M.H. and N.F. performed the data
analysis, with recommendations from K.X. and R.S. Z.M.H. produced the gures and
tables. Z.M.H. wrote the manuscript, and all authors contributed to and edited the
The authors declare no competing interests.
Supplementary information The online version contains supplementary material
available at
Correspondence and requests for materials should be addressed to Rajita Sinha.
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... Seven studies were cross-sectional. Of these, three [29][30][31] studies used communitybased cross-sectional sampling, three used exposure-selective sampling (i.e., diagnosis of MDD or PTSD is the exposure) [28,32,33], and one was a twin study where pairs of twins were selected on the basis of PTSD diagnosis [34]. The eighth study was a longitudinal pre-post stress exposure [35]. ...
... Study populations included community-based urban samples, paramedicine students, first-responders, combat veterans, twins from the Vietnam Era Twin Registry, and matched clinic and community samples with and without MDD. Two studies restricted inclusion to somatically healthy adults [28,30], and one study excluded individuals with any disorder affecting the central nervous system [33]. Three studies were restricted to males [33,34,36] ...
... By this same rationale, we might expect to see an attenuation of the association between stress and GrimAA in studies that account for somatic disease. Yet, in the studies reviewed here, we observe a trend towards the reverse: the three studies that restricted their samples based on somatic health all reported significant results [28,30,33], while the remaining studies with no health-related exclusions had mixed findings. One possible explanation may be the strong association between somatic disease and GrimAA. ...
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The notion of “biological aging” as distinct from chronological aging has been of increasing interest in psychiatry, and many studies have explored associations of stress and psychiatric illness with accelerated biological aging. The “epigenetic clocks” are one avenue of this research, wherein “biological age” is estimated using DNA methylation data from specific CpG dinucleotide sites within the human genome. Many iterations of the epigenetic clocks have been developed, but the GrimAge clock continues to stand out for its ability to predict morbidity and mortality. Several studies have now explored associations of stress, PTSD, and MDD with GrimAge acceleration (GrimAA). While stress, PTSD, and MDD are distinct psychiatric entities, they may share common mechanisms underlying accelerated biological aging. Yet, no one has offered a review of the evidence on associations of stress and stress-related psychopathology with GrimAA. In this review, we identify nine publications on associations of stress, PTSD, and MDD with GrimAA. We find that results are mixed both within and across each of these exposures. However, we also find that analytic methods — and specifically, the choice of covariates — vary widely between studies. To address this, we draw upon popular methods from the field of clinical epidemiology to offer (1) a systematic framework for covariate selection, and (2) an approach to results reporting that facilitates analytic consensus. Although covariate selection will differ by the research question, we encourage researchers to consider adjustment for tobacco, alcohol use, physical activity, race, sex, adult socioeconomic status, medical comorbidity, and blood cell composition.
... Despite the high degree of methodological variability found among the studies included in the present review, we should highlight the fact that, unlike the crosssectional and longitudinal studies, which only found significant associations between resilience and cortisol in an approximate proportion of 40%-50%, the majority of the intervention studies reported significant associations between these two variables (7 out of 9: Dimenichi et al., 2018;García-Le on, Pérez-M armol, et al., 2019;Lau et al., 2021;Mikolajczak et al., 2008;Ruiz-Robledillo et al., 2017;Simeon et al., 2007;Sun et al., 2014). Even though some differed in terms of the directionality of the relationship observed, this finding serves to highlight the potential of resilience as a modifiable key factor for modulating the stress response, as indeed has been suggested recently by different studies (Bergquist et al., 2021;Harvanek et al., 2021). Specifically, Harvanek et al. (Harvanek et al., 2021) have suggested that, among others, psychological resilience factors may moderate the deleterious effects of cumulative stress on epigenetic aging in healthy adults, thereby emphasizing the usefulness of preventive interventions aimed at fostering resilience and reducing stress in order to improve quality of life. ...
... Even though some differed in terms of the directionality of the relationship observed, this finding serves to highlight the potential of resilience as a modifiable key factor for modulating the stress response, as indeed has been suggested recently by different studies (Bergquist et al., 2021;Harvanek et al., 2021). Specifically, Harvanek et al. (Harvanek et al., 2021) have suggested that, among others, psychological resilience factors may moderate the deleterious effects of cumulative stress on epigenetic aging in healthy adults, thereby emphasizing the usefulness of preventive interventions aimed at fostering resilience and reducing stress in order to improve quality of life. Several resilience training interventions have been conducted to date, mainly focusing on strengthening resilience or diverse psychosocial factors associated with resilience, such as active coping, positive reappraisal, and self-efficacy, using different approaches such as problemsolving therapy, relaxation, and mindfulness-based training or some elements of positive psychology (Blessin et al., 2022;Chmitorz et al., 2018;Kunzler et al., 2020Kunzler et al., , 2022. ...
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Resilience or the capacity to “bend but not break” refers to the ability to maintain or regain psychobiological equilibrium during or after exposure to stressful life events. Specifically, resilience has been proposed as a potential resource for staving off pathological states that often emerge after exposure to repeated stress and that are related to alterations in circulating cortisol. The aim of this systematic review of the literature was to gather evidence related to the relationship between psychological resilience and cortisol levels in adult humans. An extensive systematic search was carried out following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) method in the PubMed and Web of Science databases. In total, 1256 articles were identified and, of these, 35 peer‐reviewed articles were included in the systematic review. We categorized findings according to (1) the short and long‐term secretion period covered by the cortisol matrices selected by studies and also according to (2) the differentiated diurnal, phasic (acute), and tonic (basal) components of the HPA output to which they refer and their relationships with resilience. Reported relationships between psychological resilience and distinct cortisol output parameters varied widely across studies, finding positive, negative, and null associations between the two variables. Notably, several of the studies that found no relationship between resilience and cortisol used a single morning saliva or plasma sample as their assessment of HPA axis activity. Despite limitations such as the great variability of the instruments and methods used by the studies to measure both resilience and cortisol, together with their high heterogeneity and small sample sizes, the evidence found in this systematic review points to the potential of resilience as a modifiable key factor to modulate the physiological response to stress. Therefore, further exploration of the interaction between the two variables is necessary for the eventual development of future interventions aimed at promoting resilience as an essential component of health prevention.
... Prediction of chronological age based on DNA methylation is commonly known as the "epigenetic clock". (Harvanek et al., 2021). ...
... Extended periods of psychological stress can directly lead to affective disorders such as major depressive disorder (MDD) 1 , post-traumatic stress disorder (PTSD) 2,3 , and anxiety disorders 2,3 . Stress also accelerates aging and the progression of neurodegenerative diseases such as Alzheimer's disease (AD) [4][5][6] . Lastly, stress can also trigger episodic psychiatric and neurological diseases, such as schizophrenia 7,8 and multiple sclerosis 9 . ...
Microglia play a pivotal role in mediating the neurological effects of chronic stress, and is implicated in stress-associated diseases such as depression. However, the biological states of microglia in the stressed brain remains unclear. Here, using single cell RNA sequencing of mouse cortical microglia, we identify a stress-associated microglia state marked by high expression of apolipoprotein E (ApoE). In the prefrontal cortex, ApoEhigh microglia are localized to layer 2/3, and are caused by stress-induced local complement activation in the upper cortical layers. In mice lacking complement component C3, chronic stress does not induce the ApoEhigh microglial state, and these mice are protected from stress-induced synapse loss, anhedonia, and working memory deficits. Our data show that spatially-restricted complement and microglia activation can result in regional vulnerability of synapses in brain disease, and highlight the role of immune processes in causing region-specific circuit defects that are associated with many psychiatric diseases.
... 29 Previous studies also described, that cumulative stress is associated with accelerated biological aging. 30 Therefore, in the context of dysglycemia, having prior exposure to stressors any time earlier in the life can lead to DBCD development and progression. ...
Dysglycemia-based chronic disease (DBCD) comprises insulin resistance, prediabetes, and type 2 diabetes, and their staged progression, complications, and impact contribute to one of the largest public health burdens worldwide. The clinical and economic effects of DBCD are fueled by external stressors related to modern society and lifestyles, especially in low socioeconomic strata. Chronic stressor exposure leads to chronic stress, characterized by immune-neuroendocrine activation and mobilization/depletion of metabolic resources. This complex adaptive response engaging many different signaling pathways is termed allostasis and principally regulates homeostatic mechanisms. Allostatic load is the metabolic cost of adaptation to environmental factors and can lead to numerous adverse health outcomes and behaviors. In this narrative review, the bidirectional relationship between allostatic load and unhealthy behaviors is analyzed in the context of DBCD development and progression, with a focus to reduce individual vulnerabilities.
The aim of this study was to evaluate the associations between psychological resilience and epigenetic clocks assessed by DNA methylation age predictions. We used data from 4018 participants in the Health and Retirement Study. Multivariable linear regression models were used to estimate the association between psychological resilience and epigenetic clocks adjusted for age, sex, race, body mass index, smoking status, and years of education. Thirteen epigenetic clocks were used in our analysis and were highly correlated with one another. A higher psychological resilience score was associated with slower DNA methylation age acceleration for the majority of epigenetic clocks after multivariable adjustment. These findings imply that people with a higher level of psychological resilience may experience slower DNA methylation age acceleration and biological aging.
Stress triggers anticipatory physiological responses that promote survival, a phenomenon termed allostasis. However, the chronic activation of energy-dependent allostatic responses results in allostatic load, a dysregulated state that predicts functional decline, accelerates aging, and increases mortality in humans. The energetic cost and cellular basis for the damaging effects of allostatic load have not been defined. Here, by longitudinally profiling three unrelated primary human fibroblast lines across their lifespan, we find that chronic glucocorticoid exposure increases cellular energy expenditure by ∼60%, along with a metabolic shift from glycolysis to mitochondrial oxidative phosphorylation (OxPhos). This state of stress-induced hypermetabolism is linked to mtDNA instability, non-linearly affects age-related cytokines secretion, and accelerates cellular aging based on DNA methylation clocks, telomere shortening rate, and reduced lifespan. Pharmacologically normalizing OxPhos activity while further increasing energy expenditure exacerbates the accelerated aging phenotype, pointing to total energy expenditure as a potential driver of aging dynamics. Together, our findings define bioenergetic and multi-omic recalibrations of stress adaptation, underscoring increased energy expenditure and accelerated cellular aging as interrelated features of cellular allostatic load.
It is well documented that social workers are at high risk for workplace stress. Practice settings have been increasingly stressful, marked by high demands and scarce resources. Although prevention and intervention self-care strategies to combat workplace stress are plentiful, some studies suggest they can be ineffective because little is known about the behavioral, cognitive, and emotional processes that undergird individual experiences of stress (Beer et al., 2021). Additionally, an individual-level approach neglects the role of the workplace itself as a potential cause of the stress experienced by its employees. Regardless of multiple contributing factors, the influence of stress on our health remains a threat to our professional and personal wellness. Stress deserves increased attention given studies that uncover the extent of negative health outcomes and the prevalence of physical health problems experienced by social workers throughout their careers (Steen & Kravitz, 2023). Alarming associations have been found between stress and physical health including increased biological age, immune system and metabolic disorders, heart disease, and DNA damage that hastens aging and age-related conditions. Prolonged states of stress can bring on oxidative stress, which is an imbalance between free radicals and antioxidants that can harm organs and tissues resulting in various diseases such as diabetes, high blood pressure, cancer, and neurodegenerative disorders (Harvanek et al., 2021; Poganik et al., 2023; Stokes, 2023). For social workers, workplace stress is associated with vicarious trauma, compassion fatigue, secondary traumatic stress, and burnout—all of which have been associated with deleterious physiological consequences. Unhealthy coping behaviors in response to stress, such as substance use and emotional eating, are also common among social workers and bear negative outcomes (Beer et al., 2021). Not surprisingly, the COVID-19 pandemic brought higher levels of workplace stress, burnout, and related health problems. Burnout rate among social workers before COVID-19 was estimated at approximately 39 percent; research suggests that burnout among social workers increased to 60 percent at the height of the pandemic (Prasad et al., 2021).
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Importance: Adverse childhood experiences (ACEs) are associated with the risk of poorer health, and identifying molecular mechanisms may lay the foundation for health promotion in people with ACEs. Objective: To investigate the associations of ACEs with changes in epigenetic age acceleration (EAA), a biomarker associated with various health outcomes in middle-aged adults, in a population with balanced race and sex demographics. Design, setting, and participants: Data for this cohort study were from the Coronary Artery Risk Development in Young Adults (CARDIA) study. Participants in CARDIA underwent 8 follow-up exams from baseline (year 0 [Y0]; 1985-1986) to Y30 (2015-2016), and participant blood DNA methylation information was obtained at Y15 (2000-2001) and Y20 (2005-2006). Individuals from Y15 and Y20 with available DNA methylation data and complete variables for ACEs and covariates were included. Data were analyzed from September 2021 to August 2022. Exposures: Participant ACEs (general negligence, emotional negligence, physical violence, physical negligence, household substance abuse, verbal and emotional abuse, and household dysfunction) were obtained at Y15. Main outcomes and measures: The primary outcome consisted of results from 5 DNA methylation-based EAA measurements known to be associated with biological aging and long-term health: intrinsic EAA (IEAA), extrinsic EAA (EEAA), PhenoAge acceleration (PhenoAA), GrimAge acceleration (GrimAA), and Dunedin Pace of Aging Calculated From the Epigenome (DunedinPACE), measured at Y15 and Y20. Linear regression and generalized estimating equations were used to assess associations of the burden of ACEs (≥4 vs <4 ACEs) with EAA adjusting for demographics, health-related behaviors, and early life and adult socioeconomic status. Results: A total of 895 participants for Y15 (mean [SD] age, 40.4 [3.5] years; 450 males [50.3%] and 445 females [49.7%]; 319 Black [35.6%] and 576 White [64.4%]) and 867 participants for Y20 (mean [SD] age, 45.4 [3.5] years; 432 males [49.8%] and 435 females [50.2%]; 306 Black [35.3%] and 561 White [64.7%]) were included after excluding participants with missing data. There were 185 participants with (20.7%) vs 710 participants without (79.3%) 4 or more ACEs at Y15 and 179 participants with (20.6%) vs 688 participants without (79.4%) 4 or more ACEs at Y20. Having 4 or more ACEs was positively associated with EAA in years at Y15 (EEAA: β = 0.60 years; 95% CI, 0.18-1.02 years; PhenoAA: β = 0.62 years; 95% CI = 0.13-1.11 years; GrimAA: β = 0.71 years; 95% CI, 0.42-1.00 years; DunedinPACE: β = 0.01; 95% CI, 0.01-0.02) and Y20 (IEAA: β = 0.41 years; 95% CI, 0.05-0.77 years; EEAA: β = 1.05 years; 95% CI, 0.66-1.44 years; PhenoAA: β = 0.57 years; 95% CI, 0.08-1.05 years; GrimAA: β = 0.57 years; 95% CI, 0.28-0.87 years; DunedinPACE: β = 0.01; 95% CI, 0.01-0.02) after adjusting for demographics, health-related behaviors, and socioeconomic status. Conclusions and relevance: In this cohort study, ACEs were associated with EAA among middle-aged adults after controlling for demographics, behavior, and socioeconomic status. These findings of the associations between early life experience and the biological aging process in midlife may contribute to health promotion in a life course perspective.
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Objective To test the hypothesis that chronic stress impairs the use of cognitive control for self‐control, we examined how chronic stress affects the relation between individual differences in general executive functioning (GEF) and self‐control in real‐life situations. Method 338 young adults with varying degrees of chronic stress underwent experience sampling of real‐life self‐control for seven days and completed a battery of nine executive function tasks from which a latent variable representing individual differences in general executive functioning (GEF) was derived. Results Structural equation models showed that higher levels of chronic stress were associated with stronger desires and a less negative relationship between GEF and desire strength. Chronic stress and GEF did not predict desire enactment in situations where effortful resistance was attempted. Conclusion These findings suggest that chronic stress may impair self‐control by reducing the use of cognitive control for “early” desire regulation strategies while leaving “late” resistance strategies unaffected. That relationships between executive functioning and real‐life self‐control can be moderated by third factors such as chronic stress may to some extent explain the common finding of weak or missing associations between laboratory measures of executive functioning and real‐life self‐control.
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DNA methylation patterns at specific cytosine-phosphate-guanine (CpG) sites predictably change with age and can be used to derive “epigenetic age”, an indicator of biological age, as opposed to merely chronological age. A relatively new estimator, called “DNAm GrimAge”, is notable for its superior predictive ability in older populations regarding numerous age-related metrics like time-to-death, time-to-coronary heart disease, and time-to-cancer. PTSD is associated with premature mortality and frequently has comorbid physical illnesses suggestive of accelerated biological aging. This is the first study to assess DNAm GrimAge in PTSD patients. We investigated the acceleration of GrimAge relative to chronological age, denoted “AgeAccelGrim” in combat trauma-exposed male veterans with and without PTSD using cross-sectional and longitudinal data from two independent well-characterized veteran cohorts. In both cohorts, AgeAccelGrim was significantly higher in the PTSD group compared to the control group (N = 162, 1.26 vs −0.57, p = 0.001 and N = 53, 0.93 vs −1.60 Years, p = 0.008), suggesting accelerated biological aging in both cohorts with PTSD. In 3-year follow-up study of individuals initially diagnosed with PTSD (N = 26), changes in PTSD symptom severity were correlated with AgeAccelGrim changes (r = 0.39, p = 0.049). In addition, the loss of CD28 cell surface markers on CD8 + T cells, an indicator of T-cell senescence/exhaustion that is associated with biological aging, was positively correlated with AgeAccelGrim, suggesting an immunological contribution to the accelerated biological aging. Overall, our findings delineate cellular correlates of biological aging in combat-related PTSD, which may help explain the increased medical morbidity and mortality seen in this disease.
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Epigenetic clocks comprise a set of CpG sites whose DNA methylation levels measure subject age. These clocks are acknowledged as a highly accurate molecular correlate of chronological age in humans and other vertebrates. Also, extensive research is aimed at their potential to quantify biological aging rates and test longevity or rejuvenating interventions. Here, we discuss key challenges to understand clock mechanisms and biomarker utility. This requires dissecting the drivers and regulators of age-related changes in single-cell, tissue- and disease-specific models, as well as exploring other epigenomic marks, longitudinal and diverse population studies, and non-human models. We also highlight important ethical issues in forensic age determination and predicting the trajectory of biological aging in an individual.
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A number of studies have assessed the effects of psychoactive drugs on stress biology, the neuroadaptations resulting from chronic drug use on stress biology, and their effects on addiction risk and relapse. This review mainly covers human research on the acute effects of different drugs of abuse (i.e., nicotine, cannabis, psychostimulants, alcohol, and opioids) on the hypothalamic-pituitary-adrenal (HPA) axis and the autonomic nervous system (ANS) responses. We review the literature on acute peripheral stress responses in naïve or light recreational users and binge/heavy or chronic drug users. We also discuss evidence of alterations in tonic levels, or tolerance, in the latter relative to the former and associated changes in the phasic stress responses. We discuss the impact of the stress system tolerance in heavy users on their response to drug- and stress-related cue responses and craving as compared to control subjects. A summary is provided of the effects of glucocorticoid responses and their adaptations on brain striatal and prefrontal cortices involved in the regulation of drug seeking and relapse risk. Finally, we summarize important considerations, including individual difference factors such as gender, co-occurring drug use, early trauma and adversity and drug use history and variation in methodologies, that may further influence the effects of these drugs on stress biology.
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It was unknown whether plasma protein levels can be estimated based on DNA methylation (DNAm) levels, and if so, how the resulting surrogates can be consolidated into a powerful predictor of lifespan. We present here, seven DNAm-based estimators of plasma proteins including those of plasminogen activator inhibitor 1 (PAI-1) and growth differentiation factor 15. The resulting predictor of lifespan, DNAm GrimAge (in units of years), is a composite biomarker based on the seven DNAm surrogates and a DNAm-based estimator of smoking pack-years. Adjusting DNAm GrimAge for chronological age generated novel measure of epigenetic age acceleration, AgeAccelGrim. Using large scale validation data from thousands of individuals, we demonstrate that DNAm GrimAge stands out among existing epigenetic clocks in terms of its predictive ability for time-to-death (Cox regression P=2.0E-75), time-to-coronary heart disease (Cox P=6.2E-24), time-to-cancer (P= 1.3E-12), its strong relationship with computed tomography data for fatty liver/excess visceral fat, and age-at-menopause (P=1.6E-12). AgeAccelGrim is strongly associated with a host of age-related conditions including comorbidity count (P=3.45E-17). Similarly, age-adjusted DNAm PAI-1 levels are associated with lifespan (P=5.4E-28), comorbidity count (P= 7.3E-56) and type 2 diabetes (P=2.0E-26). These DNAm-based biomarkers show the expected relationship with lifestyle factors including healthy diet and educational attainment. Overall, these epigenetic biomarkers are expected to find many applications including human anti-aging studies.
The aging process is characterized by the presence of high interindividual variation between individuals of the same chronical age prompting a search for biomarkers that capture this heterogeneity. Epigenetic clocks measure changes in DNA methylation levels at specific CpG sites that are highly correlated with calendar age. The discrepancy resulting from the regression of DNA methylation age on calendar age is hypothesised to represent a measure of biological ageing with a positive/negative residual signifying age acceleration /deceleration respectively. The present study examines the associations of four epigenetic clocks - Horvath, Hannum, PhenoAge, GrimAge - with a wide range of clinical phenotypes (walking speed, grip strength, Fried frailty, polypharmacy, Mini-Mental State Exam (MMSE), Montreal Cognitive Assessment (MOCA), Sustained Attention Reaction Time, 2-choice reaction time), and with all-cause mortality at up to 10-year follow-up, in a sample of 490 participants in the Irish Longitudinal Study on Ageing (TILDA). Horvath Age Acceleration (AA) and HannumAA were not predictive of health; PhenoAgeAA was associated with 4/9 outcomes (walking speed, frailty MOCA, MMSE) in minimally adjusted models, but not when adjusted for other social and lifestyle factors. GrimAgeAA by contrast was associated with 8/9 outcomes (all except grip strength) in minimally adjusted models, and remained a significant predictor of polypharmacy, frailty, and mortality in fully adjusted models. Results indicate that the GrimAge clock represents a step-improvement in the predictive utility of the epigenetic clocks for identifying age-related decline in an array of clinical phenotypes promising to advance precision medicine.
Background: Schizophrenia (SZ) is associated with increased all-cause mortality, smoking, and age-associated proteins, yet multiple previous studies found no association between SZ and biological age using Horvath's epigenetic clock, a well-established aging biomarker based on DNA methylation. However, numerous epigenetic clocks that may capture distinct aspects of aging have been developed. This study tested the hypothesis that altered aging in SZ manifests in these other clocks. Methods: We performed a comprehensive analysis of 14 epigenetic clocks categorized according to what they were trained to predict: chronological age, mortality, mitotic divisions, or telomere length. To understand the etiology of biological age differences, we also examined DNA methylation predictors of smoking, alcohol, body mass index, serum proteins, and cell proportions. We independently analyzed 3 publicly available multiethnic DNA methylation data sets from whole blood, a total of 567 SZ cases and 594 nonpsychiatric controls. Results: All data sets showed accelerations in SZ for the 3 mortality clocks up to 5 years, driven by smoking and elevated levels of 6 age-associated proteins. The 2 mitotic clocks were decelerated in SZ related to antitumor natural killer and CD8T cells, which may help explain conflicting reports about low cancer rates in epidemiological studies of SZ. One cohort with available medication data showed that clozapine is associated with male-specific decelerations up to 7 years in multiple chronological age clocks. Conclusions: Our study demonstrates the utility of studying the various epigenetic clocks in tandem and highlights potential mechanisms by which mental illness influences long-term outcomes, including cancer and early mortality.
Aging is the single most important risk factor for diseases that are currently the leading causes of morbidity and mortality. However, there is considerable inter-individual variability in risk for aging-related disease, and studies suggest that biological age can be influenced by multiple factors, including exposure to psychosocial stress. Among markers of biological age that can be affected by stress, the present article focuses on the so-called measures of epigenetic aging: DNA methylation-based age predictors that are measured in a range of tissues, including the brain, and can predict lifespan and healthspan. We review evidence linking exposure to diverse types of psychosocial stress, including early-life stress, cumulative stressful experiences, and low socioeconomic status, with accelerated epigenetic aging as a putative mediator of the effects of psychosocial environment on health and disease. The chapter also discusses methodological differences that may contribute to discordant findings across studies to date and plausible mechanisms that may underlie the effects of stress on the aging epigenome. Future studies examining the effects of adversity on epigenetic and other indicators of biological weathering may provide important insights into the pathogenesis of aging-related disease states.
Allostatic Load (AL) and epigenetic clocks both attempt to characterise the accelerated ageing of biological systems, but at present it is unclear whether these measures are complementary or distinct. This study examines the cross-sectional association of AL with Epigenetic Age Acceleration (EAA) in a sub-sample of 490 community dwelling older-adults participating in The Irish Longitudinal study on Aging (TILDA). A battery of 14 biomarkers representing the activity of 4 different physiological systems: immunological, cardiovascular, metabolic, renal, was used to construct the AL score. DNA methylation age was computed according to the algorithms described by Horvath, Hannum and Levine allowing for estimation of whether an individual is experiencing accelerated or decelerated ageing. Horvath, Hannum and Levine EAA correlated 0.05, 0.03, and 0.21 with AL respectively. Disaggregation by sex revealed that AL was more strongly associated with EAA in men compared with women as assessed using Horvath’s clock. Metabolic dysregulation was a strong driver of EAA in men as assessed using Horvath and Levine’s clock, while metabolic and cardiovascular dysregulation were associated with EAA in women using Levine’s clock. Results indicate that AL and the epigenetic clocks are measuring different age-related variance and implicate sex-specific drivers of biological ageing.
BACKGROUND: Empirical data on the link between stress and cardiovascular disease (CVD) risk among black women is limited. We examined associations of stressful life events and social strain with incident CVD among black women and tested for effect modification by resilience. METHODS AND RESULTS: Our analysis included 10 785 black women enrolled in the Women's Health Initiative Observational Study and Clinical Trials cohort. Participants were followed for CVD for up to 23 years (mean, 12.5). Multivariable Cox regression was used to estimate hazard ratios and 95% CIs for associations between stress-related exposures and incident CVD. We included interactions between follow-up time (age) and stressful life events because of evidence of nonproportional hazards. Effect modification by resilience was examined in the sub-cohort of 2765 women with resilience and stressful life events measures. Higher stressful life events were associated with incident CVD at ages 55 (hazard ratio for highest versus lowest quartile=1.80; 95% CI, 1.27-2.54) and 65 (hazard ratio for highest versus lowest quartile=1.40; 95% CI, 1.16-1.68), but not at older ages. Adjustment for CVD risk factors attenuated these associations. Similar associations were observed for social strain. In the sub-cohort of women with updated stressful life events and resilience measures, higher stressful life events were associated with incident CVD in multivariable-adjusted models (hazard ratio=1.61; 95% CI, 1.04-2.51). Resilience did not modify this association nor was resilience independently associated with incident CVD. CONCLUSIONS: In this cohort of older black women, recent reports of stressful life events were related to incident CVD. Resilience was unrelated to incident CVD.