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ARTICLE OPEN
Psychological and biological resilience modulates the effects of
stress on epigenetic aging
Zachary M. Harvanek
1
, Nia Fogelman
2
,KeXu
1,3
and Rajita Sinha
1,2,4,5
✉
© 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
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.
Translational Psychiatry (2021) 11:601 ; https://doi.org/10.1038/s41398-021-01735-7
INTRODUCTION
Cumulative stress can have adverse psychiatric and physical
effects, increasing risk for cardiometabolic diseases, mood
disorders, post-traumatic stress disorder and addiction [1–11].
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
[1,12–14]. Notably, emotional stress exposure decreases
cognitive and emotion regulation abilities [15–18], and this
effect may be modulated by cortisol [15]. Furthermore, stress
decreases self-control abilities [19–21] 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
[22–25]. 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,
stress’seffectsonphysiologyresulting in alterations in neuro-
hormonal signaling pathways as well as increased inflamma-
tion are well documented [26,28–30]. 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
[33].
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 modified by
behavioral and psychological resilience factors [34–37]. How-
ever, recent studies have demonstrated mixed results on
whether characteristics that contribute to resilience improve
or worsen the impact of stress on health [38–47]. 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
1
Department of Psychiatry, Yale University, New Haven, CT, USA.
2
Yale Stress Center, Yale University, New Haven, CT, USA.
3
Department of Psychiatry, Connecticut Veteran
Healthcare System, West Haven, CT, USA.
4
Department of Neuroscience, Yale University, New Haven, CT, USA.
5
Child Study Center, Yale University, New Haven, CT, USA.
✉email: rajita.sinha@yale.edu
www.nature.com/tp
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on personal-level, psychological skills, including self-control
and emotion regulation.
Recently developed DNA methylation-based epigenetic
“clocks”appear to provide a more accurate measure of
biological age than telomere length [48–51]. 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
asignificantly 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 significant trauma histories [59], or specific cohorts at
higher risk [60–62]. Notably, these studies did not exclude, and
often explicitly included, individuals with significant 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”,defined 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 finally, 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 Difficulties 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).
MATERIALS AND METHODS
Cohort recruitment
The participant cohort included 444 community adults between the ages
of 18–50 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]. Briefly, 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 deficits,
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
parameters
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 masters’or 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 financially 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 specific 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 “yes”to the
specific stressful event occurring led to a “1”and a sum of all the “yes”
endorsements comprised the subscale score for these events subscale. The
final subscale of chronic stress was the participant’s 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”,or“very true”scale,
with assigned scores of 0, 1, and 2, respectively. The final 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 [12,26,70–72].
PERCEIVED
ENVIRONMENT
ORGANISMAL
RESPONSE
LONG-TERM
CONSEQUENCES
Stress-
related
physiologic
changes
Psychological
resilience
Stressor
Accelerated Aging
Poor
Health
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.
2
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 [26,70–72].
Emotion regulation was assessed using the Difficulties 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]. Briefly, all samples were profiled using Illumina
Infinium 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
–12
. Intensity values showing P>10
−12
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
–12
, 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 first perform background correction and within-array normalization to
the original green/red channel intensity data using the preprocessIllumina
function in the minfiR 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://
dnamage.genetics.ucla.edu/new [51]. Data were normalized as per their
protocol, and the advanced analysis option was used. We focus on
GrimAge acceleration (GAA), which is defined 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 first 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 significant
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 coefficients)
when assessing for moderation.
All tests were two-tailed with alpha set at 0.05. Statistical significance in
both standard linear regressions and moderation analyses were assessed
from tvalues. R
2
reported on plots represent the simple relationship
between the stated variables, while adjusted R
2
values in the text represent
the model. Partial η
2
values represent the effect size for the specific
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 significant 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 defined as the
product of the coefficients of the effect of stress on BMI, of BMI on HOMA,
and of HOMA on GAA. Assessment of the individual variables’attributable
GrimAge acceleration as well as confidence intervals were calculated using
the Emmeans package using unadjusted pairwise comparisons.
RESULTS
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 Identification 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<2e−16, adjusted
R
2
=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<2e−16, partial η
2
=0.848;
model (GrimAge ~ Age +covariates) adjusted R
2
=0.912), and
this relationship remained significant 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 significantly predicted
higher GAA (CAI: t=4.82 P=2.00e−6, η
2
=0.050, adjusted R
2
=
0.0478, Fig. 2B). While there were significant differences in GAA
based on sex (P=1.33e−7), both males (CAI: P=3.35e−4,
adjusted R
2
=0.0586) and females (CAI: P=3.12e−5, adjusted
R
2
=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 significant (CAI: t=2.073, P=
0.0388, partial η
2
=0.010; model (GAA ~ CAI-total +covariates):
adjusted R
2
=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.
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Translational Psychiatry (2021) 11:601
Content courtesy of Springer Nature, terms of use apply. Rights reserved
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=6.18e−4, adjusted R
2
=0.024).
Stress-related physiology is associated with GrimAge
acceleration
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) significantly predicted GAA (Fig. 2C, HOMA: t=
2.362, P=0.0186, partial η
2
=0.013; model (GAA ~ HOMA +
Covariates): adjusted R
2
=0.389).
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=2.39e−6, η
2
=
0.049, adjusted R
2
=0.0470), though this becomes non-significant
when accounting for covariates (Cort/ACTH ratio: t=−0.721, P=
0.471, partial η
2
=0.001; model (GAA ~ Cort/ACTH +Covariates):
adjusted R
2
=0.3816). We also find a significant 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
2
=0.2197).
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 individual’s 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 Difficulties in Emotion Regulation
Scale (DERS, [65]) significantly moderated the relationship
between GAA and CAI (Fig. 3A, CAI:DERS: F=11.22, P=8.82e−4,
partial η
2
=0.025; model (GAA ~ CAI X DERS +covariates):
adjusted R
2
=0.4004), such that poor emotion regulation sig-
nificantly increased the effects of CAI on GAA. There was not a
significant 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
acceleration
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-
significant when covariates were accounted for (Fig. 3B, CAI:SCS:
F=2.303, P=0.130, partial η
2
=0.005; model (GAA ~ CAI X SCS +
Covariates: adjusted R
2
=0.3874).
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
2
=0.017; model
(HOMA ~ CAI X SCS +Covariates: adjusted R
2
=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 significantly
differ between males and females (P=0.0550).
Exploratory mediation analyses suggest stress influences
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 significant 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 findings,
we next asked whether BMI and insulin resistance act sequentially
to mediate the effects of stress on GAA. We identified a significant
indirect effect, suggesting that stress may affect GAA through
Table 1. Demographics of community population.
Category Frequency
1
/
mean
2
5th% to 95th% Stdev
Gender
1
Female 55.2%
Male 44.8%
Smoker
1
No 79.6%
Yes 20.4%
Race
1
White 73%
Black 18%
Other 9%
Marital
Status
1
Never
married
73.4%
Married 16.2%
Divorced/
other
10.4%
Regular
EtOH
use
1
Yes 70.4%
No 29.6%
AUDIT
2
5.94 0 –19 5.97
BMI
2
26.96 20–37.8 5.377
Days smoking past
4 weeks
2
4.0 0–28 9.30
Days drinking past
4 weeks
2
6.3 0–20 6.95
CAI-total score
2
19.8 6–41 10.41
DERS
2
69.9 43–108 19.73
Brief-SCS
2
45.6 31–60 8.66
Age
2
28.6 19–47 8.74
Years of Education
2
15.4 12–20 2.47
Employment Income
(monthly)
2
$1,010.59 0–$3500 $1,421.33
Cornell-biological
subscore
2
10.2 1–30 9.1
Cornell-psychological
subscore
2
5.3 0–20 6.7
Cornell-total
2
15.5 2–46 14.48
Cortisol/ACTH (AUC)
2
0.30384 0.0988–0.741 0.21344
HOMA
2
3.169 1.05 –7.13 1.9697
1
Frequency
2
Mean
Demographics and average statistics for the test population
AUDIT alcohol use disorder identification test, BMI body mass index, CAI
cumulative adversity index, DERS difficulty with emotion regulation scale,
SCS self-control scale, HOMA homeostatic model assessment of insulin
resistance
Z.M. Harvanek et al.
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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 significant 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
significant 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
significant interaction between stress and emotion regulation in
relation to GAA (CAI:DERS t=3.424, P=0.000677, partial η
2
=
0.027; model (GAA ~ CAI-total X DERS +HOMA +Cort/ACTH ratio
+SCS +Covariates): adjusted R
2
=0.4056). Notably in this model,
HOMA (t=2.308, P=0.0215, partial η
2
=0.012), BMI (t=2.641, P
=0.00857, partial η
2
=0.016), and smoking (t=10.47, P<2e−16,
partial η
2
=0.204) also demonstrate significant effects on GAA.
The impact of the cortisol/ACTH ratio on GAA is not significant
(t=−0.668, P=0.504, partial η
2
=0.001), and its removal from
the model does not impact any of the above conclusions.
Using this final linear model, we estimated the changes in
GrimAge for each significant 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 identified 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 significantly 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-significant (Supplementary Table 5).
Prior literature [51] suggests that GrimAge predicts the hazard
ratio exponentially (HR =1.1
GAA
). Thus, each additional year of
GAA would be expected to increase the relative risk of death by
approximately 10%.
DISCUSSION
In this study, we report novel findings 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 findings point to
multiple potentially modifiable biobehavioral targets of
AB
C
40
50
60
70
20 30 40 50
Chronologic Age (Years)
GrimAge (Years)
−5
0
5
10
0204060
CAI - Total Score
GrimAge Acceleration (Years)
R² = 0.857*** R² = 0.048***
P < 0.05
P < 0.01
P < 0.001
*:
**:
***:
D
−5
0
5
10
0.0 0.5 1.0
Cortisol/ACTH Ratio
GrimAge Acceleration (Years)
R² = 0.049***
−5
0
5
10
0510
HOMA
GrimAge Acceleration (Years)
R² = 0.071***
Fig. 2 GrimAge and GrimAge acceleration correlate with cumulative stress and physiologic stress pathways. A Chronologic age
significantly predicts GrimAge (P<2e−16). BCumulative stress total as measured by the CAI (CAI-Total) significantly predicts GAA before (P=
2.00e−6) and after accounting for covariates. CHigher insulin resistance (as measured by HOMA) shows a significant positive correlation with
GAA before (P=1.11e−8) and after accounting for covariates. DThe Cortisol/ACTH ratio is negatively correlated with GAA before accounting
for covariates (P=2.39e−6), but not afterward. Pand R
2
values in the figure represent simple univariate models (Y ~ X). In the main text,
models are adjusted for covariates as stated.
Z.M. Harvanek et al.
5
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 identified a significant
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
A
−2
0
2
4
0204060
CAI - Total Score
GrimAge Acceleration (Years)
Emotion regulation
Good
Fair
Poor
unadjusted: F = 15.5***
w/ covariates: F = 11.2***
P < 0.05
P < 0.01
P < 0.001
*:
**:
**:
Interaction term
C
−2
0
2
4
0 204060
CAI - Total Score
GrimAge Acceleration (Years)
Self-control
Good
Fair
Poor
unadjusted: F = 9.44**
w/ covariates: F = 2.30
3
4
5
0204060
CAI - Total Score
HOMA
Self-control
Good
Fair
Poor
unadjusted: F = 6.44*
w/ covariates: F = 7.26**
Interaction term
Interaction term
B
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=9.51e−5; GAA ~ CAI X DERS +Covariates: P=8.82e−4) and after accounting for
covariates. For panel A, “good”represents the slope at the 25th percentile of DERS, “fair”at the 50th percentile, and “poor”the 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), “good”represents the slope at the 75th percentile of B-SCS, “fair”at the
50th percentile, and “poor”the 25th percentile.
Table 2. Estimated change in GrimAge with significant variables in final model.
Independent variables Comparison Attributable GrimAge acceleration
(Years)
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 final 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 difficulty with emotion regulation scale, HOMA homeostatic model assessment of insulin resistance
Z.M. Harvanek et al.
6
Translational Psychiatry (2021) 11:601
Content courtesy of Springer Nature, terms of use apply. Rights reserved
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
findings are consistent with recent work showing that those with
significant 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 [57,81–84].
In particular, this study builds on previous findings 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 first study to investigate the impact of
cumulative stress on epigenetic aging in a healthy community
sample without significant physical or mental illness. Also it is the
first 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 specific 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 specific 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 findings 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 influence epigenetic aging. Based on prior work, inflammation
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 specific 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 identifies the
cortisol/ACTH ratio as a potential point of connection between
stress and epigenetic aging. However, this measure is somewhat
limited in that it reflects an acute measure of the HPA axis, and
this relationship becomes non-significant 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 first 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 identified
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 significant impact
on GAA, suggesting other mechanisms relating stress to aging not
identified herein are also present.
CODE AVAILABILITY
R scripts utilized for data analysis are available by contacting the authors directly.
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ACKNOWLEDGEMENTS
The authors would like to acknowledge the Yale Center of Genome Analysis for DNA
methylation profiling. 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.
AUTHOR CONTRIBUTIONS
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 figures and
tables. Z.M.H. wrote the manuscript, and all authors contributed to and edited the
manuscript.
COMPETING INTERESTS
The authors declare no competing interests.
ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41398-021-01735-7.
Correspondence and requests for materials should be addressed to Rajita Sinha.
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