Abstract
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.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11357-023-00940-0.
Keywords: Resilience, Epigenetic clock, Aging, DNA methylation
Introduction
Epigenetic clocks are predictors that utilize DNA methylation levels to estimate biological age [1]. Accelerated epigenetic aging, defined as the difference between DNA methylation-predicted age (DNAm age) and chronological age, was associated with aging-related phenotypes, including obesity, Parkinson’s disease, Alzheimer’s disease, and mortality [2]. Epigenetic clocks, as the primary measurements for epigenetic aging, are commonly used in epidemiological research to explore the relationships between epigenetic aging and other phenotypes. We briefly summarized these 13 clocks in the Supplementary Materials. It has been demonstrated that the epigenetic clocks are correlated with chronological age and associated with various factors including race, sex, and environmental risk factors [3, 4]. A previous study showed that the Levine epigenetic clock (PhenoAge) exhibited a high correlation with chronological age, while the rate of epigenetic aging varied between individuals [5]. Therefore, using epigenetic aging to explore the relationship between the epigenetic clocks and other factors to determine whether risk factors will accelerate, or delay aging has become a commonly used method. Furthermore, studies have suggested that psychological factors contribute to accelerated biological aging and can exert a more substantial impact on biological aging than other factors like smoking and sex [6]. For example, feeling unhappy or lonely may trigger biological aging and accelerate the aging process [7]. A few studies have also pointed out that psychological factors, such as stress [8], loneliness [9], and negative perceptions of aging [10], could have a significant influence on aging clocks derived from physical parameters.
Psychological resilience refers to an individual’s ability to cope with and adapt to challenging life circumstances and events [11]. In older adults, psychological resilience has been associated with several health outcomes, including better physical and mental health as well as successful aging [12]. In addition, some studies have explored the relationship between psychological resilience and biological aging and found that a decline in resilience can contribute to an increase in vulnerability to death even in people without major diseases [13, 14]. The importance of psychological resilience in promoting positive health outcomes and building community resilience has been highlighted, particularly in the era of the COVID-19 pandemic [15].
Several studies have demonstrated associations between trauma, early-life adversity, or low socioeconomic status and accelerated epigenetic aging [16, 17]. Additionally, it has been found that worse emotion regulation led to greater stress-related age acceleration in a healthy population, while higher resilience prevented a significant effect of stress on the GrimAge [6]. Despite these findings, studies are still scarce regarding a potential link between psychological resilience and epigenetic clocks. Therefore, we aimed to investigate the association between psychological resilience and epigenetic age acceleration (using thirteen epigenetic clocks) in a national representative sample from the Health and Retirement Study (HRS). We hypothesized that a higher level of psychological resilience was associated with delayed DNA methylation age acceleration (DNAmAA).
Methods
Data source and study participants
The HRS is a nationally representative longitudinal study in the USA and includes adults over the age of 50 together with their spouses. The study commenced in 1992 and recruits additional participants every 6 years, with a current sample size of almost 40,000 individuals [18]. The study collects data on several aspects of health, retirement, and aging through biennial interviews. Since 2006, participants alternate such that half the sample receives a telephone interview and the other half an extended face-to-face interview, which also includes a collection of biological specimens (such as blood and saliva) to assess various biomarkers. Follow-up is performed every 2 years, and changes in the health and well-being of the HRS participants can thereby be tracked over time.
For this study, we used two waves (2014–2016) of the HRS data. The key variables for psychological resilience were collected in the extended face-to-face interviews through the HRS Leave-Behind Questionnaire (LBQ), which was given in 2014 to half of the HRS participants and in 2016 to the other half. The analytical sample of the present study consisted of participants with complete data from LBQ. The flow chart for the selection of participants is shown in Fig. S1.
DNA methylation age acceleration (DNAmAA)
DNA methylation data were obtained from the 2016 HRS Venous Blood Study. DNA methylation assays were conducted on a non-random subsample (n = 4104) of HRS participants who consented to participate in the 2016 VBS. Of those, 4018 samples passed quality control. DNAm data were obtained from assays using the Infinium Methylation EPIC BeadChip at the University of Minnesota. Samples were randomized across plates based on essential demographic variables (i.e., age, cohort, sex, education, and race/ethnicity) with 40 pairs of blinded duplicates. The minfi package in R software was utilized for data preprocessing and quality control. The results of duplicate samples indicated a correlation > 0.97 for all CpG sites. High-quality methylation data are available for 97.9% of the samples (n = 4018). Prior to the estimation of the epigenetic clocks, missing values were imputed with mean beta methylation values of probes across all samples. DNAmAA was calculated for each clock taking the residual of the clock values regressed on age, so they can all be compared regardless of age [1].
In total, there are 13 epigenetic clocks available in HRS. Eleven of these clocks were constructed by Morgan Levine (Yale) and HRS staff to guarantee reliability. GrimAge, the twelfth clock, was constructed by HRS staff member Jonah Fisher with assistance from Steve Horvath. DundedinPoAm38, the thirteenth clock, was estimated by Thalida Arpawong (USC) with assistance from Karen Sugden (Duke). The detailed information on the construction of the 13 epigenetic clocks is described elsewhere [19].
Psychological resilience
The HRS did not incorporate a comprehensive evaluation of psychological resilience. Thus, we employed a simplified resilience score (SRS) designed to capture adjustment and management of adversity from a previous study [20] based on the 12 items of the LBQ. This SRS is guided by the Wagnild and Young Resilience Scale (1993), which includes five primary psychosocial domains [21, 22]. The Cronbach’s alpha of the resilience scale was 0.854, which indicated that the simplified resilience score had a high validity [20].
Because some items have values ranging from 1 to 7 whereas others have values ranging from 1 to 6, we created a standardized value for each item by dividing the respondent’s response by the maximum response for that item, resulting in a value from 0 to 1. Each respondent’s final psychological resilience score was created by the simple sum of these standardized values across all 12 items (Table S1). A higher score implies better psychological resilience.
Covariates
We included the following covariates in all analyses as previous studies have suggested a potential association of these variables with both DNAm patterns and psychological resilience [23]: sex, race, body mass index, smoking status, years of education, diabetes, hypertension, heart problems, and cancer.
Statistical analysis
We examined Pearson’s correlation of psychological resilience score with the 13 epigenetic clocks and accelerated aging. We then performed multiple linear regression models to examine the associations between psychological resilience and different epigenetic clocks after adjusting for the covariates. Chronological age was incorporated into the model as part of the calculation of DNAmAA (the residual of each DNA methylation age regressed upon chronological age). The HRS and HRS-VBS were approved by the Health Sciences and Behavioral Sciences Institutional Review Board at the University of Michigan (HUM00061128), and all participants provided written informed consent. A statistically significant level is set at 0.05. Statistical analyses were performed with R 4.2.1 (R Core Team, Vienna, Austria).
Results
The characteristics of study participants are shown in Table 1. Among the 4018 participants, the average chronological age was 69.45 years, and 58.46% were women. The average score of psychological resilience was 8.83 in the study sample. As shown in Fig. 1 (left panel) and Fig. S2, psychological resilience was inversely correlated with epigenetic clocks.
Table 1.
Descriptive measures of participants characteristics and DNA methylation epigenetic clocks, HRS 2014–2016
| Variables | Total (N = 4018) | ||||
|---|---|---|---|---|---|
| N | Mean | SD | Min | Max | |
| Age | 4018 | 69.45 | 9.62 | 50.00 | 100.00 |
| Horvath 1 | 4018 | 65.72 | 9.62 | 23.31 | 114.52 |
| Hannum | 4018 | 54.59 | 9.23 | 25.06 | 107.79 |
| Levine | 4018 | 57.48 | 10.08 | 26.72 | 101.68 |
| Horvath 2 | 4018 | 69.61 | 8.85 | 36.97 | 101.29 |
| Lin | 4018 | 58.41 | 11.08 | 1.91 | 133.27 |
| Weidner | 4018 | 67.29 | 11.62 | 25.22 | 148.87 |
| VidalBralo | 4018 | 63.76 | 6.19 | 36.47 | 109.95 |
| Yang | 4018 | 0.07 | 0.02 | 0.03 | 0.23 |
| Zhang | 4018 | 1.08 | 0.46 | -2.53 | 0.60 |
| Bocklandt | 4018 | 0.39 | 0.08 | 0.10 | 0.89 |
| Garagnani | 4018 | 0.72 | 0.07 | 0.43 | 0.99 |
| GrimAge | 4018 | 68.13 | 8.63 | 42.67 | 99.61 |
| DunedinPoAm38 | 4018 | 1.08 | 0.09 | 0.74 | 1.46 |
| Years of education | 3997 | 12.82 | 3.22 | 0.00 | 17.00 |
| BMI | 3973 | 34.09 | 8.47 | 6.79 | 106.80 |
| Resilience | 3129 | 8.83 | 2.08 | 0.40 | 12.00 |
| Sex | |||||
| Men | 1669 | 41.54% | |||
| Women | 2349 | 58.46% | |||
| Race | |||||
| Black or African American | 674 | 16.83% | |||
| White/Caucasian | 3013 | 75.23% | |||
| Other | 318 | 7.94% | |||
| Current smoker | |||||
| Nonsmoker | 3563 | 88.68% | |||
| Smoker | 455 | 11.32% | |||
HRS the Health and Retirement Study, BMI body mass index
Fig. 1.
(Left) Pearson correlations between epigenetic clocks and psychological resilience scores. (Right) Pearson correlations between accelerated epigenetic aging and psychological resilience scores (n = 3129)
As expected, all epigenetic clocks had an average age acceleration of zero, with the negative value indicating decelerated aging and the positive value indicating accelerated aging, which spanned from −54.70 to 58.05 years (Table 2). As shown in Fig. 1 (right panel) and Fig. S3, psychological resilience was inversely correlated with DNAmAA.
Table 2.
Descriptive measures of participants DNA methylation age acceleration epigenetic clocks, HRS 2014–2016
| Variables | Total | ||||
|---|---|---|---|---|---|
| N | Mean | SD | Min | Max | |
| Horvath 1 AccelAge* | 4018 | 0.00 | 6.46 | −36.16 | 48.39 |
| Hannum AccelAge | 4018 | 0.00 | 5.25 | −29.82 | 45.66 |
| Levine AccelAge | 4018 | 0.00 | 6.84 | −28.11 | 42.02 |
| Horvath 2 AccelAge | 4018 | 0.00 | 4.43 | −24.40 | 22.48 |
| Lin AccelAge | 4018 | 0.00 | 7.80 | −54.70 | 58.05 |
| Weidner AccelAge | 4018 | 0.00 | 10.68 | −35.20 | 71.81 |
| VidalBralo AccelAge | 4018 | 0.00 | 5.05 | −31.21 | 40.41 |
| Yang AccelAge | 4018 | 0.00 | 0.02 | −0.04 | 0.16 |
| Zhang AccelAge | 4018 | 0.00 | 0.44 | −1.36 | 1.73 |
| Bocklandt AccelAge | 4018 | 0.00 | 0.07 | −0.32 | 0.53 |
| Garagnani AccelAge | 4018 | 0.00 | 0.05 | −0.31 | 0.28 |
| GrimAge AccelAge | 4018 | 0.00 | 4.74 | −16.98 | 22.40 |
| DunedinPoAm38 AccelAge | 4018 | 0.00 | 0.09 | −0.34 | 0.39 |
Multivariable linear regression models were used to examine the associations between psychological resilience and DNAmAA (Table 3). In model 1, unadjusted for any covariate, the psychological resilience score showed negative associations with the DNAmAA of eight epigenetic clocks (Hannum, Levine, Horvath 2, Yang, Zhang, Garagnani, GrimAge, and DunedinPoAm38) (P < 0.05). Model 2 was adjusted for sex, race, and BMI and showed similar results to those of model 1. Further adjusting for smoking status and years of education did not substantially change the results albeit with wider confidence intervals for two epigenetic clocks (Yang and Horvath 2). Then we added physical health status as a covariate in the final regression model (model 4). Model 4 showed that the relationship between the acceleration age of four epigenetic clocks (Levine, Zhang, Garagnani, and GrimAge), and psychological resilience was significant.
Table 3.
Multiple linear regression analyses of psychological resilience on accelerated aging of 13 epigenetic clocks, HRS 2014–2016
| Model | Horvath1 AccelAge | Hannum AccelAge | Levine AccelAge | Horvath 2 AccelAge | Lin AccelAge | Weidner AccelAge | VidalBralo AccelAge | Yang AccelAge (β ± SE) * 100 | Zhang AccelAge | Bocklandt AccelAge | Garagnani AccelAge | GrimAge AccelAge | DunedinPoAm38 AccelAge | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | β ± SEP | −0.042 ± 0.056 0.452 | −0.126 ± 0.045 0.005* | −0.279 ± 0.058 1.81E−06* | −0.090 ± 0.038 0.018* | −0.025±0.066 0.702 | −0.035±0.091 0.698 | −0.062±0.043 0.154 | −0.049± 0.016 0.002* | −0.021 ± 0.004 1.65E−08* | −0.001 ± 0.001 0.369 | −0.002 ± 0.000 0.001* | −0.276 ± 0.040 7.02E−12* | −0.004 ± 0.001 2.98E−08* |
| 2 | β ± SEP | −0.033 ± 0.056 0.560 | −0.130 ± 0.044 0.003* | −0.268 ± 0.059 5.51E−06* | −0.088 ± 0.038 0.021* | −0.015 ± 0.066 0.827 | −0.045 ± 0.092 0.622 | −0.064 ± 0.043 0.138 | −0.049± 0.016 0.002* | −0.022 ± 0.004 1.39E−09* | −0.001 ± 0.001 0.400 | −0.001 ± 0.000 0.002* | −0.288 ± 0.038 5.20E−14* | −0.004 ± 0.001 6.53E−08* |
| 3 | β ± SEP | −0.039 ± 0.058 0.497 | −0.114 ± 0.045 0.012* | −0.219 ± 0.060 2.71E−04* | −0.072 ± 0.039 0.066 | −0.073 ± 0.068 0.280 | −0.069 ± 0.094 0.463 | −0.071 ± 0.044 0.109 | −0.020±0.016 0.203 | −0.015 ± 0.004 7.64E−05* | 0.000 ± 0.001 0.677 | −0.001 ± 0.000 0.006* | −0.138 ± 0.035 6.30E−05* | −0.002 ± 0.001 0.006* |
| 4 | β ± SEP | −0.019 ± 0.058 0.744 | −0.085 ± 0.046 0.063 | −0.169 ± 0.060 0.005* | −0.055 ± 0.039 0.161 | −0.046 ± 0.069 0.500 | −0.044 ± 0.095 0.644 | −0.052 ± 0.044 0.240 | −0.021 ± 0.016 0.204 | −0.011 ± 0.004 0.003* | 0.000 ± 0.001 0.601 | −0.001 ± 0.000 0.009* | −0.092 ± 0.034 0.007* | −0.001 ± 0.001 0.082 |
Accelerated aging is the residual from a regression of the clock on chronological age
Model 1: no covariates; model 2: model 1 + sex, race, BMI; model 3: model 2 + smoking, years of education; model 4: model 3+ diabetes, cancer, hypertensive, heart problems
Significant regression coefficients are marked in bold
HRS the Health and Retirement Study; BMI body mass index, SE standard error
*P value < 0.05 (two-sided)
Multiple testing corrections were performed using the Bonferroni approach (Table S2). We performed covariates adjustment by including more variables and obtained similar results (Table S3). We additionally performed regression analysis on the epigenetic clocks with psychological resilience. The results are shown in Table S4, which shows similar results to the acceleration clock. We also conducted stratified analyses by sex and race (Table S5-Table S9) and effect modification analysis (Table S10). The point estimates of the relationships between psychological resilience and DNAmAA remained largely the same, although the confidence intervals were wider with reduced sample sizes. The correlation heat maps show that the results are more pronounced in women (Fig. S4 and Fig. S5).
Discussion
In the present study based on HRS, we found that higher levels of psychological resilience were associated with a deceleration of biological aging measured by multiple epigenetic clocks in a nationwide sample of older adults. The association was largely independent of potential confounders including age, sex, body mass index, smoking, and physical health status. Through the stratified analysis, we found that the relationship was more pronounced in women. This finding suggests that psychological resilience might affect biological aging through epigenetic manifestations.
A few studies have previously investigated the associations of psychological resilience and stress with epigenetic aging. For example, a study with 429 participants found a correlation between an increased burden of psychiatric disorder and epigenetic age acceleration in a cross-sectional setting and suggested the importance of medical management of patients with multiple psychiatric comorbidities as well as the potential usefulness of epigenetic clocks in early detection of psychiatric disorders [24]. Likewise, the Canadian Longitudinal Study on aging found an association between cumulative adverse childhood experiences and a higher DNAm GrimAge acceleration (β: 0.07; 95% CI: 0.02, 0.11) after adjusting for covariates [25]. This highlights the importance of prevention of adverse childhood experiences and early intervention among individuals with such exposure to promote healthy aging. A negative correlation between psychological resilience and epigenetic clock acceleration was also observed in a prior study [6], which showed that cumulative stress was associated with epigenetic aging in a cohort of 444 participants and that the association was modifiable by biobehavioral resilience factors. Emotion regulation, as one of the resilience factors, moderated the association between stress and aging (P = 8.82E−4). Namely, among individuals with poor emotion regulation, cumulative stress could predict higher-than-expected GrimAge acceleration even after accounting for demographic, physiologic, and behavioral covariates. Another study showed that the sense of purpose in life was associated with reduced epigenetic age acceleration in older adults [26]. A third study recruited 47 business executives and evaluated the simultaneous impact of perceived chronic stress and resilience on GrimAge acceleration [27]. Findings from this study showed that perceived stress over the past month was positively correlated with age acceleration and that the resilience of stress-coping ability was inversely correlated with age acceleration. Interestingly, the absolute magnitude of correlation was larger for the resilience of stress coping ability than that of perceived stress. Taken together, the findings of the present study as well as those from the existing studies add further evidence to the role of psychological resilience as an important contributor to aging biology and suggest the potential for new opportunities to reduce biological age acceleration and late-life disease by enhancing psychological resilience.
The biological mechanisms underlying the relationship between psychological resilience and epigenetic clocks remain poorly understood but likely include both psychosociological and biological aspects. The psychosociological explanations mostly center on macro-level exposures and mediators, such as coping abilities to tackle poverty, violence, and discrimination, which could lead to a higher level of socioeconomic status followed by healthy lifestyles and slower biological age acceleration [6]. In addition, epigenetic aging appears to be modulated via specific psychological traits, including emotion regulation and self-control. People with better emotion regulation and higher levels of self-control have been shown to have less age acceleration, compared to others, even at similar levels of stress exposure [6]. Some evidence has also been accumulated regarding potential biological explanations. For example, the initial establishment of the epigenome, protective environmental exposures across life, protective factors in counteracting adverse influences, and genetic moderation of environmentally induced epigenetic modifications are all considered as different functions of epigenetic mechanisms in resilience [28]. Experimental studies of biological aging have shown that genes that influence aging-related phenotypes and longevity in model organisms are commonly involved in resilience [14]. The most intensively studied aging pathways (IGF-1/AKT/FOXO3A, TP53/P21/P16, and mTOR/S6K) work together to regulate particular cellular responses to stress and damage, such as apoptosis and senescence, which play major roles in shaping resilience and its decline with age [14]. Impaired resilience to psychological stress has been linked to the age-associated epigenetic upregulation of the FKBP5 gene involved in glucocorticoid signaling and binding to rapamycin [29]. Furthermore, genome-wide variation in DNA methylation has been associated with resilience biomarkers such as the ability to complete the glucocorticoid stress response through negative feedback [30].
This study has several advantages, including a large sample size with different ethnicities, the use of multiple epigenetic clocks, and the inclusion of various covariates to control for potential confounding factors. The heat map presented in Fig. 1 allows for easy visualization of the patterns of relationships between psychological resilience and different epigenetic scores. Some limitations should also be acknowledged. For example, the present study design is cross-sectional in nature, which makes it difficult to establish a causal relationship between psychological resilience and epigenetic clocks. Additionally, the study population is limited to older adults, not representative of the general population. To control for confounding bias, the study employed multiple linear regression models adjusted for various covariates, such as sex, race, body mass index, smoking status, years of education, and physical health status. However, information bias, such as the measurement error of psychological resilience, is still possible. Further studies using a longitudinal design with repeated measurements of extensively assessed psychological resilience scale and epigenetic clocks could improve validity.
In summary, by using data from a large nationwide sample, we demonstrated that a higher level of psychological resilience was associated with slower epigenetic aging and that this association was independent of multiple potential confounders. The findings suggest that interventions aimed at promoting psychological resilience may have implications for mitigating biological age acceleration and promoting healthy aging.
Supplementary information
Acknowledgements
The author would like to thank Health and Retirement Research for providing data for this article.
Funding
This study was supported by a start-up grant from Sun Yat-Sen University, the Pearl River Scholar Program of Guangdong Province (Health Science Section, No: 0920220206), and the International Distinguished Teacher Program of Guangdong Provincial Department of Science and Technology.
Data availability
The data are publicly available.
Declarations
Conflict of interest
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
A. Zhang and Y. Zhang contributed equally to this work.
Contributor Information
Liqiong Zhou, Email: zhoulq28@mail.sysu.edu.cn.
Yiqiang Zhan, Email: zhanyq8@mail.sysu.edu.cn.
References
- 1.Crimmins EM, Thyagarajan B, Levine ME, et al. Associations of age, sex, race/ethnicity, and education with 13 epigenetic clocks in a nationally representative U.S. sample: the health and retirement study. J Gerontol A Biol Sci Med Sci. 2021;76(6):1117–1123. doi: 10.1093/gerona/glab016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Mehta D, Bruenig D, Lawford B, et al. Accelerated DNA methylation aging and increased resilience in veterans: the biological cost for soldiering on. Neurobiol Stress. 2018;8:112–119. doi: 10.1016/j.ynstr.2018.04.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Horvath S, Gurven M, Levine ME, et al. An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol. 2016;17(1):171. doi: 10.1186/s13059-016-1030-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Schmitz LL, Zhao W, Ratliff SM, et al. The socioeconomic gradient in epigenetic ageing clocks: evidence from the multi-ethnic study of atherosclerosis and the health and retirement study. Epigenet. 2022;17(6):589–611. doi: 10.1080/15592294.2021.1939479. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Levine ME, Lu AT, Quach A, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY), 2018;10(4): 573-591. 10.18632/aging.101414. [DOI] [PMC free article] [PubMed]
- 6.Harvanek ZM, Fogelman N, Xu K, et al. Psychological and biological resilience modulates the effects of stress on epigenetic aging. Transl Psychiatry. 2021;11(1):601. doi: 10.1038/s41398-021-01735-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Galkin F, Kochetov K, Koldasbayeva D, et al. Psychological factors substantially contribute to biological aging: evidence from the aging rate in Chinese older adults. Aging (Albany NY). 2022;14(18):7206-7222. 10.18632/aging.204264. [DOI] [PMC free article] [PubMed]
- 8.Boks MP, Van Mierlo HC, Rutten BP, et al. Longitudinal changes of telomere length and epigenetic age related to traumatic stress and post-traumatic stress disorder. Psychoneuroendocrin. 2015;51:506–512. doi: 10.1016/j.psyneuen.2014.07.011. [DOI] [PubMed] [Google Scholar]
- 9.Mutz J, Roscoe CJ, Lewis CM. Exploring health in the UK Biobank: associations with sociodemographic characteristics, psychosocial factors, lifestyle and environmental exposures. BMC Med. 2021;19(1):240. doi: 10.1186/s12916-021-02097-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Wurm S, Warner LM, Ziegelmann JP, et al. How do negative self-perceptions of aging become a self-fulfilling prophecy? Psychol Aging. 2013;28(4):1088–1097. doi: 10.1037/a0032845. [DOI] [PubMed] [Google Scholar]
- 11.Fontes AP, Neri AL. Resilience in aging: literature review. Cien Saude Colet. 2015;20(5):1475–1495. doi: 10.1590/1413-81232015205.00502014. [DOI] [PubMed] [Google Scholar]
- 12.Lavretsky H. Health, resilience, and successful aging in the older US veterans. Am J Geriatr Psychiat. 2021;29(3):257–259. doi: 10.1016/j.jagp.2020.08.018. [DOI] [PubMed] [Google Scholar]
- 13.Merchant RA, Aprahamian I, Woo J, et al. Editorial: resilience and successful aging. J Nutr Health Aging. 2022;26(7):652–656. doi: 10.1007/s12603-022-1818-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Ukraintseva S, Arbeev K, Duan M, et al. Decline in biological resilience as key manifestation of aging: potential mechanisms and role in health and longevity. Mech Ageing Dev. 2021;194:111418. doi: 10.1016/j.mad.2020.111418. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Baskin RG, Bartlett R. Healthcare worker resilience during the COVID-19 pandemic: an integrative review. J Nurs Manag. 2021;29(8):2329–2342. doi: 10.1111/jonm.13395. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Palma-Gudiel H, Fananas L, Horvath S, et al. Psychosocial stress and epigenetic aging. Int Rev Neurobiol. 2020;150:107–128. doi: 10.1016/bs.irn.2019.10.020. [DOI] [PubMed] [Google Scholar]
- 17.Sumner JA, Colich NL, Uddin M, et al. Early experiences of threat, but not deprivation, are associated with accelerated biological aging in children and adolescents. Biol Psychiatry. 2019;85(3):268–278. doi: 10.1016/j.biopsych.2018.09.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Sonnega A, Faul JD, Ofstedal MB, et al. Cohort Profile: the Health and Retirement Study (HRS) Int J Epidemiol. 2014;43(2):576–585. doi: 10.1093/ije/dyu067. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.EILEEN . CRIMMINS J K K J F, JESSICA FAUL. HRS epigenetic clocks – release 1. Ann Arbor Michigan: Survey Research Center, Institute for Social Research; 2020. [Google Scholar]
- 20.Manning LK, Carr DC, Kail BL. Do higher levels of resilience buffer the deleterious impact of chronic illness on disability in later life? Gerontol. 2016;56(3):514–524. doi: 10.1093/geront/gnu068. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Wagnild GM, Young HM. Development and psychometric evaluation of the Resilience Scale. J Nurs Meas. 1993;1(2):165–178. [PubMed] [Google Scholar]
- 22.Zeng Y, Shen K. Resilience significantly contributes to exceptional longevity. Curr Gerontol Geriatr Res. 2010;2010:525693. doi: 10.1155/2010/525693. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018;19(6):371–384. doi: 10.1038/s41576-018-0004-3. [DOI] [PubMed] [Google Scholar]
- 24.Yusupov N, Dieckmann L, Erhart M, et al. Transdiagnostic evaluation of epigenetic age acceleration and burden of psychiatric disorders. Neuropsychopharmacol. 2023; 10.1038/s41386-023-01579-3. [DOI] [PMC free article] [PubMed]
- 25.Joshi D, Gonzalez A, Lin D, et al. The association between adverse childhood experiences and epigenetic age acceleration in the Canadian longitudinal study on aging (CLSA) Aging Cell. 2023;22(2):e13779. doi: 10.1111/acel.13779. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Kim ES, Nakamura JS, Strecher VJ, et al. Reduced epigenetic age in older adults with high sense of purpose in life. J Gerontol A Biol Sci Med Sci. 2023; 10.1093/gerona/glad092. [DOI] [PMC free article] [PubMed]
- 27.Bergquist SH, Wang D, Smith AK, et al. Hormetic association between perceived stress and human epigenetic aging based on resilience capacity. Biogerontol. 2022;23(5):615–627. doi: 10.1007/s10522-022-09985-8. [DOI] [PubMed] [Google Scholar]
- 28.Smeeth D, Beck S, Karam EG, et al. The role of epigenetics in psychological resilience. Lancet Psychiatry. 2021;8(7):620–629. doi: 10.1016/S2215-0366(20)30515-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Sabbagh JJ, O'leary JC, 3RD, Blair LJ, et al. Age-associated epigenetic upregulation of the FKBP5 gene selectively impairs stress resiliency. PLoS One. 2014;9(9):e107241. 10.1371/journal.pone.0107241. [DOI] [PMC free article] [PubMed]
- 30.Taff CC, Campagna L, Vitousek MN. Genome-wide variation in DNA methylation is associated with stress resilience and plumage brightness in a wild bird. Mol Ecol. 2019;28(16):3722–3737. doi: 10.1111/mec.15186. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data are publicly available.