Positive social factors prospectively predict younger epigenetic age: Findings from the Health and Retirement Study.

OBJECTIVES: Positive social factors may slow biological aging, but this has yet to be rigorously tested. This study investigated whether baseline levels or changes over time in social support and contact frequency prospectively predicted epigenetic age. METHOD: Health and Retirement Study participants (N = 1912, 46.3 % male, aged 42-87 at baseline) reported longitudinal social support and contact frequency data up to 3 times between 2006 and 2016 and provided blood in 2016. Baseline levels (intercepts) and changes over time (slopes) in social support from and contact frequency with spouses, children, friends, and other family were outputted from multilevel models and used to predict epigenetic age, estimated from Horvath, Hannum, GrimAge, PhenoAge, and Dunedin Pace of Aging. RESULTS: In models adjusted for demographic and health characteristics, higher baseline levels of support from and contact frequency with friends were prospectively associated with a slower Pace of Aging (support: p = .002; contact: p = 0.009) and a lower GrimAge (contact: p = .001). In addition, higher contact frequency with children at baseline was prospectively associated with a lower GrimAge (p < .001), and higher contact frequency with family at baseline and an increase in family contact over time was associated with a lower Hannum age (baseline: p = .005; slope: p = .015). CONCLUSIONS: Perceived support from and contact with close others, particularly friends, may have implications for healthy biological aging. Notably, the effect sizes for friends were comparable to the effect of body mass index on epigenetic age. Positive social factors were generally associated with second- and third-generation clocks, which may be more sensitive to psychosocial factors than first-generation clocks.

Life-course socioeconomic factors are associated with markers of epigenetic aging in a population-based study.

Adverse socioeconomic circumstances negatively affect the functioning of biological systems, but the underlying mechanisms remain only partially understood. Here, we explore the associations between life-course socioeconomic factors and four markers of epigenetic aging in a population-based setting. We included 684 participants (52 % women, mean age 52.6 ± 15.5 years) from a population and family-based Swiss study. We used nine life-course socioeconomic indicators as the main exposure variables, and four blood-derived, second generation markers of epigenetic aging as the outcome variables (Levine's DNAmPhenoAge, DunedinPoAm38, GrimAge epigenetic age acceleration (EAA), and the mortality risk score (MS)). First, we investigated the associations between socioeconomic indicators and markers of epigenetic aging via mixed-effect linear regression models, adjusting for age, sex, participant's recruitment center, familial structure (random-effect covariate), seasonality of blood sampling, and technical covariates. Second, we implemented counterfactual mediation analysis to investigate life-course and intermediate mechanisms underlying the socioeconomic gradient in epigenetic aging. Effect-size estimates were assessed using regression coefficients and counterfactual mediation parameters, along with their respective 95 % confidence intervals. Individuals reporting a low father's occupation, adverse financial conditions in childhood, a low income, having financial difficulties, or experiencing unfavorable socioeconomic trajectories were epigenetically older and had a higher mortality risk score than their more advantaged counterparts. Specifically, this corresponded to an average increase of 1.1-1.5 years for Levine's epigenetic age (ß and 95 %CI range, ß (minimum and maximum): 1.1-1.5 95 %CI[0.0-0.2; 2.3-3.0]), 1.1-1.5 additional years for GrimAge (ß: 1.1-1.5 95 %CI[0.2-0.6; 1.9-3.0]), a 1-3 % higher DunedinPoAm38 age acceleration (ß: 0.01-0.03 95 %CI[0.00; 0.03-0.04]), and a 10-50 % higher MS score (ß: 0.1-0.4 95 %CI[0.0-0.2; 0.3-0.4]) for the aforementioned socioeconomic indicators. By exploring the life-course mechanisms underlying the socioeconomic gradient in epigenetic aging, we found that both childhood and adulthood socioeconomic factors contributed to epigenetic aging, and that detrimental lifestyle factors mediated the relation between socioeconomic circumstances in adulthood and EAA (31-89 % mediated proportion). This study provides emerging evidence for an association between disadvantaged life-course socioeconomic circumstances and detrimental epigenetic aging patterns, supporting the "sensitive-period" life-course model. Counterfactual mediation analyses further indicated that the effect of socioeconomic factors in adulthood operates through detrimental lifestyle factors, whereas associations involving early-life socioeconomic factors were less clear.

Associations of Age, Sex, Race/Ethnicity, and Education With 13 Epigenetic Clocks in a Nationally Representative U.S. Sample: The Health and Retirement Study.

BACKGROUND: Many DNA methylation-based indicators have been developed as summary measures of epigenetic aging. We examine the associations between 13 epigenetic clocks, including 4 second generation clocks, as well as the links of the clocks to social, demographic, and behavioral factors known to be related to health outcomes: sex, race/ethnicity, socioeconomic status, obesity, and lifetime smoking pack-years. METHODS: The Health and Retirement Study is the data source which is a nationally representative sample of Americans over age 50. Assessment of DNA methylation was based on the EPIC chip and epigenetic clocks were developed based on existing literature. RESULTS: The clocks vary in the strength of their relationships with age, with each other and with independent variables. Second generation clocks trained on health-related characteristics tend to relate more strongly to the sociodemographic and health behaviors known to be associated with health outcomes in this age group. CONCLUSIONS: Users of this publicly available data set should be aware that epigenetic clocks vary in their relationships to age and to variables known to be related to the process of health change with age.