Associations of genetics, behaviors, and life course circumstances with a novel aging and healthspan measure: Evidence from the Health and Retirement Study

• Published in: PLoS medicine 2019-06, Vol.16 (6), p.e1002827-e1002827
• Main Authors: Liu, Zuyun, Chen, Xi, Gill, Thomas M, Ma, Chao, Crimmins, Eileen M, Levine, Morgan E
• Format: Article
• Language: English
• Subjects: Adults; Aged; Aged, 80 and over; Aging; Aging (Biology); Aging - genetics; Aging - psychology; Biology and Life Sciences; Children; Chronic illnesses; Cluster analysis; Coronary artery; Coronary artery disease; Coronary heart disease; Demographic aspects; Demographics; Elderly; Evaluation; Female; Gene-Environment Interaction; Genes; Genetic aspects; Genetic research; Genetics; Health; Health aspects; Health behavior; Health Behavior - physiology; Health disparities; Healthy Aging - genetics; Healthy Aging - psychology; Heart diseases; Humans; Life Change Events; Male; Measurement methods; Medicine and Health Sciences; Middle Aged; Morbidity; Mortality; Novels; Phenotype; Retirement - psychology; Retirement - trends; Social Sciences; Socioeconomic factors; Standard error; Subpopulations
• ISSN: 1549-1676; 1549-1277; 1549-1676
• DOI: 10.1371/journal.pmed.1002827

An individual's rate of aging directly influences his/her susceptibility to morbidity and mortality. Thus, quantifying aging and disentangling how various factors coalesce to produce between-person differences in the rate of aging, have important implications for potential interventions. We recently developed and validated a novel multi-system-based aging measure, Phenotypic Age (PhenoAge), which has been shown to capture mortality and morbidity risk in the full US population and diverse subpopulations. The aim of this study was to evaluate associations between PhenoAge and a comprehensive set of factors, including genetic scores, childhood and adulthood circumstances, and health behaviors, to determine the relative contributions of these factors to variance in this aging measure. Based on data from 2,339 adults (aged 51+ years, mean age 69.4 years, 56% female, and 93.9% non-Hispanic white) from the US Health and Retirement Study, we calculated PhenoAge and evaluated the multivariable associations for a comprehensive set of factors using 2 innovative approaches-Shapley value decomposition (the Shapley approach hereafter) and hierarchical clustering. The Shapley approach revealed that together all 11 study domains (4 childhood and adulthood circumstances domains, 5 polygenic score [PGS] domains, and 1 behavior domain, and 1 demographic domain) accounted for 29.2% (bootstrap standard error = 0.003) of variance in PhenoAge after adjustment for chronological age. Behaviors exhibited the greatest contribution to PhenoAge (9.2%), closely followed by adulthood adversity, which was suggested to contribute 9.0% of the variance in PhenoAge. Collectively, the PGSs contributed 3.8% of the variance in PhenoAge (after accounting for chronological age). Next, using hierarchical clustering, we identified 6 distinct subpopulations based on the 4 childhood and adulthood circumstances domains. Two of these subpopulations stood out as disadvantaged, exhibiting significantly higher PhenoAges on average. Finally, we observed a significant gene-by-environment interaction between a previously validated PGS for coronary artery disease and the seemingly most disadvantaged subpopulation, suggesting a multiplicative effect of adverse life course circumstances coupled with genetic risk on phenotypic aging. The main limitations of this study were the retrospective nature of self-reported circumstances, leading to possible recall biases, and the unrepresentative racial/ethnic makeup of