Date Published: June 18, 2019
Publisher: Public Library of Science
Author(s): Zuyun Liu, Xi Chen, Thomas M. Gill, Chao Ma, Eileen M. Crimmins, Morgan E. Levine, Jonas Mengel-From
Abstract: BackgroundAn 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.Methods and findingsBased 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 the population.ConclusionsIn a sample of US older adults, genetic, behavioral, and socioenvironmental circumstances during childhood and adulthood account for about 30% of differences in phenotypic aging. Our results also suggest that the detrimental effects of disadvantaged life course circumstances for health and aging may be further exacerbated among persons with genetic predisposition to coronary artery disease. Finally, our finding that behaviors had the largest contribution to PhenoAge highlights a potential policy target. Nevertheless, further validation of these findings and identification of causal links are greatly needed.
Partial Text: One major driver in the pathogenesis of many chronic diseases is presumed to be aging [1,2], a complex multifactorial process characterized by increasing dysregulation and loss of function across multiple levels and systems . 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, even among healthy individuals [4,5]. PhenoAge is meant to capture age-related dysregulation and can facilitate identification of individuals at risk for a number of chronic diseases or causes of death. It can also be applied to basic and observational research, shedding light on genetic and environmental factors that alter the pace of aging.
Based on data from a large sample of older adults in the US, our comprehensive analyses showed that childhood and adulthood circumstances, behaviors, and genetic factors were associated with differences in a novel multi-system signature of aging. Collectively, the factors we evaluated accounted for just under one-third of the variance in phenotypic aging. Using many of the variables characterizing childhood and adulthood circumstances, we were able to group participants into 6 subpopulations—2 of which appeared to reflect disadvantaged subpopulations exhibiting substantially increased phenotypic aging. Finally, we observed a significant gene-by-environment interaction between a previously validated PGS for CAD and the seemingly most disadvantaged subpopulation, suggesting a multiplicative effect of adverse life course circumstances coupled with genetic risk on phenotypic aging. Taken together, these results may inform potential interventions to reduce morbidity and mortality risk experienced throughout the life course. While causality needs to be formally evaluated, the results from the current study highlight the socioenvironmental circumstances and behavioral factors that potentially have the largest influence over level of phenotypic aging. As such, targeting these factors may lead to improvements in health and diminish corresponding disparities over the life course.