Liu Z, et al. - Researchers investigated how a comprehensive set of factors, including genetic scores, health behaviors, and childhood and adulthood circumstances, are correlated with PhenoAge (a novel multi-system-based aging measure, Phenotypic Age, that can capture mortality and morbidity risk), to ascertain their relative impacts to variance in this aging measure. From the US Health and Retirement Study, data from 2,339 adults (aged 51+ years, mean age 69.4 years, 56% female, and 93.9% non-Hispanic white) were obtained and PhenoAge was calculated. The multivariable correlations for a comprehensive set of factors were evaluated using Shapley value decomposition (the Shapley approach hereafter) and hierarchical clustering. Eleven domains (four childhood and adulthood circumstances domains, five polygenic score domains, one behavior domain, and one demographic domain) accounted for about 30% of the variance in phenotypic aging. The greatest contribution to variance in PhenoAge was behaviors (9.2%), which was closely followed by adulthood adversity (9%). Given the modifiability of these factors, these seem to be potential policy target. Subpopulations of participants were identified (represented by shared childhood and adulthood circumstances) that displayed either faster or slower phenotypic aging. Those in the most disadvantaged subpopulation show further phenotypic aging in correlation to the genetic predisposition to coronary artery disease.