As the US rapidly transitions into an aging society, aging-associated diseases are increasing in both prevalence and cost. Compounding this concern is evidence that cohorts entering adulthood and midlife today are less healthy than preceding generations were at those ages. The faster rate of aging among recent cohorts is cause for concern, and necessitates earlier detection of aging and aging-associated diseases. Determining the individual physiological changes linked to aging and health outcomes provides important information about influences on the aging process, as well as identifying potential areas for intervention to increase healthspan and longevity. A variety of approaches to measuring physiological aging using sets of biomarkers have been suggested in recent years?but currently little is known about how they correlate with each other, how those relationships change over the life course, and to what degree the biomarkers of aging are generalizable to population subgroups (by sex, race/ethnicity, and socioeconomic status (SES)). Early life social contextual exposures, such as poverty and trauma, show strong and lasting associations with aging and aging-related diseases. However, a key unanswered question is the degree to which biomarkers of aging across the life course link early life context to later life health. To understand how biomarkers of aging correlate across the life course and link to SES and social context we draw upon survey and biorepository data and samples from three large nationally representative panel studies: the Health and Retirement Study (HRS; representative of US population over age 50; biomarker data for ages 51-110), the National Longitudinal Study of Adolescent to Adult Health (AH; representative of adolescents in grades 7-12 in 1994; biomarker data ages 24-42) and the Fragile Families and Child Wellbeing Study (FF; representative of birth in large US Cities 1998-2000; biomarker data ages 9 to 24). The harmonization of biomarkers and survey data across these three panel studies provides an unprecedented opportunity to discover how biomarkers of aging correlate over the life course and how they correlate with SES and other social contextual factors associated with aging. Aim 1 will produce harmonized data and measures for the research community from 3 national panel studies with special focus on biomarkers: aging blood-based biomarkers (TAME assays: IL-6, TNFa, CRP, GDF15, IGF-1, Cystatin C, NT-proBNP, and hbA1c), DNA methylation (Illumina EPIC chip), and gene expression (RNA Seq). Aim 2 will examine how the biomarkers of aging are distributed and correlate with each other over the life course and across several key demographics. Also, using already generated immune cell methylation (FF at ages 9 and 15) and RNA (AH age 24-32) we will predict subsequent adult biomarkers of aging. Using the harmonized survey data, Aim 3 will examine the link between biomarkers of aging a set of contextual measures of early life and adult health phenotypes. Results will provide insight into which the measures from TAME, methylation, and RNA can be used to examine context influences on aging long before observable symptoms arise.