Health and Aging Associations with Omic Data in the Health and Retirement Study

This study will investigate the biological pathways linking social and demographic factors to aging and health outcomes, including mortality, disease morbidity, physical functioning, and cognitive functioning. It will link publicly available data from the Health and Retirement Study (HRS) to epigenetics data available from the National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS).

This study will examine links between social factors (e.g., social stress, social position, discrimination, social support and isolation) to health outcomes (e.g., mortality, disease morbidity, physical functioning, and cognitive functioning) via epigenetics, with a particular focus on aging. This study will utilize machine learning and regression techniques to identify epigenetic markers associated with aging and health and/or sociodemographics. This study will also calculate epigenetic clocks and surrogates not available in the public data releases.

This project will also investigate menopause timing and characteristics, sex hormones, hysterectomy and oophorectomy, and other sex and aging factors as predictors and outcomes of epigenetic factors. This will include epigenome-wide analyses, machine learning analyses, and clock and surrogate-based analyses.

This project will also investigate how DNAm pipeline procedures may affect epigenetic results, including how decisions surrounding normalization procedures affect EWAS and epigenetic clock results.

This project will identify epigenetic factors linking social and demographic factors to health outcomes. It will also examine how sex and aging related factors may affect or be affected by epigenetics. It will also examine bioinformatic and biostatistical decisions that might affect epigenetic results.