Understanding aging and ADRD disparities using a representative epigenetic clock

Disadvantaged groups may experience accelerated biological aging, due to the cumulative impact of repeated experiences with socioeconomic adversity and marginalization. This may cause early physical and cognitive health deterioration leading to disparities in aging and Alzheimer's disease and related dementias (ADRD) outcomes, as evident in associations of epigenetic clocks with worse aging and ADRD outcomes. Epigenetic clocks apply machine learning to DNA methylation profiles from blood samples to estimate biological aging. Black American adults show accelerated epigenetic aging compared to White American adults and socioeconomic factors contribute to this racial disparity. However, epigenetic clocks have been developed using non-representative datasets consisting predominantly of White adults and therefore may not provide accurate estimates of epigenetic aging in other racial and ethnic groups. We will use machine learning to develop and validate a representative epigenetic clock (REpiClock) to more accurately predict epigenetic age in Black adults. As the accuracy of epigenetic age estimates may be influenced by the genetic distance of a target individual from the average genotype of the training dataset, we will apply a data-driven method to correct epigenetic age predictions for the individual’s genetic distance from the training set. In a deeply-phenotyped biobank, we will assess the relationship of the REpiClock with ADRD pathology, using plasma biomarkers, and ADRD risk and aging outcomes, using electronic health record data. This will allow us to establish whether a representative, genetic distance-corrected epigenetic clock, more precisely estimates disparities in epigenetic aging and whether these disparities underlie disparities in ADRD pathology and risk.