We designed a sub-cohort of individuals from the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study that would enable us to address the effects of race and SES on DNAm age. Based on power and sample size calculations, and extensive discussions with our collaborative team, we have selected 508 participants from Wave 1 of HANDLS. We chose these individuals with peripheral blood DNA samples from both Wave 1 and Wave 3, which will allow us to perform longitudinal study of accelerated DNAm age. These individuals were comprised of above and below poverty, AA and white, male and female across the age-span of the HANDLS study (30-64 yrs.). We measured genome-wide DNA methylation (866,836 CpGs) using the Illumina MethylationEPIC BeadChip in blood DNA extracted from 487 middle-aged AA (N=244) and white (N=243), men (N=248) and women (N=239). The mean (sd) age was 48.4 (8.8) in AA and 49.0 (8.7) in whites (p=0.48). We identified 4,930 significantly associated aDMPs in AAs and 469 in whites. Of these, 75.6% and 53.1% were novel, largely driven by the increased number of measured CpGs in the EPIC array, in AA and whites, respectively. AAs had more age-associated DNAm changes than whites in genes implicated in age-related diseases and cellular pathways involved in growth and development. We assessed three epigenetic age acceleration measures (universal, intrinsic and extrinsic). AAs had a significantly slower extrinsic aging compared to whites. Furthermore, compared to AA women, both AA and white men had faster aging in the universal age acceleration measure (+2.04 and +1.24 years, respectively, p<0.05). AAs have more wide-spread methylation changes than whites. Race and sex interact to underlie biological age acceleration suggesting altered DNA methylation patterns may be important in age-associated health disparities. In future, we will also analyze genome-wide DNA methylation longitudinally using DNA samples from wave 3. Once the wave 3 methylation measurement is complete, we will follow similar quality control standards, and perform longitudinal data analysis using linear mixed-effects regression models (i) to assess the effect of sex, race, SES, and their interaction on the longitudinal changes of epigenetic age acceleration; and (ii) to identify age-associated differentially methylated CpG sites and differentially methylated regions that change longitudinally. We will also perform genome-wide methylation analysis on the longitudinal data to identify differentially methylated CpG positions and regions associated with age to identify genes that could play role in the aging processes and pathways. Data generated will help to understand factors affecting the longitudinal changes in epigenetic age acceleration as well as in genome-wide CpG methylation. Finally, we will measure 188 endogenous metabolites belonging to six metabolite classes: amino acids and amino acid metabolites, biogenic amines, acylcarnitines, glycerolphospholipids, sphingolipids, and hexoses to assess the association between epigenetic age acceleration measures and plasma metabolites and to assess the association between plasma metabolites and genome-wide CpG methylation levels cross-sectionally in this cohort.
