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.). As expected, during the wave 1 DNA methylation profiling process some samples failed (7%). This is lower than our estimated 10% sample failure rate. Of the 487 Wave 1 samples with EPIC methylation data, 17 were excluded because either they had poor methylation detection rate (P >0.01), sex mismatch, or were outliers. We have compared the demographic characteristics between included and excluded samples and found no systematic differences in the distributions of age, sex, race and SES (P >0.61 for all t-test and chi-squared tests). Therefore, Wave 1 DNA methylation analysis in a total of 487 samples has been completed using the Illumina Infinium Methylation EPIC arrays (EPIC array, contains 866,836 CpG sites). Using this wave 1 DNA methylation data, we have estimated the Horvath (DNAmAgeHO) and Hannum (DNAmAgeHA) epigenetic clocks cross-sectionally. Epigenetic age acceleration (AgeAccel) was calculated as the residuals of regressing DNAmAgeHO or DNAmAgeHA on chronological age, and are abbreviated as AgeAccelHO and AgeAccelHA. Two additional epigenetic age acceleration measures were also derived by considering white blood cell (WBC) counts. We have estimated WBC counts based on genome-wide DNA methylation level. These are intrinsic epigenetic age acceleration (IEAA) and extrinsic epigenetic age acceleration (EEAA). The IEAA is the residuals of regressing chronological age and estimated WBC counts on DNAmAgeHO, thus the IEAA represents an epigenetic age acceleration measure that is independent of chronological age and WBC counts. On the other hand, the residuals derived in the EEAA are an enhanced version of the Hannum age acceleration measure where WBC types whose abundance changes over time were given additional weight (3). By design, the EEAA measures both biological and immune system cell aging rates. We have also identified novel age-associated differentially methylated CpG positions (aDMPs) in AA and in whites. 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.
