Saturday, February 18, 2017
Exhibit Hall (Hynes Convention Center)
Crystal Grant, Emory University, Atlanta, GA
Background: Age is the main risk factor for many chronic diseases in humans. Age-related cardiovascular diseases, cancers, and neurodegenerative disorders are among the dominant health problems faced by the population. Aging is marked by widespread, reproducible changes in biological processes and the epigenome. The most studied epigenetic mechanism, DNA methylation (DNAm), has been found to show robust and widespread age-related changes across the genome. DNAm profiles from whole blood can be used to predict human aging rates with great accuracy, suggesting that DNAm can act as a biomarker of aging. Since age is a major risk factor for most chronic diseases, we investigated DNA methylation as a potential mediator of this relationship. Specifically, we sought to test whether DNAm-based predictions of age contribute to risk factors for diabetes, with the goal of identifying risk factors potentially mediated by DNAm. Methods: Our participants were 43 post-menopausal women, between 50 and 76 years of age, enrolled in clinical trials within the Women’s Health Initiative (WHI). We obtained methylation data via the Illumina 450K Methylation array on whole blood samples from participants at three time points (covering on average 16 years per participant). We employed the method and software of Horvath which uses DNAm at 353 CpGs to form a DNAm-based estimate of chronological age (DNAm age). We then calculated the difference between participants’ chronological age and DNAm age (termed Δage) at each time point. This allowed us to observe longitudinal changes in participants’ Δage and DNAm age. We fit linear mixed models to characterize how Δage contributed to a longitudinal model of aging and diabetes risk factors. Results: For most participants, Δage remained constant over the course of the follow-up period, indicating that age acceleration is generally stable over time. We found Δage contributed significantly to models of body mass index (BMI) and waist circumference (p=0.0007 and p=0.0200 respectively), with the contribution to BMI maintaining significance after correction for multiple testing. Other risk factors tested (blood pressure, fasting glucose and insulin levels) did not reach significance. Conclusions: Our results suggest DNAm has the potential to act as a mediator between aging and risk of having a high BMI, and that further studies of the role of DNAm in age-related BMI changes are warranted. Though our power is currently low, replication in a larger cohort of 157 WHI participants with DNAm and phenotype data at two time points is pending.