Accelerated epigenetic aging and prospective morbidity and mortality among U.S. veterans

Abstract

Epigenetic measures of aging derived from DNA methylation are promising biomarkers associated with prospective morbidity and mortality, but require validation in real-world medical settings. Using data from 2,216 post-9/11 veterans, we examined whether accelerated DunedinPACE aging scores were associated with chronic disease morbidity, predicted healthcare costs, and mortality assessed over an average of 13.1 years of follow up in VA electronic health records. Veterans with faster DunedinPACE aging scores developed more chronic disease and showed larger increases in predicted healthcare costs over the subsequent 5, 10, and 15 years. Faster aging was associated with incident myocardial infarction, stroke, diabetes, cancer, liver disease, and renal disease, as well greater risk of mortality due to all-causes and chronic disease. These findings provide evidence that accelerated epigenetic aging is associated with worsening prospective health across multiple chronic diseases and organ systems assessed using electronic health records from an integrated healthcare system.

Keywords: Biological aging; DNA methylation; aging biomarker; chronic disease; mortality; veterans.

Figure 1.

Panel A presents mean CCI scores over time grouped by DunedinPACE aging scores. Four groups were created by standardizing DunedinPACE scores and creating cutoffs at the mean and 0.75 SD above and below the mean, corresponding to DunedinPACE values of “slowest aging” ≤ 0.98 (n = 517, 23.3%), “slow aging” between 0.98 to 1.07 (n = 656, 29.6%), “fast aging” between 1.07 to 1.15 (n = 561, 25.3%), and 1.15 ≤ for “fastest aging” (n = 482, 21.8%). Groups were created using a priori SD cutoffs to roughly approximate quartlies for illustrative purposes—all model estimates used full DunedinPACE aging scores. Panel B presents the study sample by baseline year of enrollment in the PDMH and years of follow-up to the censor date. Baseline PDMH enrollment included the blood draw used to derive DNA methylation data from whole blood.

Figure 2.

Visualization of the HRs for each of the CCI chronic disease categories over the follow-up period. Effects represent HRs per 1 SD difference in DunedinPACE. All estimates include demographic and technical DNAm covariates. Number of cases and excluded participants for each estimate are presented in Table 2. Error bars represent 95% confidence intervals.

Figure 3.

Visualization of diabetes onset (1 – survival) across the follow-up period, as an illustrative example of chronic disease incidence. The model included demographic and technical covariates and excluded 28 veterans with a baseline diagnosis of diabetes or missing covariate data. The four groups were created by standardizing DunedinPACE aging scores and creating cutoffs, as with Figure 1. No. at risk represents veterans at risk of diabetes onset at each period on the x-axis. Over the follow up, 45 of 511 (8.8%) slowest aging veterans, 110 of 651 (16.9%) slow aging veterans, 145 of 556 (26.1%) fast aging veterans, and 179 of 470 (38.1%) of fastest aging veterans developed diabetes. Compared to the slowest aging veterans, all other groups were more likely to develop diabetes; slow aging HR, 1.90 (95% CI, 1.33–2.70), fast aging HR, 3.21 (95% CI, 2.27–4.56), fastest aging HR, 5.13 (95% CI, 3.57–7.38, all ps < .001).