Research Paper Volume 11, Issue 11 pp 3487—3504
Investigation of bidirectional longitudinal associations between advanced epigenetic age and peripheral biomarkers of inflammation and metabolic syndrome
- 1 National Center for PTSD at VA Boston Healthcare System, Boston, MA 02130, USA
- 2 Department of Psychiatry, Boston University School of Medicine, Boston, MA 02118, USA
- 3 Biomedical Genetics, Boston University School of Medicine, Boston, MA 02118, USA
- 4 Pharmacogenomics Analysis Laboratory, Research Service, Central Arkansas Veterans Healthcare System, Little Rock, AR 72205, USA
- 5 Geriatric Research Educational and Clinical Center and Translational Research Center for TBI and Stress Disorders, VA Boston Healthcare System, Boston, MA 02130, USA
- 6 Department of Psychiatry, Harvard Medical School, Boston, MA 02115, USA
Received: March 12, 2019 Accepted: May 20, 2019 Published: June 7, 2019https://doi.org/10.18632/aging.101992
How to Cite
Copyright: Morrison et al. This is an open‐access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Epigenetic age estimations based on DNA methylation (DNAm) can predict human chronological age with a high level of accuracy. These DNAm age algorithms can also be used to index advanced cellular age, when estimated DNAm age exceeds chronological age. Advanced DNAm age has been associated with several diseases and metabolic and inflammatory pathology, but the causal direction of this association is unclear. The goal of this study was to examine potential bidirectional associations between advanced epigenetic age and metabolic and inflammatory markers over time in a longitudinal cohort of 179 veterans with a high prevalence of posttraumatic stress disorder (PTSD) who were assessed over the course of two years. Analyses focused on two commonly investigated metrics of advanced DNAm age derived from the Horvath (developed across multiple tissue types) and Hannum (developed in whole blood) DNAm age algorithms. Results of cross-lagged panel models revealed that advanced Hannum DNAm age at Time 1 (T1) was associated with increased (i.e., accounting for T1 levels) metabolic syndrome (MetS) severity at Time 2 (T2; p = < 0.001). This association was specific to worsening lipid panels and indicators of abdominal obesity ( p = 0.001). In contrast, no baseline measures of inflammation or metabolic pathology were associated with changes in advanced epigenetic age over time. No associations emerged between advanced Horvath DNAm age and any of the examined biological parameters. Results suggest that advanced epigenetic age, when measured using an algorithm developed in whole blood, may be a prognostic marker of pathological metabolic processes. This carries implications for understanding pathways linking advanced epigenetic age to morbidity and mortality.