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; = < 0.001). This association was specific to worsening lipid panels and indicators of abdominal obesity ( = 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.
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http://dx.doi.org/10.18632/aging.101992 | DOI Listing |
Genes (Basel)
November 2024
Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
Background/objectives: A-kinase-interacting protein 1 (AKIP1) has been discovered to be a pivotal signaling adaptor in the regulation of human labor and associated with preterm birth, but its effect on fetal growth was still unclear. Meanwhile, the regulation role of DNA methylation (DNAm) on placental and fetal development has been demonstrated. Therefore, we aimed to investigate the association of DNAm in maternal peripheral blood with placental development and full-term small for gestational age (FT-SGA) neonates, and to explore whether placenta mediate the association between DNAm and FT-SGA; Methods: This study was a case-control study including 84 FT-SGAs and 84 FT-AGAs derived from the Shenzhen Birth Cohort Study.
View Article and Find Full Text PDFElectrophoresis
January 2025
Forensic Sciences Laboratory, Section of Legal Medicine, Department of Medicine and Surgery, Santa Maria Hospital, University of Perugia, Terni, Italy.
The increasing interest in DNA methylation (DNAm) analysis within the forensic scientific community prompted a collaborative project by Ge.F.I.
View Article and Find Full Text PDFScand J Med Sci Sports
January 2025
University School of Health and Sport, University of Girona, Girona, Spain.
Physical exercise has been shown to induce epigenetic modifications with various health implications, directly affect DNA methylation (DNAm), as well as reverse the epigenetic age. Hence, we aimed to identify differential methylation changes and assess the epigenetic age in the saliva of 7-9-year-old school children following a 3-month integrated neuromuscular training (INT), as well as to explore if any of the methylation changes are in core genes. Core genes are defined as genes of high relevance and essential importance within the human genome.
View Article and Find Full Text PDFAging (Albany NY)
January 2025
Department of Public Health Sciences, University of Chicago, Chicago, IL 60615, USA.
Background: DNA methylation (DNAm) data from human samples has been leveraged to develop "epigenetic clock" algorithms that predict age and other aging-related phenotypes. Some DNAm clocks were trained using DNAm obtained from blood cells, while other clocks were trained using data from diverse tissue/cell types. To assess how DNAm clocks perform across non-blood tissue types, we applied DNAm algorithms to DNAm data generated from 9 different human tissue types.
View Article and Find Full Text PDFClin Epigenetics
December 2024
MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Background: Multiple studies have shown that DNA methylation (DNAm) models of protein abundance can be informative about exposure, phenotype and disease risk. Here we investigate and provide descriptive details of the capacity of DNAm to capture non-genetic variation in protein abundance across the lifecourse.
Methods: We evaluated the performance of 14 previously published DNAm models of protein abundance (episcores) in peripheral blood from a large adult population using the Avon Longitudinal Study of Parents and Children (ALSPAC) at ages 7-24 and their mothers antenatally and in middle age (N range = 145-1464).
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