TL;DR
These exploratory findings indicate that hypertension-associated epigenetic changes are captured by multi-generational epigenetic clocks and may implicate inflammation-related pathways, warranting confirmation in longitudinal studies.
BACKGROUND Men exhibit greater susceptibility to cardiovascular diseases and metabolic disorders, with an earlier onset and more aggressive progression, potentially driven by epigenetic modifications, particularly DNA methylation. Our goal was to comprehensively characterize the epigenetic landscape of a broad cardiometabolic burden in a cohort composed exclusively of men. RESULTS We generated novel DNA methylation profiles from whole blood samples of men with cardiometabolic disturbances (hypertension, ischemic heart disease, obesity, dyslipidemia) and age-matched healthy controls. Cases demonstrated significant epigenetic age acceleration, most pronounced for second-generation clocks (GrimAge, GrimAge2) and pace of aging measures (DunedinPACE), accompanied by shortened epigenetic telomere length (DNAmTL). Notably, none of the 19 evaluated first-generation epigenetic clocks exhibited sensitivity to the studied diseases. Epigenome-wide association analysis identified differentially methylated positions (DMPs), predominantly hypomethylated in cases compared to controls. Gene set enrichment analysis of genes annotated to these DMPs revealed nine distinct biological pathway clusters that reflect the multifactorial processes associated with cardiometabolic burden, including chronic inflammation, GPCR signaling dysregulation, metabolic disturbances, mitochondrial dysfunction, vascular remodeling, and renal electrolyte regulation. Key findings, including GrimAge acceleration, DunedinPACE elevation, DNAmTL shortening, and enrichment of inflammatory and GPCR pathways, were replicated in an independent cohort of men with atherosclerosis. CONCLUSIONS Men with cardiometabolic disturbances exhibit accelerated epigenetic aging and distinct DNA methylation signatures associated with cardiometabolic burden. Analysis of a broad battery of epigenetic clock models revealed that only the second-generation (GrimAge) and third-generation (DunedinPACE) models demonstrated a pronounced sensitivity to the uncomplicated diseases evaluated. In contrast, the first-generation models, trained to predict chronological age, failed to detect significant differences between the groups, suggesting limited applicability in these pathologies. The concordance of results across original and independent replication cohorts underscores the fundamental nature of these epigenetic alterations. Our findings suggest candidate biomarkers measurable in minimally invasive blood samples that may assist in early risk stratification and monitoring of disease progression in men, warranting further prospective evaluation to facilitate clinical translation.
Elevated plasma homocysteine impairs DNA methylation capacity by raising the intracellular S-adenosylhomocysteine (SAH)/S-adenosylmethionine (SAM) ratio, thereby inhibiting DNA methyltransferases. Prior epigenome-wide association studies (EWAS) of homocysteine have been conducted almost exclusively in European populations, and data from East Asian populations remain limited. We performed an EWAS of fasting plasma homocysteine in 1,510 Korean adults from the Korea Genome and Epidemiology Study (KoGES/KARE) cohort using the Illumina MethylationEPIC 850 K array. Blood cell type proportions were estimated by reference-based deconvolution (EpiDISH, 12 cell types) and included as covariates alongside age, sex, BMI, smoking status, array position, and global mean methylation. We tested 845,023 CpG sites for association with log-transformed homocysteine using ordinary least squares regression. Differentially methylated regions (DMRs) were identified by positional clustering and Stouffer combined Z-scores with Benjamini-Hochberg FDR correction. We additionally performed sensitivity analyses adjusting for alcohol consumption, serum creatinine, folate, vitamin B12, and MTHFR C677T genotype. The study population had a mean homocysteine of 14.19 ± 5.51 µmol/L and a hyperhomocysteinemia prevalence of 22.7% (> 16 µmol/L). The genomic inflation factor was λ = 1.227. Three probes on chromosome 1 reached FDR < 0.05. Regional analysis identified three known imprinted loci among the top-ranked DMRs: the L3MBTL1 locus (chr20q12, 31 CpGs, Stouffer p = 2.0 × 10 −20 ), the PEG3 / ZIM2 / MIMT1 domain (chr19q13.43, 40 CpGs, p = 5.2 × 10 −20 ), and the BLCAP / NNAT locus (chr20q11.21, 55 CpGs, p = 1.4 × 10 −15 ). The L3MBTL1 and PEG3 domains remained significant after adjustment for folate, vitamin B12, MTHFR C677T, alcohol, and creatinine (e.g., PEG3 FDR = 5.1 × 10 −15 after MTHFR adjustment); the BLCAP / NNAT signal was attenuated by folate/B12 adjustment. The three chromosome-1 probes lost epigenome-wide significance after MTHFR C677T adjustment (FDR ≈ 0.4), consistent with cis -meQTL confounding. This EWAS of plasma homocysteine in a Korean cohort—one of the largest population-based homocysteine EWAS reported in an East Asian population to date—highlights three imprinted genomic domains ( L3MBTL1 , PEG3 / ZIM2 / MIMT1 , BLCAP / NNAT ) as the top-ranked regional methylation signals, of which L3MBTL1 and PEG3 are robust to one-carbon-biomarker and genotype adjustment. The findings generate a biologically plausible hypothesis linking one-carbon metabolism dysregulation to altered methylation at imprinting-associated domains. Replication in independent East Asian cohorts is warranted.
Aging is a multifactorial process that is best described in terms of the progressive acquisition of multiple layers of phenotypic changes, such as epigenetic modifications, inflammation, and metabolic dysregulation. DNA methylation clocks have been extensively used to construct epigenetic clocks based on the DNAm profiles that can be used to estimate biological age and predict age-associated outcomes. Nevertheless, the vast majority of clocks constructed so far have been based on linear models, which are unlikely to fully account for the heterogeneity and non-linearity of survival-related DNAm signatures. In this work, we constructed a heterogeneous stacked ensemble survival model based on DNAm data obtained from the Framingham Heart Study. We first identified 190 CpG loci using elastic net Cox regression and subsequently constructed a survival prediction model based on the fusion of five complementary survival models by means of a neural network meta-learner. The prediction power of the survival model was evaluated in an external validation cohort, where we observed strong performance for predicting all-cause mortality that significantly exceeded PhenoAge and was statistically comparable to GrimAge. These performance estimates were derived in cohorts of European ancestry and externally validated in postmenopausal women aged 50-79 years, and should therefore be interpreted as applicable only to demographically similar populations.
Recent advances in machine learning have applied novel tools to aging research, yet the relationship between the gut microbiome and epigenetic aging remains underexplored. This proof-of-concept study investigates whether gut microbial composition is associated with biological aging pace independent of chronological age. Using paired 16S rRNA gene sequencing and DNA methylation data from 123 monocyte-enriched samples in a cohort including Native Hawaiian and Pacific Islander participants, we developed “EpiBiome” models to predict epigenetic age acceleration residuals and DunedinPACE, a DNA methylation biomarker that estimates the instantaneous pace of biological aging. Models predicting residuals of traditional clocks (Horvath, Levine, GrimAge2) showed no predictive signal at either taxonomic rank. By contrast, the EpiBiome-Accel model for DunedinPACE reached statistical significance at both the species level (R2 = 0.152, Spearman ρ = 0.408, p = 0.012; permutation p < 0.001) and the genus level (R2 = 0.099, permutation p = 0.036). Adding chronological age as a feature did not improve performance (ΔR2 = − 0.046 at species level), indicating age-independence. SHAP analysis of the species-level ElasticNet model identified Bifidobacterium adolescentis as the dominant contributor and the strongest predictor of decelerated aging, with Succinivibrio dextrinosolvens showing the strongest association with accelerated aging. These findings reveal specific gut taxa as hypothesis-generating candidates for mechanistic follow-up, rather than as individual-level diagnostic markers.
ABSTRACT Epigenetic aging biomarkers are well‐established hallmarks of biological aging, yet their metabolic underpinnings remain largely unexplored. Here, we characterized metabolic signatures associated with five epigenetic aging biomarkers (HorvathAge, HannumAge, DNAmPhenoAge, DunedinPACE, and DNAmTL) and examined their clinical relevance and potential determinants in 7162 Chinese older adults from two cohorts (primary and validation). We observed both shared and distinct metabolic associations across epigenetic aging biomarkers. Metabolic signatures of epigenetic aging biomarkers were derived using elastic net regression, showing moderate correlations with the corresponding epigenetic aging biomarkers (r = 0.21–0.36 in internal testing set, p < 0.05), with external replication further validating metabolic signatures of DNAmPhenoAge, DunedinPACE, and DNAmTL (r = 0.18–0.29, p < 0.05). These five metabolic signatures of epigenetic age acceleration (EAA) exhibited 279 significant associations with aging‐related phenotypes including higher disease risk, poorer health status, and adverse clinical indicators. Gallstones, chronic kidney disease, and hepatitis, along with renal‐, hepatic‐ and metabolic‐related clinical indicators, were consistently associated with multiple metabolic signatures of EAA. Smoking status, alcohol consumption, body mass index (BMI), and physical activity were identified as modifiable lifestyle factors associated with metabolic signatures of EAA, with BMI showing the most consistent associations. Metabolic signatures of DunedinPACE and DNAmPhenoAA exhibited the most extensive associations with aging‐related phenotypes and modifiable lifestyle factors in both primary and validation cohorts. These findings provide novel insights into the metabolic correlates of epigenetic aging biomarkers and underscore the potential of metabolomics‐informed metrics of epigenetic aging as informative indicators of physiological decline and lifestyle effects.
Findings reveal novel genetic contributors to melatonin regulation and establish their putative causal relationship with hypertension.