Blood-Based DNA Methylation Models Improve Short-term Cardiovascular Risk Stratification in Individuals With Type 2 Diabetes

Leukocyte DNA methylation-based signatures for atherosclerotic cardiovascular disease risk prediction in the Million Veteran Program

Examining the potential causal relationships among smoking, blood DNA methylation, and type 2 diabetes development: A Mendelian randomization study

INTRODUCTION Type 2 diabetes (T2D) is a major global health burden with rising prevalence and significant morbidity and mortality. Smoking has been recognized as an independent risk factor for T2D, but the exact underlying mechanisms are not yet fully understood. METHODS We conducted a two-sample Mendelian randomization (MR) study to investigate the effects of smoking behaviors and smoking-related DNA methylation on type 2 diabetes (T2D) risk. This secondary analysis was based on publicly available summary statistics. Smoking traits were obtained from a large meta-GWAS of over 1.2 million individuals of European ancestry. DNA methylation data were derived from the CHARGE Consortium EWAS (n=15907) and mQTLs from the GoDMC (n=27750). T2D outcome data were obtained from a European GWAS including 74124 cases and 824006 controls. Replication was performed in FinnGen (17268 cases, 184778 controls) and BioBank Japan (40250 cases, 170615 controls). Stringently selected genetic instruments were applied, and causal effects were estimated primarily using two-sample MR with inverse-variance weighted analysis, complemented by MR-Egger and weighted median approaches, and further validated by colocalization analysis. RESULTS In our genomic analysis, we presented convincing evidence supporting the association between genetically predicted smoking initiation and an increased susceptibility to T2D. In epigenetic analysis, we identified methylation at 21 smoking-related CpG sites associated with T2D risk. Subsequently, 14 of these CpG sites were validated using the FinnGen dataset, and 12 of them were validated using the BBJ dataset. Furthermore, we observed strong colocalization evidence for four CpG sites, including cg23756272 (BCL2), cg03864215 (KCNJ11), cg09861057 (TDRD10) and cg10672416 (C12orf65), and T2D risk. CONCLUSIONS This research provides novel findings of the impact of smoking on T2D susceptibility, highlighting the role of epigenetic DNA methylation.

Predictive value of epigenetic circulating free DNA (cfDNA) typing for cardiac function stratification in patients with cardiovascular disease.

OBJECTIVE To evaluate the use of circulating free DNA (cfDNA) epigenetic characteristics in cardiac function stratification and risk prediction in patients with cardiovascular disease (CVD). METHODS This retrospective study included 624 CVD patients diagnosed from January 2023 to January 2025. Patients were grouped according to the New York Heart Association (NYHA) classification into mild (I-II) and severe (III-IV) cardiac insufficiency groups. Genome-wide methylation level, hypermethylation proportion, and "risk-type cfDNA" (defined by hypermethylation of cardioprotective gene promoters and hypomethylation of injury-promoting gene promoters) were assessed. Logistic regression, Random Forest, and XGBoost models were constructed to identify predictors of severe cardiac dysfunction. RESULTS Severe cardiac dysfunction was significantly associated with higher genome-wide methylation levels, hypermethylation proportion, and risk-type cfDNA (all P < 0.05). cfDNA concentration was an independent protective factor for endpoint events (OR = 0.946, 95% CI: 0.893-0.997, P = 0.0443). The combined model (cfDNA + left ventricular ejection fraction [LVEF] + N-terminal pro-brain natriuretic peptide [NT-proBNP] + age) achieved an area under the ROC curve (AUC) of 0.994. Machine learning models identified age, cfDNA concentration, and LVEF as the top three predictors of severe cardiac dysfunction. CONCLUSION Epigenetic characteristics of cfDNA are closely associated with the severity of cardiac dysfunction in CVD patients and may serve as effective noninvasive molecular markers for cardiac function stratification. Integrating cfDNA epigenetic features with traditional clinical indicators significantly improves risk prediction accuracy.

DNA Methylation Biomarkers Capture Residual Biological Risk Beyond PREVENT

Metabolomic profiling refines cardiovascular risk stratification beyond SCORE2-diabetes in patients with concurrent type 2 diabetes and pulmonary dysfunction

Patients presenting with both type 2 diabetes (T2D) and lung function impairment (LFI) are exposed to an amplified risk of major adverse cardiovascular events (MACE). Although the SCORE2-Diabetes algorithm is widely endorsed for clinical risk assessment, its prognostic accuracy within this specific multimorbid phenotype remains inadequately explored. This study sought to evaluate the baseline utility of SCORE2-Diabetes and to investigate whether integrating a novel, machine learning-derived metabolomic signature could optimize 10-year MACE prediction in this highly vulnerable population. We conducted a prospective analysis of UK Biobank participants with type 2 diabetes (T2D) and no cardiovascular disease at baseline. A reference cohort of 12,130 participants with preserved lung function was used to assess the base clinical model, and the primary cohort comprised 3,706 participants with lung function impairment (LFI). We profiled 249 plasma metabolites using nuclear magnetic resonance (NMR) spectroscopy. A machine-learning framework combining least absolute shrinkage and selection operator (LASSO) Cox regression, random forest survival analysis, and extreme gradient boosting (XGBoost) was used to derive a consensus signature. Internal validation used 1,000 bootstrap resamples; the complete modeling pipeline was repeated within each resample. Incremental performance beyond SCORE2-Diabetes was assessed using Harrell’s concordance index (C-index), net reclassification improvement (NRI), calibration, and decision curve analysis (DCA). During a median follow-up of 12 years, SCORE2-Diabetes showed lower discrimination in the LFI cohort than in the reference cohort (C-index, 0.670 vs. 0.707). Among the 3,706 participants with LFI, 845 developed major adverse cardiovascular events (MACE). Adding the 12-metabolite signature increased the C-index from 0.670 to 0.722 (absolute difference, 0.052; P < 0.001), representing a statistically significant but moderate improvement in discrimination. The five-metabolite model had a C-index of 0.708, while the two-metabolite sensitivity model yielded a C-index of 0.699 (95% CI, 0.683-0.716). Using the 2023 European Society of Cardiology SCORE2-Diabetes categories, the categorical NRI was 14.8% (95% CI, 10.8%-18.8%). Internal calibration and DCA suggested improved agreement and net benefit, but these results require external validation. In participants with T2D and LFI, adding a metabolomic signature to SCORE2-Diabetes produced a statistically significant but moderate improvement in risk discrimination. These internally validated findings support further evaluation in independent cohorts and prospective impact studies; they do not establish immediate clinical usefulness.

Intergenerational and life-course links between a cardiovascular health-related methylation score and early vascular changes

An LE8-derived DNAm score was associated with lower cIMT across the life course and, to a lesser extent, across generations, suggesting that blood DNAm reflects cumulative cardiovascular health and vascular burden and may complement conventional cardiovascular risk assessment.