DNA methylation-based signatures were associated with incident ASCVD and modestly improved risk prediction beyond that of traditional risk factors, and an agnostic probe reliability-based approach was developed.
Abstract
Background and Aims: The contribution of DNA methylation signatures to atherosclerotic cardiovascular disease (ASCVD) risk prediction remains unclear. We developed methylation risk scores (MRS) for incident ASCVD and assessed whether they improved risk prediction beyond established risk factors. Methods: We studied 44,674 Million Veteran Program participants with leukocyte DNA methylation data, divided into two independent subcohorts: a prevalent ASCVD cohort (n=27,560) used for epigenome-wide association analyses (EWAS) to inform cytosine-phosphate-guanine dinucleotide selection, and a cohort free of ASCVD at blood draw (n=17,114), split into training and testing sets for MRS development and evaluation. MRS for incident ASCVD were developed using elastic net regression. Incremental prediction beyond clinical risk factors was assessed by improvement in discrimination ({Delta}CPE), reclassification (NRI), and calibration. Results: Three MRS were developed: MRS-1A, informed by prevalent ASCVD EWAS and probe reliability; MRS-1B, informed by EWAS alone; and MRS-2, using an agnostic probe reliability-based approach. Among 17,114 participants (mean [SD] age, 58.9 [14.1] years; 89.6% men; 54.2% European), 2,789 developed ASCVD over a median follow-up of 7.4 years. Each MRS was associated with incident ASCVD (HR per 1-SD: 1.97 [95% CI, 1.72-2.26] for MRS-1A, 2.08 [1.83-2.37] for MRS-1B, and 2.07 [1.78-2.39] for MRS-2) and modestly improved discrimination beyond clinical risk factors ({Delta}CPE: 0.014 [0.006, 0.021], 0.016 [0.007, 0.023], and 0.013 [0.006, 0.021], respectively). MRS improved risk stratification, driven by the downward reclassification of non-events (non-event NRI: 3.6% [2.6-4.7], 5.4% [4.3-6.5], and 3.6% [2.6-4.6], respectively), while maintaining calibration. Conclusions: DNA methylation-based signatures were associated with incident ASCVD and modestly improved risk prediction beyond that of traditional risk factors.
DNAmScore identified residual biological risk beyond PREVENT-like clinical predictors, with strong independent mortality associations and modest, consistent improvements in cross-fitted prediction performance, which support development and external validation of CVD-specific DNAm biomarkers.
D.-G. Xing, M. S. Bhuiyan, S. Conrad et al.· medRxiv· 0 citations
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 and risk prediction accuracy.
S. Cai, Qingyuan Cai, Chunwen Jia· American journal of translat...· 0 citations
Background: DNA methylation (DNAm) signatures capture cumulative lifestyle exposures and biological aging. This prospective study evaluated whether DNAm-based scores and epigenetic aging clocks are associated with clinical outcomes and mortality in a multinational cohort of patients with heart failure (HF). Methods: We studied 2,594 patients with HF from 40 countries in the Global Congestive Heart Failure (G-CHF) registry with whole-blood DNAm data. Fifteen published DNAm-based scores and epigenetic aging clocks reflecting lifestyle, environmental and physiological exposures, inflammation, frailty, mortality risk, and biological aging were derived. Associations with HF hospitalization, cardiovascular death, and all-cause death were assessed using multivariable Cox regression adjusted for age, sex, ancestry, the MAGGIC risk score, and NT-proBNP. Incremental prognostic value was compared to MAGGIC score and NT-proBNP. Extreme DNAm profiles were defined as scores or clocks exceeding {+/-}1.5 standard deviations (s.d.) from the population mean. Results: Mean age was 62.7{+/-}14.0 years, 66.2% were male, and mean left ventricular ejection fraction was 40.1{+/-}14.1%. During a median follow-up of 3.0 years, 338 patients were hospitalized for HF, 349 died from cardiovascular causes, and 565 died from any cause. Higher epigenetic age and DNAm scores for CRP, frailty, and mortality were associated with increased risk, whereas higher diet-related DNAm scores were inversely associated. For all-cause death, adjusted hazard ratios per 1-s.d. were 1.36 (95% CI, 1.24-1.50) for GrimAge, 1.27 (95% CI, 1.17-1.38) for the DNAm score for CRP, 1.44 (95% CI, 1.28-1.63) for the DNAm score for frailty, and 1.48 (95% CI, 1.32-1.65) for the DNAm score for mortality, compared with 0.81 (95% CI, 0.75-0.88) and 0.86 (95% CI, 0.79-0.94) for the DNAm scores for Alternative Healthy Eating Index and Mediterranean Diet Score. These patterns were directionally consistent for cardiovascular death and weaker for HF hospitalization and were more pronounced among patients with lower clinical risk (MAGGIC<17, Pinteraction<0.05), particularly for all-cause death. Patients with 4-5 extreme-high DNAm scores or clocks had more than twice the risk of death (HR, 2.27; 95% CI, 1.65-3.13). Conclusions: DNAm-based scores and epigenetic aging clocks reflect multiple dimensions of biological vulnerability in HF and are associated with clinical outcomes and mortality beyond clinical risk factors.
P. Meyre, M. Chong, E. Shemesh et al.· medRxiv· 0 citations
Summary Background Guideline-recommended clinical risk scores such as AusCVDRisk underestimate cardiovascular disease (CVD) risk in a substantial proportion of individuals who later experience events, with up to 65% initially classified as low or intermediate risk. This limitation is most consequential in the intermediate-risk group, where treatment decisions are uncertain and additional risk refinement could alter management. Circulating lipid species and inherited genetic variation capture complementary molecular aspects of atherosclerotic risk that are not fully reflected by conventional clinical variables, but are not routinely incorporated into primary-care risk assessment. We investigated whether selective integration of lipidomic and genomic risk signals into AusCVDRisk improves 5-year CVD prediction and reclassification, with a focus on individuals at intermediate clinical risk. Methods A lipidomic score comprising 689 lipid species measured by liquid chromatography–tandem mass spectrometry was derived using regularised Cox regression in 8082 participants from the Australian Diabetes, Obesity and Lifestyle Study (1999–2000). A genome-wide coronary artery disease polygenic score (PGS002048; 762,124 variants) was optimised in 3328 participants from the Busselton Health Study (1994–95). Each score was adjusted for AusCVDRisk predictors to isolate independent effects and incorporated into Cox models retaining the AusCVDRisk linear predictor as a fixed offset, generating lipidomic-enhanced (L.CVDRisk), genomic-enhanced (G.CVDRisk), and combined (LG.CVDRisk) scores. Internal and external validation was performed across five Australian cohorts totalling 13,521 adults without baseline CVD. Discrimination (Harrell’s concordance index; C-statistic), calibration, categorical net reclassification improvement (NRI), and decision-curve analyses were assessed. Findings LG.CVDRisk showed modest gains in discrimination compared with AusCVDRisk (pooled ΔC among intermediate-risk individuals 0.071, 95% CI 0.033–0.109; overall 0.012, 95% CI 0.000–0.024). Risk classification improved substantially (pooled NRI in the intermediate-risk group 0.305, 95% CI 0.212–0.397; overall 0.080, 95% CI 0.031–0.129), with net event and non-event reclassification of 38.2% (95% CI 29.3–47.0%) and −6.8% (95% CI −9.3 to −4.2%) among intermediate-risk individuals. Decision-curve analysis showed the greatest net benefit when molecular profiling was selectively applied to individuals with intermediate AusCVDRisk (5–<10%). In a coronary imaging cohort, LG.CVDRisk reclassified 17 (41%) of 41 intermediate-risk individuals with extensive coronary calcification into the high-risk category. Interpretation Selective augmentation of an established clinical risk algorithm with lipidomic and genomic information improves cardiovascular risk stratification among individuals at intermediate baseline risk. This approach supports targeted molecular testing within existing primary-care pathways to inform personalised prevention. Funding National Heart Foundation, Australia, Australian Government Medical Research Future Fund, National Health and Medical Research Council, Victorian Government.
Aleksandar Dakic, Jing-Qin Wu, Tingting Wang et al.· EClinicalMedicine· 0 citations
This study supports methylation risk scores as novel biomarkers of stress-related CHD and uncovers epigenetic regulation in monocytes as a potential underlying mechanism of stress-related CHD, highlighting biological pathways linking stress and disease and may promote personalized interventions in high-risk populations.
Sofia Benavides, Hazel Milla, Helena Palma-Gudiel et al.· medRxiv· 0 citations
Background It remains unclear whether cardiovascular-kidney-metabolic (CKM) syndrome and genetic susceptibility are associated with cancer incidence. This study aims to evaluate the associations between CKM health status, genetic susceptibility, and cancer incidence, and to develop a machine learning-based prediction model for cancer risk in patients with advanced CKM syndrome. Methods This study included 399,034 UK Biobank participants (389,289 with genetic data). CKM health was categorized into stages 0–4. Genetic susceptibility was assessed via a polygenic risk score (PRS). Associations were evaluated using Cox proportional hazard models. For participants with advanced CKM, candidate predictors were screened via the Boruta algorithm, LASSO regression, and multivariable logistic regression. Eight machine learning models were constructed and validated, and their predictive discrimination, calibration performance, and clinical practical value were further assessed. Additionally, Shapley Additive Explanations (SHAP) were adopted to interpret the internal mechanism of the optimal model. The dose–response relationship was assessed using restricted cubic splines (RCS), and mediation analysis was further performed to explore the underlying mechanism of the observed associations. Results During a median follow-up of 13.7 years, 48,247 incident cancer cases were identified. Compared to CKM stage 0, multivariable-adjusted hazard ratios (HRs) were 1.01 (95% CI: 0.91-1.12) for stage 1, 1.21 (1.10–1.33) for stage 2, and 2.17 (1.95–2.42) for advanced CKM (stage 3–4). Participants with high PRS and advanced CKM faced the highest cancer risk (HR 3.24, 95% CI 2.68–3.93). A total of 14 predictors were retained after screening. The GBM model achieved the best predictive performance (test AUC = 0.801). A web-based calculator was developed to realize individualized cancer risk prediction. RCS analyses indicated that diastolic blood pressure (Pnonlinearity<0.05), neutrophil count(Pnonlinearity<0.05), alkaline phosphatase (Pnonlinearity=0.001), and TyG-BMI (Pnonlinearity<0.05) were significantly and nonlinearly associated with cancer risk. Total bilirubin the strongest positive mediating effect, explaining 44.45% of the total association (P < 0.001). Conclusions Advanced CKM syndrome independently increases incident cancer risk, and this elevated risk is significantly amplified by high genetic susceptibility. The validated GBM model provides an interpretable tool for individualized risk estimation, which serves as a valuable complementary tool for clinical risk stratification and cancer patients with, cancer.
Xue He, J. An, Chun-Jia Yan et al.· Frontiers in Endocrinology· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
