Table 1 DNAm-based prediction models for CVD, HF and Heart Age
From: DNA methylation in cardiovascular disease and heart failure: novel prediction models?
Research article(s) | Prediction model(s) | Cohort(s) | Main finding(s) |
|---|---|---|---|
Meder et al. [46] | 3-marker methylation panel for HF caused by DCM | Screening cohort: patients with DCM (n = 41) and clinical controls (n = 31). Heart and blood samples Replication cohort I: Heart sample, patients with DCM (n = 18) and healthy controls (n = 8); Blood samples, patients with DCM (n = 9) and healthy controls (n = 28) Replication cohort II: Blood samples, patients with DCM (n = 82) and healthy controls (n = 109) | The 3-marker methylation panel exhibits excellent diagnostic performance for DCM and outperforms the NT-proBNP |
Westerman et al. [65] | CVD Risk CSL | Training cohorts: participants from WHI (n = 2023; CVD cases, n = 1009), FHS‐JHU (n = 484; CVD cases, n = 125), LBC (n = 818; CVD cases, n = 297), and FHS‐UM (n = 2103; CVD cases, n = 180) Replication cohort: participants from REGICOR (n = 391; CVD cases, n = 191) | The CSL associates with CVD time‐to‐event, performs best in individuals with lower Framingham Risk Scores and predicts MI status |
Cappozzo et al [66] | Composite biomarker predictive of CVD risk | Training cohort: participants from EPIC Italy (n = 1803) Validation cohorts: participants from Understanding Society (n = 1174), TILDA (n = 490), EXPOsOMICS CVD (n = 315), GSE174818 (n = 127), HRS (n = 2146) and NICOLA (n = 1728) | The DNAmCVDscore shows high performance in predicting short-term CV events outperforming current state-of-the-art CVD prediction models based on traditional risk factors |
Zhao et al (2022) [71] | Composite HFmeRisk score | Training cohort: participants from FHS Offspring cohort (n = 797; HFpEF n = 59) Testing cohort: participants from FHS Offspring cohort (n = 171; HFpEF n = 32) | The HFmeRisk score integrates both clinical and epigenetic features and outperforms models with clinical characteristics or DNAm alone, and published chronic HF risk prediction models |
Tropiceanu et al [68] | DNAm Age scores (AgeHannum, AgeHorvath, PhenoAge, and GrimAge) relation to CV risk factors | Study cohort: participants from MRC NSHD (n = 498) | By the age of 60, participants with accelerated DNAm have older, weaker, and more electrically impaired hearts |
Mongelli et al [70] | Mongelli & Panunzi (M&P) cardiac and blood clocks | Study cohort: Patients undergoing cardiac surgery (n = 383; CABG, n = 289; AVR, n = 94) Training cohort: n = 288 Testing cohort: n = 95 | The M&P cardiac and blood clocks consist of 31 specific CpG sites and reveal similarity between chronological and biological age in the blood and heart |
Chybowska et al [67] | Composite CVD EpiScore | Study cohort: participants from GS (n = ≥ 12,657 CVD cases, ≥ 1274) Training cohort: GS (n = 6880) Testing cohort: GS (n = 3659) | The composite CVD EpiScore, based on 45 protein EpiScores, is a significant predictor of CVD risk independent of ASSIGN and cTnI concentration |
Carbonneau et al [69] | DNAm Age scores (DunedinPACE, PhenoAge, GrimAge, and DNAmTL) relation to CV health, CVD and all-cause mortality | Study cohort: participants from FHS (n = 5682) | DNAm Age scores mediate the associations between the LE8 score and incident CVD, CVD‐specific mortality, and all‐cause mortality |