ArticleAmerican journal of translational research2026
Predictive value of epigenetic circulating free DNA (cfDNA) typing for cardiac function stratification in patients with cardiovascular disease.
Article in American journal of translational research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo evaluate the use of circulating free DNA (cfDNA) epigenetic characteristics in cardiac function stratification and risk prediction in patients with cardiovascular disease (CVD).
methodsThis 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.
resultsSevere 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.
conclusionEpigenetic 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.
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