ArticlePloS one2018
Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it, 110 citations in OpenAlex.
- Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI.GigaScience · 2022Pooled it
- Epigenetic Age Acceleration Reflects Long-Term Cardiovascular Health.Circulation research · 2021Trial
- ACTB hypermethylation in blood is a potential biomarker as predictor to the risk of coronary heart disease.Scientific reports · 2026Article
- Development and validation of machine learning-based risk prediction models of coronary artery disease using Porphyromonas gingivalis concentration.BMC oral health · 2026Article
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
- The Genetic Risk Score with Variants in PDGFs and PDGFRB for the Risk of Major Cardiovascular Adverse Events in Patients with Coronary Artery Disease.Journal of atherosclerosis and thrombosis · 2025Article
- Article
- AESurv: autoencoder survival analysis for accurate early prediction of coronary heart disease.Briefings in bioinformatics · 2024Article
- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- Machine Learning in Cardiovascular Risk Prediction and Precision Preventive Approaches.Current atherosclerosis reports · 2023Review
- Epigenetics of Early Cardiometabolic Disease: Mechanisms and Precision Medicine.Circulation research · 2023Review
- The Reversion of the Epigenetic Signature of Coronary Heart Disease in Response to Smoking Cessation.Genes · 2023Article
- Towards AI-driven longevity research: An overview.Frontiers in aging · 2023Review
- Article
- Do Loneliness and Per Capita Income Combine to Increase the Pace of Biological Aging for Black Adults across Late Middle Age?International journal of environmental research and public health · 2022Article
- Identification of DNA methylation-regulated genes as potential biomarkers for coronary heart disease via machine learning in the Framingham Heart Study.Clinical epigenetics · 2022Article
- DNA methylation-based predictors of health: applications and statistical considerations.Nature reviews. Genetics · 2022Review
- Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach.International journal of molecular sciences · 2022Article
- Artificial Intelligence and Cardiovascular Genetics.Life (Basel, Switzerland) · 2022Review
- Current and Future Applications of Artificial Intelligence in Coronary Artery Disease.Healthcare (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
An improved method for detecting coronary heart disease (CHD) could have substantial clinical impact. Building on the idea that systemic effects of CHD risk factors are a conglomeration of genetic and environmental factors, we use machine learning techniques and integrate genetic, epigenetic and phenotype data from the Framingham Heart Study to build and test a Random Forest classification model for symptomatic CHD. Our classifier was trained on n = 1,545 individuals and consisted of four DNA methylation sites, two SNPs, age and gender. The methylation sites and SNPs were selected during the training phase. The final trained model was then tested on n = 142 individuals. The test data comprised of individuals removed based on relatedness to those in the training dataset. This integrated classifier was capable of classifying symptomatic CHD status of those in the test set with an accuracy, sensitivity and specificity of 78%, 0.75 and 0.80, respectively. In contrast, a model using only conventional CHD risk factors as predictors had an accuracy and sensitivity of only 65% and 0.42, respectively, but with a specificity of 0.89 in the test set. Regression analyses of the methylation signatures illustrate our ability to map these signatures to known risk factors in CHD pathogenesis. These results demonstrate the capability of an integrated approach to effectively model symptomatic CHD status. These results also suggest that future studies of biomaterial collected from longitudinally informative cohorts that are specifically characterized for cardiac disease at follow-up could lead to the introduction of sensitive, readily employable integrated genetic-epigenetic algorithms for predicting onset of future symptomatic CHD.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.