ArticleCardiovascular diabetology2023
An innovative model for predicting coronary heart disease using triglyceride-glucose index: a machine learning-based cohort study.
Article in Cardiovascular diabetology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed, 38 citations in OpenAlex.
- Triglyceride-glucose waist-to-height ratio improves risk stratification for depressive symptoms: evidence from NHANES.European archives of psychiatry and clinical neuroscience · 2026Article
- Diagnostic value of systemic immune-inflammation composite index combined with triglyceride-glucose index in type 2 diabetes patients with coronary heart disease: a retrospective diagnostic model study.BMC cardiovascular disorders · 2026Article
- Correlation between surrogate indicators of insulin resistance and all-cause mortality in patients with severe hemorrhagic stroke: a multicenter retrospective cohort study in the United States.Cardiovascular diabetology · 2026Article
- Machine Learning-Based Prediction of Coronary Artery Disease Using Clinical and Behavioral Data: A Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
- Association of pericoronary fat attenuation index and insulin resistance for the risk of cardiometabolic multimorbidity: a cross-sectional study.Frontiers in endocrinology · 2026Article
- Predicting anti-CCP positivity and early rheumatoid arthritis onset from routine laboratory parameters: a SHAP-explained machine learning pipeline.Frontiers in medicine · 2026Article
- Machine learning based model for predicting cardiovascular disease using dynamic triglyceride-glucose index: a longitudinal study cohort CHARLS database.Journal of geriatric cardiology : JGC · 2025Article
- Exploration and analysis of risk factors for coronary artery disease with type 2 diabetes based on SHAP explainable machine learning algorithm.Scientific reports · 2025Article
- Triglyceride-Glucose Index Predicts Major Adverse Cardiovascular and Cerebrovascular Events in Non-Diabetic IndividualsBalkan medical journal · 2025Article
- Association of cardiovascular health with MAFLD and mortality in overweight and obese adults and mediation by inflammation and insulin resistance.Scientific reports · 2025Article
- Serum uric acid and coronary artery disease risk: a 10-year prospective cohort study in healthy adults.BMC cardiovascular disorders · 2025Observational
- Article
- Machine learning-based coronary heart disease diagnosis model for type 2 diabetes patients.Frontiers in endocrinology · 2025Article
- Application of machine learning to predict the occurrence of venous thromboembolism in patients hospitalized for coronary artery disease: a single-center retrospective study.Frontiers in cardiovascular medicine · 2025Article
- An explainable machine learning model for predicting chronic coronary disease and identifying valuable text features.Frontiers in cardiovascular medicine · 2025Article
- Visual analysis of research trends in diabetes-associated dry eyeInternational journal of ophthalmology · 2025Article
- The association between the triglyceride-glucose index and serum anti-aging protein α- Klotho: a population-based study.Diabetology & metabolic syndrome · 2024Article
- Association of triglyceride-glucose-body mass index with all-cause and cardiovascular mortality among individuals with chronic kidney disease.Scientific reports · 2024Article
- Association between triglyceride-glucose related indices and mortality among individuals with non-alcoholic fatty liver disease or metabolic dysfunction-associated steatotic liver disease.Cardiovascular diabetology · 2024Article
- Which surrogate insulin resistance indices best predict coronary artery disease? A machine learning approach.Cardiovascular diabetology · 2024Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVarious predictive models have been developed for predicting the incidence of coronary heart disease (CHD), but none of them has had optimal predictive value. Although these models consider diabetes as an important CHD risk factor, they do not consider insulin resistance or triglyceride (TG). The unsatisfactory performance of these prediction models may be attributed to the ignoring of these factors despite their proven effects on CHD. We decided to modify standard CHD predictive models through machine learning to determine whether the triglyceride-glucose index (TyG-index, a logarithmized combination of fasting blood sugar (FBS) and TG that demonstrates insulin resistance) functions better than diabetes as a CHD predictor.
methodsTwo-thousand participants of a community-based Iranian population, aged 20-74 years, were investigated with a mean follow-up of 9.9 years (range: 7.6-12.2). The association between the TyG-index and CHD was investigated using multivariate Cox proportional hazard models. By selecting common components of previously validated CHD risk scores, we developed machine learning models for predicting CHD. The TyG-index was substituted for diabetes in CHD prediction models. All components of machine learning models were explained in terms of how they affect CHD prediction. CHD-predicting TyG-index cut-off points were calculated.
resultsThe incidence of CHD was 14.5%. Compared to the lowest quartile of the TyG-index, the fourth quartile had a fully adjusted hazard ratio of 2.32 (confidence interval [CI] 1.16-4.68, p-trend 0.04). A TyG-index > 8.42 had the highest negative predictive value for CHD. The TyG-index-based support vector machine (SVM) performed significantly better than diabetes-based SVM for predicting CHD. The TyG-index was not only more important than diabetes in predicting CHD; it was the most important factor after age in machine learning models.
conclusionWe recommend using the TyG-index in clinical practice and predictive models to identify individuals at risk of developing CHD and to aid in its prevention.
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