ArticleCardiovascular research2023
A machine learning based approach to identify carotid subclinical atherosclerosis endotypes.
Article in Cardiovascular research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- A multi-omics approach uncovers causality of IL6R on endotypes of subclinical carotid atherosclerosis and the possible role of the IL6R/OSMR pathway.Cardiovascular research · 2025Pooled it
- Development and Internal Validation of a Risk Prediction Model for Carotid Atherosclerosis in the Hyperuricemia Population.Vascular health and risk management · 2024Trial
- Super learner model for predicting carotid plaque regression in type 2 diabetes on PCSK9 inhibitors.Acta diabetologica · 2026Article
- Inflammaging and cytokine-driven cardiovascular senescence in age-related myocardial infarction: mechanisms, biomarkers, and precision immunotherapy strategies.Biogerontology · 2026Review
- Artificial Intelligence in heart and brain ultrasound: the least you need to know.Internal and emergency medicine · 2026Review
- Radiomic features and carotid stenosis in periodontitis a two stage bootstrap and multimodal machine learning study.Scientific reports · 2026Article
- Interpretable machine learning analysis for relationships betweenFrontiers in cellular and infection microbiology · 2026Article
- Effect of information motivation behavioral skills model based dietary patterns on carotid atherosclerosis in elderly hypertensive patients.Scientific reports · 2025Article
- Review
- Anti-senescence therapies: a new concept to address cardiovascular disease.Cardiovascular research · 2025Review
- Development and Validation of a Nomogram for Predicting Carotid Atherosclerosis in Non-Obese Patients with Type 2 Diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- SHAP Analysis of Machine Learning-Based Carotid Atherosclerosis Prediction of Older Adults with OSA, a Multicenter Study.Nature and science of sleep · 2025Article
- The role of vitamin D in sleep regulation: mechanisms, clinical advances, and future directions.Frontiers in nutrition · 2025Review
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Authors and funding
15 authors at 9 institutions in 6 countries.
Funding
Abstract
aimsTo define endotypes of carotid subclinical atherosclerosis. METHODS AND
resultsWe integrated demographic, clinical, and molecular data (n = 124) with ultrasonographic carotid measurements from study participants in the IMPROVE cohort (n = 3340). We applied a neural network algorithm and hierarchical clustering to identify carotid atherosclerosis endotypes. A measure of carotid subclinical atherosclerosis, the c-IMTmean-max, was used to extract atherosclerosis-related features and SHapley Additive exPlanations (SHAP) to reveal endotypes. The association of endotypes with carotid ultrasonographic measurements at baseline, after 30 months, and with the 3-year atherosclerotic cardiovascular disease (ASCVD) risk was estimated by linear (β, SE) and Cox [hazard ratio (HR), 95% confidence interval (CI)] regression models. Crude estimates were adjusted by common cardiovascular risk factors, and baseline ultrasonographic measures. Improvement in ASCVD risk prediction was evaluated by C-statistic and by net reclassification improvement with reference to SCORE2, c-IMTmean-max, and presence of carotid plaques. An ensemble stacking model was used to predict endotypes in an independent validation cohort, the PIVUS (n = 1061). We identified four endotypes able to differentiate carotid atherosclerosis risk profiles from mild (endotype 1) to severe (endotype 4). SHAP identified endotype-shared variables (age, biological sex, and systolic blood pressure) and endotype-specific biomarkers. In the IMPROVE, as compared to endotype 1, endotype 4 associated with the thickest c-IMT at baseline (β, SE) 0.36 (0.014), the highest number of plaques 1.65 (0.075), the fastest c-IMT progression 0.06 (0.013), and the highest ASCVD risk (HR, 95% CI) (1.95, 1.18-3.23). Baseline and progression measures of carotid subclinical atherosclerosis and ASCVD risk were associated with the predicted endotypes in the PIVUS. Endotypes consistently improved measures of ASCVD risk discrimination and reclassification in both study populations.
conclusionsWe report four replicable subclinical carotid atherosclerosis-endotypes associated with progression of atherosclerosis and ASCVD risk in two independent populations. Our approach based on endotypes can be applied for precision medicine in ASCVD prevention.
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