Machine learning models incorporating HDL-related inflammatory biomarkers achieved high discrimination (AUC 0.8892, accuracy 96.55%) for identifying cross-sectional associations with CHD prevalence.
Cross-Sectional (n=14,745)
Do HDL-related inflammatory ratios associate with coronary heart disease prevalence in US adults?
Machine learning models utilizing readily available HDL-related inflammatory ratios from routine blood tests demonstrate high discrimination for identifying prevalent coronary heart disease, offering potential utility in cardiovascular risk stratification.
Effect estimate: AUC 0.8892
High-density lipoprotein (HDL)-related inflammatory ratios (monocyte-to-HDL cholesterol ratio MHR, lymphocyte-to-HDL cholesterol ratio, neutrophil-to-HDL cholesterol ratio NHR, platelet-to-HDL cholesterol ratio) represent composite biomarkers integrating lipid metabolism and inflammatory pathways. We developed machine learning models to evaluate their utility in coronary heart disease (CHD) classification using a large population-based dataset. We analyzed data from the National Health and Nutrition Examination Survey 2009 to 2020, including 14,745 US adults aged ≥20 years (mean age 51.8 ± 17.6 years). Self-reported CHD diagnosis was the outcome variable. Machine learning models (eXtreme gradient boosting, random forest, logistic regression) were developed to evaluate cross-sectional associations between HDL-related inflammatory ratios and CHD prevalence. Self-reported CHD prevalence was 5.7% (n = 840). All HDL-related inflammatory ratios were significantly elevated in CHD patients: MHR (0.54 ± 0.35 vs 0.42 ± 0.23, P < .001), lymphocyte-to-HDL cholesterol ratio (2.05 ± 3.12 vs 1.55 ± 1.02, P < .001), and NHR (4.06 ± 2.89 vs 3.11 ± 1.77, P < .001). eXtreme gradient boosting demonstrated optimal performance with an area under the receiver operating characteristic curve of 0.8892, accuracy of 96.55%, and precision of 86.00%. SHapley Additive exPlanations analysis identified age as the most important predictor, with MHR and NHR ranking among the top 5 features. Machine learning models incorporating HDL-related inflammatory biomarkers achieved high discrimination (area under the receiver operating characteristic curve = 0.8892) for identifying cross-sectional associations with CHD prevalence. These findings reveal significant cross-sectional associations between HDL-related inflammatory ratios and CHD prevalence, rather than predictive relationships for incident events. These readily available biomarkers from routine blood tests provide substantial value for cardiovascular risk stratification. Prospective validation is warranted to establish their utility for predicting incident CHD events.
Cai et al. (Fri,) conducted a cross-sectional in Coronary heart disease (n=14,745). Machine learning models using HDL-related inflammatory ratios was evaluated on Self-reported CHD diagnosis (AUC 0.8892). Machine learning models incorporating HDL-related inflammatory biomarkers achieved high discrimination (AUC 0.8892, accuracy 96.55%) for identifying cross-sectional associations with CHD prevalence.