HDL-Triglyceride Index: A Kinetically-Derived Biomarker for Time-Integrated Triglyceride Exposure and Cardiometabolic Risk

Abstract

BackgroundHigh-density lipoprotein (HDL) particles undergo dynamic remodeling through triglyceride (Tg) enrichment via cholesteryl ester transfer protein-mediated exchange, yet clinical assessment tools capturing this temporal integration remain lacking. Traditional single-point Tg measurements fail to account for metabolic fluctuations occurring throughout HDL particle lifespan. ObjectiveTo derive and validate a mathematical equation estimating time-weighted average Tg exposure based on HDL particle kinetics. MethodsWe developed HDLTg through kinetic modeling incorporating HDL residence time ({tau} {approx} 5 days) and lipid exchange dynamics. The equation HDLTg = 42.5 x (Total Tg/HDL-cholesterol) was calibrated using regression analysis and validated across laboratory tests of 1,247 subjects stratified by metabolic phenotype. Comparative analyses employed receiver operating characteristic curves and correlation coefficients against established biomarkers. ResultsHDLTg demonstrated superior predictive performance versus fasting Tg for metabolic syndrome development (AUC 0.824 vs. 0.742, {Delta}AUC = 0.082, p = 0.003) with 12.4% net reclassification improvement (p = 0.008). Strong correlations emerged with Tg-glucose index (r = 0.856) and seven-day Tg averaging (r = 0.867). Population stratification revealed progressive elevation: healthy controls 78.4 {+/-} 18.6 mg/dL, prediabetes 118.7 {+/-} 31.4 mg/dL, metabolic syndrome 167.9 {+/-} 45.2 mg/dL, diabetes 203.6 {+/-} 58.7 mg/dL (p < 0.001). Optimal sex-specific cutoffs achieved sensitivity >78% and specificity >76% for metabolic syndrome identification. ConclusionHDLTg provides clinically actionable assessment of integrated Tg burden, offering enhanced cardiometabolic risk stratification through biologically-grounded temporal averaging superior to conventional single-measurement approaches.