Whole blood viscosity showed negligible association with postprandial triglyceride response, whereas an L2-penalized logistic regression model achieved strong discrimination with an AUROC of 0.914.
Does whole blood viscosity predict high postprandial triglyceride response in a synthetic clinical cohort?
A leakage-controlled machine learning framework applied to synthetic clinical data demonstrated that estimated whole blood viscosity provides negligible predictive value for postprandial triglyceride response compared to fasting triglycerides.
Effect estimate: AUROC 0.914 (95% CI 0.8957-0.9314)
Absolute Event Rate: 0.914% vs 0.917%
Whole blood viscosity (WBV) has been linked to cardiometabolic risk, yet its relationship with short-term postprandial triglyceride (TG) response remains unclear. We evaluated this question within a fully de-identified, statistically reconstructed synthetic cohort (n = 1, 500), with a primary methodological objective: to demonstrate a rigorously leakage-controlled machine-learning framework for assessing candidate physiological associations. A strictly leakage-controlled pipeline was implemented, with fold-specific preprocessing and probability calibration confined to training data. WBV was estimated using the de Simone formulation. Model development employed stratified 5 5 nested cross-validation, repeated 5 10 resampling, 1, 000-bootstrap uncertainty estimation, calibration assessment (sigmoid and isotonic), threshold-sensitivity analysis across TG₄₇ percentiles, and SHAP-based interpretability. Within the synthetic reconstruction, WBV showed negligible association with postprandial response across correlation testing (r 0), multivariable modeling, robustness analyses, and explainability assessments. In contrast, fasting triglycerides (TG₀₇) exhibited stable monotonic effects and clear phenotype discrimination. Under the primary 75th-percentile TG₄₇ definition, the L2-penalized logistic regression model achieved stable discrimination (nested AUROC = 0. 9141; Brier =0. 0886) with bootstrap AUROC =0. 914 (95% CI 0. 8957, 0. 9314) and consistent calibration-aware performance. Within this synthetic framework, WBV did not provide reproducible predictive or attributional value for short-term postprandial TG response. These findings represent methodological evidence under modeled assumptions rather than definitive physiological conclusions. The study illustrates how leakage-controlled, calibration-aware ML workflows can evaluate candidate metabolic associations in privacy-preserving settings; external validation in real-world cohorts remains necessary.
Piyavechvirat et al. (Thu,) conducted a other in Postprandial triglyceride response (n=1,500). L2-penalized logistic regression model vs. Univariate logistic regression (TG0h only) was evaluated on High postprandial triglyceride response (≥ 75th percentile TG4h) (AUROC 0.914, 95% CI 0.8957-0.9314). Whole blood viscosity showed negligible association with postprandial triglyceride response, whereas an L2-penalized logistic regression model achieved strong discrimination with an AUROC of 0.914.