Medication-Stratified Analysis of LDL-C Equation Miscalibration in Diabetes: Evidence from the All of Us Research Program and a Medication-Agnostic Machine-Learning Correction.

ABSTRACT:

Objective

Standard LDL-C equations were derived in cohorts largely untreated with modern combination diabetes therapies. With medication-treated patients comprising 84% on statins, 53% on insulin, and 25% on GLP-1 receptor agonists-often in combination-we quantified medication-specific miscalibration in LDL-C equations and evaluated a machine learning correction that operates without requiring medication data.

Research design and methods

Using All of Us Research Program data (n=3,477; test n=696), we compared Friedewald, Martin-Hopkins, and Sampson (NIH) Equation 2 against direct LDL-C measurements. We developed a stacked ensemble model (elastic net, random forest, XGBoost, neural network) trained solely on routine laboratory values. Accuracy was assessed within medication g

SUBMITTER: Doku R 

PROVIDER: S-EPMC12633579 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature