Unsupervised Learning Analysis of Triglycerides, Inflammation, Cholesterol, and the Risks of Incident Cardiovascular Disease and Type 2 Diabetes in the Women's Health Study.

ABSTRACT:

Background

Traditional cardiovascular risk assessment entails investigator-defined exposure levels and individual risk markers in multivariable analysis. We sought to determine whether an alternative unbiased learning analysis might provide further insights into vascular risk.

Methods

We conducted an unsupervised learning (k-means cluster) analysis in the Women's Health Study (N=26 443) using baseline levels of triglycerides, high-sensitivity C-reactive protein, and low-density lipoprotein cholesterol to form novel exposures. We then evaluated cluster-based risk of incident coronary, cerebrovascular, and limb events using the Kaplan-Meier method and multivariable Cox models, followed by comparison with established clinical biomarker thresholds. Finally, we illustrated clinic

SUBMITTER: Duran EK 

PROVIDER: S-EPMC12554421 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature