Clustering beyond the binary: Stable machine-learning phenotypes of metabolic syndrome.

Abstract excerpt

BACKGROUND AND OBJECTIVES: Metabolic syndrome (MetS) is defined by binary thresholds on waist circumference, triglycerides, HDL-C, blood pressure, and fasting glucose, which can obscure risk gradients and within-group heterogeneity. This study evaluates whether unsupervised clustering using physiology-anchored composite indices produces more stable and clinically interpretable cardiometabolic phenotypes than...

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