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

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 clustering based on traditional MetS components in a full adult population. Specifically, the analysis tests whether such an approach recovers a graded metabolic risk strata extending beyond the binary MetS definition.

METHODS: NHANES adults (1999-2023) were analysed after excluding users of antihypertensive, lipid-lowering, or glucose-lowering medications. The final analytic cohort comprised 33,597 adults, with a MetS prevalence of 27.5%. Candidate features included traditional MetS components and derived indices. All feature combinations of size 3-8 were screened using k-means clustering (k = 4) with robust scaling. For each combination, clusters were ranked by MetS prevalence. A feature set passed the screen if the union of the two highest-prevalence clusters (Highest and Medium-High) contained ≥75% of MetS+ and ≤15% of MetS- participants. Retained sets were re-fitted across 10 random seeds and evaluated for stability (bootstrap Jaccard with Hungarian alignment), internal validity (silhouette, Davies-Bouldin index), and external alignment (normalised mutual information, purity). The best-performing Derived set and the Traditional benchmark were selected for detailed analysis.

RESULTS: The Derived set concentrated 79.26% of all MetS+ in Highest and Medium-High clusters, while including 14.62% of all MetS-. The Traditional set captured 27.63% of MetS+ and 2.51% of MetS- in the top two clusters. Internal validity favoured the Derived solution (silhouette 0.274 vs. 0.210; Davies-Bouldin 1.152 vs. 1.366), as did stability (min-mean Jaccard 0.963 vs. 0.920). Heatmaps and radar plots showed two distinct intermediate phenotypes in the Derived set, hypertensive-leaning and dyslipidaemic-leaning, whereas the Traditional solution displayed crossovers and weaker gradients. Demographic associations (sex, ethnicity, age) were consistent with known epidemiology.

CONCLUSIONS: Composite indices capturing insulin resistance, atherogenic lipids, visceral adiposity, and hepatic fat yielded more reproducible and interpretable strata than traditional components alone, with markedly higher recall of MetS+. These findings suggest improved stratification of metabolic dysfunction across a continuum and may support earlier identification of at-risk individuals not yet meeting binary thresholds, pending validation in independent cohorts.