Novel metabolic syndrome subgroups and their association with cardiovascular outcomes: A data‐driven cluster analysis of the UK Biobank
Authors' conclusion
This clustering reveals clinical heterogeneity within the MS population. Identifying these subgroups provides a more nuanced approach to risk assessment than traditional criteria counting. This framework may help clinicians to better identify high-risk individuals who could benefit from more intensive monitoring and targeted management strategies.
Study context
- Population
- Drug-naïve individuals with metabolic syndrome (n = 62,776) and controls without metabolic syndrome (MS), UK Biobank; clustering on waist circumference, systolic and diastolic blood pressure (BP), TG, HDL-C and fasting glucose62,776 with MS
- Exposure
- MS clusters 1–3 vs non-MS controls
- Non-MSn = 230,999reference
- Outcome
- 3P-MACE:
- MI
- Stroke
- Cardiovascular disease (CVD)-related mortality
Methodological detailsUK Biobank, data-driven cluster analysis, separate clustering for men and women, Cox and logistic regression
- UK Biobank
- Data-driven cluster analysis
- Separate clustering for men and women
- Cox and logistic regression
Main finding
As reported in the abstract
3P-MACE
Each group vs Non-MSAdjusted hazard ratio (95% CI)
Cluster 1
1.37(1.30–1.46)
Cluster 2
1.61(1.53–1.70)
Cluster 3
1.87(1.74–2.00)
Observational data: associations, not effects.
Further findings
Clustering vs traditional criteria counting
- Significant risk heterogeneity even within groups with the same number of MS criteria; clustering substantially more sensitive in identifying highest-risk individuals than counting
Original publication
Novel metabolic syndrome subgroups and their association with cardiovascular outcomes: A data‐driven cluster analysis of the UK Biobank. Diabetes Obes Metab. Published 12 January 2026. doi:10.
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