Anthropometric metabolic subtypes and health outcomes: A data-driven cluster analysis

Area of Science:

• Metabolic health and anthropometry
• Obesity research
• Data-driven subtype identification

Background:

• Overweight and obesity are complex conditions with varied health implications.
• Existing classifications may not fully capture the diverse metabolic profiles associated with excess weight.
• Need for data-driven approaches to define obesity subtypes.

Purpose of the Study:

• To develop and validate WHOLISTIIC, a novel data-driven cluster analysis for identifying anthropometric metabolic subtypes.
• To investigate the association of these subtypes with long-term adverse health outcomes.

Main Methods:

• K-means cluster analysis of 397,424 UK Biobank participants.
• Utilized five domains: central obesity (waist-to-height ratio), general obesity (body mass index [BMI]), limb strength (handgrip strength), insulin resistance (triglyceride to high-density lipoprotein cholesterol [HDLc] ratio), and inflammatory condition (neutrophil-to-lymphocyte ratio).
• Replication in the NHANES cohort and Cox proportional hazards regression for outcome associations.

Main Results:

• Six replicable anthropometric metabolic clusters were identified.
• Cluster 2 (high handgrip strength) showed increased cardiovascular mortality and MACE risk but decreased respiratory mortality and dementia risk.
• Clusters 3-6 (low strength, high insulin resistance, high inflammation, or highest BMI) exhibited substantially increased risks for all-cause, cardiovascular, cancer mortality, MACE, and chronic renal failure.
• Associations were replicated in the NHANES cohort.

Conclusions:

• Easily accessible anthropometric and metabolic parameters can define distinct subtypes of overweight and obesity.
• These identified subtypes are associated with significantly different long-term risks of adverse health outcomes.
• WHOLISTIIC provides a valuable tool for stratifying risk in individuals with overweight and obesity.