This study aimed to identify integrated body composition patterns and evaluate their sex-specific associations with incident type 2 diabetes (T2D). Among 258,500 UK Biobank participants free of diabetes at baseline, principal component analysis (PCA) was applied to 28 anthropometric, fat, lean, bone, and muscle strength traits. Cox models assessed associations with incident T2D, and least absolute shrinkage and selection operator (LASSO)-penalized Cox and weighted quantile sum (WQS) regression identified key components. During a mean follow-up of 11 years, 33,636 incident T2D cases were identified. PCA yielded seven interpretable patterns. LASSO consistently identified generalized adiposity, leg-dominant lean distribution, and central leanness as robust components across sexes. Compared with the low-score reference group, generalized adiposity was associated with higher T2D risk in men and women (hazard ratio [HR] 1.73 [95% CI 1.33–2.24] and 2.21 [1.55–3.16], respectively), whereas leg-dominant lean distribution was associated with lower risk (HR 0.57 [0.48–0.68] and 0.50 [0.40–0.63]). Central leanness was associated with lower risk in women (HR 0.41 [0.34–0.51]) and showed a nonmonotonic association with risk in men. In WQS analyses, central leanness contributed more strongly to the inverse mixture association in men, whereas leg-dominant lean distribution contributed more strongly in women. These findings highlight sex-specific fat-lean configurations in diabetes risk.
- Conventional obesity measures do not capture the multidimensional nature of body composition or the integrated distribution of fat and lean tissue.
- We aimed to identify integrated body composition patterns and determine their sex-specific associations with incident type 2 diabetes risk.
- We identified distinct composite body composition patterns associated with diabetes risk in men and women, with generalized adiposity linked to higher risk and leg-dominant lean distribution or central leanness linked to lower risk.
- These findings suggest that multidimensional fat-muscle patterns provide complementary information beyond conventional obesity measures for understanding diabetes risk.
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