Beyond BMI: The integrated impact of obese-metabolic-anthropometric phenotypes on chronic kidney disease risk

Chronic kidney disease (CKD) is a global health challenge. Body mass index (BMI) fails to capture the heterogeneity of fat distribution and metabolic status in obesity. We aimed to investigate whether integrated obese-metabolic-anthropometric phenotypes, which simultaneously consider adiposity, metabolic health, and body shape, provide a superior framework for identifying individuals at high risk of CKD.

This prospective cohort study included 343,993 participants from the UK Biobank without pre-existing CKD. Obese-metabolic-anthropometric phenotypes were defined by integrating BMI, a metabolic health score, and body shape (based on A Body Shape Index and Hip Index). Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident CKD. Population attributable risk (PAR) was calculated to quantify the CKD burden attributable to different phenotypes. K-modes cluster analysis classified individuals into four distinct subtypes.

Over a mean follow-up of 13.6 years, 16,037 incident CKD cases were recorded. Compared to the reference group (metabolically healthy non-obese with slim shape), both metabolically unhealthy obese wide-shaped (MUOW) and apple-shaped (MUOA) phenotypes demonstrated substantially elevated CKD risk, with fully adjusted HRs of 2.26 (95% CI: 2.10-2.43) and 2.68 (95% CI: 2.48-2.89), respectively. PAR analysis revealed that the integrated phenotype contributed most to the population-level CKD burden (PAR: 29.3%, 95% CI: 20.8-38.3%), far exceeding the contribution of any single component. Cluster analysis further delineated a high-risk cluster characterized by co-existing obesity and metabolic dysfunction, which exhibited an 89% increased risk of CKD (HR: 1.89, 95% CI: 1.80-1.97).

The confluence of obesity, metabolic dysfunction, and an adverse body shape synergistically substantially elevates CKD risk. Moving beyond BMI to multidimensional phenotyping enables precision identification of high-risk individuals for targeted preventive strategies.