Identifying Four Obesity Axes Through Integrative Multiomics and Imaging Analysis

We aimed to identify distinct axes of obesity using advanced magnetic resonance imaging (MRI)–derived pheno-types. We used 24 MRI-derived fat distribution and muscle volume measures (UK Biobank; N = 33,122) to construct obesity axes through principal component analysis. Genome-wide association studies were performed for each axis to uncover genetic factors, followed by pathway enrichment, genetic correlation, and Mendelian randomi-zation analyses to investigate disease associations. Four primary obesity axes were identified: 1) general obesity, reflecting higher fat accumulation in all regions (visceral, subcutaneous, and ectopic fat); 2) muscle dominant, indi-cating greater muscle volume; 3) peripheral fat, associated with higher subcutaneous fat in abdominal and thigh regions; and 4) lower-body fat, characterized by increased lower-body subcutaneous fat and reduced ectopic fat. Each axis was associated with distinct genetic loci and pathways. For instance, the lower-body fat axis was associated with RSPO3 and COBLL1, which are emerging as promising candidates for therapeutic targeting. Disease risks varied across axes; the general obesity axis was correlated with higher risks of metabolic and cardiovascular diseases, whereas the lower-body fat axis seemed to pro-tect against type 2 diabetes and cardiovascular disease. This study highlights the heterogeneity of obesity through the identification of obesity axes and emphasizes the potential to extend beyond BMI in defining and treating obesity for obesity-related disease management.