Identifying four obesity axes through integrative multi-omics and imaging analysis

Abstract

We aimed to identify distinct axes of obesity using advanced MRI-derived phenotypes. We used 24 MRI-derived fat distribution and muscle volume measures (UK Biobank, n= 33,122) to construct obesity axes through principal component analysis (PCA). Genome-wide association studies were performed for each axis to uncover genetic factors, followed by pathway enrichment, genetic correlation, and Mendelian randomization 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, indicating 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 correlated with higher risks of metabolic and cardiovascular diseases; the Lower Body Fat Axis appeared protective 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.