<b>Identifying four obesity axes through integrative multi-omics and imaging analysis</b>

<p dir="ltr">We aimed to identify distinct axes of obesity using advanced MRI-derived phenotypes.</p><p dir="ltr">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.</p><p dir="ltr">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 <i>RSPO3</i> and <i>COBLL1</i> 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.</p><p dir="ltr">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.</p>