Identifying Four Obesity Axes Through Integrative Multiomics and Imaging Analysis

Area of Science:

• Medical imaging and genetics
• Obesity research
• Metabolic and cardiovascular disease

Background:

• Body mass index (BMI) is a limited measure of obesity.
• Obesity has diverse fat distribution patterns.
• Understanding obesity heterogeneity is crucial for disease management.

Purpose of the Study:

• To identify distinct axes of obesity using advanced MRI phenotypes.
• To explore the genetic underpinnings of these obesity axes.
• To investigate associations between obesity axes and disease risks.

Main Methods:

• Principal component analysis (PCA) of 24 MRI-derived measures in 33,122 UK Biobank participants.
• Genome-wide association studies (GWAS) for each obesity axis.
• Pathway enrichment, genetic correlation, and Mendelian randomization analyses.

Main Results:

• Four obesity axes identified: General Obesity, Muscle-Dominant, Peripheral Fat, and Lower Body Fat.
• Each axis demonstrated distinct genetic loci and associated pathways.
• General Obesity linked to increased metabolic/cardiovascular risk; Lower Body Fat showed protective effects against type 2 diabetes and cardiovascular disease.

Conclusions:

• Obesity is heterogeneous, with distinct axes identifiable via advanced imaging.
• These axes have unique genetic profiles and disease associations.
• Moving beyond BMI offers potential for personalized obesity treatment and disease prevention.