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Identifying Four Obesity Axes Through Integrative Multiomics and Imaging Analysis.

Chiemela S Odoemelam1, Afreen Naz1, Marjola Thanaj2

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We identified four distinct obesity axes using MRI, revealing varied genetic links and disease risks. This approach moves beyond BMI for better obesity management and treatment.

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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.