A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes

genetics
metabolism
GWAS
phenome-wide analysis
  • Core Finding: The study used genetic data to define two distinct obesity profiles that exhibit highly contrasting, or discordant, effects on the risk of developing type 2 diabetes (T2D).
  • Phenotypic Differences: The two profiles showed key differences across a wide range of clinical and molecular traits, including cardiovascular mortality, liver metabolism, specific lipid fractions, and blood pressure.
  • Mechanism: Instrumental analyses highlighted the prominent causal role of factors like waist-to-hip ratio and blood pressure in driving the differences in T2D risk between the two genetic obesity subtypes.
Published

23 January 2026

PubMed: 36703017 DOI: 10.1038/s42255-022-00731-5 Overview generated by: Gemini 2.5 Flash, 28/11/2025

Key Findings: Two Genetic Obesity Profiles

This study addresses the high degree of heterogeneity in the relationship between obesity and type 2 diabetes (T2D) by using a genetic-driven approach to define two distinct obesity profiles. These profiles convey highly concordant and discordant diabetogenic effects, indicating that the genetic mechanism of obesity strongly influences its specific consequences for metabolic health.

Methods and Study Design

The researchers employed a genetic-driven approach to identify the two obesity profiles. They then conducted a phenome-wide comparative analysis to annotate and compare the genetic association signals for these profiles across various clinical and molecular phenotypic layers.

  • Data Sources: The analysis utilized individual-level data from large biobanks, specifically the UK Biobank and BioVU.
  • Ethics: The study adhered to the ethical principles of the Declaration of Helsinki, with both data sources having received the necessary institutional ethics approvals, and all participants providing written informed consent.

Results: Phenotypic Differences

The comparative analysis identified key differences between the two obesity profiles across a wide spectrum of traits, illustrating the diverse pathways through which obesity can influence health.

Trait Discordance

Significant differences were identified in various clinical and molecular traits, including: * Cardiovascular Mortality: The profiles convey different risks for cardiovascular outcomes. * Fat Distribution: Variations were observed in how fat is distributed in the body. * Metabolism: Key differences were found in liver metabolism, blood pressure, and specific lipid fractions. * Molecular Traits: Differences were noted in blood levels of proteins involved in extracellular matrix remodelling. * Microbiome: Marginal differences were found in the abundance of Bacteroidetes and Firmicutes bacteria in the gut.

Causal Mechanisms

Instrumental analyses indicated prominent causal roles for certain risk factors in driving the discordance between the profiles: * Waist-to-Hip Ratio (WHR): This measure of central adiposity plays a key causal role. * Blood Pressure: Elevated blood pressure is a prominent causal factor. * Cholesterol Content of High-Density Lipoprotein (HDL): The genetic influence on HDL cholesterol content is also a significant causal differentiator.

Conclusions

The research defines a genetic basis for the heterogeneity observed in the relationship between obesity and T2D. By identifying obesity profiles with distinct diabetogenic effects and differential impacts on cardiovascular and metabolic health, the study supports a more refined, mechanism-based approach to understanding and treating T2D risk. This paves the way for a precision medicine framework where genetic profiles could inform specific intervention strategies.