Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Identifying Four Obesity Axes Through Integrative Multiomics and Imaging Analysis

24 Apr 2025

Abstract excerpt

We aimed to identify distinct axes of obesity using advanced magnetic resonance imaging (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. Genome-wide association studies were performed for each axis to uncover genetic factors, followed by pathway enrichment, genetic correlation, and...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Adipose Tissue and Metabolism
Share to Diabetes, Cardiovascular Risks, and Lipoproteins
Share to Genetic Associations and Epidemiology

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Source

Identifiers

PMID: 40272846
DOI: 10.2337/db24-1103

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identifying Four Obesity Axes Through Integrative Multiomics and Imaging AnalysisPMID 40272846Read ↗

Uncovering covariance patterns across energy balance traits enables the discovery of new obesity‐related genesSemantic proximity Read ↗

Genomics reveals eleven obesity endotypes with distinct biological and phenotypic signaturesSemantic proximity Read ↗

Genetic and molecular signatures highlight diverse pathways linking obesity to type 2 diabetes.Semantic proximity Read ↗

A conserved transcriptomic model defines metabolic resilience and vulnerability in obesitySemantic proximity Read ↗

Genetic subtyping of obesity reveals biological insights into the uncoupling of adiposity from its cardiometabolic comorbidities.Semantic proximity Read ↗

Body composition subphenotypes, cardiometabolic risk and incident outcomes: validation in the population-based NAKO and UK Biobank imaging cohortsSemantic proximity Read ↗

Organ fat, not general obesity, defines risk for diabetes, inflammation, and comorbiditiesSemantic proximity Read ↗

Modeling the genomic architecture of adiposity and anthropometrics across the lifespan.Semantic proximity Read ↗

Genomics and phenomics of body mass index reveals a complex disease network.Semantic proximity Read ↗

Genetic subtyping of obesity reveals biological insights into the uncoupling of adiposity from its cardiometabolic comorbiditiesSemantic proximity Read ↗

Discordant Obesity Severity Classification Between the Edmonton Obesity Staging System and the Lancet Commission ModelSemantic proximity Read ↗

Genome-wide association study provides novel insight into the genetic architecture of severe obesity.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.