ArticleeLife2021
Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 167 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
167 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence-based models for quantification of intra-pancreatic fat deposition and their clinical relevance: a systematic review of imaging studies.European radiology · 2026Pooled it
- Spleen volume in relation to ulcerative colitis and Crohn's disease: a Mendelian randomization study.Scientific reports · 2025Pooled it
- Causal relationship between nonalcoholic fatty liver disease and different sleep traits: a bidirectional Mendelian randomized study.Frontiers in endocrinology · 2023Trial
- Nationwide organ volume distributions and cross-sectional age-associated differences in abdominal CT from Japan.Japanese journal of radiology · 2026Article
- Abdominal MRI-Derived Liver, Visceral Fat, and Pancreatic Phenotypes Improve Prediction of Incident Type 2 Diabetes.Diabetes/metabolism research and reviews · 2026Article
- AI-derived whole-body MRI metrics in patients with multiple myeloma reveal unique insights into body composition and outcomes.Blood advances · 2026Observational
- Inferring multi-organ genetic connections using imaging and clinical data through Mendelian randomization.Nature biomedical engineering · 2026Article
- Multilevel genome-wide analysis reveals shared genetic architecture between acute pancreatitis and body fat distribution.World journal of emergency medicine · 2026Article
- Effects of Schizophrenia, Bipolar Disorder, and Depression on Cardiopulmonary and Abdominal Organ Structure.Biological psychiatry global open science · 2026Article
- Genetic association stratified by HLA-DR3 and HLA-DR4 status reveals heterogeneity in pathways of progression to type 1 diabetes.Diabetologia · 2026Article
- Causal effects of life-course obesity on sleep apnoea: a Mendelian randomization study.Internal and emergency medicine · 2026Article
- Age-related changes in adiposity and cardiometabolic disease risk: a longitudinal and prospective study in the UK Biobank.International journal of obesity (2005) · 2026Article
- Genetic analysis of imaging-derived phenotypes.Nature reviews. Genetics · 2026Review
- Mapping histologic and functional maturation of human endocrine pancreas across early postnatal periods.Nature communications · 2026Article
- Hundreds of cardiac MRI traits derived using 3D diffusion autoencoders share a common genetic architecture.Nature communications · 2026Article
- Insulin resistance drives GGT elevation in MASLD with diabetes: evidence from clinical cohorts, mendelian randomization and functional genomics.Nutrition & diabetes · 2026Article
- Brain-heart-eye axis revealed by multi-organ imaging genetics and proteomics.Nature biomedical engineering · 2026Article
- Shared genetic architecture between obesity and psychiatric disorders reveals comorbid etiology and therapeutic targets.Life medicine · 2026Article
- Multi-organ imaging and genetics show the impact of sleep patterns on the human brain and body.Communications medicine · 2026Article
- Article
107 more citing papers are in PubMed but not listed here.
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Authors and funding
8 authors.
Funding
Abstract
Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health, but biobank-scale studies are still in their infancy. Using over 38,000 abdominal MRI scans in the UK Biobank, we used deep learning to quantify volume, fat, and iron in seven organs and tissues, and demonstrate that imaging-derived phenotypes reflect health status. We show that these traits have a substantial heritable component (8-44%) and identify 93 independent genome-wide significant associations, including four associations with liver traits that have not previously been reported. Our work demonstrates the tractability of deep learning to systematically quantify health parameters from high-throughput MRI across a range of organs and tissues, and use the largest-ever study of its kind to generate new insights into the genetic architecture of these traits.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.