ArticleJournal of cachexia, sarcopenia and muscle2024
Association between myosteatosis and impaired glucose metabolism: A deep learning whole-body magnetic resonance imaging population phenotyping approach.
Article in Journal of cachexia, sarcopenia and muscle, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Associations Between Body Composition and Intratumoural Immune Cells in Patients With Uterine Cervical Cancer.Journal of cachexia, sarcopenia and muscle · 2026Article
- Artificial Intelligence-derived Measurements of Myosteatosis from Coronary Artery Calcium CT Scans to Predict COPD: The Multi-Ethnic Study of Atherosclerosis.Radiology. Cardiothoracic imaging · 2026Article
- A clinical prediction model for low psoas muscle radiodensity in adults with severe obesity: development and internal validation.Frontiers in nutrition · 2026Article
- Myosteatosis in type 1 diabetes: immunometabolic mechanisms, biomarkers, and therapeutic targets.Frontiers in endocrinology · 2026Review
- Association between myosteatosis and impaired glucose metabolism: A deep learning whole-body magnetic resonance imaging population phenotyping approach.Journal of cachexia, sarcopenia and muscle · 2024Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundThere is increasing evidence that myosteatosis, which is currently not assessed in clinical routine, plays an important role in risk estimation in individuals with impaired glucose metabolism, as it is associated with the progression of insulin resistance. With advances in artificial intelligence, automated and accurate algorithms have become feasible to fill this gap.
methodsIn this retrospective study, we developed and tested a fully automated deep learning model using data from two prospective cohort studies (German National Cohort [NAKO] and Cooperative Health Research in the Region of Augsburg [KORA]) to quantify myosteatosis on whole-body T1-weighted Dixon magnetic resonance imaging as (1) intramuscular adipose tissue (IMAT; the current standard) and (2) quantitative skeletal muscle (SM) fat fraction (SMFF). Subsequently, we investigated the two measures for their discrimination of and association with impaired glucose metabolism beyond baseline demographics (age, sex and body mass index [BMI]) and cardiometabolic risk factors (lipid panel, systolic blood pressure, smoking status and alcohol consumption) in asymptomatic individuals from the KORA study. Impaired glucose metabolism was defined as impaired fasting glucose or impaired glucose tolerance (140-200 mg/dL) or prevalent diabetes mellitus.
resultsModel performance was high, with Dice coefficients of ≥0.81 for IMAT and ≥0.91 for SM in the internal (NAKO) and external (KORA) testing sets. In the target population (380 KORA participants: mean age of 53.6 ± 9.2 years, BMI of 28.2 ± 4.9 kg/m
conclusionsQuantitative SMFF, but not IMAT, is an independent predictor of impaired glucose metabolism, and discrimination is not significantly different from BMI, making it a promising alternative for the currently established approach. Automated methods such as the proposed model may provide a feasible option for opportunistic screening of myosteatosis and, thus, a low-cost personalized risk assessment solution.
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