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Grune, E. ; Nattenmüller, J.* ; Kiefer, L.S.* ; Machann, J. ; Peters, A. ; Bamberg, F.* ; Schlett, C.L.* ; Rospleszcz, S.

Subphenotypes of body composition and their association with cardiometabolic risk - Magnetic resonance imaging in a population-based sample.

Metabolism 164:156130 (2025)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
BACKGROUND: For characterizing health states, fat distribution is more informative than overall body size. We used population-based whole-body magnetic resonance imaging (MRI) to identify distinct body composition subphenotypes and characterize associations with cardiovascular disease (CVD) risk. METHODS: Bone marrow, visceral, subcutaneous, cardiac, renal, hepatic, skeletal muscle and pancreatic adipose tissue were measured by MRI in n = 299 individuals from the population-based KORA cohort. Body composition subphenotypes were identified by data-driven k-means clustering. CVD risk was calculated by established scores. RESULTS: We identified five body composition subphenotypes, which differed substantially in CVD risk factor distribution and CVD risk. Compared to reference subphenotype I with favorable risk profile, two high-risk phenotypes, III&V, had a 3.8-fold increased CVD risk. High-risk subphenotype III had increased bone marrow and skeletal muscle fat (26.3 % vs 11.4 % in subphenotype I), indicating ageing effects, whereas subphenotype V showed overall high fat contents, and particularly elevated pancreatic fat (25.0 % vs 3.7 % in subphenotype I), indicating metabolic impairment. Subphenotype II had a 2.7-fold increased CVD risk, and an unfavorable fat distribution, probably smoking-related, while BMI was only slightly elevated. Subphenotype IV had a 2.8-fold increased CVD risk with comparably young individuals, who showed high blood pressure and hepatic fat (17.7 % vs 3.0 % in subphenotype I). CONCLUSIONS: Whole-body MRI can identify distinct body composition subphenotypes associated with different degrees of cardiometabolic risk. Body composition profiling may enable a more comprehensive risk assessment than individual fat compartments, with potential benefits for individualized prevention.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Adipose Tissue ; Body Composition ; Cardiometabolic Risk ; Clustering ; Magnetic Resonance Imaging ; Obesity ; Population-based; Subcutaneous Adipose-tissue; Fatty Liver-disease; Ectopic Fat; Pancreas; Kora
Sprache englisch
Veröffentlichungsjahr 2025
Prepublished im Jahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 0026-0495
e-ISSN 1532-8600
Quellenangaben Band: 164, Heft: , Seiten: , Artikelnummer: 156130 Supplement: ,
Verlag Elsevier
Verlagsort 1600 John F Kennedy Boulevard, Ste 1800, Philadelphia, Pa 19103-2899 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology (EPI)
Institute of Diabetes Research and Metabolic Diseases (IDM)
POF Topic(s) 30202 - Environmental Health
90000 - German Center for Diabetes Research
Forschungsfeld(er) Genetics and Epidemiology
Helmholtz Diabetes Center
PSP-Element(e) G-504000-010
G-502400-001
Förderungen German Federal Ministry of Education and Research (BMBF)
state of Bavaria
University Hospital of Augsburg
Munich Center of Health Sciences (MC-Health)
German Research Foundation
Centre for Diabetes Research (DZD e. V., Neuherberg, Germany)
Siemens Healthcare
Scopus ID 85213888603
PubMed ID 39743039
Erfassungsdatum 2025-01-30