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| Item Type: | Preprint |
|---|---|
| Title: | Body composition subphenotypes, cardiometabolic risk and incident outcomes: validation in the population-based NAKO and UK Biobank imaging cohorts |
| Creators: |
Grune, Elena  ORCID: https://orcid.org/0009-0001-8986-5670, Haueise, Tobias ORCID: https://orcid.org/0000-0002-1462-7539, von Itter, Marc-Nicolas ORCID: https://orcid.org/0009-0005-2339-4270, Jung, Matthias ORCID: https://orcid.org/0000-0002-1124-4284, Bamberg, Fabian ORCID: https://orcid.org/0000-0002-7460-3942, Bibi, Saima, Friedrich, Christoph M., Fromherz, Patricia ORCID: https://orcid.org/0000-0002-0931-8651, Kauczor, Hans-Ulrich ORCID: https://orcid.org/0000-0002-6730-9462, Kellner, Elias ORCID: https://orcid.org/0000-0001-9494-2354, Köttgen, Anna ORCID: https://orcid.org/0000-0002-4671-3714, Krist, Lilian, Kroencke, Thomas, Lieb, Wolfgang ORCID: https://orcid.org/0000-0003-2544-4460, Machann, Jürgen ORCID: https://orcid.org/0000-0002-4458-5886, Nattenmüller, Johanna ORCID: https://orcid.org/0000-0003-4032-378X, Niedermayer, Fiona ORCID: https://orcid.org/0000-0002-6798-2630, Niendorf, Thoralf ORCID: https://orcid.org/0000-0001-7584-6527, Nonnenmacher, Tobias, Norajitra, Tobias ORCID: https://orcid.org/0000-0002-1788-8646, Pischon, Tobias ORCID: https://orcid.org/0000-0003-1568-767X, Reisert, Marco ORCID: https://orcid.org/0000-0003-2742-1940, Schlett, Christopher L. ORCID: https://orcid.org/0000-0002-1576-1481, Schulz-Menger, Jeanette ORCID: https://orcid.org/0000-0003-3100-1092, Weiß, Jakob ORCID: https://orcid.org/0000-0002-0336-8519, Peters, Annette ORCID: https://orcid.org/0000-0001-6645-0985, Boulesteix, Anne-Laure ORCID: https://orcid.org/0000-0002-2729-0947 and Rospleszcz, Susanne ORCID: https://orcid.org/0000-0002-4788-2341
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| Abstract: | Background Anthropometric measures do not adequately capture heterogeneity in body fat distribution and corresponding cardiometabolic risk, whereas magnetic resonance imaging (MRI) enables precise differentiation and quantification of adipose tissue compartments and ectopic fat. We aimed to validate previously derived MRI-based body composition subphenotypes and their cardiometabolic risk profiles in two independent European cohorts. Methods Using deep learning–based image analysis, we quantified bone marrow, visceral, subcutaneous, cardiac, renal sinus, hepatic, skeletal muscle, and pancreatic fat in the imaging substudies of two population-based cohorts: the German National Cohort (NAKO, N=29,314, age range 19-74 years) and the UK Biobank (N=36,109, age range 40-69 years). Body composition subphenotypes, previously identified by k-means clustering, were evaluated using a rigorous statistical cluster validation framework with method-based and results-based approaches. In NAKO, cross-sectional associations between subphenotypes and estimated cardiovascular disease risk scores were examined using linear regression. In UK Biobank, longitudinal associations between subphenotypes and incident cardiometabolic outcomes, ascertained through hospital record linkage, were analysed using Cox regression. Findings All five body composition subphenotypes were robustly validated across both cohorts, and showed distinct fat distribution patterns and cardiometabolic risk profiles: I “lean”, II “average adiposity”, III “bone and muscle adiposity”, IV “hepato-abdominal adiposity”, and V “general and pancreatic adiposity”. Subphenotypes I–III showed progressive adipose tissue remodelling patterns likely reflecting ageing trajectories. The “hepato-abdominal adiposity” subphenotype showed highest risk of incident diabetes, whereas the “general and pancreatic adiposity” subphenotype showed highest overall cardiovascular disease burden and metabolic impairment. Interpretation MRI-derived body composition subphenotypes represent distinct fat distribution patterns that reflect ageing- and disease-related processes, which supports the potential of body composition phenotyping for improved cardiometabolic risk stratification and targeted prevention. |
| Source: | medRxiv |
| Publisher: | Cold Spring Harbor Laboratory Press |
| Article Number: | 2026.06.18.26355957 |
| Date: | 22 June 2026 |
| Official Publication: | https://doi.org/10.64898/2026.06.18.26355957 |
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