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This underscores the need for a systems-level framework integrating key organ fat measurements to disentangle the heterogeneous pathways through which adiposity shapes differential cardiometabolic risk profiles. Such an approach could advance mechanistic understanding and enable more precise risk stratification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In the UK Biobank, 24 935 participants without overt cardiac disease were studied. Six adiposity phenotypic groups were identified using unsupervised clustering of MRI-derived adiposity measures (subcutaneous, visceral, pericardial, liver, pancreatic, and muscle fat). These phenotypes were characterized in terms of body composition, cardiac remodelling, and the development of major cardiometabolic diseases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Each phenotype showed a distinct pattern of fat distribution and body composition. A pancreatic-fat dominant phenotype, marked by visceral adiposity and sarcopenic features, showed a cardiorenal-metabolic risk profile. A muscle-fat dominant phenotype, characterized by subcutaneous adiposity and sarcopenic features, was associated with increased heart failure risk. While pericardial-fat dominant and liver-fat dominant phenotypes did not associate with cardiac disease risk, they exhibited distinctive cardiac remodelling patterns, revealing phenotype-specific metabolic-cardiac interactions. Lastly, normal-weight individuals with mild multi-organ fat showed elevated chronic ischaemic heart disease risk, highlighting the value of phenotype-based risk assessment beyond general weight measures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Distinct patterns of multi-organ fat accumulation were associated with differential body composition, cardiac remodelling, and cardiometabolic disease risk profiles. The identified adiposity phenotypic groups capture clinically meaningful heterogeneity across the cardiorenal-metabolic spectrum and may inform future personalized, multisystem approaches to prevention and management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/eurheartj\/ehag702","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T11:48:26Z","timestamp":1787140106000},"source":"Crossref","is-referenced-by-count":1,"title":["Mapping organ fat distribution and body composition: towards precision profiling of cardiometabolic disease risk"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6726-2425","authenticated-orcid":false,"given":"Yeshe Manuel","family":"Kway","sequence":"first","affiliation":[{"name":"Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford , Oxford OX3 9DU ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahra","family":"Raisi-Estabragh","sequence":"additional","affiliation":[{"name":"Centre for Advanced Cardiovascular Imaging, William Harvey Research Institute, Queen Mary University of London, NIHR Barts Biomedical Research Centre, Barts Heart Centre, Barts Health NHS Trust , London ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios","family":"Vavilis","sequence":"additional","affiliation":[{"name":"Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford , Oxford OX3 9DU ,","place":["UK"]},{"name":"Department of Cardiology, Heart and Vascular Center, Karolinska University Hospital , Stockholm ,","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charalambos","family":"Antoniades","sequence":"additional","affiliation":[{"name":"Acute Multidisciplinary Imaging and Interventional Centre, Radcliffe Department of Medicine, University of Oxford , Oxford ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4622-5160","authenticated-orcid":false,"given":"Steffen E","family":"Petersen","sequence":"additional","affiliation":[{"name":"William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London , Charterhouse Square, London ,","place":["UK"]},{"name":"Barts Heart Centre, St Bartholomew\u2019s Hospital, Barts Health NHS Trust , London ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas E","family":"Nichols","sequence":"additional","affiliation":[{"name":"Big Data Institute, Nuffield Department of Population Health, University of Oxford , Oxford OX3 7LF ,","place":["UK"]},{"name":"Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford , Oxford ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1239-9608","authenticated-orcid":false,"given":"Betty","family":"Raman","sequence":"additional","affiliation":[{"name":"Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford , Oxford OX3 9DU ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3385-1242","authenticated-orcid":false,"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford , Oxford OX3 9DU ,","place":["UK"]},{"name":"Big Data Institute, Nuffield Department of Population Health, University of Oxford , Oxford OX3 7LF ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9017-5645","authenticated-orcid":false,"given":"Stefan","family":"Neubauer","sequence":"additional","affiliation":[{"name":"Oxford Centre for Clinical Magnetic Resonance Research (OCMR), Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford , Oxford OX3 9DU ,","place":["UK"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"2026090412112978500_ehag702-B1","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.pop.2015.10.001","article-title":"Obesity statistics","volume":"43","author":"Smith","year":"2016","journal-title":"Prim Care"},{"key":"2026090412112978500_ehag702-B2","doi-asserted-by":"publisher","first-page":"2218","DOI":"10.1093\/eurjpc\/zwac187","article-title":"Obesity and cardiovascular disease: mechanistic insights and management strategies. 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