{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T04:54:41Z","timestamp":1790916881856,"version":"4.1.0"},"reference-count":41,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82373665"],"award-info":[{"award-number":["82373665"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Metabolism Res"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>To evaluate whether abdominal magnetic resonance imaging (MRI)\u2010derived metabolic phenotypes improve the opportunistic prediction of incident type 2 diabetes (T2D) beyond clinical risk models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>This study included 36,833 UK Biobank participants who underwent abdominal MRI and were free of T2D at imaging baseline. The original Cambridge Diabetes Risk Score (CDRS) was evaluated, and glycated haemoglobin (HbA1c) was added to form the clinical CDRS model. MRI phenotypes were selected using a LASSO\u2010based selection\u2010frequency analysis and added to construct the clinical CDRS\u2010MRI model. Model performance was assessed in an internal validation set using Harrell's C\u2010index, continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>During a median follow\u2010up of 7.38\u00a0years, 570 participants developed incident T2D. The selection\u2010frequency analysis retained four MRI phenotypes: liver proton density fat fraction, liver volume, visceral fat volume, and pancreas volume. In the internal validation set, C\u2010indices were 0.765 (95% CI, 0.731\u20130.800) for the original CDRS model, 0.820 (95% CI, 0.786\u20130.849) for the clinical CDRS model, and 0.849 (95% CI, 0.818\u20130.877) for the clinical CDRS\u2010MRI model. The clinical CDRS\u2010MRI model improved the C\u2010index by 0.084 versus the original CDRS model and by 0.029 versus the clinical CDRS model, with a continuous NRI of 56.2% (95% CI, 40.6%\u201370.0%) and an IDI of 0.022 (95% CI, 0.010\u20130.037).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Abdominal MRI\u2010derived phenotypes improved the prediction of incident T2D beyond established clinical risk models. These findings support secondary use of available abdominal MRI data for metabolic risk stratification, pending external validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/dmrr.70235","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T04:25:56Z","timestamp":1790915156000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Abdominal MRI\u2010Derived Liver, Visceral Fat, and Pancreatic Phenotypes Improve Prediction of Incident Type 2 Diabetes"],"prefix":"10.1002","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5550-4853","authenticated-orcid":false,"given":"Ruotong","family":"Peng","sequence":"first","affiliation":[{"name":"Department of Health and Chronic Disease Management School of Nursing Chinese Academy of Medical Sciences &amp; Peking Union Medical College  Beijing 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