{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T04:59:14Z","timestamp":1785473954876,"version":"3.56.0"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>To characterize cardiac magnetic resonance (CMR)-derived phenotypes in a population-based cohort and to evaluate their incremental prognostic value for major adverse cardiovascular events (MACE) beyond traditional risk factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Participants from the UK Biobank imaging cohort without prior cardiovascular diseases were included. Unsupervised k-means clustering and Kaplan\u2013Meier analyses identified distinct CMR-derived phenotypes. Sequential XGBoost models were developed to evaluate the incremental predictive value of CMR features over conventional risk factors, with SHAP used for interpretation. Cross-modality transportability was assessed in an independent Asian cohort using echocardiographic data as a surrogate for CMR-derived variables.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 27,254 participants, 785 (2.88%) experienced MACE over a median follow-up of approximately 5 years. Two phenotypes were identified, with the higher-risk phenotype characterized by increased cardiac volumes, impaired cardiac function, a tendency toward myocardial hypertrophy, and reduced myocardial strain. The CMR-enhanced model improved MACE prediction compared with traditional risk factors alone (AUC: 0.76, 95% CI: 0.73\u20130.79; vs. AUC: 0.61, 95% CI: 0.58\u20130.64,\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt; 0.001), with strong performance for heart failure (AUC: 0.90, 95% CI: 0.86\u20130.94). In the cross-modality validation cohort, the simplified model yielded an AUC of 0.69 (95% CI: 0.56\u20130.83). Higher age, left ventricular mass index, global wall thickness, and lower levels of high-density lipoprotein cholesterol, left atrial ejection fraction, right atrial stroke volume were the key contributors to increased MACE risk.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>CMR-derived phenotypes provide incremental prognostic value beyond traditional risk factors and may improve early identification of individuals at elevated cardiovascular risk.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1852547","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T04:24:35Z","timestamp":1785471875000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Incremental predictive value of CMR phenotyping for major adverse cardiovascular events: an interpretable machine learning study from UK Biobank"],"prefix":"10.3389","volume":"13","author":[{"given":"Peiqi","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University","place":["Xi'an, China"]},{"name":"Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University","place":["Xi'an, China"]},{"name":"Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University","place":["Xi'an, China"]},{"name":"Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University","place":["Xi'an, China"]},{"name":"Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University","place":["Xi'an, China"]},{"name":"Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zi","family":"Kou","sequence":"additional","affiliation":[{"name":"Department of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University","place":["Xi'an, China"]},{"name":"Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University","place":["Xi'an, China"]},{"name":"Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuxian","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Meishan Branch of the Third Affiliated Hospital, Yan'an University School of Medicine","place":["Meishan, Sichuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dengfeng","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University","place":["Xi'an, China"]},{"name":"Xi'an Jiaotong University","place":["Xi'an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1161\/CIRCRESAHA.117.308903","article-title":"Reducing the global burden of cardiovascular disease, Part 1: the epidemiology and risk factors","volume":"121","author":"Joseph","year":"2017","journal-title":"Circ Res."},{"key":"B2","doi-asserted-by":"publisher","first-page":"e223008","DOI":"10.1148\/radiol.223008","article-title":"Cardiac MRI: state of the art","volume":"307","author":"Rajiah","year":"2023","journal-title":"Radiology."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.1038\/s41591-024-02971-2","article-title":"Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging","volume":"30","author":"Wang","year":"2024","journal-title":"Nat Med."},{"key":"B4","doi-asserted-by":"publisher","first-page":"106998","DOI":"10.1016\/j.compbiomed.2023.106998","article-title":"Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: a review","volume":"160","author":"Jafari","year":"2023","journal-title":"Comput Biol Med."},{"key":"B5","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.1093\/eurheartj\/ehw128","article-title":"2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) developed with the special contribution of the Heart Failure Association (HFA) of the ESC","volume":"37","author":"Ponikowski","year":"2016","journal-title":"Eur Heart J."},{"key":"B6","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1161\/CIRCULATIONAHA.107.699579","article-title":"General cardiovascular risk profile for use in primary care: the Framingham Heart Study","volume":"117","author":"D'Agostino","year":"2008","journal-title":"Circulation."},{"key":"B7","doi-asserted-by":"publisher","first-page":"e0213653","DOI":"10.1371\/journal.pone.0213653","article-title":"Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423,604 UK Biobank participants","volume":"14","author":"Alaa","year":"2019","journal-title":"PLoS ONE."},{"key":"B8","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1161\/CIRCRESAHA.117.311312","article-title":"Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis","volume":"121","author":"Ambale-Venkatesh","year":"2017","journal-title":"Circ Res."},{"key":"B9","doi-asserted-by":"publisher","first-page":"e1001779","DOI":"10.1371\/journal.pmed.1001779","article-title":"UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age","volume":"12","author":"Sudlow","year":"2015","journal-title":"PLoS Med."},{"key":"B10","doi-asserted-by":"publisher","first-page":"1980","DOI":"10.1016\/S0140-6736(07)60924-6","article-title":"UK Biobank: bank on it","volume":"369","author":"Palmer","year":"2007","journal-title":"Lancet."},{"key":"B11","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1186\/s12968-018-0471-x","article-title":"Automated cardiovascular magnetic resonance image analysis with fully convolutional networks","volume":"20","author":"Bai","year":"2018","journal-title":"J Cardiovasc Magn Reson."},{"key":"B12","doi-asserted-by":"publisher","first-page":"1654","DOI":"10.1038\/s41591-020-1009-y","article-title":"A population-based phenome-wide association study of cardiac and aortic structure and function","volume":"26","author":"Bai","year":"2020","journal-title":"Nat Med."},{"key":"B13","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1016\/j.jcmg.2021.08.011","article-title":"AI based CMR assessment of biventricular function: clinical significance of intervendor variability and measurement errors","volume":"15","author":"Wang","year":"2022","journal-title":"JACC Cardiovasc Imaging."},{"key":"B14","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1016\/j.jcmg.2021.10.008","article-title":"Building confidence in AI-interpreted CMR","volume":"15","author":"Lima","year":"2022","journal-title":"JACC Cardiovasc Imaging."},{"key":"B15","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s11886-019-1107-y","article-title":"Epidemiology of diabetes mellitus and cardiovascular disease","volume":"21","author":"Glovaci","year":"2019","journal-title":"Curr Cardiol Rep."},{"key":"B16","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1161\/CIRCRESAHA.120.315913","article-title":"Basic mechanisms of diabetic heart disease","volume":"126","author":"Ritchie","year":"2020","journal-title":"Circ Res."},{"key":"B17","doi-asserted-by":"publisher","first-page":"e002188","DOI":"10.1161\/JAHA.115.002188","article-title":"Left ventricular structure and risk of cardiovascular events: a Framingham Heart Study cardiac magnetic resonance study","volume":"4","author":"Tsao","year":"2015","journal-title":"J Am Heart Assoc."},{"key":"B18","doi-asserted-by":"publisher","first-page":"e011337","DOI":"10.1161\/CIRCIMAGING.120.011337","article-title":"Right ventricular ejection fraction for the prediction of major adverse cardiovascular and heart failure-related events: a cardiac MRI based study of 7131 patients with known or suspected cardiovascular disease","volume":"14","author":"Purmah","year":"2021","journal-title":"Circ Cardiovasc Imaging."},{"key":"B19","doi-asserted-by":"publisher","first-page":"101118","DOI":"10.1016\/j.jocmr.2024.101118","article-title":"Impact of cardiovascular magnetic resonance-derived right ventricular ejection fraction on adverse outcomes: a robust Bayesian model-averaged meta-analysis","volume":"26","author":"Kitano","year":"2024","journal-title":"J Cardiovasc Magn Reson."},{"key":"B20","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.5551\/jat.48843","article-title":"Development of a cardiovascular disease risk prediction model using the Suita Study, a population-based prospective cohort study in Japan","volume":"27","author":"Nakai","year":"2020","journal-title":"J Atheroscler Thromb."},{"key":"B21","doi-asserted-by":"publisher","first-page":"2439","DOI":"10.1093\/eurheartj\/ehab309","article-title":"SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe","volume":"42","year":"2021","journal-title":"Eur Heart J"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1056\/NEJM199005313222203","article-title":"Prognostic implications of echocardiographically determined left ventricular mass in the Framingham Heart Study","volume":"322","author":"Levy","year":"1990","journal-title":"N Engl J Med."},{"key":"B23","doi-asserted-by":"publisher","first-page":"2478","DOI":"10.1093\/eurheartj\/ehp270","article-title":"Increase in end-systolic volume after exercise independently predicts mortality in patients with coronary heart disease: data from the heart and soul study","volume":"30","author":"Turakhia","year":"2009","journal-title":"Eur Heart J."},{"key":"B24","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1093\/ehjci\/jey123","article-title":"Left ventricular global function index predicts incident heart failure and cardiovascular disease in young adults: the coronary artery risk development in young adults (CARDIA) study","volume":"20","author":"Nwabuo","year":"2019","journal-title":"Eur Heart J Cardiovasc Imaging."},{"key":"B25","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1016\/j.jacc.2024.05.050","article-title":"Myocardial strain measured by cardiac magnetic resonance predicts cardiovascular morbidity and death","volume":"84","author":"Chadalavada","year":"2024","journal-title":"J Am Coll Cardiol."},{"key":"B26","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1161\/01.CIR.96.1.116","article-title":"End-systolic volume index at 90 to 180 minutes into reperfusion therapy for acute myocardial infarction is a strong predictor of early and late mortality. The Global Utilization of Streptokinase and t-PA for Occluded Coronary Arteries (GUSTO)-I Angiographic Investigators","volume":"96","author":"Migrino","year":"1997","journal-title":"Circulation."},{"key":"B27","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1093\/ehjci\/jeab266","article-title":"Left atrial structure and function are associated with cardiovascular outcomes independent of left ventricular measures: a UK Biobank CMR study","volume":"23","author":"Raisi-Estabragh","year":"2022","journal-title":"Eur Heart J Cardiovasc Imaging."}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1852547\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T04:24:36Z","timestamp":1785471876000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1852547\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,31]]},"references-count":27,"alternative-id":["10.3389\/fmed.2026.1852547"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2026.1852547","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,31]]},"article-number":"1852547"}}