{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T08:52:46Z","timestamp":1791276766180,"version":"4.1.0"},"reference-count":48,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T00:00:00Z","timestamp":1791244800000},"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":["82270254"],"award-info":[{"award-number":["82270254"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82271609"],"award-info":[{"award-number":["82271609"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82371573"],"award-info":[{"award-number":["82371573"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dom-pubs.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Obesity Metabolism"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Cardiometabolic diseases (CMDs) often manifest in the form of cardiometabolic multimorbidity. Traditional risk assessment methods that rely on single biomarkers fail to adequately characterise the heterogeneity among patients across various subtypes. This study seeks to utilise routine biomarkers to identify clinical subtypes with unique characteristics, thereby improving risk assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We included 337\u2009334 UK Biobank participants without type 2 diabetes (T2D), ischaemic heart disease (IHD) or stroke at baseline. Multi\u2010task Deep LASSO selected 12 variables for K\u2010means clustering. Multi\u2010state Cox models assessed transitions to first cardiometabolic disease (FCMD), cardiometabolic multimorbidity (CMM) and death. Analyses of 251 plasma metabolic measures and circulating proteins characterised molecular correlates; metabolite panels were evaluated in held\u2010out testing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Four risk\u2010informed phenotypes were identified: metabolically healthy (MH), older cardio\u2010renal\u2010metabolic (OCRM), lean hypercholesterolaemia (LHC) and early\u2010onset metabolic syndrome (EO\u2010MetS). During a median follow\u2010up of approximately 13\u2009years, OCRM showed the highest FCMD and FCMD\u2010to\u2010CMM risks. In primary models, OCRM and EO\u2010MetS showed strong T2D associations, whereas LHC showed stronger relative associations with IHD and stroke than with T2D. Age adjustment attenuated several OCRM and LHC associations, particularly with IHD and stroke; T2D associations remained largely unchanged. Phenotypes exhibited differing metabolic profiles, and exploratory decomposition showed statistical overlap with phenotype\u2013outcome associations without establishing causality. Protein enrichment highlighted complement\/coagulation and vascular\u2010remodelling processes. Metabolite panels modestly improved discrimination beyond full clinical models, increasing the 10\u2010year CMM area under the receiver operating characteristic curve from 0.834 to 0.848; phenotype membership added little further improvement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Routine biomarker\u2010derived subtypes showed distinct cardiometabolic trajectories and multi\u2010omic profiles. These risk\u2010informed phenotypes support exploratory description of cardiometabolic heterogeneity, pending external validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.71423","type":"journal-article","created":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T08:23:09Z","timestamp":1791274989000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data\u2010Driven Cluster Subtyping and Its Association With Cardiometabolic Disease Progression: Integrating Multi\u2010State Modelling, Metabolomics and Proteomics"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-9809-0689","authenticated-orcid":false,"given":"Yixing","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Cardiology Sun Yat\u2010Sen Memorial Hospital, Sun Yat\u2010Sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhao","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Cardiology Sun Yat\u2010Sen Memorial Hospital, Sun Yat\u2010Sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maoxiong","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Cardiology Sun Yat\u2010Sen Memorial Hospital, Sun Yat\u2010Sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingyuan","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Cardiology Sun Yat\u2010Sen Memorial Hospital, Sun Yat\u2010Sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7917-2806","authenticated-orcid":false,"given":"Jingwei","family":"Gao","sequence":"additional","affiliation":[{"name":"Department of Cardiology Sun Yat\u2010Sen Memorial Hospital, Sun Yat\u2010Sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Cardiology Sun Yat\u2010Sen Memorial Hospital, Sun Yat\u2010Sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5827-7876","authenticated-orcid":false,"given":"Jingfeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Cardiology Sun Yat\u2010Sen Memorial Hospital, Sun Yat\u2010Sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangxin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Cardiology Sun Yat\u2010Sen Memorial Hospital, Sun Yat\u2010Sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Cardiology Sun Yat\u2010Sen Memorial Hospital, Sun Yat\u2010Sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingting","family":"Mai","sequence":"additional","affiliation":[{"name":"Department of Cardiology Sun Yat\u2010Sen Memorial Hospital, Sun Yat\u2010Sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Woliang","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Cardiology Sun Yat\u2010Sen Memorial Hospital, Sun Yat\u2010Sen University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,6]]},"reference":[{"key":"e_1_2_12_2_1","first-page":"2982","article-title":"Global Burden of Cardiovascular Diseases and Risk Factors, 1990\u20102019: Update From the GBD 2019 Study","volume":"76","author":"Roth G. 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