{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T22:20:12Z","timestamp":1792016412637,"version":"4.4.0"},"reference-count":42,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2025JJ20091"],"award-info":[{"award-number":["2025JJ20091"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2023JJ30780"],"award-info":[{"award-number":["2023JJ30780"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2023ZD0503705"],"award-info":[{"award-number":["2023ZD0503705"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82271601"],"award-info":[{"award-number":["82271601"]}],"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"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Diabetic macrovascular complications continue to drive substantial morbidity, yet early detection tools and deeper mechanistic understanding remain scarce. This study aimed to pinpoint circulating protein biomarkers for diabetic macrovascular complications while elucidating their biological significance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We combined proteome\u2010wide Mendelian randomization (MR), Cox proportional hazards regression, and proteome\u2010wide association study (PWAS) to rank priority proteins. From these prioritized proteins, we constructed a machine learning\u2010derived protein risk score. Functional enrichment, multi\u2010omics integration, and longitudinal trajectory modeling were conducted, supplemented by phenome\u2010wide MR analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 43 proteins were identified, clustering in pathways associated with immune\u2010inflammatory cascades and extracellular matrix remodeling. The resulting 12\u2010protein panel achieved robust discrimination (AUC\u2009=\u20090.793), maintained reliable performance over a 15\u2010year period, and delivered clear risk stratification. Multi\u2010omics integration revealed synchronized links to cardiometabolic dysregulation and cardiac structural alterations. Longitudinal trajectory analyses demonstrated that protein perturbations emerged as early as 10\u201312\u2009years prior to clinical onset. Phenome\u2010wide MR uncovered pleiotropic associations across various disease categories and provided causal support for several prioritized proteins.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This work identifies a robust circulating protein panel suitable for early forecasting of diabetic macrovascular events and sheds light on core biological drivers of disease progression. The findings underscore the value of integrating proteomics, time\u2010series evaluation, and causal inference for biomarker discovery and mechanistic insight.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.70807","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T09:19:07Z","timestamp":1776849547000},"page":"5968-5981","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Integrative Proteomic and Machine Learning Analysis Identifies Novel Predictors and Risk Model for Diabetic Macrovascular Complications"],"prefix":"10.1111","volume":"28","author":[{"given":"Chenggong","family":"Ma","sequence":"first","affiliation":[{"name":"Department of Cardiology The Second Xiangya Hospital of Central South University  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongling","family":"Dai","sequence":"additional","affiliation":[{"name":"Department of Cardiology The Second Xiangya Hospital of Central South University  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bingqing","family":"Xiao","sequence":"additional","affiliation":[{"name":"Xiangya School of Medicine Central South University  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuke","family":"Chen","sequence":"additional","affiliation":[{"name":"Xiangya School of Medicine Central South University  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyao","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Blood Transfusion The Second Xiangya Hospital of Central South University  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5802-2910","authenticated-orcid":false,"given":"Shi","family":"Tai","sequence":"additional","affiliation":[{"name":"Department of Cardiology The Second Xiangya Hospital of Central South University  Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10654-024-01168-8"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-025-57830-6"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.124.070454"},{"key":"e_1_2_12_5_1","article-title":"A Plasma Proteomic Signature for Atherosclerotic Cardiovascular Disease Risk Prediction in the UK Biobank Cohort","author":"Gupte T. 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