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This study aimed to use a machine learning technique combined with proteomics to develop personalized models for predicting\n                      <jats:styled-content style=\"fixed-case\">CVD<\/jats:styled-content>\n                      in patients with\n                      <jats:styled-content style=\"fixed-case\">T2DM<\/jats:styled-content>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>\n                      In total, 874 patients with\n                      <jats:styled-content style=\"fixed-case\">T2DM<\/jats:styled-content>\n                      and 2,920 Olink proteins obtained from the\n                      <jats:styled-content style=\"fixed-case\">UK<\/jats:styled-content>\n                      Biobank were used in this study. Proteins were screened using Cox regression and\n                      <jats:styled-content style=\"fixed-case\">LASSO<\/jats:styled-content>\n                      regression. A basic model containing clinical features and a full model combining proteome and clinical features were constructed using the random survival forest algorithm. The area under the receiver operating characteristic (\n                      <jats:styled-content style=\"fixed-case\">ROC<\/jats:styled-content>\n                      ) curve (\n                      <jats:styled-content style=\"fixed-case\">AUC<\/jats:styled-content>\n                      ) was used to evaluate the predictive performance of the models and compare them with other\n                      <jats:styled-content style=\"fixed-case\">CVD<\/jats:styled-content>\n                      predictive models. Compared with the basic model, the full model performed better in predicting\n                      <jats:styled-content style=\"fixed-case\">CVD<\/jats:styled-content>\n                      , with time\u2010dependent\n                      <jats:styled-content style=\"fixed-case\">AUCs<\/jats:styled-content>\n                      of 0.81 (3\u2009years), 0.74 (5\u2009years) and 0.74 (10\u2009years) (0.77, 0.69 and 0.67). We calculated the risk scores of the Framingham,\n                      <jats:styled-content style=\"fixed-case\">ASCVD<\/jats:styled-content>\n                      and Score2\u2010Diabetes models. The results revealed that the prediction performance of the full model was also better than that of the abovementioned models. In terms of differentiation accuracy, the results of the net reclassification improvement index and integrated discrimination improvement index showed that the full model can identify high\u2010risk individuals more accurately (accuracy rate: 79% vs. 69%).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Proteomics can be used to predict cardiovascular complications in diabetic patients. It is also necessary to consider the applicability of the model due to the limitations of the sample size and the constraints of proteomics in clinical applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.70064","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T09:59:06Z","timestamp":1756720746000},"page":"6594-6604","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Proteomics\u2010enabled learning machine algorithms enhance the prediction of cardiovascular diseases in patients with type 2 diabetes mellitus"],"prefix":"10.1111","volume":"27","author":[{"given":"Bowei","family":"Yu","sequence":"first","affiliation":[{"name":"Institute and Department of Endocrinology and Metabolism Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine  Shanghai China"},{"name":"Department of Medicine and Therapeutics The Chinese University of Hong Kong  Hong Kong China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiang","family":"Li","sequence":"additional","affiliation":[{"name":"Institute and Department of Endocrinology and Metabolism Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuefeng","family":"Yu","sequence":"additional","affiliation":[{"name":"Institute and Department of Endocrinology and Metabolism Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Sun","sequence":"additional","affiliation":[{"name":"Institute and Department of Endocrinology and Metabolism Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuying","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute and Department of Endocrinology and Metabolism Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4869-1352","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute and Department of Endocrinology and Metabolism Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3992-5812","authenticated-orcid":false,"given":"Xiao","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Medical Sciences Uppsala University  Uppsala Sweden"},{"name":"School of Public Health Zhejiang University  Hangzhou China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5117-1614","authenticated-orcid":false,"given":"Yingli","family":"Lu","sequence":"additional","affiliation":[{"name":"Institute and Department of Endocrinology and Metabolism Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9591-6991","authenticated-orcid":false,"given":"Ningjian","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute and Department of Endocrinology and Metabolism Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lijie","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute and Department of Endocrinology and Metabolism Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine  Shanghai China"},{"name":"Department of Urology Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine  Shanghai China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,9,1]]},"reference":[{"key":"e_1_2_12_2_1","unstructured":"IDF Diabetes Atlas 10th Edition. 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