{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T09:13:08Z","timestamp":1785229988492,"version":"3.55.0"},"reference-count":27,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["7202011"],"award-info":[{"award-number":["7202011"]}],"id":[{"id":"10.13039\/501100004826","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":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Due to the high cost and complexity, the oral glucose tolerance test is not adopted as the screening method for identifying diabetes patients, which leads to the misdiagnosis of patients with isolated post\u2010challenge hyperglycemia (IPH), that is., patients with normal fasting plasma glucose (&lt;7.0\u00a0mmoL\/L) and abnormal 2\u2010h postprandial blood glucose (\u226511.1\u00a0mmoL\/L). We aimed to develop a model to differentiate individuals with IPH from the normal population.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data from 54301 eligible participants were obtained from the Risk Evaluation of Cancers in Chinese Diabetic Individuals: a longitudinal (REACTION) study in China. Data from 37740 participants were used to develop the diagnostic system. External validation was performed among 16561 participants. Three machine learning algorithms were used to create the predictive models, which were further evaluated by various classification algorithms to establish the best predictive model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Ten features were selected to develop an IPH diagnosis system (IPHDS) based on an artificial neural network. In external validation, the AUC of the IPHDS was 0.823 (95% CI 0.811\u20130.836), which was significantly higher than the AUC of the Taiwan model [0.799 (0.786\u20130.813)] and that of the Chinese Diabetes Risk Score model [0.648 (0.635\u20130.662)]. The IPHDS model had a sensitivity of 75.6% and a specificity of 74.6%. This model outperformed the Taiwan and CDRS models in subgroup analyses. An online site with instant predictions was deployed at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/app-iphds-e1fc405c8a69.herokuapp.com\/\">https:\/\/app\u2010iphds\u2010e1fc405c8a69.herokuapp.com\/<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed IPHDS could be a convenient and user\u2010friendly screening tool for diabetes during health examinations in a large general population.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/dmrr.3832","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T03:44:06Z","timestamp":1718941446000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Development and validation of a machine learning\u2010based model to predict isolated post\u2010challenge hyperglycemia in middle\u2010aged and elder adults: Analysis from a multicentric study"],"prefix":"10.1002","volume":"40","author":[{"given":"Rui","family":"Hou","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Health Statistics School of Public Health Capital Medical University  Beijing China"},{"name":"Beijing Center for Disease Prevention and Control  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0375-6527","authenticated-orcid":false,"given":"Jingtao","family":"Dou","sequence":"additional","affiliation":[{"name":"Department of Endocrinology The First Medical Center Chinese PLA General Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijuan","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Epidemiology and Health Statistics School of Public Health Capital Medical 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