{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T02:16:40Z","timestamp":1770085000440,"version":"3.49.0"},"reference-count":31,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Diabetes Invest"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Identifying patient characteristics predictive of treatment response is crucial for optimizing type 2 diabetes outcomes. Using data from three phase 2\/3 imeglimin trials in Japan, this analysis applied machine learning to determine characteristics associated with HbA1c improvement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Regression tree and random forest methods identified baseline characteristics predictive of HbA1c improvement. Partial dependence plots (PDP) visualized the relationship between HbA1c change and continuous variables deemed important by Boruta.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>For monotherapy, key predictors were baseline HbA1c, hypertension, smoking, type 2 diabetes duration, body mass index (BMI), low\u2010density lipoprotein\u2010cholesterol (LDL\u2010C), metabolic syndrome, and estimated glomerular filtration rate. Nonsmokers with HbA1c \u22658.35% and LDL\u2010C\u2009&lt;\u20093.26\u2009mmol\/L at baseline showed the greatest improvement in HbA1c (\u22121.24%). Random forest analysis and Boruta identified baseline HbA1c, BMI, fatty liver index, smoking, and hypertension as significant predictors of HbA1c improvement. PDPs identified a positive correlation between higher baseline HbA1c, and a negative correlation between BMI and fatty liver index, and HbA1c improvement. For imeglimin add\u2010on to insulin therapy, key predictors were BMI, age, LDL\u2010C, type 2 diabetes duration, systolic blood pressure, and alanine transaminase (ALT). Patients with BMI &lt;25.9, LDL\u2010C\u2009&lt;\u20092.68\u2009mmol\/L, and ALT &lt;21\u2009U\/L showed the greatest HbA1c improvement (\u22121.48%). Random forest analysis and Boruta confirmed BMI, age, and LDL\u2010C as significant predictors. PDPs identified a positive correlation between older age, and a negative correlation between higher BMI and LDL\u2010C, and HbA1c improvement.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Machine learning effectively identified baseline characteristics predictive of HbA1c response to imeglimin, supporting the potential for personalized type 2 diabetes treatment strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jdi.70215","type":"journal-article","created":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T09:15:42Z","timestamp":1765185342000},"page":"214-226","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predictors of glycemic control with imeglimin for type 2 diabetes: Results of machine learning analyses using clinical trial data"],"prefix":"10.1111","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1389-1474","authenticated-orcid":false,"given":"Katsuhiko","family":"Hagi","sequence":"first","affiliation":[{"name":"Medical Affairs Sumitomo Pharma Co., Ltd.  Tokyo Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazumasa","family":"Yoshida","sequence":"additional","affiliation":[{"name":"Data Science, Sumitomo Pharma Co., Ltd.  Tokyo Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hirotaka","family":"Watada","sequence":"additional","affiliation":[{"name":"Department of Metabolism and Endocrinology Juntendo University Graduate School of Medicine  Tokyo Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1574-0565","authenticated-orcid":false,"given":"Kohei","family":"Kaku","sequence":"additional","affiliation":[{"name":"Department of Medicine Kawasaki Medical School  Okayama Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kohjiro","family":"Ueki","sequence":"additional","affiliation":[{"name":"Diabetes Research Center National Institute of Global Health and Medicine, Japan Institute for Health Security  Tokyo 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