{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T04:38:54Z","timestamp":1792471134544,"version":"4.5.0"},"reference-count":29,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T00:00:00Z","timestamp":1783987200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","award":["RS\u20102025\u201002309552"],"award-info":[{"award-number":["RS\u20102025\u201002309552"]}],"id":[{"id":"10.13039\/501100003710","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":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>While HbA1c is the standard for monitoring long\u2010term glycaemic control, it fails to capture glycaemic variability. We investigated the discriminatory capacity of longitudinal continuous glucose monitoring (CGM) metrics and identified CGM metric patterns associated with diabetic kidney disease (DKD) in individuals with type 1 diabetes (T1D) using machine learning (ML).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We analysed cross\u2010sectional data from 282 T1D patients with 1\u2010year consecutive CGM data. DKD was defined by persistent laboratory abnormalities (urine albumin\u2010creatinine ratio\u2009\u2265\u200930\u2009mg\/g or estimated glomerular filtration rate\u2009&lt;\u200960\u2009mL\/min\/1.73\u2009m\n                      <jats:sup>2<\/jats:sup>\n                      ) confirmed by at least two measurements within the 1\u2010year period. LightGBM, XGBoost, Random Forest, and Logistic Regression (LR) were developed. Feature importance was assessed using SHAP analysis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The LightGBM model achieved the highest performance (AUROC\u2009=\u20090.91 [95% CI, 0.88\u20130.93], F1 score\u2009=\u20090.65). All tree\u2010based ML models outperformed the LR model. SHAP analysis identified the standard deviation (SD) of monthly time in range (TIR) and time in tight range (TITR) as the most influential features. In contrast, the CV of sensor glucose did not differ significantly between groups (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.416). Even in the early DKD subgroup, the SD of monthly TIR and TITR remained significantly elevated.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The SD of monthly TIR and TITR is strongly associated with DKD in T1D, whereas the CV of sensor glucose is not. ML\u2010based integration of these longitudinal metrics offers improved discrimination of concurrent DKD status beyond conventional glycaemic markers.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/dom.71117","type":"journal-article","created":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T04:10:02Z","timestamp":1784088602000},"page":"9044-9053","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Association of Variability of Monthly Continuous Glucose Monitoring\u2010Derived Metrics With Diabetic Kidney Disease in Type 1 Diabetes"],"prefix":"10.1111","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1446-2401","authenticated-orcid":false,"given":"Byeongjae","family":"Kang","sequence":"first","affiliation":[{"name":"Medical AI Research Center, Research Institute for Future Medicine Samsung Medical Center  Seoul Republic of Korea"},{"name":"Department of Digital Health SAIHST, Sungkyunkwan University  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1945-9279","authenticated-orcid":false,"given":"Rosa","family":"Oh","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Metabolism, Department of Medicine Chungbuk National University Hospital  Cheongju Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2591-0129","authenticated-orcid":false,"given":"Taeyoung","family":"Kim","sequence":"additional","affiliation":[{"name":"Medical AI Research Center, Research Institute for Future Medicine Samsung Medical Center  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2536-6274","authenticated-orcid":false,"given":"Jee Hee","family":"Yoo","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Metabolism, Department of Internal Medicine Chung\u2010Ang University Gwangmyeong Hospital, Chung\u2010Ang University College of Medicine  Gwangmyeong Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0244-7714","authenticated-orcid":false,"given":"Danbee","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Clinical Research Design &amp; Evaluation Samsung Advanced Institute for Health Sciences &amp; Technology (SAIHST), Sungkyunkwan University  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2242-2816","authenticated-orcid":false,"given":"Gyuri","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Metabolism, Department of Medicine Samsung Medical Center, Sungkyunkwan University School of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5929-3627","authenticated-orcid":false,"given":"Sang\u2010Man","family":"Jin","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Metabolism, Department of Medicine Samsung Medical Center, Sungkyunkwan University School of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6271-3343","authenticated-orcid":false,"given":"Myung Jin","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Radiology and Medical AI Research Center Samsung Medical Center  Seoul Republic of Korea"},{"name":"Department of Data Convergence and Future Medicine Sungkyunkwan University School of Medicine  Suwon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5001-963X","authenticated-orcid":false,"given":"Jae Hyeon","family":"Kim","sequence":"additional","affiliation":[{"name":"Division of Endocrinology and Metabolism, Department of Medicine Samsung Medical Center, Sungkyunkwan University School of Medicine  Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,14]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.11491116"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2023.04.052"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.metabol.2023.155763"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.2337\/dc24-0908"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.2196\/64979"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00125-024-06171-y"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13098-025-01749-x"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12933-020-01085-6"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.2337\/db20-1081"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/jdi.12728"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2022.109209"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2025.112951"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.2337\/dci19-0028"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12882-017-0767-3"},{"key":"e_1_2_8_16_1","first-page":"S219","article-title":"11. 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