{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T06:00:14Z","timestamp":1785218414956,"version":"3.55.0"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T00:00:00Z","timestamp":1785196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Diabetic kidney disease (DKD) is a major microvascular complication of type 2 diabetes mellitus (T2DM) and the leading cause of end-stage renal disease in China. Limited disease awareness and insufficient early screening tools hinder timely intervention for DKD patients. This study aimed to develop and internally validate a non-invasive machine learning-based prediction model for early DKD risk identification among T2DM patients using routine clinical laboratory indicators.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective cross-sectional study was conducted with 602 eligible T2DM patients (457 without DKD, 145 with DKD) recruited from Sihui People\u2019s Hospital between January 2023 and June 2024. Subjects were randomly split into an 8:2 training set (n=481) and independent test set (n=121). Baseline clinical characteristics were compared between groups. Univariate and multivariate logistic regression analyses were performed to screen independent DKD predictors. Six machine learning algorithms including logistic regression, XGBoost, random forest, AdaBoost, support vector classifier (SVC), and Gaussian naive Bayes (GNB) were constructed and comprehensively assessed via AUC, accuracy, sensitivity, specificity, calibration curves, decision curve analysis (DCA), and SHapley Additive exPlanations (SHAP) interpretability analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Baseline comparisons showed that DKD patients presented worse glycolipid, hepatic, renal, hematological and urinary protein indicators than patients with isolated T2DM, while sex, age, BMI, DBP and FPG showed no significant intergroup differences (all P&amp;gt;0.05). Multivariate logistic regression identified glycated hemoglobin (HbA1c), \u03b2\n                      <jats:sub>2<\/jats:sub>\n                      -microglobulin (\u03b2\n                      <jats:sub>2<\/jats:sub>\n                      MG), and urine protein (PRO) as independent risk factors, while serum albumin (ALB) and estimated glomerular filtration rate (eGFR) acted as protective factors. Single indicator ROC analysis showed PRO and \u03b2\n                      <jats:sub>2<\/jats:sub>\n                      MG achieved the highest diagnostic AUC of 0.944. All six machine learning models exhibited excellent discriminative performance with validation AUCs over 0.975. Logistic regression was selected as the optimal model, yielding a test-set AUC of 0.979, sensitivity of 89.7%, and specificity of 92.4%. The model demonstrated favorable calibration and sustained positive net clinical benefit across almost all threshold probabilities in DCA. SHAP analysis ranked HbA1c, ALB, PRO, eGFR, and \u03b2\n                      <jats:sub>2<\/jats:sub>\n                      MG as the top five predictive features, clarifying the individual risk contribution of each biomarker.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The interpretable logistic regression model built on routine non-invasive clinical indicators reliably identifies DKD risk in T2DM patients. Glycemic control and renal injury biomarkers serve as core predictive factors. This tool provides convenient, low-cost early risk stratification for clinical practice, especially for primary care settings. Further external multicenter prospective validation is required to generalize its clinical application.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1888060","type":"journal-article","created":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T05:35:34Z","timestamp":1785216934000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and internal validation of a machine-learning-based risk identification model for diabetic kidney disease in patients with type 2 diabetes"],"prefix":"10.3389","volume":"17","author":[{"given":"Jianquan","family":"Luo","sequence":"first","affiliation":[{"name":"Department of Endocrinology Laboratory, Sihui People\u2019s Hospital","place":["Sihui, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoxin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Endocrinology Laboratory, Sihui People\u2019s Hospital","place":["Sihui, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiacheng","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Endocrinology Laboratory, Sihui People\u2019s Hospital","place":["Sihui, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.2337\/dc14-1296","article-title":"Diabetic kidney disease: a report from an ADA Consensus Conference","volume":"37","author":"Tuttle","year":"2014","journal-title":"Diabetes Care"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/s0140-6736(12)60033-6","article-title":"Prevalence of chronic kidney disease in China: a cross-sectional survey","volume":"379","author":"Zhang","year":"2012","journal-title":"Lancet"},{"key":"B3","doi-asserted-by":"publisher","first-page":"S173","DOI":"10.37766\/inplasy2026.1.0023","article-title":"Disease burden of diabetic kidney disease in China: a systematic literature review","volume":"29","author":"Tang","year":"2026","journal-title":"Val Health"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1161\/circulationaha.120.050583","article-title":"Machine learning in medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"B5","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1093\/jamia\/ocw042","article-title":"Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review","volume":"24","author":"Goldstein","year":"2017","journal-title":"J Am Med Inform Assoc"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1177\/0272989x06295361","article-title":"Decision curve analysis: a novel method for evaluating prediction models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med Decis Making"},{"key":"B7","article-title":"A unified approach to interpreting model predictions","volume-title":"Advances in Neural Information Processing Systems 30 (Nips 2017)","author":"Lundberg","year":"2017"},{"key":"B8","doi-asserted-by":"publisher","first-page":"4","DOI":"10.2337\/diacare.27.2007.s133","article-title":"Chinese guidelines for the prevention and treatment of type 2 diabetes (2020 edition)","volume":"13","year":"2021","journal-title":"Chin J Diabetes"},{"key":"B9","article-title":"Expert consensus on the diagnosis and treatment of diabetic kidney disease (2019 edition)","volume":"35","year":"2019","journal-title":"Chin J Nephrol"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.2337\/dc24-S011","article-title":"Chronic kidney disease and risk management: Standards of Care in Diabetes\u20142024","volume":"47","year":"2024","journal-title":"Diabetes Care"},{"key":"B11","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.1016\/j.kint.2022.06.008","article-title":"KDIGO 2022 clinical practice guideline for diabetes management in chronic kidney disease","volume":"102","year":"2022","journal-title":"Kidney Int"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.2337\/dci22-0027","article-title":"Diabetes management in chronic kidney disease: a consensus report by the ADA and KDIGO","volume":"45","author":"de Boer","year":"2022","journal-title":"Diabetes Care"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.10924","article-title":"Clinical manifestations of kidney disease among US adults with diabetes, 1988\u20132014","volume":"316","author":"Afkarian","year":"2016","journal-title":"JAMA"},{"key":"B14","doi-asserted-by":"publisher","first-page":"15018","DOI":"10.1016\/j.mpmed.2022.08.002","article-title":"Diabetic kidney disease","volume":"1","author":"Thomas","year":"2015","journal-title":"Nat Rev Dis Primers"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.11491116","article-title":"Diabetic kidney disease: challenges, progress, and possibilities","volume":"12","author":"Alicic","year":"2017","journal-title":"Clin J Am Soc Nephrol"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa2024816","article-title":"Dapagliflozin in patients with chronic kidney disease","volume":"383","author":"Heerspink","year":"2020","journal-title":"N Engl J Med"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1056\/nejmoa1811744","article-title":"Canagliflozin and renal outcomes in type 2 diabetes and nephropathy","volume":"380","author":"Perkovic","year":"2019","journal-title":"N Engl J Med"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1056\/nejmoa2025845","article-title":"Effect of finerenone on chronic kidney disease outcomes in type 2 diabetes","volume":"383","author":"Bakris","year":"2020","journal-title":"N Engl J Med"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehab777","article-title":"Cardiovascular and kidney outcomes with finerenone in patients with type 2 diabetes and chronic kidney disease: the FIDELITY pooled analysis","volume":"43","author":"Agarwal","year":"2022","journal-title":"Eur Heart J"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-150-9-200905050-00006","article-title":"A new equation to estimate glomerular filtration rate","volume":"150","author":"Levey","year":"2009","journal-title":"Ann Intern Med"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1056\/nejmoa2102953","article-title":"New creatinine- and cystatin C-based equations to estimate GFR without race","volume":"385","author":"Inker","year":"2021","journal-title":"N Engl J Med"},{"key":"B22","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/a:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"B23","doi-asserted-by":"crossref","DOI":"10.1145\/2939672.2939785","article-title":"XGBoost: A scalable tree boosting system","author":"Chen","year":"2016"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1705.07874","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"B25","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From local explanations to global understanding with explainable AI for trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat Mach Intell"},{"key":"B26","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.jclinepi.2014.11.010","article-title":"Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis: the TRIPOD statement","volume":"162","author":"Collins","year":"2015","journal-title":"Ann Intern Med"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.5870","article-title":"Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view","volume":"18","author":"Luo","year":"2016","journal-title":"J Med Internet Res"}],"container-title":["Frontiers in Endocrinology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2026.1888060\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T05:35:36Z","timestamp":1785216936000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fendo.2026.1888060\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,28]]},"references-count":27,"alternative-id":["10.3389\/fendo.2026.1888060"],"URL":"https:\/\/doi.org\/10.3389\/fendo.2026.1888060","relation":{},"ISSN":["1664-2392"],"issn-type":[{"value":"1664-2392","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,28]]},"article-number":"1888060"}}