[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121542-en":3,"doc-seo-121542-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121542,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",7,"Healthcare","Machine learning model based on routine blood and biochemical parameters for early diagnosis of diabetic kidney disease - read online","Machine learning enables earlier identification of diabetic kidney disease (DKD), yet clinical early detection remains difficult because standard biomarkers have limited reliability and sensitivity. This study develops and validates machine learning models using routine blood and biochemical parameters for early DKD prediction. A retrospective cohort was combined with external validation, comparing seven algorithms and using SHAP for feature importance. Logistic regression delivered the best performance and highlighted TyG, HbA1c, and globulin as key predictors for cost-effective screening.","TYPE Original Research PUBLISHED 28 January 2026  \nDOI 10.3389/fendo.2026.1720574  \nOPEN ACCESS  \nEDITED BY  \nCem Haymana,  \nUniversity of Health Sciences, Türkiye  \nREVIEWED BY  \nAshwin Dhakal,  \nThe University of Missouri, United States Sheng Ding,  \nThe Central Hospital of Wuhan, China  \n*CORRESPONDENCE  \nJun-jie Gao  \n[wnmcjykjj@163.com](wnmcjykjj@163.com)[ ](wnmcjykjj@163.com)Ruo-xue Cao  \n [caoruoxue9490@163.com](caoruoxue9490@163.com)  \n†These authors have contributed equally to this work  \nRECEIVED 08 October 2025  \nREVISED 28 December 2025  \nACCEPTED 05 January 2026  \nPUBLISHED 28 January 2026  \nCITATION  \nYong W, Peng D-d, Ye K, Gao J-jand Cao R-x (2026) Machine learning model based on routine blood and biochemical parameters for early diagnosis of diabetic kidney disease.  \nFront. Endocrinol. 17:1720574 .  \ndoi: 10.3389/fendo.2026.1720574  \nCOPYRIGHT  \n© 2026 Yong, Peng, Ye, Gao and Cao. This isan open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning model based on routine blood and biochemical parameters for early diagnosis of diabetic kidney disease  \nWei Yong1†, Dan-dan Peng 1†, Kai Ye1, Jun-jie Gao 1* and Ruo-xue Cao 2,3*  \n1 Department of Clinical Laboratory, The Second Affiliated Hospital of Wannan Medical College, Wuhu, China, 2 Department of Laboratory Medicine, The Second People ’s Hospital of Lianyungang, Lianyungang, China, 3 Department of Laboratory Medicine, The Second People’s Hospital of Lianyungang Affiliated with Kangda College of Nanjing Medical University, Lianyungang, China  \nBackground: Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease globally, yet early diagnosis remains challenging due to conventional biomarker limitations, including UACR variability and reduced eGFR sensitivity. While machine learning shows promise in diabetes prediction, its application to early DKD identification using routine parameters remains underexplored. This study aimed to develop and validate machine learning models incorporating routine blood and biochemical parameters for early DKD prediction.  \nMethods: This retrospective study analyzed 3,114 diabetic patients from the Second Affiliated Hospital of Wannan Medical College (EDN1) and 1,496 patients from NHANES 2005-2018 (EDN2) for external validation. Early DKD was defined as UACR 30–300 mg/g with eGFR ≥60 ml/min/1 .73m² . Seven machine learning algorithms were compared. Feature importance was assessed using SHAP framework, and Mendelian randomization explored causal relationships. Results: Among 3,114 patients, 1,333 (42 . 8%) had early DKD. Logistic regression achieved optimal performance (AUC = 0 . 689, sensitivity=40 .5%, specificity=81 .3%) . Top predictors included triglyceride-glucose index (TyG), gender, creatinine, globulin, and age. External validation confirmed significant associations for HbA1c, globulin, TyG, and neutrophil-to-albumin ratio.  \nConclusions: The machine learning model successfully identified early DKD using routine parameters, with TyG index, HbA1c, and globulin as key predictors, demonstrating potential as a cost-effective screening tool.  \nKEYWORDS  \ndiabetic kidney disease, early diagnosis, machine learning, risk prediction, routine blood parameters  \nFrontiers in Endocrinology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nDiabetic kidney disease (DKD), the most common and severe microvascular complication of diabetes, has become the leading cause of end-stage renal disease (ESRD) worldwide (1) . Its prevalence continues to rise alongside the global diabetes epidemic, imposing a substantial burden on patient","cbCainEph8QrJAW4","https://ap.wps.com/l/cbCainEph8QrJAW4","pdf",4090808,1,14,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusions\n# Introduction","[{\"question\":\"How is early diabetic kidney disease defined in this study?\",\"answer\":\"Early DKD is defined as UACR 30–300 mg/g with eGFR ≥ 60 ml/min/1.73m².\"},{\"question\":\"Which machine learning model performed best?\",\"answer\":\"Logistic regression achieved the optimal performance (AUC 0.689) with reported sensitivity and specificity values.\"},{\"question\":\"What predictors were identified as most important?\",\"answer\":\"Top predictors included the triglyceride-glucose index (TyG), gender, creatinine, globulin, and age, with external validation supporting HbA1c, globulin, TyG, and neutrophil-to-albumin ratio.\"}]","Machine learning model based on routine blood and biochemical parameters for early diagnosis of diabetic kidney disease - read online | PDF",1785736166,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-model-based-on-routine-blood-and-biochemical-parameters-for-early-diagnosis-of-diabetic-kidney-disease","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-model-based-on-routine-blood-and-biochemical-parameters-for-early-diagnosis-of-diabetic-kidney-disease/121542/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How is early diabetic kidney disease defined in this study?","Question",{"text":75,"@type":76},"Early DKD is defined as UACR 30–300 mg/g with eGFR ≥ 60 ml/min/1.73m².","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning model performed best?",{"text":80,"@type":76},"Logistic regression achieved the optimal performance (AUC 0.689) with reported sensitivity and specificity values.",{"name":82,"@type":73,"acceptedAnswer":83},"What predictors were identified as most important?",{"text":84,"@type":76},"Top predictors included the triglyceride-glucose index (TyG), gender, creatinine, globulin, and age, with external validation supporting HbA1c, globulin, TyG, and neutrophil-to-albumin ratio.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]