{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T05:09:15Z","timestamp":1778476155688,"version":"3.51.4"},"reference-count":49,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"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":"\n Introduction and aims<\/jats:title>\n Complications of type 2 diabetes are a primary cause of public health challenges in the field of diabetes. The emergence of metabolomics and proteomics provides a direct perspective for revealing the mechanisms of metabolic diseases. Our research aims to explore the relationship between omics components and complications, as well as their clinical predictive performance.<\/jats:p>\n <\/jats:sec>\n \n Materials and methods<\/jats:title>\n This prospective study utilized data from the UK Biobank, including over 1,400 proteins and more than 280 metabolites, to analyze outcomes such as type 2 diabetes, microvascular complications, macrovascular complications, neurological complications, kidney complications, retinal complications, cardiovascular complications, peripheral vascular complications, metabolic disorder complications, and all-cause mortality. A total of 50,021 participants without type 2 diabetes were included in the analysis. The baseline time frame spanned from 2006 to 2010, with an average follow-up duration of 12.0 to 12.03 years. Researchers used LASSO Cox and LightGBM to search for new markers of complications, and employed SHAP methods to explain the contributions of these markers within the machine learning models. Subsequently, a comprehensive prediction model was established to reveal the potential of new markers for the early diagnosis of complications under nonlinear patterns, utilizing nine specific machine learning methods (CatBoost, LightGBM, Random Forest, XGBoost, logistic regression, multi-layer perceptron, single-layer neural network, Naive Bayes, and support vector machine).<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n GDF15 alone is more accurate than blood glucose and HbA1c in reflecting future kidney complications, especially in differentiating those who develop the disease within the next five years (GDF15 AUC=0.94, blood glucose AUC=0.68, HbA1c AUC=0.85). Within the framework of the comprehensive prediction model, the GDF15 model improved the accuracy of early screening for kidney complications compared with models constructed using traditional indicators (5-year Max AUC=0.92, 10-year Max AUC=0.88). In conclusion, both machine learning and statistical methods support the correlation between GDF15 and kidney complications, reflecting its robustness.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n The results highlight the association of GDF15 during the early asymptomatic stage of various complications, especially kidney complications, revealing the potential role of GDF15 at the molecular pathological level during disease progression. In distinguishing participants who developed complications after the baseline period, the comprehensive GDF15 model provides a method for the early warning of various complications, particularly kidney complications.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fendo.2026.1758267","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T04:25:36Z","timestamp":1778473536000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Plasma protein GDF15 has a good predictive potential for the kidney complications of type 2 diabetes"],"prefix":"10.3389","volume":"17","author":[{"given":"Ming","family":"Hao","sequence":"first","affiliation":[{"name":"Department of Endocrinology, the First Affiliated Hospital of Harbin Medical University","place":["Harbin, 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