Frontiers in endocrinology · 2026
Deep exploration and precise identification of key risk factors for diabetic peripheral neuropathy using the random forest algorithm.
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Li Yongnan, Li Yongsheng, Sen Gan, Hu Bin
Аннотация
BACKGROUND: Diabetic peripheral neuropathy (DPN) is a prevalent and highly disabling complication of diabetes mellitus, associated with markedly increased rates of disability and mortality. Timely intervention and effective management have been consistently shown to substantially reduce the risk of DPN onset and progression.
METHODS: This retrospective cohort study analyzed 1, 004 hospitalized patients with type 2 diabetes mellitus (T2DM) admitted to the endocrinology department of a hospital in Jiangsu Province, China. A risk prediction model for DPN was developed using the Random Forest (RF) algorithm, while logistic regression analysis was employed to identify the major risk factors. The overarching aim was to provide a reliable risk assessment tool for clinical application.
FINDINGS: Five principal factors were identified as significantly associated with DPN risk: age (OR = 1.257, 95% CI [1.188-1.367], p < 0.001), serum 25(OH)D3 levels (OR = 0.791, 95% CI [0.759-0.854], p < 0.001), duration of diabetes (OR = 1.431, 95% CI [1.285-1.617], p < 0.001), glycated hemoglobin (HbA1c) (OR = 1.236, 95% CI [1.197-1.391], p < 0.001), and glycated serum protein (GSP) (OR = 1.091, 95% CI [1.047-1.201], p = 0.017). A DPN risk prediction model incorporating these variables achieved an area under the receiver operating characteristic curve (AUC) of 0.829 (95% CI: 0.802-0.857), demonstrating excellent discriminatory performance.
INTERPRETATION: The Random Forest-based DPN risk prediction model successfully identified five critical risk factors, offering a solid theoretical foundation for personalized strategies in DPN prevention and management among patients with diabetes. This model exhibits high predictive utility in clinical practice.
METHODS: This retrospective cohort study analyzed 1, 004 hospitalized patients with type 2 diabetes mellitus (T2DM) admitted to the endocrinology department of a hospital in Jiangsu Province, China. A risk prediction model for DPN was developed using the Random Forest (RF) algorithm, while logistic regression analysis was employed to identify the major risk factors. The overarching aim was to provide a reliable risk assessment tool for clinical application.
FINDINGS: Five principal factors were identified as significantly associated with DPN risk: age (OR = 1.257, 95% CI [1.188-1.367], p < 0.001), serum 25(OH)D3 levels (OR = 0.791, 95% CI [0.759-0.854], p < 0.001), duration of diabetes (OR = 1.431, 95% CI [1.285-1.617], p < 0.001), glycated hemoglobin (HbA1c) (OR = 1.236, 95% CI [1.197-1.391], p < 0.001), and glycated serum protein (GSP) (OR = 1.091, 95% CI [1.047-1.201], p = 0.017). A DPN risk prediction model incorporating these variables achieved an area under the receiver operating characteristic curve (AUC) of 0.829 (95% CI: 0.802-0.857), demonstrating excellent discriminatory performance.
INTERPRETATION: The Random Forest-based DPN risk prediction model successfully identified five critical risk factors, offering a solid theoretical foundation for personalized strategies in DPN prevention and management among patients with diabetes. This model exhibits high predictive utility in clinical practice.