ArticleFrontiers in endocrinology2024
Construction and validation of a nomogram model for predicting diabetic peripheral neuropathy.
Article in Frontiers in endocrinology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Exploration of a Multimodal Machine Learning Model Integrating Ultrasound and Clinical Indicators for the Diagnosis of Diabetic Peripheral Neuropathy.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2026Article
- A multi-machine learning framework identifies novel PANoptosis-related biomarkers and their immune landscape in ulcerative colitis: Insights from transcriptomics and experimental validation.Frontiers in immunology · 2026Article
- Development and Validation of a Machine Learning-Based Predictive Model for Peripheral Neuropathy Risk in Elderly Patients with Type 2 Diabetes.Risk management and healthcare policy · 2026Article
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5 authors.
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Abstract
Objective: Diabetic peripheral neuropathy (DPN) is a chronic complication of diabetes that can potentially escalate into ulceration, amputation and other severe consequences. The aim of this study was to construct and validate a predictive nomogram model for assessing the risk of DPN development among diabetic patients, thereby facilitating the early identification of high-risk DPN individuals and mitigating the incidence of severe outcomes. Methods: 1185 patients were included in this study from June 2020 to June 2023. All patients underwent peripheral nerve function assessments, of which 801 were diagnosed with DPN. Patients were randomly divided into a training set (n =711) and a validation set (n = 474) with a ratio of 6:4. The least absolute shrinkage and selection operator (LASSO) logistic regression analysis was performed to identify independent risk factors and develop a simple nomogram. Subsequently, the discrimination and clinical value of the nomogram was extensively validated using receiver operating characteristic (ROC) curves, calibration curves and clinical decision curve analyses (DCA). Results: Following LASSO regression analysis, a nomogram model for predicting the risk of DPN was eventually established based on 7 factors: age (OR = 1.02, 95%CI: 1.01 - 1.03), hip circumference (HC, OR = 0.94, 95%CI: 0.92 - 0.97), fasting plasma glucose (FPG, OR = 1.06, 95%CI: 1.01 - 1.11), fasting C-peptide (FCP, OR = 0.66, 95%CI: 0.56 - 0.77), 2 hour postprandial C-peptide (PCP, OR = 0.78, 95%CI: 0.72 - 0.84), albumin (ALB, OR = 0.90, 95%CI: 0.87 - 0.94) and blood urea nitrogen (BUN, OR = 1.08, 95%CI: 1.01 - 1.17). The areas under the curves (AUC) of the nomogram were 0.703 (95% CI 0.664-0.743) and 0.704 (95% CI 0.652-0.756) in the training and validation sets, respectively. The Hosmer-Lemeshow test and calibration curves revealed high consistency between the predicted and actual results of the nomogram. DCA demonstrated that the nomogram was valuable in clinical practice. Conclusions: The DPN nomogram prediction model, containing 7 significant variables, has exhibited excellent performance. Its generalization to clinical practice could potentially help in the early detection and prompt intervention for high-risk DPN patients.
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