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ORIGINAL RESEARCH article

Front. Endocrinol.
Sec. Clinical Diabetes
Volume 15 - 2024 | doi: 10.3389/fendo.2024.1419115
This article is part of the Research Topic Understanding and Managing Diabetic Neuropathy: Current Perspectives and Future Directions View all 7 articles

Construction and Validation of a Nomogram Model for Predicting Diabetic Peripheral Neuropathy

Provisionally accepted
Hanying Liu Hanying Liu Qiao Liu Qiao Liu *Mengdie Chen Mengdie Chen Chaoyin Lu Chaoyin Lu *Ping Feng Ping Feng *
  • Department of Endocrinology, Taizhou Central Hospital, Taizhou, China

The final, formatted version of the article will be published soon.

    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 Cpeptide (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.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.

    Keywords: Diabetic peripheral neuropathy, nomogram, age, Hip circumference, Fasting plasma glucose, C-Peptide, albumin, Blood Urea Nitrogen

    Received: 17 Apr 2024; Accepted: 02 Dec 2024.

    Copyright: © 2024 Liu, Liu, Chen, Lu and Feng. This is an 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) or licensor 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.

    * Correspondence:
    Qiao Liu, Department of Endocrinology, Taizhou Central Hospital, Taizhou, China
    Chaoyin Lu, Department of Endocrinology, Taizhou Central Hospital, Taizhou, China
    Ping Feng, Department of Endocrinology, Taizhou Central Hospital, Taizhou, China

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