Liu Hanying, Liu Qiao, Chen Mengdie, Lu Chaoyin, Feng Ping
Department of Endocrinology, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, China.
Front Endocrinol (Lausanne). 2024 Dec 16;15:1419115. doi: 10.3389/fendo.2024.1419115. eCollection 2024.
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.
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).
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.
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.
糖尿病周围神经病变（DPN）是糖尿病的一种慢性并发症，可能会发展为溃疡、截肢及其他严重后果。本研究旨在构建并验证一种预测列线图模型，用于评估糖尿病患者发生DPN的风险，从而有助于早期识别DPN高危个体并降低严重后果的发生率。
本研究纳入了2020年6月至2023年6月期间的1185例患者。所有患者均接受了外周神经功能评估，其中801例被诊断为DPN。患者按6:4的比例随机分为训练集（n =711）和验证集（n = 474）。采用最小绝对收缩和选择算子（LASSO）逻辑回归分析来确定独立危险因素并建立一个简单的列线图。随后，使用受试者工作特征（ROC）曲线、校准曲线和临床决策曲线分析（DCA）对列线图的辨别能力和临床价值进行了广泛验证。
经过LASSO回归分析，最终基于7个因素建立了一个预测DPN风险的列线图模型：年龄（OR = 1.02，95%CI：1.01 - 1.03）、臀围（HC，OR = 0.94，95%CI：0.92 - 0.97）、空腹血糖（FPG，OR = 1.06，95%CI：1.01 - 1.11）、空腹C肽（FCP，OR = 0.66，95%CI：0.56 - 0.77）、餐后2小时C肽（PCP，OR = 0.78，95%CI：0.72 - 0.84）、白蛋白（ALB，OR = 0.90，95%CI：0.87 - 0.94）和血尿素氮（BUN，OR = 1.08，95%CI：1.01 - 1.17）。列线图在训练集和验证集的曲线下面积（AUC）分别为0.703（95%CI 0.664 - 0.743）和0.704（95%CI 0.652 - 0.756）。Hosmer-Lemeshow检验和校准曲线显示列线图的预测结果与实际结果高度一致。DCA表明该列线图在临床实践中具有价值。
包含7个显著变量的DPN列线图预测模型表现出色。将其推广应用于临床实践可能有助于早期发现高危DPN患者并及时进行干预。
