Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Sichuan, China.
School of Basic Medical Sciences and School of Stomatology, Mudanjiang Medical University, Heilongjiang, China.
Front Endocrinol (Lausanne). 2024 Aug 22;15:1349117. doi: 10.3389/fendo.2024.1349117. eCollection 2024.
Currently, distinct use of clinical data, routine laboratory indicators or the detection of diabetic autoantibodies in the diagnosis and management of diabetes mellitus is limited. Hence, this study was aimed to screen the indicators, and to establish and validate a multifactorial logistic regression model nomogram for the non-invasive differential prediction of type 1 diabetes mellitus.
Clinical data, routine laboratory indicators, and diabetes autoantibody profiles of diabetic patients admitted between September 2018 and December 2022 were retrospectively analyzed. Logistic regression was used to select the independent influencing factors, and a prediction nomogram based on the multiple logistic regression model was constructed using these independent factors. Moreover, the predictive accuracy and clinical application value of the nomogram were evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).
A total of 522 diabetic patients were included in this study. These patients were randomized into training and validation sets in a 7:3 ratio. The predictors screened included age, prealbumin (PA), high-density lipoprotein cholesterol (HDL-C), islet cells autoantibodies (ICA), islets antigen 2 autoantibodies (IA-2A), glutamic acid decarboxylase antibody (GADA), and C-peptide levels. Based on these factors, a multivariate model nomogram was constructed, which had an Area Under Curve (AUC) of 0.966 and 0.961 for the training set and validation set, respectively. Subsequently, the calibration curves demonstrated a strong accuracy of the graph; the DCA and CIC results indicated that the graph could be used as a non-invasive valid predictive tool for the differential diagnosis of type 1 diabetes mellitus, clinically.
The established prediction model combining patient's age, PA, HDL-C, ICA, IA-2A, GADA, and C-peptide can assist in differential diagnosis of type 1 diabetes mellitus and type 2 diabetes mellitus and provides a basis for the clinical as well as therapeutic management of the disease.
目前，在糖尿病的诊断和管理中，临床数据、常规实验室指标或糖尿病自身抗体的单独使用存在一定局限性。因此，本研究旨在筛选指标，建立并验证一种多因素逻辑回归模型列线图，用于对 1 型糖尿病进行非侵入性鉴别预测。
回顾性分析 2018 年 9 月至 2022 年 12 月期间收治的糖尿病患者的临床数据、常规实验室指标和糖尿病自身抗体谱。采用逻辑回归筛选独立影响因素，并基于这些独立因素构建基于多因素逻辑回归模型的预测列线图。此外，还通过受试者工作特征（ROC）曲线、校准曲线、决策曲线分析（DCA）和临床影响曲线（CIC）评估列线图的预测准确性和临床应用价值。
本研究共纳入 522 例糖尿病患者，按 7:3 的比例随机分为训练集和验证集。筛选出的预测指标包括年龄、前白蛋白（PA）、高密度脂蛋白胆固醇（HDL-C）、胰岛细胞自身抗体（ICA）、胰岛抗原 2 自身抗体（IA-2A）、谷氨酸脱羧酶抗体（GADA）和 C 肽水平。基于这些因素，构建了一个多变量模型列线图，在训练集和验证集中的 AUC 分别为 0.966 和 0.961。随后，校准曲线表明图谱具有很强的准确性；DCA 和 CIC 结果表明，该图谱可作为 1 型糖尿病鉴别诊断的一种有效无创预测工具，具有临床应用价值。
建立的预测模型结合患者的年龄、PA、HDL-C、ICA、IA-2A、GADA 和 C 肽水平，可以辅助 1 型糖尿病和 2 型糖尿病的鉴别诊断，为临床治疗提供依据。
