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ORIGINAL RESEARCH article

Front. Endocrinol.
Sec. Clinical Diabetes
Volume 15 - 2024 | doi: 10.3389/fendo.2024.1349117
This article is part of the Research Topic Deciphering the immunological and neuronal regulators of diabesity View all articles

The Application of Predictive Value of Diabetes Autoantibody Profile Combined with Clinical Data and Routine Laboratory Indexes in the Classification of Diabetes Mellitus

Provisionally accepted
Jiawen Xian Jiawen Xian 1Hui Yuan Hui Yuan 2Jingyuan Li Jingyuan Li 1Qin Pei Qin Pei 1Yongjie Hao Yongjie Hao 1Xi Zeng Xi Zeng 1Jingying Wang Jingying Wang 1Ting Ye Ting Ye 1*
  • 1 Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China
  • 2 School of Basic Medical Sciences and School of Stomatology, Mudanjiang Medical University, Mudanjiang, China

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

    Objective: 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. Methods: 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). Results: 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), and glutamic acid decarboxylase antibody (GADA) 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. Conclusions: 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.

    Keywords: diabetes autoantibody profile1, clinical data2, routine laboratory indicators3, diabetes typing4, Nomogram5

    Received: 04 Dec 2023; Accepted: 24 Jul 2024.

    Copyright: © 2024 Xian, Yuan, Li, Pei, Hao, Zeng, Wang and Ye. 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: Ting Ye, Department of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China

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