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Letter to the Editor
©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Diabetes. Nov 15, 2025; 16(11): 112939
Published online Nov 15, 2025. doi: 10.4239/wjd.v16.i11.112939
Reevaluating the relationship between COVID-19 and type 1 diabetes mellitus: Methodological considerations
Er-Min Liang, Hong-Cheng Luo
Er-Min Liang, Department of Respiratory Medicine, Anting Hospital of Jiading District, Shanghai 201805, China
Hong-Cheng Luo, Department of Urology, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen 518033, Guangdong Province, China
Hong-Cheng Luo, Division of Vascular and Endovascular Surgery, Department of Surgery, University of Hong Kong Medical Centre, Queen Mary Hospital, Hong Kong 999077, China
Author contributions: Liang EM wrote the original draft; Luo HC provided supervision, critical revisions, and final approval of the manuscript. All authors have read and approved the final version.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Hong-Cheng Luo, MD, Department of Urology, The Eighth Affiliated Hospital of Sun Yat-sen University, No. 3025 Shennan Zhong Road, Futian District, Shenzhen 518033, Guangdong Province, China. drlhc96@163.com
Received: August 11, 2025
Revised: August 25, 2025
Accepted: October 20, 2025
Published online: November 15, 2025
Processing time: 96 Days and 6.1 Hours
Core Tip

Core Tip: Carmon et al’s study suggests a link between the coronavirus disease 2019 (COVID-19) pandemic and rising type 1 diabetes mellitus cases. This letter provides a constructive critique of their methodology. The authors point out that ecological analysis without individual infection data can be misleading, unaddressed confounders and lack of COVID-19 stratification weaken causal inference, and pandemic-related diagnostic delays (reflected in higher diabetic ketoacidosis rates) may have inflated case counts. The authors also caution that biases in viral surveillance data complicate the interpretation of “reduced” non-COVID infections. Addressing these issues with more granular data and analyses will improve future research on the type 1 diabetes mellitus-COVID relationship.