We read with great interest the recent article by Zhang et al. on the effectiveness and safety of Azvudine in hospitalized COVID-19 patients with pre-existing diabetes (1). This multicenter real-world study represents a valuable contribution to the evolving evidence base on antiviral use in high-risk populations. However, several methodological limitations merit attention to better contextualize the findings and guide future research. We wish to offer the following observations.
First, while the application of Kaplan–Meier and Cox proportional hazards regression for time-to-event outcomes is methodologically sound, the analysis does not adequately account for competing risks. Specifically, discharge alive represents a strong competing event that may informatively censor the outcome of in-hospital mortality. The use of cumulative incidence functions and the Fine–Gray model would provide more accurate estimates of absolute risk and avoid potential overestimation of mortality probabilities (2). Additionally, the definition of Azvudine exposure as a time-fixed covariate from admission may introduce immortal time bias, as patients must survive long enough to receive treatment. The analysis does not report the distribution of time from hospital admission to first administration, and immortal time bias cannot be ruled out. Modeling treatment as a time-varying covariate or applying a landmark analysis would help mitigate this bias.
Second, the authors have commendably adjusted for numerous clinical covariates and used propensity score matching to balance baseline characteristics (1). Nevertheless, several potential confounders—including vaccination status (3), smoking (4), and calendar time of admission (5)—were not considered. These factors are plausibly associated with both treatment allocation and COVID-19 outcomes, and their omission may bias the estimated treatment effect. The most consequential sources of unmeasured confounding factor is vaccination status. Whether patients included in the study had received a COVID-19 vaccine prior to illness significantly impacts their final outcomes, thereby further influencing the assessment of mortality rates. Furthermore, the analysis does not account for center-level variability, despite the multicenter design. Incorporating hospital fixed or random effects, or using cluster-robust standard errors, would improve the validity of inferences.
Third, the subgroup analyses are informative but should be interpreted with caution. The authors state that no significant interactions were observed, yet a nominally significant interaction (p=0.044) is reported for HbA1c in the composite outcome (1). This inconsistency, coupled with the absence of multiplicity correction and limited power in some subgroups, underscores the exploratory nature of these findings. Pre-specification of key subgroups and clearer reporting of interaction estimates with confidence intervals would enhance interpretability.
Finally, the safety analysis assessed adverse events from Azvudine initiation until five half-lives after the last dose, but the corresponding risk window for controls was not clearly defined. Asymmetric assessment periods and variable missingness in laboratory data may bias safety comparisons. Standardizing the at-risk period, accounting for informative missingness, and qualifying p-values for multiple testing would strengthen the safety conclusions.
In summary, this study offers important insights into Azvudine use in a high-risk diabetic cohort, but some shortcomings need to be improved and refined. We hope that these analyses and recommendations will be useful for future studies, and together we will promote the progress.
Statements
Author contributions
SG: Writing – original draft. JZ: Writing – original draft. JS: Writing – original draft.
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References
1
Zhou Y Yang Z Zhang S Zhang D Luo H Zhu D et al . A multicenter, real-world cohort study: effectiveness and safety of Azvudine in hospitalized COVID-19 patients with pre-existing diabetes. Front Endocrinol (Lausanne). (2025) 16:1467303. doi: 10.3389/fendo.2025.1467303
2
Austin PC Fine JP . Practical recommendations for reporting Fine-Gray model analyses for competing risk data. Stat Med. (2017) 36:4391–400. doi: 10.1002/sim.7501
3
Grapsa E Adamos G Andrianopoulos I Tsolaki V Giannakoulis VG Karavidas N et al . Association between vaccination status and mortality among intubated patients with COVID-19-related acute respiratory distress syndrome. JAMA Netw Open. (2022) 5:e2235219. doi: 10.1001/jamanetworkopen.2022.35219
4
Grigg J . Smoking, nicotine, and COVID-19. Lancet Respir Med. (2022) 10:818–9. doi: 10.1016/S2213-2600(22)00258-2
5
Cojocaru L Pahlavan A Tadbiri H Seung H Reddy R Mangione ME et al . Temporal trend of COVID-19 clinical severity and the ethnic/racial disparity: A report from the maryland study group. Am J Perinatol. (2023) 40:115–21. doi: 10.1055/s-0042-1757391
Summary
Keywords
Azvudine, COVID-19, diabetes, propensity score matching, safety, subgroup analysis
Citation
Guo S, Zhang J and Shu J (2026) Commentary: A multicenter, real-world cohort study: effectiveness and safety of Azvudine in hospitalized COVID-19 patients with pre-existing diabetes. Front. Endocrinol. 17:1735040. doi: 10.3389/fendo.2026.1735040
Received
29 October 2025
Revised
28 December 2025
Accepted
06 January 2026
Published
20 January 2026
Volume
17 - 2026
Edited by
Eleonore Fröhlich, Medical University of Graz, Austria
Reviewed by
Yingkai Xu, Wuhan University of Science and Technology, China
Zain Chagla, McMaster University, Canada
Updates
Copyright
© 2026 Guo, Zhang and Shu.
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) and the copyright owner(s) 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: Juan Shu, chanjuan-124@163.com
†These authors have contributed equally to this work
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.