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DOI 10.1186/s12967-026-07717-x

Медицинская библиотека · Journal of translational medicine · 2026

The silent epidemic within the pandemic: pathophysiology and prediction of post-COVID-19 diabetes.

Fang Hongjuan, Wang Qiang

СтатьяОбзорПоддержка исследования: вне правительства СШАПеревод в очереди

Аннотация

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has presented extraordinary challenges to global public health, with impacts reaching beyond acute respiratory manifestations to include long-term metabolic disturbances. Emerging evidence indicates a significant link between SARS-CoV-2 infection and the onset of diabetes mellitus, establishing this condition as a major element of the post-acute sequelae of COVID-19, often referred to as Long COVID. MAIN BODY: This review synthesizes epidemiological findings that demonstrate a elevated incidence of new-onset diabetes following COVID-19, particularly among certain high-risk demographic groups. We examine the molecular mechanisms underpinning this association, such as viral entry into pancreatic β-cells via ACE2 receptors, systemic inflammation leading to insulin resistance, and the potential diabetogenic effects of glucocorticoids used in COVID-19 treatment. Furthermore, this review outlines biomarker profiles that distinguish COVID-19-associated diabetes from traditional type 2 diabetes, underscoring important pathophysiological differences. Additionally, we evaluate advances and ongoing challenges in developing predictive risk models that combine clinical and molecular data to identify individuals at elevated risk for post-COVID diabetes.

CONCLUSIONS

By integrating multidisciplinary evidence, this comprehensive narrative review aims to guide future research and shape clinical approaches for early detection, prevention, and management of diabetes following COVID-19, thereby confronting a latent health crisis emerging within the broader pandemic context.

Для цитирования: Fang Hongjuan, Wang Qiang. The silent epidemic within the pandemic: pathophysiology and prediction of post-COVID-19 diabetes.. Journal of translational medicine. 2026. DOI: 10.1186/s12967-026-07717-x.

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