Back

Epidemiological characteristics of newly diagnosed Graves' disease following the widespread outbreak of COVID-19 in Guangzhou, China

Lu, Y.; Zhong, R.; Shi, W.; Zhou, D.; Ran, Y.; Yang, Q.; Xu, D.; Lin, H.; Yao, L.; Liao, S.; Zhang, X.; Li, L.; Li, W.; Hu, z.

2025-12-29 endocrinology
10.64898/2025.12.28.25343112 medRxiv
Show abstract

ObjectivesThere is currently insufficient evidence linking COVID-19 infection with Graves disease (GD). Following the complete lifting of COVID-19 restrictions on December 13, 2022, widespread infection in Guangzhou provides a basis for this study. This research aims to investigate the correlation between COVID-19 infection and GD onset, explore the epidemiological characteristics of newly diagnosed GD post-infection, and offer a scientific basis for treatment. MethodsThe study population included 494 GD outpatients treated in the Department of Endocrinology at the Second Affiliated Hospital of Guangzhou Medical University from January 1 to June 30 each year between 2021 and 2023. They were divided into two groups: 2023 (N=219) and 2021-2022 (N=275), based on the time node of widespread COVID-19 infection in 2023. The new diagnosis rate, general clinical characteristics, and serological test results of GD patients were analyzed before and after the outbreak of COVID-19. ResultsCompared with the 2021-2022 group, the new diagnosis rate of GD patients in 2023 showed a significant increase (12.8% vs. 8.4%, P<0.001). Furthermore, there was a significant decrease in pre-treatment thyrotropin receptor antibody levels (P=0.01), white blood cell count (P=0.02), and neutrophil proportion (P=0.04), while there was a significant increase in the proportion of patients with a family history (P=0.047). Follow-up until June 30 of that year revealed that the proportion of newly diagnosed GD patients developing hypothyroidism during treatment in 2023 significantly increased compared to the 2021-2022 group (P<0.001). ConclusionsAfter widespread infection of COVID-19, the diagnosis rate of newly diagnosed GD increased, which may influence the epidemiological characteristics of related GD patients before initial treatment and during treatment.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.