Safety of COVID-19 Vaccines among Patients with Type 2 Diabetes Mellitus: Real-World Data Analysis

Diabetes Metab J. 2023 May;47(3):356-365. doi: 10.4093/dmj.2022.0129. Epub 2023 Mar 6.

Abstract

Background: Little is known about the adverse events (AEs) associated with coronavirus disease 2019 (COVID-19) vaccination in patients with type 2 diabetes mellitus (T2DM).

Methods: This study used vaccine AE reporting system data to investigate severe AEs among vaccinated patients with T2DM. A natural language processing algorithm was applied to identify people with and without diabetes. After 1:3 matching, we collected data for 6,829 patients with T2DM and 20,487 healthy controls. Multiple logistic regression analysis was used to calculate the odds ratio for severe AEs.

Results: After COVID-19 vaccination, patients with T2DM were more likely to experience eight severe AEs than controls: cerebral venous sinus thrombosis, encephalitis myelitis encephalomyelitis, Bell's palsy, lymphadenopathy, ischemic stroke, deep vein thrombosis (DVT), thrombocytopenia (TP), and pulmonary embolism (PE). Moreover, patients with T2DM vaccinated with BNT162b2 and mRNA-1273 were more vulnerable to DVT and TP than those vaccinated with JNJ-78436735. Among patients with T2DM administered mRNA vaccines, mRNA-1273 was safer than BNT162b2 in terms of the risk of DVT and PE.

Conclusion: Careful monitoring of severe AEs in patients with T2DM may be necessary, especially for those related to thrombotic events and neurological dysfunctions after COVID-19 vaccination.

Keywords: Adverse effects; COVID-19; Diabetes mellitus, type 2; Vaccines.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • 2019-nCoV Vaccine mRNA-1273
  • Ad26COVS1
  • BNT162 Vaccine
  • COVID-19 Vaccines / adverse effects
  • COVID-19* / prevention & control
  • Data Analysis
  • Diabetes Mellitus, Type 2* / complications
  • Humans

Substances

  • COVID-19 Vaccines
  • BNT162 Vaccine
  • 2019-nCoV Vaccine mRNA-1273
  • Ad26COVS1

Grants and funding

This research was supported by the Bio Industry Technology Development Program (No. 20015086) funded by the Ministry of Trade, Industry, & Energy (MOTIE, Korea), as well as supported by a grant from the Information and Communications Promotion Fund through the National IT Industry Promotion Agency (NIPA), funded by the Ministry of Science and ICT (MSIT), Republic of Korea.