Already the incidence of new-onset type 1 diabetes (T1D) increased during the COVID-19 pandemic, and this increase has been associated with SARS-CoV-2 infection.
https://pubmed.ncbi.nlm.nih.gov/35072727/
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0266809
The US CDC reported that pediatric patients with COVID-19 were more likely to be diagnosed with diabetes after infection, although types 1 and 2 were not separated.
https://pubmed.ncbi.nlm.nih.gov/35025851/
The study findings were published in the peer reviewed journal: JAMA Network Open.
https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2796649
The Table shows population characteristics before and after matching. The study population included 1 091 494 pediatric patients: 314 917 with COVID-19 and 776 577 with non–COVID-19 respiratory infections.
The matched cohort included 571 256 pediatric patients: 285 628 with COVID-19 and 285 628 with non–COVID-19 respiratory infections.
By 6 months after COVID-19, 123 patients (0.043%) had received a new diagnosis of T1D, but only 72 (0.025%) were diagnosed with T1D within 6 months after non–COVID-19 respiratory infection. At 1, 3, and 6 months after infection, risk of diagnosis of T1D was greater among those infected with SARS-CoV-2 compared with those with non–COVID-19 respiratory infection (1 month: HR, 1.96 [95%CI, 1.26-3.06]; 3 months: HR, 2.10 [95% CI, 1.48-3.00]; 6 months: HR, 1.83 [95% CI, 1.36-2.44]) and in subgroups of patients aged 0 to 9 years, a group unlikely to develop type 2 diabetes, and 10 to 18 years (Figure). Similar increased risks were observed among children infected with SARS-CoV-2 compared with other control cohorts at 6 months (fractures: HR, 2.09 [95% CI, 1.41- 3.10]; well child visits: HR, 2.10 [95% CI, 1.61- 2.73]).


Respiratory infections have previously been associated with onset of T1D,6 but this risk was even higher among those with COVID-19 in our study, raising concern for long-term, post–COVID-19 autoimmune complications among youths.
Study limitations include potential biases owing to the observational and retrospective design of the electronic health record analysis, including the possibility of misclassification of diabetes as type 1 vs type 2, and the possibility that additional unidentified factors accounted for the association.
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