Immunogenicity and safety of SARS-CoV-2 mRNA vaccines in a cohort of patients with type 1 diabetes
Bibliographic record
Abstract
Patients with type 1 diabetes (T1D) may develop severe outcomes during COVID-19 disease, but their ability to generate an immune response against the SARS-CoV-2 messenger RNA (mRNA) vaccines remains to be established. Here we evaluated the safety, immunogenicity and glycometabolic effects of the SARS-CoV-2 mRNA vaccines in patients with T1D. A total of 375 patients, 326 with T1D and 49 non-diabetics, who received two doses of the SARS-CoV-2 mRNA vaccines (mRNA-1273, BNT162b2) between March and April 2021 at the ASST FBF-Sacco Milan, Italy, were included in this monocentric observational study (NCT04905823). Local and systemic adverse events were reported in both groups after SARS-CoV-2 mRNA vaccination without statistical differences between them. While both T1D patients and non-diabetic subjects exhibited a parallel increase in anti-SARS-CoV-2S titers after vaccination, the vast majority of T1D patients (70% and 78% respectively) did not show any increase in the SARS-CoV-2-specific cytotoxic response as compared to the robust increase observed in all non-diabetic subjects. A reduced secretion of the T cell-related cytokines IL-2 and TNF-alpha in vaccinated patients with T1D was also observed. No glycometabolic alterations were evident in patients with T1D using continuous glucose monitoring during follow-up. Administration of the SARS-CoV-2 mRNA vaccine is associated with an impaired cellular SARS-CoV-2-specific cytotoxic immune response in T1D patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".