Association between dipeptidyl peptidase-4 inhibitors and glucagon-like peptide-1 receptor agonists and COVID-19 infection and adverse outcomes: a cohort study
Bibliographic record
Abstract
INTRODUCTION: People with type 2 diabetes (T2DM) have an elevated risk of adverse outcomes from COVID-19. Dipeptidyl peptidase-4 inhibitors (DPP4is) and glucagon-like peptide-1 receptor agonists (GLP1RAs) might have favorable effects on COVID-19 outcomes. RESEARCH DESIGN AND METHODS: We conducted a population-based cohort study in Ontario, Canada. We compared the risk of both COVID-19 infection as well as adverse outcomes between users of DPP4i or GLP1RA and users of sodium-glucose cotransporter-2 inhibitors (SGLT2is) or sulfonylureas (SUs). The study population was persons ≥66 years with T2DM taking metformin who had ≥1 COVID-19 PCR test between January 2020 and July 2021. We compared (1) COVID-19 infection and (2) adverse outcomes at 30 days among COVID-19 positive patients (major cardiovascular (CV) events, hospitalizations, intensive care unit admission, all-cause mortality, venous thromboembolism, mechanical ventilation). We reported weighted risk differences (RDs) and relative risks (RRs). RESULTS: There were 26,485 DPP4i/GLP1RA users (mean age 76, 47% female, 91% DPP4i users) and 14,487 SGLT2i/SU users (mean age 75, 39% female, 65% SGLT2i users). The weighted rate of COVID-19 infection in DPP4i/GLP1RA users was 10.3% compared with 10.4% among SGLT2i/SU users (weighted RD -0.06, 95% CI -0.79 to 0.66; RR 0.99, 95% CI 0.93 to 1.07). Among COVID-19 positive patients, the weighted RD for all-cause hospitalization for DPP4i/GLP1RA users versus SGLT2i/SU users was -6.72% (95% CI -3.02 to -10.4) and the adjusted weighted RR was 0.79 (95% CI 0.70 to 0.89). For major CV events, the weighted RD was -1.91% (95% CI -4.00 to 0.18) and RR 0.73 (95% CI 0.54 to 1.00). CONCLUSIONS: DPP4i/GLP1RA use was not associated with reduced risk of COVID-19 infection compared with SGLT2i/SU use. DPP4i/GLP1RA use was associated with reduced risk of 30-day hospitalization among COVID-19 positive older adults and a possible trend towards a lower associated risk of CV events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".