Association Between DPP-4 Inhibitors and COVID-19–Related Outcomes Among Patients With Type 2 Diabetes
Bibliographic record
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for coronavirus disease 2019 (COVID-19), uses angiotensin-converting enzyme 2 to invade human cells. However, recent evidence suggested that dipeptidyl peptidase 4 (DPP-4) may be used as a coreceptor when SARS-CoV-2 enters the target cells (1). Interestingly, upregulation of DPP-4 is associated with older age, respiratory or cardiovascular disease, and diabetes (2), all of which were reported to exacerbate COVID-19. Given the pathophysiological evidence, DPP-4 inhibitors were suggested to have beneficial effects on COVID-19. Given the high fatality rate of COVID-19 among patients with diabetes, there is an urgent need to understand the effect DPP-4 inhibitors may have on COVID-19. Therefore, we aimed to determine whether use of DPP-4 inhibitors reduces the risk of adverse COVID-19–related outcomes among patients with type 2 diabetes (T2D). We conducted a nationwide cohort study using the Health Insurance Review and Assessment Service database linked with the Korea Disease Control and Prevention Agency database, which covers the entire South Korean population of ≥50 million, from 1 January 2017 to 15 May 2020. We included patients who had a positive test result for COVID-19 as of 15 May 2020, had been diagnosed with T2D within the preceding 3 years before COVID-19 diagnosis (cohort entry), and had ≥1 antidiabetic prescription within the 180 days before cohort entry. We excluded patients aged <18 years; those prescribed metformin monotherapy only, to restrict inclusion to patients who were on second- or third-line therapy for T2D; those prescribed insulin monotherapy as they are likely to be patients with type 1 diabetes; those prescribed only insulin and metformin as they do not belong to …
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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 | 0.000 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".