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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".