Does metformin affect outcomes in COVID‐19 patients with new or pre‐existing diabetes mellitus? A systematic review and meta‐analysis
Bibliographic record
Abstract
Aims The COVID‐19 pandemic is a global public health emergency and patients with diabetes mellitus (DM) are disproportionately affected, exhibiting more severe outcomes. Recent studies have shown that metformin is associated with improved outcomes in patients with COVID‐19 and DM and may be a potential candidate for drug repurposing. We aimed to investigate the effects of metformin on outcomes in patients with COVID‐19 and DM. Methods Databases (PubMed, Scopus, Web of Science, EMBASE, Clinicaltrials.gov and Cochrane library) were searched up to 10 April 2021 for studies reporting data on metformin use in COVID‐19 patients with DM. The risk of bias was assessed using the Newcastle–Ottawa scale. Certainty of evidence was rated using the GRADE approach. The primary outcome was mortality reported as odds ratio (OR). A random‐effects meta‐analysis was carried out on both unadjusted and adjusted ORs. This study is registered with PROSPERO, CRD42020221842. Results In total, 2 916 231 patients from 32 cohort studies were included in the quantitative and qualitative synthesis. The meta‐analysis showed that metformin was significantly associated with lower mortality in COVID‐19 patients with DM in both unadjusted (OR 0.61 [95% confidence interval: 0.53–0.71], P < .00001, I2 = 70%) and adjusted (OR 0.78 [95% confidence interval: 0.69–0.88], P < .00001, I2 = 67%) models. Conclusion Poor outcomes in COVID‐19 patients with DM can be attributed to inadequate glycaemic control and weakened immune responses. Metformin has multiple effects that can improve outcomes in patients with DM and our findings highlight a possible role of its use. However, robust randomised trials are needed to thoroughly assess its use.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".