Effect of metformin on cardiovascular outcomes: a systematic review and meta-analysis of observational studies and RCTs
Bibliographic record
Abstract
BackgroundMetformin is an oral medication most commonly prescribed to lower blood glucose levels. However, previous systematic reviews have cast doubt on its effectiveness in reducing the risk of cardiovascular disease (CVD), the costliest side effect of type 2 diabetes mellitus (T2DM). ObjectiveThis study aimed to combine data from observational studies and randomised controlled trials to determine the impact of metformin on cardiovascular outcomes in diabetic and non-diabetic population. MethodsOn February 24, 2023, a thorough article search was performed in PubMed, EBSCO, Scopus, Web of Science, and ProQuest using keywords and synonyms of Metformin and CVD, coupled with specific terms for different CVDs. Study quality was evaluated using the Cochrane risk of bias tool and Newcastle–Ottawa Scale. Statistical analysis of the data was conducted using R software. PROSPERO registration: CRD42023404151.ResultsA total of 40,087 studies were found through a literature search, of which 22 studies were identified as eligible, involving 612,823 participants, for the meta-analysis. The overall pooled effect estimate for CVD outcome with metformin treatment was found to be a Risk Ratio (RR) of 0.88 (95% CI:0.76-1.03). The pooled effect estimate indicated a significant reduction in CVD-related mortality with metformin treatment, with an RR of 0.75 (95% CI: 0.60-0.93).ConclusionsThis study provides evidence that metformin treatment may not have a significant effect on composite CVD outcomes or individual outcomes such as stroke, MI, HF, and MACE. However, we observed a potential reduction in CVD mortality with metformin 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 | 0.033 | 0.079 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.044 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".