Metformin use and cardiovascular events in patients with type 2 diabetes and chronic kidney disease
Bibliographic record
Abstract
AIMS: Metformin could have benefits on cardiovascular disease and kidney disease progression but is often withheld from individuals with diabetes and chronic kidney disease (CKD) because of a concern that it may increase the risk of lactic acidosis. MATERIALS AND METHODS: All-cause mortality, cardiovascular death, cardiovascular events (death, hospitalization for heart failure, myocardial infarction, stroke or myocardial ischemia), end stage renal disease (ESRD) and the kidney disease composite (ESRD or death) were compared in metformin users and non-users with diabetes and CKD enrolled in the Trial to Reduce Cardiovascular Events with Aranesp (darbepoeitin-alfa) Therapy (TREAT) (NCT00093015). Outcomes were compared after propensity matching of users and non-users and in multivariable proportional hazards models. RESULTS: There were 591 individuals who used metformin at baseline and 3447 non-users. Among propensity-matched users, the crude incidence rate for mortality, cardiovascular mortality, cardiovascular events and the combined endpoint was lower in metformin users than in non-users, but ESRD was marginally higher (4.0% vs 3.6%). Metformin use was independently associated with a reduced risk of all-cause mortality (HR, 0.49; 95% CI, 0.36-0.69), cardiovascular death (HR, 0.49; 95% CI, 0.32-0.74), the cardiovascular composite (HR, 0.67, 95% CI, 0.51-0.88) and the kidney disease composite (HR, 0.77; 95% CI, 0.61-0.98). Associations with ESRD (HR, 1.01; 95% CI, 0.65-1.55) were not significant. Results were qualitatively similar in adjusted analyses of the full population. Two cases of lactic acidosis were observed. CONCLUSIONS: Metformin may be safer for use in CKD than previously considered and may lower the risk of death and cardiovascular events in individuals with stage 3 CKD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".