The “Real-World” Effect of Anti-hyperglycemic Drugs on the Development of Chronic Kidney Disease in a Retrospective Cohort of Patients With Incident Diabetes: A Research Letter
Bibliographic record
Abstract
Recent clinical trials suggest benefit of anti-hyperglycemic drugs on kidney outcomes. However, there is a paucity of information available on the real-world impact.We aimed to study the real-world impact of anti-hyperglycemic drugs (metformin, sodium-glucose cotransporter-2 (SGLT-2) inhibitors, dipeptidyl peptidase-4 (DPP-4) inhibitors, and glucagon-like peptide-1receptor (GLP-1R) agonists) using a cohort of patients with incident diabetes derived from the Alberta Tomorrow Project (ATP) database. A retrospective cohort was created from the ATP database using administrative data from October 1, 2000, to March 31, 2021. We examined the effect of anti-hyperglycemic medications including metformin (as a control), SGLT-2 inhibitors, DPP-4 inhibitors, and GLP-1R agonists on a composite kidney outcome including chronic kidney disease, kidney failure, dialysis, kidney transplant, and kidney-related death using a Cox-regression analysis. The study included 3001 patients with an incident diagnosis of diabetes. The average follow-up was 6.7 ± 4.6 years after diagnosis, and 628 (20.9%) patients reached the composite outcome with a mean of 5.6 ± 4.2 years to the first event. A total of 1749 (58.8%) patients were on metformin, 360 (12.0%) on SGLT-2 inhibitors, 313 (10.4%) on DPP-4 inhibitors, and 188 (6.3%) on GLP-1R agonists. Only the patients prescribed SGLT-2 inhibitors had a significant reduction in the composite outcome (hazard ratio (HR) 0.23, 95% CI 0.09-0.62, P -value = .003), and a dose-related effect was observed. Our study has shown that SGLT-2 inhibitors result in significant reduction of composite kidney outcomes, including chronic kidney disease, suggesting a renally protective effect over long term.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".