Dulaglutide and Kidney Function-Related Outcomes in Type 2 Diabetes: Post Hoc Analysis from the REWIND Trial
Bibliographic record
Abstract
Background: Over the median follow-up of 5.4 years in the REWIND trial, which included participants with type 2 diabetes (T2D) and multiple cardiovascular (CV) risk factors, dulaglutide (DU) use was associated with a reduction in composite renal outcomes, defined as the first occurrence of new macroalbuminuria, sustained decline in estimated glomerular filtration rate (eGFR) of ≥30% (using the modification of diet in renal disease [MDRD] equation), or chronic renal replacement therapy. This posthoc analysis evaluated the potential effects of dulaglutide on renal outcomes using an alternative endpoint definition that is commonly used in renal outcomes studies; defined as the composite endpoint of sustained eGFR decline ≥40% (using the chronic kidney disease-epidemiology collaboration [CKD-EPI] equation), end-stage renal disease (ESRD), or all-cause death. Methods: REWIND participants were randomized (1:1) to DU 1.5 mg once weekly or placebo. Cox proportional hazards model for time-to-first event analysis was used to determine the risk of renal outcomes. Subgroup analyses were conducted by baseline eGFR and albuminuria status. Results: At baseline, treatment groups had similar eGFR values (mean±SD: DU=77.6 ± 19.4 mL/min/1.73 m2; placebo=77.1 ± 19.6 mL/min/1.73 m2). The incidence rate of the composite endpoint was significantly lower for the DU group compared with placebo. This effect was consistent regardless of baseline eGFR or albuminuria status (Table). Conclusions: Treatment with DU 1.5 mg was associated with a 17% risk reduction in the composite renal outcome, suggesting potential delay in progression of diabetic kidney disease in patients with T2D at CV risk. Funding: Commercial Support - Eli Lilly and Company
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".