Biomarker Changes Associated With Both Dulaglutide and Cardiovascular Events in the REWIND Randomized Controlled Trial: A Nested Case-Control Post Hoc Analysis
Bibliographic record
Abstract
OBJECTIVE: The glucagon-like peptide-1 receptor agonist dulaglutide reduced MACE in the Researching Cardiovascular Events with a Weekly Incretin in Diabetes (REWIND) trial. This article expores the relationship of selected biomarkers to both dulaglutide and major adverse cardiovascular events (MACE). RESEARCH DESIGN AND METHODS: In this post hoc analysis, stored fasting baseline and 2-year plasma samples from 824 REWIND participants with MACE during follow-up and 845 matched non-MACE participants were analyzed for 2-year changes in 19 protein biomarkers. Two-year changes in 135 metabolites were also analyzed in 600 participants with MACE during follow-up and in 601 matched non-MACE participants. Linear and logistic regression models were used to identify proteins that were associated with both dulaglutide treatment and MACE. Similar models were used to identify metabolites that were associated with both dulaglutide treatment and MACE. RESULTS: Compared with placebo, dulaglutide was associated with a greater reduction or lesser 2-year rise from baseline in N-terminal prohormone of brain natriuretic peptide (NT-proBNP), growth differentiation factor 15 (GDF-15), high-sensitivity C-reactive protein, and a greater 2-year rise in C-peptide. Compared with placebo, dulaglutide was also associated with a greater fall from baseline in 2-hydroxybutyric acid and a greater rise in threonine (P < 0.001). Increases from baseline in two of the proteins (but neither metabolite) were associated with MACE, including NT-proBNP (OR 1.267; 95% CI 1.119, 1.435; P < 0.001) and GDF-15 (OR 1.937; 95% CI 1.424, 2.634; P < 0.001). CONCLUSIONS: Dulaglutide was associated with a reduced 2-year rise from baseline of NT-proBNP and GDF-15. Higher rises of these biomarkers were also associated with MACE.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| 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 | 0.001 |
| 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".