Trial Design and Baseline Characteristics from REMODEL: A Mechanistic Study of Semaglutide vs. Placebo in People with Type 2 Diabetes and CKD
Bibliographic record
Abstract
Background: Type 2 diabetes (T2D) is the leading cause of chronic kidney disease (CKD) and kidney failure globally. Semaglutide, a glucagon-like peptide-1 receptor agonist, reduces risks of major kidney, cardiovascular, and mortality outcomes in people with T2D and CKD as shown in the FLOW trial. Despite the clinical benefits, the precise kidney-specific mode-of-action (MoA) of semaglutide remains unclear. Methods: REMODEL is a 52-week phase 3b trial investigating the kidney-specific MoA for semaglutide (once-weekly subcutaneous 1.0 mg) vs. placebo in people with T2D and CKD (Figure 1).Primary endpoints are magnetic-resonance imaging (MRI)-based measures of changes in kidney oxygenation, global kidney perfusion, inflammation and fibrosis. A subset of participants opted to undergo sequential kidney biopsies both at baseline and at the end of treatment for tissue-based interrogation including single nucleus transcriptomics, pathology, and morphometric examination. Results: REMODEL enrolled 106 participants across 8 countries (Table 1). All participants were prescribed concomitant medications at baseline with 98.1% using RAAS inhibition, and 38.7% using SGLT2i. Baseline kidney biopsies were performed in 33 participants with characteristics similar to the entire cohort. Conclusion: REMODEL will comprehensively assess multiple proposed physiological pathways for the kidney-specific MoA of semaglutide and will help elucidate kidney-specific benefits seen in the FLOW trial. The REMODEL trial has successfully enrolled a representative population of people with T2D and CKD, ensuring the generalizability and clinical relevance of the findings. Funding: Commercial Support - NOVO NORDISK AS
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".