Mechanistic Effects of Semaglutide on Kidney Disease in Type 2 Diabetes: The REMODEL Trial
Bibliographic record
Abstract
Background: Semaglutide reduces risks of losing kidney function and kidney failure in type 2 diabetes (T2D) with chronic kidney disease (CKD), yet the mechanisms remain unclear. The study aimed to discover the basis of kidney protection using a pathophysiologically integrative design combining functional magnetic resonance imaging with tissue-based analytics. Methods: Adults with T2D, estimated glomerular filtration rate (eGFR) of 30-75 mL/min/1.73 m2 and urinary albumin-to-creatinine ratio (UACR) 20-<5000 mg/g were randomized 2:1 to subcutaneous semaglutide 1.0 mg once-weekly or placebo for 52 weeks. A subgroup underwent paired kidney biopsy for histology, single-nuclear, and spatial transcriptomics. Results: At baseline, participants (N=106) were 65 ±10 years old and 24% (n=25) were female. Mean eGFR was 51 ±10 mL/min/1.73m2 and median UACR was 187 (interquartile range 60-546) mg/g. At 52 weeks, the mean estimated treatment effects for semaglutide versus placebo included 40% lower UACR and 12 mL/min higher creatinine clearance (Figure 1A). Perirenal and sinus fat volumes decreased by 25% and 13%, respectively, and the cortical apparent diffusion coefficient increased (estimated treatment ratio [ETR] 1.05, 95% CI 1.01-1.09) (Figure 1B). Semaglutide reduced the renal arterial resistive index (ETR 0.96, 0.93-0.99), while cortical oxygenation (ETR 0.98, 0.96-1.01) and perfusion (ETR 1.10, 0.98-1.24) trended higher (Figure 1C). In the biopsy subgroup (N=33), the arteriolar intimal area of the most diseased vessel decreased by 10%, and glomerular endothelial-cell transcriptomes revealed downregulation of genes in metabolic, inflammatory, and fibrotic pathways by semaglutide (Figure 1D). Conclusion: In participants with T2D and CKD, semaglutide reduced kidney fat, improved glomerular hemodynamics, and ameliorated endothelial injury through metabolic reprogramming and mitigation of inflammation and fibrosis. Funding: Commercial Support - Novo Nordisk A/S
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".