Effects of oral semaglutide on kidney outcomes in people with type 2 diabetes: a nationwide, multicentre, retrospective, observational study (Renal_ENDO2S-RWD substudy)
Bibliographic record
Abstract
Background: Subcutaneous semaglutide has shown kidney-protective effects in people with type 2 diabetes (PWT2D), but data on oral semaglutide remain limited. This multicentre real-world study evaluates the clinical effectiveness of oral semaglutide on kidney outcomes in PWT2D. Methods: We included PW2TD ≥18 years of age who initiated oral semaglutide in routine practice between 2021 and 2022 in the Spanish National Health System, with at least one report of clinical follow-up (FU) data at 3 months. Co-primary endpoints were changes in urine albumin:creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) slope at 6-12 months. We also assessed baseline predictors of response, drug persistence and safety by CKD severity. Results: , UACR 12 mg/g, 45.8% female, median FU 8.96 months). Oral semaglutide decreased UACR by 30.3% and 40.0% in the overall cohort, by 40.2% and 50.7% in those with a UACR ≥30 mg/g and by 40.6% and 49.9% in those with a UACR ≥300 mg/g at 6 and 12 months of FU, respectively. PWT2D with a low age-adjusted risk of liver fibrosis by the Fibrosis-4 index had increased odds of achieving a >30% reduction in UACR [adjusted odds ratio 5.50 (95% confidence interval 1.6-18.7)] regardless of baseline background. Metabolic and weight loss effectiveness, safety and persistence of oral semaglutide were consistent across CKD severities. Conclusions: In a real-world setting, oral semaglutide treatment for up to 52 weeks resulted in clinically meaningful reductions in albuminuria without changes in the eGFR slope in PWT2D. Effectiveness, safety and tolerability were not influenced by CKD severity.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".