FP482EGFR LOSS WITH GLUCAGON-LIKE PEPTIDE-1 (GLP-1) ANALOGUE TREATMENT: DATA FROM SUSTAIN 6 AND LEADER
Bibliographic record
Abstract
INTRODUCTION: Previous SUSTAIN 6 and LEADER cardiovascular (CV) outcomes trials data indicate the GLP-1 analogues semaglutide and liraglutide may have beneficial effects on kidney function. This post hoc analysis investigated the semaglutide and liraglutide effects on change in eGFR evaluated as total eGFR slope. METHODS: SUSTAIN 6 and LEADER assessed CV, kidney and safety outcomes with semaglutide and liraglutide vs placebo, in 3297 and 9340 patients with type 2 diabetes and at high CV risk, respectively. Median treatment duration was 2.1 and 3.8 years, respectively. In the current analysis, eGFR change over time was evaluated by overall population and baseline eGFR subgroup (<60 vs ≥60 mL/min/1.73 m2) for semaglutide (1.0 mg) and liraglutide vs placebo using a linear regression model with random slope and intercept; treatment differences between annual slopes were estimated (ETDs). RESULTS: In the overall population, a slower rate of annual eGFR reduction was observed with semaglutide vs placebo (mean annual ETD of 0.87 mL/min/1.73 m2 favouring semaglutide); this effect appeared more pronounced for baseline eGFR <60 mL/min/1.73 m2, (annual ETD: 1.62 mL/min/1.73 m2 slower eGFR reduction, Table). In LEADER, the annual eGFR reduction was slower for liraglutide vs placebo for the overall population; the effect was more marked in patients with baseline eGFR <60 mL/min/1.73 m2 (annual ETD: 0.67 mL/min/1.73 m2 slower eGFR reduction, Table). CONCLUSIONS: Annual loss of kidney function was slower in patients treated with semaglutide or liraglutide vs placebo. The benefit appears more pronounced in patients with pre‑existing chronic kidney disease.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| 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 | 0.001 |
| 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".