FP483EFFECTS OF SEMAGLUTIDE AND LIRAGLUTIDE ON URINARY ALBUMIN-TO-CREATININE RATIO (UACR) – A POOLED ANALYSIS OF SUSTAIN 6 AND LEADER
Bibliographic record
Abstract
INTRODUCTION: UACR is a marker of renal damage and renal disease progression risk; UACR reduction has been shown to correlate with renal protection. This post hoc analysis of pooled data from SUSTAIN 6 and LEADER investigated the effects of glucagon-like peptide-1 (GLP-1) analogues semaglutide and liraglutide vs placebo on UACR. METHODS: Primary outcome in both trials: major adverse cardiovascular events. Nephropathy events and change in renal biochemistry, including UACR, were secondary outcomes. In this analysis (N=11812), patients were stratified by baseline UACR class (normo-: <30 mg/g, micro-: 30–300 mg/g and macroalbuminuria: >300 mg/g). Risk of categorical changes in UACR (time to persistent: progression to micro- or macroalbuminuria, regression to micro- or normoalbuminuria, 30% reduction) and UACR change from baseline to 2 years were analysed by baseline UACR using a Cox regression model (with treatment as a fixed factor and stratified by study) and a mixed model on log-transformed values, respectively. RESULTS: GLP-1 analogues reduced risk of progression to micro- or macroalbuminuria and increased the likelihood of regression in albuminuria category vs placebo. The likelihood of reaching 30% UACR reduction was greater with the GLP-1 analogues vs placebo (p<0.0001 for all UACR subgroups) (Table). UACR increased relatively more from baseline to 2 years for placebo vs GLP-1 analogues for the pooled population and patients with baseline normo-, micro- and macroalbuminuria (24%, 20%, 30% and 21%, respectively). CONCLUSIONS: This pooled analysis shows beneficial effects on UACR for GLP-1 analogues semaglutide and liraglutide vs placebo irrespective of baseline UACR.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".