Tirzepatide Associated With Reduced Albuminuria in Participants With Type 2 Diabetes: Pooled Post Hoc Analysis From the Randomized Active- and Placebo-Controlled SURPASS-1–5 Clinical Trials
Bibliographic record
Abstract
OBJECTIVE: Tirzepatide, a long-acting, glucose-dependent insulinotropic polypeptide/glucagon-like peptide 1 receptor agonist, reduced urine albumin-to-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) decline in people with type 2 diabetes and high cardiovascular risk in the SURPASS-4 trial. To examine the generalizability of these findings, we assessed change from baseline in UACR for tirzepatide (5, 10, and 15 mg) compared with active and placebo treatment in a broad population from the SURPASS-1-5 trials. RESEARCH DESIGN AND METHODS: This post hoc analysis examined data from the overall pooled SURPASS-1-5 population and subgroups defined by baseline UACR ≥30 mg/g. A mixed model for repeated measures was used to analyze on-treatment data from baseline to the end-of-treatment visit. Study identifier was included in the model as a covariate. RESULTS: The adjusted mean percent change from baseline in UACR for tirzepatide 5, 10, or 15 mg compared with all pooled comparators was -19.3% (95% CI -25.5, -12.5), -22.0% (-28.1, -15.3), and -26.3 (-32.0, -20.0), respectively, at week 40/42. Results were similar across pooled placebo, active, and insulin comparator studies. UACR lowering appeared more pronounced in subgroups with UACR ≥30 mg/g. Mediation analysis findings suggested that approximately one-half of the reduction in albuminuria associated with tirzepatide may be weight loss related. There was no difference in eGFR between tirzepatide and pooled comparators at week 40/42. CONCLUSIONS: In this post hoc analysis in people with type 2 diabetes, including those with chronic kidney disease, tirzepatide was associated with a clinically relevant decreased UACR versus comparators, suggesting a potential kidney-protective effect.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.025 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".