Tirzepatide Reduces Albuminuria in Patients with T2D: Post-hoc Pooled Analysis of SURPASS 1-5
Bibliographic record
Abstract
Does tirzepatide (5, 10, 15 mg) reduce urine albumin-creatinine ratio (UACR) compared to placebo or other diabetes medications? Methods : In this post-hoc analysis, UACR (% difference) for TZP (5, 10, 15 mg) vs comparators (COMPs) was analysed. Analyses were conducted in the pooled entire SURPASS 1-5 population and populations pooled by COMP: placebo (SURPASS 1 & 5); active (SURPASS 2 [semaglutide 1 mg] & SURPASS 3-4 [insulins]); and insulins. In each pooled population, data were examined in all patients and in subgroups defined by baseline UACR ≥30 mg/g or eGFR<60 mL/min/1.73m^2. Mixed model for repeated measures was used to analyse on-treatment data from baseline up to the end of treatment visit. Results : UACR data was available in 6263 patients of whom 1846 had UACR ≥30 mg/g and 537 had eGFR<60 mL/min/1.73m^2. UACR decreased more with TZP 5, 10, and 15 mg vs COMPs in pooled SURPASS 1-5 and consistently across pooled placebo, active, and insulin COMP studies. UACR lowering appeared more pronounced in subgroups with baseline UACR ≥30 mg/g or eGFR<60 mL/min/1.73m^2. Conclusion : In people with T2D, including those with reduced kidney function, TZP decreased UACR vs COMPs to a clinically relevant degree, supporting a potential kidney protective effect. ^ denotes square root of in this submission Publication History Article published online: 18 April 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 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) | 0.003 | 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".