318-OR: Tirzepatide Reduces Albuminuria in Patients with T2D—Post Hoc Pooled Analysis of SURPASS 1–5
Bibliographic record
Abstract
In SURPASS 4, the GIP/GLP-1 receptor agonist tirzepatide (TZP) showed a potential kidney protective effect in people with T2D and high CV risk by slowing the rate of eGFR decline and reducing urine albumin-creatinine ratio (UACR) vs insulin glargine over 2 years. In this post-hoc analysis, we explored effects of TZP on UACR changes in SURPASS 1-5 trials. UACR (% difference) for TZP (5, 10, 15 mg) vs comparators (COMPs) was analyzed. 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/1.73m2. Mixed model for repeated measures was used to analyze on-treatment data from baseline up to the end of treatment visit. UACR data was available in 6263 patients of whom 1846 had UACR ≥30 mg/g and 537 had eGFR <60 mL/min/1.73m2. 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 (Table). UACR lowering appeared more pronounced in subgroups with baseline UACR ≥30 mg/g or eGFR <60 mL/min/1.73m2. 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. Disclosure H.L.Heerspink: Consultant; AstraZeneca, Boehringer Ingelheim International GmbH, Bayer Inc., Eli Lilly and Company, Chinook Therapeutics Inc., CSL Behring, Gilead Sciences, Inc., George Clinical, Merck & Co., Inc., Janssen Research & Development, LLC, Traveere Pharmaceuticals, Novo Nordisk. K.R.Tuttle: Consultant; Lilly, AstraZeneca, Gilead Sciences, Inc., Research Support; Bayer Inc., Boehringer Ingelheim (Canada) Ltd., Novo Nordisk, Goldfinch Bio, Inc., Traveere Pharmaceuticals. I.Pavo: Employee; Eli Lilly and Company. A.Haupt: Employee; Lilly, Stock/Shareholder; Lilly. Z.Yang: Employee; Eli Lilly and Company. R.Wiese: Employee; Eli Lilly and Company. A.Hemmingway: Employee; Eli Lilly and Company, Stock/Shareholder; Eli Lilly and Company. D.Cherney: Other Relationship; Boehringer Ingelheim-Lilly, Merck, AstraZeneca, Sanofi, Mitsubishi-Tanabe, Abbvie, Janssen, Bayer, Prometic, BMS, Maze, Gilead, CSL-Behring, Otsuka, Novartis, Youngene, Lexicon and Novo-Nordisk, Research Support; Boehringer Ingelheim-Lilly, Merck, Janssen, Sanofi, AstraZeneca, CSL-Behring and Novo-Nordisk. N.Sattar: Advisory Panel; Amgen Inc., Boehringer Ingelheim and Eli Lilly Alliance, Novartis, Roche Diagnostics, Consultant; Afimmune Limited, Hanmi Pharm. Co., Ltd., Pfizer Inc., Other Relationship; Abbott, AstraZeneca, Boehringer Ingelheim Pharma GmbH&Co.KG, Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Novo Nordisk, Sanofi, Research Support; AstraZeneca, Boehringer Ingelheim Pharma GmbH&Co.KG, Novartis, Roche Diagnostics. Funding Eli Lilly and Company
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.018 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".