Effects of tirzepatide versus insulin glargine 100U/ml on kidney outcomes in people with type 2 diabetes in SURPASS-4
Bibliographic record
Abstract
Question Does treatment with tirzepatide (TZP 5, 10, 15mg) result in more favourable changes of markers of diabetic kidney disease in people with T2D and high CV risk compared to iGlar? Methodology We compared progression to pre-specified kidney endpoints between TZP and iGlar. Composite kidney outcomes in SURPASS-4 were analysed: endpoint 1 (eGFR [CKD-EPI] decline ≥ 40% from baseline, renal death, progression to end stage kidney disease, new onset macroalbuminuria) and endpoint 2 (endpoint 1 without new onset macroalbuminuria). Data were examined within the entire study population, and in subgroups defined by baseline SGLT2i use, urine albumin-creatinine ratio (UACR) ≥ 30mg/g, eGFR<60ml/min/1.73m 2 and in those at high risk for kidney related outcomes, defined as eGFR<75ml/min per 1.73m 2 and macroalbuminuria, or eGFR<45 ml/min per 1.73m 2 . Results At baseline, participants (N=1995, age 63.6 years, HbA1c 8.5%) had a mean eGFR of 81.3ml/min per 1.73m 2 ; 17% had eGFR<60 ml/min per 1.73m 2 , 28% microalbuminuria (UACR 30-300mg/g) and 8% macroalbuminuria (UACR > 300mg/g). During the follow-up to 104 weeks, TZP participants experienced significantly fewer renal outcomes versus iGlar (HR [95% CI]=0.58 [0.43, 0.80]), especially new onset of macroalbuminuria (0.41 [0.26, 0.66]) was reduced, while the new onset of eGFR decline ≥ 40% (0.86 [0.56, 1.33]) was not significantly different between groups. Conclusions In people with T2D and high cardiovascular risk, TZP reduced markers of diabetic kidney disease risk. Publication History Article published online: 02 May 2023 © 2023. Thieme. All rights reserved. Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".