Effects of Tirzepatide Versus Insulin Glargine on Cystatin C–Based Kidney Function: A SURPASS-4 Post Hoc Analysis
Bibliographic record
Abstract
OBJECTIVE: Tirzepatide reduces HbA1c and body weight, and creatinine-based estimated glomerular filtration rate (eGFR) decline. Unlike creatine-derived eGFR (eGFR-creatinine), cystatin C-derived eGFR (eGFR-cystatin C) is unaffected by muscle mass changes. We assessed effects of tirzepatide on eGFR-creatinine and eGFR-cystatin C. RESEARCH DESIGN AND METHODS: Our primary outcome was eGFR change from baseline at 52 weeks with pooled tirzepatide (5, 10, and 15 mg) and titrated insulin glargine in adults with type 2 diabetes and high cardiovascular risk (SURPASS-4). RESULTS: Least squares mean (SE) eGFR-creatinine (mL/min/1.73 m2) changes from baseline with tirzepatide and insulin glargine were -2.5 (0.38) and -3.9 (0.38) (between-group difference, 1.4 [95% CI 0.3-2.4]) and -3.5 (0.37) and -5.3 (0.37) (between-group difference, 1.8 [95% CI 0.8-2.8]) for eGFR-cystatin C. Baseline, 1-year, and 1-year change from baseline values significantly correlated between eGFR-cystatin C and eGFR-creatinine. Measures of eGFR changes did not correlate with body weight changes. CONCLUSIONS: Tirzepatide slows the eGFR decline rate, supporting a 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".