Effects of Tirzepatide vs. Insulin Glargine on Kidney Function Evaluated by Cystatin C-Based eGFR: A Post Hoc Analysis From the SURPASS-4 Trial
Bibliographic record
Abstract
Background: In patients with type 2 diabetes and increased cardiovascular risk (SURPASS-4 trial, N=1995; mean age 64 years, HbA1c 8.5%, BMI 33 kg/m2, eGFR [CKD-EPI-creatinine] 81.3 ± 21 mL/min/1.73m2), including 707 (35%) with UACR>30 mg/g and 342 (17%) with eGFR<60 mL/min/1.73m2, tirzepatide (TZP) treatment markedly reduced weight and slowed creatinine-based eGFR (eGFRcreatinine) decline vs. insulin glargine (iGLAR). As weight reduction affects muscle mass, eGFRcreatinine may change independently of kidney function, while cystatin C-based eGFR (eGFRcystatin C) is not similarly affected. The aim of this analysis was to determine whether the effect of TZP on kidney function was confirmed by eGFRcystatin C. Methods: Mixed model for repeated measurements over time was used to analyze on-treatment eGFR data. Results: After 1-year in the overall study population, the decline from baseline in eGFRcystatin C was significantly less with TZP vs. iGLAR (Table). No statistically significant interaction was observed in subgroup analyses by baseline UACR, eGFRcreatinine, BMI, smoking status, or SGLT2i treatment (Table). Baseline (r=0.765, p<0.0001), 1-year (r=0.771, p<0.0001), and 1-year change from baseline (r=0.326, p<0.0001) values correlated between cystatin- and creatinine-based eGFR. eGFRcystatin C reductions at 1 year were dose dependent (between group difference vs. iGLAR 1.2 [-0.2, 2.7], 2.1 [0.7, 3.6] and 2.0 [0.6, 3.5] mL/min/1.73m2 with 5, 10, and 15 mg, respectively). 1-year changes in body weight did not correlate with changes in eGFRcystatin C (r=0.054, p=0.125) or eGFRcreatinine (r=0.012, p=0.728). Conclusions: The effect of TZP on the slowing of eGFR decline is confirmed by cystatin C-based measurements, supporting the concept of a kidney-protective effect. Funding: Commercial Support - Eli Lilly and Company
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".