Authors' Reply: Tirzepatide and Kidney Protection in Obesity: Unanswered Questions
Bibliographic record
Abstract
We thank Drs. Zhu and Ren1 for their Letter and agree that racial/ethnic heterogeneity in obesity-related kidney disease and response to tirzepatide treatment and other incretin-based therapies is of clinical relevance. In our analysis of the SURMOUNT 1 and 2 trials,2 published in JASN, the majority of participants were White. The ongoing SURMOUNT MMO trial will enroll a more diverse and representative global population of approximately 15,000 participants from 27 countries around the world. This large outcome trial will provide more insight into the consistency of the potential cardiovascular and kidney protective effects of tirzepatide in adults with overweight or obesity but without diabetes across races and regions.3 We also agree that additional biomarker research will help to explore mechanisms of kidney protective effects in people with overweight or obesity. Experimental studies have shown that tirzepatide reduces kidney injury molecule-1 and markers of oxidative stress.4 Whether these results translate to the clinical setting is currently being investigated in the TREASURE-CKD trial. Whether the dual glucagon like peptide -1 (GLP-1)/glucose-dependent insulinotropic polypeptide agonism of tirzepatide provides additional benefit compared with GLP-1 agonism alone has been studied before. Two clinical trials compared tirzepatide with semaglutide in adults with type 2 diabetes and overweight or obesity (SURMOUNT-5).5,6 Both trials demonstrated superior effects of tirzepatide in improving glycemic and body weight control, which could translate into greater kidney protective effects. A registry study showed that tirzepatide compared with GLP-1 receptor agonists was associated with a lower risk of all-cause mortality, cardiovascular, and kidney outcomes in patients with type 2 diabetes.7 In summary, the field involving GLP-1–based therapies is rapidly evolving, yet many mechanistic and clinical questions remain unresolved. Fortunately, large trials enrolling diverse cohorts of patients and mechanistic studies are underway that will hopefully deliver some answers.
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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 | 0.009 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.038 | 0.052 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".