Potential kidney protection with liraglutide and semaglutide: Exploratory mediation analysis
Bibliographic record
Abstract
Abstract Aims To investigate whether effects on chronic kidney disease risk factors could explain the apparent reduction in kidney outcomes (composite of macroalbuminuria, doubling of serum creatinine, renal replacement therapy, or renal death), primarily driven by changes in albuminuria, after treatment with the glucagon‐like peptide‐1 receptor agonists (GLP‐1RAs) liraglutide and semaglutide in patients with type 2 diabetes in the LEADER and SUSTAIN 6 trials. Materials and Methods We evaluated the mediation effect of glycated haemoglobin (HbA1c), systolic blood pressure (BP), and body weight on the kidney effects of GLP‐1RAs. Diastolic BP, haemoglobin, heart rate, low‐density lipoprotein and total cholesterol, and white blood cell count were also investigated. The mediation effect was estimated by the novel Vansteelandt statistical method. Subgroups with estimated glomerular filtration rate (eGFR) <60 and ≥60 mL/min/1.73 m 2 were examined in LEADER. Results We observed that HbA1c mediated 25% (95% confidence interval [CI] −7.1; 67.3) and 26% (95% CI noncalculable), and systolic BP 9% (95% CI 2.8; 22.7) and 22% (95% CI noncalculable) of kidney effects of GLP‐1RAs in LEADER and SUSTAIN 6, respectively. Small or no mediation was observed for the other parameters; for example, body weight mediated 9% (95% CI −7.9; 35.5) in the former and did not mediate effects in the latter study. Mediation by HbA1c was greater in patients with eGFR ≥60 mL/min/1.73 m 2 (57%) versus those with eGFR <60 mL/min/1.73 m 2 (no mediation). Conclusions Our results suggest that HbA1c and systolic BP may moderately mediate kidney benefits of liraglutide and semaglutide, with all other variables having a small to no effect. Potential kidney benefits may be driven by other mediators or potentially by direct mechanisms.
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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 | 0.036 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".