Empagliflozin and Decreased Risk of Nephrolithiasis: A Potential New Role for SGLT2 Inhibition?
Bibliographic record
Abstract
CONTEXT: Diabetes mellitus is a risk factor for nephrolithiasis. A recent observational study found that in patients with type 2 diabetes (T2D), SGLT2 inhibitor use was associated with a 49% lower risk of nephrolithiasis compared with GLP-1 receptor agonists. OBJECTIVE: We examined the association between nephrolithiasis and the SGLT2 inhibitor empagliflozin, using existing data from randomized clinical trials. METHODS: We pooled data from 15 081 T2D patients randomized to empagliflozin (n = 10 177) or placebo (n = 4904) from 20 phase I-IV trials, including the large cardiovascular outcome trial, EMPA-REG OUTCOME. Incident urinary tract stone events were captured using a predefined collection of MedRA terms. A sensitivity analysis using a narrower definition was also performed. Incidence rate ratios (IRR) and 95% CIs were calculated using the relative risk estimate, stratified by study. RESULTS: The median exposures to study drug were 543 days (placebo) and 549 days (empagliflozin); 183 patients experienced an incident urolithiasis during follow-up (placebo, 79; empagliflozin, 104), yielding annual incidence rates of 1.01 vs 0.63 events/100 patient-years in the 2 respective groups. The IRR was 0.64 (95% CI, 0.48-0.86), in favor of empagliflozin. In the sensitivity analysis, the results were similar (IRR, 0.62 [95% CI, 0.45-0.85]). CONCLUSION: Compared with placebo, empagliflozin therapy was associated with an approximate 40% reduced risk of urinary tract stone events in T2D patients. The underlying mechanisms are unknown but may involve altered lithogenic profile of the urine. Dedicated randomized prospective clinical trials are warranted to confirm these initial observations in patients with and without T2D.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".