Empagliflozin and uric acid metabolism in diabetes: A post hoc analysis of the <scp>EMPA‐REG OUTCOME</scp> trial
Bibliographic record
Abstract
AIM: To evaluate the effect of empagliflozin on uric acid (UA) levels, antigout medication and gout episodes in the EMPA-REG OUTCOME trial (NCT01131676). MATERIALS AND METHODS: A total of 7020 patients with type 2 diabetes (T2D) were randomized to either empagliflozin (10 or 25 mg) or placebo. The effects of empagliflozin versus placebo on UA concentration were assessed using mixed linear models. A composite outcome of new prescription of antigout medication or gout episode was studied with Cox proportional hazards models. RESULTS: Empagliflozin reduced serum UA levels versus placebo: week 52 adjusted mean treatment difference = -0.37 (95% confidence interval [CI] -0.42, -0.31) mg/dL; this was more pronounced in patients with baseline UA ≥ 7.0 mg/dL versus <7.0 mg/dL: week 52 adjusted mean treatment difference = -0.56 (95% CI -0.68, -0.43) and -0.30 (95% CI -0.37, -0.24) mg/dL, respectively. Among 6607 patients not taking antigout medications at baseline, 5.2% had a gout episode or initiated antigout treatment versus 3.6% in the placebo and empagliflozin groups, respectively: hazard ratio 0.67 (95% CI 0.53, 0.85; P = 0.001). Both components of the composite outcome contributed to the reduction with empagliflozin in the composite. Risk reduction was similar with both empagliflozin doses. CONCLUSIONS: Empagliflozin reduced UA levels and the composite of gout episodes or prescription of antigout medication. These clinically important findings expand the utility of empagliflozin as a potential antigout treatment in patients with T2D, beyond its well-established cardio-renal benefits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".