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 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".