Empagliflozin lowers serum uric acid in chronic kidney disease: exploratory analyses from the EMPA-KIDNEY trial
Bibliographic record
Abstract
BACKGROUND: Hyperuricaemia and gout are common in chronic kidney disease (CKD). We aimed to assess the effects of sodium-glucose co-transporter-2 (SGLT2) inhibition on uric acid (urate) and gout in patients with CKD. METHODS: The EMPA-KIDNEY trial randomised 6609 patients with CKD to receive either empagliflozin 10 mg daily or matching placebo over a median of 2 years of follow-up. Serum uric acid was measured at randomisation then at 2 and 18 months of follow-up and the effects of empagliflozin were analysed using a pre-specified mixed model repeated measures approach. Participant-reported gout events were analysed in Cox regression models (first events) with the Andersen-Gill extension (total events). A post hoc composite outcome included new initiation of uric acid-lowering therapy or colchicine. EMPA-KIDNEY primary and kidney disease progression outcomes were also assessed in subgroups of baseline serum uric acid. RESULTS: Baseline mean ± standard deviation serum uric acid concentration was 431 ± 114 µmol/l. Allocation to empagliflozin resulted in a study-average between-group difference in serum uric acid of -25.6 µmol/l [95% confidence interval (CI) -30.3 to -21.0], with larger effects in those with higher eGFR (trend P < .001) and without diabetes (heterogeneity P < .001). Compared with placebo, empagliflozin did not significantly reduce first or total gout events [hazard ratio 0.87 (95% CI 0.74-1.02) for the 595 first events and 0.86 (0.72-1.03) for the 869 total events] with similar hazard ratios for the post hoc composite and across subgroups, including by diabetes and eGFR. The effect of empagliflozin on the primary outcome and kidney disease progression outcomes were similar irrespective of the baseline level of uric acid. CONCLUSIONS: SGLT2 inhibition reduces serum uric acid in patients with CKD, with larger effects at higher eGFR and in the absence of diabetes. However, the effect on uric acid is modest and did not translate into reduced risk of gout in EMPA-KIDNEY.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".