Association between uric acid levels and cardio‐renal outcomes and death in patients with type 2 diabetes: A subanalysis of EMPA‐REG OUTCOME
Bibliographic record
Abstract
In the EMPA-REG OUTCOME trial, we explored the association between pre-randomization uric acid level tertile (<309.30 μmol/L; 309.30 to <387.21 μmol/L; ≥387.21 μmol/L) and cardiovascular (CV) death, hospitalization for heart failure (HHF), HHF or CV death, all-cause mortality, three-point major adverse CV events (MACE), and incident or worsening nephropathy. Patients with type 2 diabetes and CV disease received empagliflozin or placebo. The median baseline plasma uric acid level was 344.98 μmol/L, and patients' baseline characteristics were mainly balanced across tertiles. Baseline uric acid levels were associated with cardio-renal outcomes: in the placebo group, for the highest versus lowest tertile, the multivariable hazard ratios for three-point MACE, HHF or CV death, and incident or worsening nephropathy were 1.22 (95% confidence interval [CI] 0.89-1.67; P = 0.2088), 1.51 (95% CI 1.02-2.23; P = 0.0396) and 1.77 (95% CI 1.33-2.34; P < 0.0001), respectively. When tested as a continuous variable, baseline uric acid was associated with all outcomes in the placebo group. Empagliflozin improved all cardio-renal outcomes across tertiles, with all interaction P values >0.05. Further investigation of these relationships is required.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".