Correlation Between Serum Uric Acid and Renal Function in Patients With Stable Coronary Artery Disease and Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to investigate the relationship between serum uric acid and renal function, expressed as estimated glomerular filtration rate (eGFR), in patients with stable coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM) in China. METHODS: Serum levels of uric acid and creatinine were determined in 526 enrolled patients diagnosed with stable CAD and T2DM. To assess renal function, eGFR was calculated using a modified MDRD formula suitable for the Chinese population. Patients' anthropometric measurements were collected using standardized protocols, and 6-month follow-up results were collected and analyzed. Pearson's correlation coefficient r was calculated and multivariate analysis was performed to evaluate the relationship between uric acid and renal function. Patients with eGFR < 60 mL/min/1.73 m(2) were classified as having reduced renal function or chronic kidney disease (CKD) in this study. RESULTS: Uric acid levels were negatively associated with eGFR (P = 0.002), especially in patients with CKD (eGFR < 60 mL/min/1.73 m(2)) (P < 0.001). In patients with reduced renal function, the risk in the highest quartile of uric acid levels was higher than in the lowest quartile (odds ratio 9.18, 95% confidence interval: 4.01 - 21.01, P < 0.001). These associations remained after multiple adjustments for potential confounders and were recapitulated after 6-month follow-up. CONCLUSIONS: Serum uric acid level is negatively associated with renal function, as assessed by eGFR, and serves as an independent predictor for CKD in patients with stable CAD and T2DM.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".