The association between hyperuricemia and coronary artery calcification development: A systematic review and meta‐analysis
Bibliographic record
Abstract
Hyperuricemia coincides with coronary artery calcification (CAC) development, but the role of serum uric acid (SUA) as a risk factor for CAC remains unclear. The objective of this study was to gain an insight into the association between SUA and CAC in adults by performing a meta-analysis. MEDLINE, EMBASE, the Cochrane Library, and EBSCO (CINAHL) were searched for relevant observational studies published until 2 June 2019. Studies were included only if they reported data on CAC presence (Agatston score > 0) or progression related to hyperuricemia in subclinical adult patients. The pooled estimates of crude and adjusted odds ratios (ORs) and 95% confidence interval (CI) were calculated to evaluate the association between CAC presence or progression and hyperuricemia. A total of 11 studies were identified involving 11 108 adults. The pooled OR based on the frequency of CAC presence showed that patients in the high SUA group had 1.806-fold risk for developing CAC (95% CI: 1.491-2.186) under the minimal threshold of hyperuricemia (more than 6 mg/dL or 357 μmoL/L). When SUA levels were analyzed as categorical variables, the pooled estimate of adjusted ORs was 1.48 (95% CI: 1.23-1.79) for CAC presence. Additionally, for each increase of 1 mg/dL of SUA level, the risk of CAC progression was increased by 31% (95% CI: 1.15-1.49) with an average follow-up duration ranged from 4.6 to 6.1 years. Hyperuricemia is closely associated with increased risk of CAC development and CAC progression in asymptomatic patients.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".