Serum Urate Is Not Associated with Coronary Artery Calcification: The NHLBI Family Heart Study
Bibliographic record
Abstract
OBJECTIVE: urate may have effects on vascular remodeling and atherosclerosis. We had shown an association between serum uric acid (SUA) and carotid atherosclerotic plaques. Inflammation and vascular remodeling in atherosclerosis promote coronary artery calcification (CAC), a preclinical marker for atherosclerosis. Here, we examined whether SUA is associated with CAC, using the same study sample and methods as for our previous carotid atherosclerosis study. METHODS: the national Heart, Lung, and Blood Institute Family Heart Study is a multicenter study designed to assess risk factors for heart disease. Participants were recruited from population-based cohorts in the US states of Massachusetts, North Carolina, Minnesota, Utah, and Alabama. CAC was assessed with helical computed tomography (CT). We conducted sex-specific and family-cluster analyses, as well as additional analyses among persons without risk factors related to both cardiovascular disease and hyperuricemia, adjusting for potential confounders as we had in the previous study of carotid atherosclerosis. RESULTS: for the CAC study, 2412 subjects had both SUA and helical CT results available (55% women, age 58 ± 13 yrs, body mass index 27.6 ± 5.3). We found no association of SUA with CAC in men or women [OR in men: 1.0, 1.11, 0.86, 0.90; women: 1.0, 0.83, 1.00, 0.87 for increasing categories of SUA: < 5 (referent group), 5 to < 6, 6 to < 6.8, ≥ 6.8 mg/dl, respectively], nor in subgroup analyses. CONCLUSION: replicating the methods used to demonstrate an association of SUA with carotid atherosclerosis did not reveal any association between SUA and CAC, suggesting that SUA likely does not contribute to atherosclerosis through effects on arterial calcification. The possibility that urate has divergent pathophysiologic effects on atherosclerosis and artery calcification merits further study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".