Two still unanswered questions about uric acid and cardiovascular prevention: Is a specific uric acid cut-off needed? Is hypouricemic treatment able to reduce cardiovascular risk?
Bibliographic record
Abstract
AIMS: The most frequent consequence of elevated uric acid (UA) levels is the development of gout and urate kidney disease. Besides these effects, several studies have investigated the association between hyperuricemia and cardiovascular (CV) disease. High serum UA has been identified as an important determinant of all-cause and CV mortality and CV events (acute and chronic coronary syndrome, stroke and peripheral artery disease). Despite the high number of publications on this topic, there are two questions that are still unanswered: do we need a specific CV cut-off of serum UA to better refine the CV risk? Is urate lowering treatment (ULT) able to reduce CV risk in asymptomatic patients? In this review, we will focus on these two points. DATA SYNTHESIS: Although no doubt exists that the relationship between CV events starts at lower levels than the actually used cut-off, different papers found dissimilar cut-offs. Furthermore, heterogeneity is present depending on the specific CV events evaluated and none of the found cut-off have been tested in external populations (in order to confirm its discriminatory capacity). Furthermore, only few randomized clinical trials on the role of hypouricemic agents in reducing the CV risk have been published giving heterogeneous results. The last published one (ALL-HEART) has strong limitations, that we will deeply discuss. CONCLUSIONS: A definitive answer to the two questions is impossible with the actually published paper but, over identifying current gaps in knowledge we try to individuate how they can be overruled.
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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 | 0.032 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".