Hyperuricemia and Hypertension: Links and Risks
Bibliographic record
Abstract
Douglas J Stewart,1 Valerie Langlois,1,2 Damien Noone1,2 1Division of Nephrology, The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada; 2Department of Paediatrics, University of Toronto, Toronto, Ontario M5G 1X8, CanadaCorrespondence: Damien Noone Email damien.noone@sickkids.caAbstract: Hyperuricemia has long been recognized to be associated with increased cardiovascular risk, including risk of developing hypertension. Epidemiological findings suggest that the link with hypertension is stronger in children and adolescents. Uric acid acts as a strong antioxidant compound in the extracellular environment but has pro-inflammatory effects within the intracellular setting. A chronic phase of microvascular injury is known to occur after prolonged periods of hyperuricemia. This is proposed to contribute to afferent arteriolopathy and elevation of blood pressure that may become unresponsive to uric acid-lowering therapies over time. Studies have struggled to infer direct causality of hyperuricemia due to a vast number of confounders including body mass index. The aim of this review is to present the available data and highlight the need for large scale prospective randomized controlled trials in this area. At present, there is limited evidence to support a role for uric acid-lowering therapies in helping mitigate the risk of hypertension.Keywords: hyperuricemia, hypertension, urate, cardiovascular, chronic kidney disease
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.004 | 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".