Red blood cell urate levels are linked to hemolysis in vitro and post‐transfusion as a function of donor sex, population and genetic polymorphisms in <scp>SLC2A9</scp> and <scp>ABCG2</scp>
Bibliographic record
Abstract
BACKGROUND: Storage of packed red blood cells (RBCs) for transfusion leads to biochemical and morphological changes, increasing hemolysis risk. Urate levels in blood bags at donation contribute to the molecular heterogeneity and hemolytic propensity of stored RBCs. However, studies to date have been underpowered to investigate at scale the contribution of donor demographics and genetics to the heterogeneity in urate levels across donations. STUDY DESIGN AND METHODS: Urate levels were measured in 13,091 RBC units from the REDS study. Characteristics tested included hemolysis parameters (spontaneous, osmotic, oxidative) at storage end and post-transfusion hemoglobin (Hb) increments in recipients. Donor demographics, urate levels, and genetic variants were analyzed for associations with these outcomes. RESULTS: Elevated urate levels were linked to male sex, older age, high BMI, and Asian descent. Units with high urate levels exhibited increased spontaneous and osmotic hemolysis, while oxidative hemolysis was unaffected. Genetic variants in SLC2A9 (V282I) and ABCG2 (Q141K) were strongly associated with elevated urate, particularly in Asian donors. Post-transfusion analyses revealed that units from female donors carrying these variants were associated with reduced Hb increments, with up to a 31% reduction in efficacy. This effect was not observed in male donors. DISCUSSION: RBC urate levels and genetic traits significantly impact storage quality and transfusion outcomes. These findings highlight the importance of donor molecular characteristics for optimizing transfusion strategies. Moreover, genetic and metabolic insights may inform donor recruitment efforts, providing health feedback to volunteers while ensuring effective transfusion products.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".