Impact of taurine on red blood cell metabolism and implications for blood storage
Bibliographic record
Abstract
ABSTRACT BACKGROUND Taurine is an antioxidant that is abundant in some common energy drinks. Here we hypothesized that the antioxidant activity of taurine in red blood cells (RBCs) could be leveraged to counteract storage‐induced oxidant stress. STUDY DESIGN AND METHODS Metabolomics analyses were performed on plasma and RBCs from healthy volunteers (n = 4) at baseline and after consumption of a whole can of a common, taurine‐rich (1000 mg/serving) energy drink. Reductionistic studies were also performed by incubating human RBCs with taurine ex vivo (unlabeled or 13C15N‐labeled) at increasing doses (0, 100, 500, and 1000 μmol/L) at 37°C for up to 16 hours, with and without oxidant stress challenge with hydrogen peroxide (0.1% or 0.5%). Finally, we stored human and murine RBCs under blood bank conditions in additives supplemented with 500 μmol/L taurine, before metabolomics and posttransfusion recovery studies. RESULTS Consumption of energy drinks increased plasma and RBC levels of taurine, which was paralleled by increases in glycolysis and glutathione (GSH) metabolism in the RBC. These observations were recapitulated ex vivo after incubation with taurine and hydrogen peroxide. Taurine levels in the RBCs from the REDS‐III RBC‐Omics donor biobank were directly proportional to the total levels of GSH and glutathionylated metabolites and inversely correlated to oxidative hemolysis measurements. Storage of human RBCs in the presence of taurine improved energy and redox markers of storage quality and increased posttransfusion recoveries in FVB mice. CONCLUSION Taurine modulates RBC antioxidant metabolism in vivo and ex vivo, an observation of potential relevance to transfusion medicine.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".