Stored <scp>RBC</scp> metabolism as a function of caffeine levels
Bibliographic record
Abstract
BACKGROUND: Coffee consumption is extremely common in the United States. Coffee is rich with caffeine, a psychoactive, purinergic antagonist of adenosine receptors, which regulate red blood cell energy and redox metabolism. Since red blood cell (purine) metabolism is a critical component to the red cell storage lesion, here we set out to investigate whether caffeine levels correlated with alterations of energy and redox metabolism in stored red blood cells. STUDY DESIGN AND METHODS: We measured the levels of caffeine and its main metabolites in 599 samples from the REDS-III RBC-Omics (Recipient Epidemiology Donor Evaluation Study III Red Blood Cell-Omics) study via ultra-high-pressure-liquid chromatography coupled to high-resolution mass spectrometry and correlated them to global metabolomic and lipidomic analyses of RBCs stored for 10, 23, and 42 days. RESULTS: Caffeine levels positively correlated with increased levels of the main red cell antioxidant, glutathione, and its metabolic intermediates in glutathione-dependent detoxification pathways of oxidized lipids and sugar aldehydes. Caffeine levels were positively correlated with transamination products and substrates, tryptophan, and indole metabolites. Expectedly, since caffeine and its metabolites belong to the family of xanthine purines, all xanthine metabolites were significantly increased in the subjects with the highest levels of caffeine. However, high-energy phosphate compounds ATP and DPG were not affected by caffeine levels, despite decreases in glucose oxidation products-both via glycolysis and the pentose phosphate pathway. CONCLUSION: Though preliminary, this study is suggestive of a beneficial correlation between the caffeine levels and improved antioxidant capacity of stored red cells.
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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 | 0.001 |
| 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) | 0.002 | 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".