mRNA Biomarkers in Dried Blood Spots May Improve Detection of Autologous Blood Micro‐Transfusions Using an Individualized Approach
Bibliographic record
Abstract
ABSTRACT Autologous blood transfusions (ABTs) are prohibited by the World Anti‐Doping Agency (WADA), yet detecting autologous blood micro‐transfusions (ABMTs) remains a challenge. Due to smaller transfused volumes, ABMTs cause attenuated biomarker changes, limiting detection sensitivity within the Athlete Biological Passport (ABP). This study assessed whether mRNA expression of 5‐aminolevulinic acid synthase ( ALAS2 ) and carbonic anhydrase 1 ( CA1 ), measured from dried blood spots (DBS), could serve as sensitive biomarkers of ABMT. In a randomized, placebo‐controlled design, 47 trained individuals (24 ♀; mean VO 2 peak 56 ± 7 mL·min −1 ·kg −1 ) were allocated to an ABMT group ( n = 23; ♀ = 12) or placebo group ( n = 24; ♀ = 12). The ABMT group donated 450 mL of blood and received a 130 mL packed red blood cell reinfusion 4 weeks later. Blood sampling occurred regularly before and after both donation and reinfusion. ALAS2 and CA1 mRNA expression from DBS, and reticulocyte percentage (RET%) from venous blood, were analyzed. Following blood donation, ALAS2 , CA1 , and RET% increased by 270%, 200%, and 150%, respectively. However, no consistent group‐level changes were observed after ABMT. Individualized analysis identified more outliers for ALAS2 than for CA1 , and blinded interpretation of individual mRNA profiles achieved > 95% sensitivity and specificity for detecting ABMT. These findings suggest that ALAS2 mRNA expression, assessed via minimally invasive DBS sampling, is a promising biomarker for identifying ABMT. This approach may enhance current anti‐doping strategies by improving sensitivity to small‐volume autologous transfusions that evade detection through traditional ABP biomarkers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".