Detection of a Small‐Volume Autologous Blood Transfusion by Hepcidin, Erythroferrone, and the Athlete Biological Passport
Bibliographic record
Abstract
The World Anti‐Doping Agency prohibits autologous blood transfusion (ABT) but misuse remains difficult to detect, and evalution and identification of current and novel biomarkers are warranted. This study investigated the hypothesis that plasma hepcidin and erythroferrone (ERFE) can detect a 130 mL ABT and that the Athlete Biological Passport (ABP) benefits including reticulocyte percentage (RET%) and the Abnormal Blood Profile Score (ABPS) algorithm as stand‐alone markers in both males and females. Using a randomized, placebo‐controlled design, two weekly baseline samples were collected from 48 well‐trained males and females. Participants (12 males, 12 females) donated 450 mL blood, from which 130 mL packed red blood cells was re‐infused after four weeks with blood samples collected 3, 6 and 24 hours as well as 2, 3 and 6 days following reinfusion. The remaining participants (12 males, 12 females) served as controls with samples collected with the same time frame. Endurance exercise performance was evaluated in 13 participants. Independent of sex, hepcidin levels increased (49%), while ERFE levels decreased (43%) six hours following reinfusion (P<0.05). Using an individual approach, a sensitivity of 20% with a specificity of ~98% was evident for both hepcidin and ERFE across all time points. For the ABP the most sensitive marker was the combined algorithm score Off‐hr (P<0.05), although inclusion of RET% and ABPS increased the sensitivity. However, ABPS also reduced the specificity. As opposed to combining all time points after both donation and re‐infusion and ABP markers, which provide a sensitivity of 88%, sensitivity at any single time point was 8% and 58% following reinfusion and donation, respectively. Combining the findings for hepcidin and ERFE with the ABP results following re‐infusion yielded a sensitivity across all time points of 50% with no sex‐specific difference evident for either iron markers or the ABP. Yet, numerically more females were identified with an outlier after reinfusion using the ABP. Finally, we observed increased mean power output during a 650 kCal time‐trial 24 hours (+6.4%, P<0.01) and six days following reinfusion (+5.8%, P<0.01). In conclusion, iron biomarkers may aid in the detection of ABT. However, additional research is required, as natural fluctuations remain a challenge.
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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) | 0.000 | 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) | 0.001 | 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".