Effects of altitude and recombinant human erythropoietin on iron metabolism: a randomized controlled trial
Bibliographic record
Abstract
Current markers of iron deficiency (ID), such as ferritin and hemoglobin, have shortcomings, and hepcidin and erythroferrone (ERFE) could be of clinical relevance in relation to early assessment of ID. Here, we evaluate whether exposure to altitude-induced hypoxia (2,320 m) alone, or in combination with recombinant human erythropoietin (rHuEPO) treatment, affects hepcidin and ERFE levels before alterations in routine ID biomarkers and stress erythropoiesis manifest. Two interventions were completed, each comprising a 4-wk baseline, a 4-wk intervention at either sea level or altitude, and a 4-wk follow-up. Participants ( n = 39) were randomly assigned to 20 IU·kg body wt−1 rHuEPO or placebo injections every second day for 3 wk during the two intervention periods. Venous blood was collected weekly. Altitude increased ERFE ( P ≤ 0.001) with no changes in hepcidin or routine iron biomarkers, making ERFE of clinical relevance as an early marker of moderate hypoxia. rHuEPO treatment at sea level induced a similar pattern of changes in ERFE ( P < 0.05) and hepcidin levels ( P < 0.05), demonstrating the impact of accelerated erythropoiesis and not of other hypoxia-induced mechanisms. Compared with altitude alone, concurrent rHuEPO treatment and altitude exposure induced additive changes in hepcidin ( P < 0.05) and ERFE ( P ≤ 0.001) parallel with increases in hematocrit ( P < 0.001), demonstrating a relevant range of both hepcidin and ERFE. A poor but significant correlation between hepcidin and ERFE was found ( R2 = 0.13, P < 0.001). The findings demonstrate that hepcidin and ERFE are more rapid biomarkers of changes in iron demands than routine iron markers. Finally, ERFE and hepcidin may be sensitive markers in an antidoping context.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".