Mechanisms of haemoglobin mass expansion following heat stress
Bibliographic record
Abstract
Abstract Haemoglobin mass is a critical determinant of oxygen delivery to working muscle, with even modest increases enhancing maximal oxygen uptake () and endurance performance. While altitude training has long been utilised to increase haemoglobin mass, its efficacy is increasingly debated, due to concerns over maladaptation, inter‐individual variability, cost and environmental impact. More recently, heat training – traditionally employed for acclimatisation ahead of competition in warm climates – has emerged as a feasible long‐term stimulus capable of eliciting comparable haemoglobin mass expansion. Long‐term heat acclimation (≥5 weeks) shows promise as an environmental intervention to improve oxygen‐carrying capacity via haematological adaptation, with each study published to date reporting meaningful (∼2–4%) haemoglobin mass expansion and concurrent improvements in . Erythropoiesis underpins the haematological adaptations to long‐term heat acclimation, though it appears to diverge from established hypoxia‐driven mechanisms. This review will discuss how heat exposure may stimulate erythropoietin via distinct cellular stress signalling, altered renal oxygen tension or plasma volume perturbations. These responses may act through distinct pathways, complementing or deviating from those traditionally associated with hypoxia. While published long‐term heat acclimation studies have primarily utilised exercise under heat stress conditions, we discuss the potential for passive heating methods to yield similar adaptations. Based on our review of the literature, we highlight the need for research that elucidates precise mechanisms, compares differing modes of heat stress, and explores broader applications of long‐term heat acclimation. However, current evidence supports long‐term heat acclimation as an effective alternative or adjunct to altitude training for enhancing oxygen‐carrying capacity. image
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".