Modelling the relationships between haemoglobin oxygen affinity and the oxygen cascade in humans
Bibliographic record
Abstract
Key points Haemoglobin affinity is an integral concept in exercise physiology that impacts oxygen uptake, delivery and consumption. How chronic alterations in haemoglobin affinity impact physiology is unknown. Using human haemoglobin variants, we demonstrate that the affinity of haemoglobin for oxygen is highly correlated with haemoglobin concentration. Using the Fick equation, we model how altered haemoglobin affinity and the associated haemoglobin concentration influences oxygen consumption at rest and during exercise via alterations in cardiac output and mixed‐venous . The combination of low oxygen affinity haemoglobin and reduced haemoglobin concentration seen in vivo may be unable to support oxygen uptake during moderate or heavy exercise. Abstract The physiological implications, with regard to exercise, of altered haemoglobin affinity for oxygen are not fully understood. Data from the Mayo Clinic Laboratories database of rare human haemoglobin variants reveal a strong inverse correlation (r = −0.82) between blood haemoglobin concentration and P50, an index of oxygen affinity [Hb = −0.3135(P50) + 23.636]. In the present study, observed P50 values for high, normal and low oxygen‐affinity haemoglobin variants (13, 26 and 39 mmHg) and corresponding haemoglobin concentrations (19.5, 15.5 and 11.4 g dL−1 respectively) are used to model oxygen consumption as a fraction of delivery at rest ( = 0.25 L min−1, cardiac output = 5.70 L min−1) and during exercise ( = 2.75 L min−1, cardiac output = 18.9 l min−1). With high‐affinity haemoglobin, the model shows that normal levels of oxygen consumption can be achieved at rest and during exercise at the assumed cardiac output levels, with reduced oxygen extraction both at rest (16.8% high affinity vs. 21.7% normal) and during exercise (55.8% high affinity vs. 72.2% normal). With low‐affinity haemoglobin, which predicts low haemoglobin concentration, oxygen consumption at rest can be sustained with the assumed cardiac output, with increased oxygen extraction (31.1% low affinity vs. 21.7% normal). However, exercise at 2.75 l min−1 cannot be achieved with the assumed cardiac output, even with 100% oxygen extraction. In conclusion, the model indicates chronic alterations in P50 associate directly with Hb concentration, highlighting that human Hb variants can serve as ‘experiments of nature’ to address fundamental hypotheses on oxygen transport and exercise.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".