Optimal hematocrit theory - A review.

blood doping blood viscosity hematocrit on exertion high altitude adaptation optimal hematocrit theory

Journal

Journal of applied physiology (Bethesda, Md. : 1985)
ISSN: 1522-1601
Titre abrégé: J Appl Physiol (1985)
Pays: United States
ID NLM: 8502536

Informations de publication

Date de publication:
30 May 2024
Historique:
medline: 30 5 2024
pubmed: 30 5 2024
entrez: 30 5 2024
Statut: aheadofprint

Résumé

In humans and higher animals, a trade-off between a sufficiently high concentration of erythrocytes (hematocrit), to bind oxygen and sufficiently low blood viscosity to allow rapid blood flow has been achieved during evolution. The optimal value lies between the extreme cases of pure blood plasma, which cannot practically transport any oxygen, and 100\% hematocrit, which would imply very slow blood flow or none at all. As oxygen delivery to tissues is the main task of the cardiovascular system, it is reasonable to expect that maximum oxygen delivery has been achieved during evolution. Optimal hematocrit theory, based on this optimality principle, has been successful in predicting hematocrit values of about 0.3-0.5, which are indeed observed in humans and many animal species. Similarly, the theory can explain why higher than normal hematocrit, ranging from 0.5 to 0.7, can promote better exertional performance. Here we present a review of theoretical approaches to the calculation of the optimal hematocrit value under different conditions and discuss them in a broad physiological context. Several physiological and medical implications are outlined, e.g. in view of blood doping, temperature adaptation, and life at high altitudes.

Identifiants

pubmed: 38813609
doi: 10.1152/japplphysiol.00034.2024
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Michal Sitina (M)

Department of anesthesiology and intensive care medicine, International Clinical Research Center of St. Anne's University Hospital Brno, Brno, Czech Republic.

Heiko Stark (H)

Institut of Zoologie and Evolutionary Research, Friedrich Schiller University Jena, Jena, Germany.

Stefan Schuster (S)

Department of Bioinformatics, Friedrich Schiller University Jena, Germany.

Classifications MeSH