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Enregistrement W1965248369 · doi:10.1113/jphysiol.2003.042812

Exercise: a paradigm for multi‐system control of acid–base state

2003· letter· en· W1965248369 sur OpenAlexaff
Michael I. Lindinger

Notice bibliographique

RevueThe Journal of Physiology · 2003
Typeletter
Langueen
DomaineMedicine
ThématiqueRenal function and acid-base balance
Établissements canadiensUniversity of Guelph
Organismes subventionnairesnon disponible
Mots-clésChemistrypCO2Base (topology)Acid–base reactionAcid–base homeostasisSodiumPotassiumChlorideBiochemistryMathematicsInternal medicineOrganic chemistryMedicine

Résumé

récupéré en direct d'OpenAlex

During the past two decades, acid-base physiology has returned to the physicochemical roots described in the first three decades of the 20th century by Lawrence Henderson and Donald van Slyke and those that came prior to these better-known individuals. The physicochemical approach to understanding acid-base balance goes far beyond the mere description of pH in relation to PCO2 and [HCO3−] expressed by the Henderson-Hasselbach equation. In his 1981 monograph, Peter Stewart developed anew the rationale and power of a complete physicochemical analysis of body fluid acid-base status. Stewart's approach recognizes that pH (or [H+]) and [HCO3−] are dependent acid-base variables because one cannot change either of these (or indeed any of the other dependent variables) without initially (and concurrently) changing the concentrations of independent acid-base variables. The independent variables include carbon dioxide (represented by the PCO2), weak anions and cations such as the plasma proteins and some amino acids (represented by [Atot]) and strong anions and cations such as lactate, chloride, sodium and potassium (represented by the strong ion difference - [SID]). The strength of this approach lies in the ability to quantitatively determine the contributions of each of the independent variables of acid-base balance to the separate changes in [H+] and [HCO3−]. For example, when one drinks a solution of NaHCO3 for the purpose of enhancing athletic performance, the contribution of increased plasma [Na+] and hence increased plasma [SID] to the decrease in plasma [H+] and increase in plasma [HCO3−] can be quantified. Exercise results in many simultaneous changes in the plasma concentrations of strong and weak ions and CO2. Each of these changes directly affects the physical and chemical interactions between H+ and −OH in solution, thereby altering [H+] and other dependent variables. Clearly, the acid-base responses to exercise, and also to many disease conditions, are complex and multifactorial and often originate in varied tissues and organs within the body (Johnson et al. 1996). Furthermore, acid-base state is different within different portions of the circulation (e.g. femoral venous plasma vs. arterial plasma vs. antecubital venous plasma during exercise) and within different tissues (arterial plasma vs. skeletal muscle). Despite these complexities, the systematic application of the approach described by Stewart (1981) allows the researcher and clinician to determine the contributions, or importance, of the independent variables, singly or in combination, to the changes in [H+] and [HCO3−] (Kowalchuk et al. 1988; Johnson et al. 1996). In contrast, reliance on the Henderson-Hasselbach equation limits the interpretation to an incomplete, semi-quantitative description that largely ignores changes in strong and weak ion concentrations. Arguments against the application of the physicochemical approach to the assessment of acid-base disturbances have centred on the need to measure multiple variables (at the least [Na+], [K+], [Cl−], [plasma proteins] and PCO2) as opposed to measuring only pH and PCO2. However, advances in electrode technologies easily permit the measurement of each of these variables, and more, on a single instrument using 200 μl or less of plasma. Another argument is that errors inherent in the collection, handling and measurement of fluid samples for analysis can be important and compounded, thus yielding only a semi-quantitative analysis. These arguments are clearly refuted by the many excellent papers using this approach that have appeared in the peer-reviewed, clinical and research journals during the past 15 years. Taken together, this research demonstrates a substantial increase in understanding the origins of acid-base disturbances and the development of treatments specific for different types of acid-base disturbances. Norman Jones and George Heigenhauser, having spent time with Peter Stewart at McMaster University in the mid-1980s, have been driving forces in the application of the physicochemical approach. In this issue of The Journal of Physiology, Putman et al. (2003) describe, for the first time, the factors contributing to arterial and femoral venous plasma acid-base disturbances during the first 15 min of exercise at three different submaximal intensities, both before and after 7 days of submaximal intensity training. Previous exercise studies have focussed on very high-intensity exercise of very short duration, where increases in [Atot] and PCO2 and decreased [SID] all contribute to acidification (Kowalchuk et al. 1988; Lindinger et al. 1992). Clearly changes in these independent variables are less pronounced during submaximal exercise and their contributions to the origins of the acid-base disturbance are markedly different than during high-intensity exercise. Accumulating data from studies spanning a range of exercise intensities and from different vascular ‘compartments’ and skeletal muscle allow us to better understand acid-base physiology. The application of Stewart's physicochemical approach continues to provide an improved appreciation of how the body generates acid-base disturbances, how different tissues respond to the disturbances and how some tissues play a regulatory role in both the acute and chronic correction of the disturbances.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,251
Score d'incertitude au seuil0,557

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,024
Tête enseignante GPT0,274
Écart entre enseignants0,250 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2003
Routes d'admission1
Résumé présentoui

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