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Record W1965248369 · doi:10.1113/jphysiol.2003.042812

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

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

Bibliographic record

VenueThe Journal of Physiology · 2003
Typeletter
Languageen
FieldMedicine
TopicRenal function and acid-base balance
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsChemistrypCO2Base (topology)Acid–base reactionAcid–base homeostasisSodiumPotassiumChlorideBiochemistryMathematicsInternal medicineOrganic chemistryMedicine

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.251
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.274
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations8
Published2003
Admission routes1
Has abstractyes

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