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Lactate: metabolic fuel or poison

2011· article· en· W2113879345 on OpenAlexaff
Michael I. Lindinger

Bibliographic record

VenueExperimental Physiology · 2011
Typearticle
Languageen
FieldMedicine
TopicRenal function and acid-base balance
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsChemistry

Abstract

fetched live from OpenAlex

Dr Robergs has written an interesting commentary in response to my recent Viewpoint espousing lactate as a metabolic fuel (Lindinger, 2011). While the title of the Viewpoint was designed to attract attention, criticism was levelled at the fact that I did not discuss the ‘benefits’ of lactate accumulate on muscle function, and that I did not make remarks about the stoichiometry between lactate efflux and apparent H+ efflux from contracting muscle fibres. These topics do not freely arise from the paper by Kitaoka et al. (2011) and, given the word constraints on Viewpoints, did not warrant comment at the time. Robergs refers to biochemical reactions represented by published biochemical equations as a proof of how metabolic pathways in contracting skeletal muscle actually work (Robergs, 2011; Robergs et al. 2005). These equations and reaction schemes are constructs that represent the current understanding of some, but not all, people who are interested in skeletal muscle function. The reality may be quite different from these contructs. One example is provided by the proposed functioning of the monocarboxylate transporter, which is purportedly a lactate−–H+ symport. There is no physical evidence that a proton is actually transported. There is also no evidence that a proton can physically be transported by the sarcolemma by any means. Physical studies reveal that it is hydronium (H3O+) that is ‘sensed’ by electrodes and in turn interpreted by us as a proton. Furthermore, a given molecule of hydronium (or proton) is in fleeting physical existence, about 10−6 s (see Lindinger et al. 2005 for recent discussion of this topic). Thus, this ion (H3O+ or H+) is not amenable to any transmembrane transport process. One might therefore ask why some researchers evoke proton transport mechanisms. The main reason is that charge balance for transport across membranes is usually maintained (except in the case of the Na+,K+-ATPase) and that there is a measurable change in the pH of the extracellular solution in which the cellular studies are performed. One should then question why the pH of the extracellular, or intracellular, solution changes. There is agreement that a decrease in pH is associated with an increase in the concentrations of H+ and/or hydronium. There is no agreement that such a change in pH must occur by the physical transport of a ‘proton’ from one side of the membrane to the other. Physical and chemical studies can be performed (for example, see classical texts by Edsall & Wyman, 1958; Harned & Owen, 1958) to show that physically transporting lactate− from one side of a semi-permeable membrane to the other will raise the pH on the side where lactate− concentration is decreased and lower the pH on the side where lactate− concentration is increased. It can also be demonstrated physically and chemically that the pH change on either side of the membrane is due to changes in the association of hydronium with H+ and water and of H+ with HO− (Edsall & Wymann, 1958; Harned & Owen, 1958). Unfortunately, physiologists and many biochemists are not familiar with the physical characteristics of physiological solutions, and this has led to some unreasonable interpretations of physiological phenomena. Robergs also appears to refute the concept that intracellular acidosis is a contributor to skeletal muscle fatigue and urges us to take an ‘evidence-based view of the benefits of lactate production’‘rather than a traditional blame of fatigue and acidosis’. This is a strange comment given the evidence-based research supporting both the benefits, as well as the detriments, of intracellular lactate− accumulation on muscle fatigue/function, and of intracellular acidification on muscle fatigue/function (see Bangsbo & Juel, 2006 and related commentary). It is contrived to assert that lactate− does not have to have a ‘direct negative role’ in order to be implicated in skeletal muscle fatigue. Lactate−, by virtue of its physical and chemical properties in physiological solutions, affects solution acid–base chemistry and this, in turn, has effects on many cellular functions. While accumulation of muscle lactate is not evil, there is evidence-based research that it is a contributor to intracellular acidosis and to skeletal muscle fatigue using in vivo and in situ research models (Fitts, 1994; Bangsbo & Juel, 2006; Cairns, 2006; Messonnier et al. 2007; Cairns & Lindinger, 2008; Juel, 2008). Where is the in vivo and in situ evidence-based research showing that intracellular and extracellular lactate accumulation and acidification have beneficial effects on exercise performance?

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.035
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0030.015
Scholarly communication0.0070.012
Open science0.0040.004
Research integrity0.0240.039
Insufficient payload (model declined to judge)0.0100.010

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 head0.051
GPT teacher head0.307
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

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".

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Citations0
Published2011
Admission routes1
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