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Record W2201272619 · doi:10.1139/apnm-2015-0057

Discussion: “The kinetics of blood lactate in boys during and following a single and repeated all-out sprints of cycling are different than in men” — Do children indeed release and remove lactate faster than adults?

2015· letter· en· W2201272619 on OpenAlexaffvenue
Raffy Dotan, Bareket Falk

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2015
Typeletter
Languageen
FieldAgricultural and Biological Sciences
TopicPharmacological Effects and Assays
Canadian institutionsBrock University
Fundersnot available
KeywordsBlood lactateKineticsRealmPsychologyMedicineInternal medicineHeart ratePhysicsHistory

Abstract

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In their just-published paper, Engel et al. (2015) re-establish 2 very important facts in the realm of pediatric exercise physiology: First, that pre-pubertal boys, even when trained, do not reach the levels of blood lactate (La) concentrations attained by men. Second, that in repeated short-term high-intensity exercise, boys fatigue considerably less than do their adult counterparts. The authors, however, go beyond comparing La concentrations and performance capacities and venture into the more complex topic of child–adult differences in La kinetics. The boys’ La kinetics is clearly different than that of the men (Fig. 2; Engel et al. 2015), with the boys’ La concentration peaking earlier, but at considerably lower levels than themen’s. Less obvious are the reasons for these differences and this is where, we believe, the authors went astray. As the first of their study’smain findings, the authors claim that boys demonstrated “...a faster release of lactate into the blood and more rapid subsequent removal from the blood...” As will be explained below, we consider this claim and its reasoning to be largely unacceptable. The root problem appears to be the authors’ stated reliance on a model previously employed by Beneke et al. for the very same purpose (Beneke et al. 2005). That model, in turn, was one earlier used by Dost to assess pharmacokinetics (Dost 1968). While the 2 may appear similar, there is an important distinction between drug and La kinetics. While drug kinetics is typically measured in hours and days, La responses are timed in minutes and seconds. Therefore, in typical pharmacokinetics, a factor that affects the kinetics by a few seconds or minutes would be imperceptible and thus inconsequential for how a drug would be administered. On the other hand, a comparable effect on La kinetics does make a considerable difference. Such a factor does indeed exist. It has been termed “circulation time” or “transit time”. It has long been shown that circulation (out and back transit) time increases with body mass and blood volume from infancy to adulthood (Seckel 1936), and Cumming, for example, showed it to be shorter in children versus adults during exercise (Cumming 1978). In this respect, children’s faster La response to exercise is not much different than their corresponding fasteroxygenuptakekinetics (Carter et al. 2005). The Dost/Beneke model has 2 components (i.e., release into the blood and elimination from the blood) and it may be suitable when comparing drug or La kinetics in individuals of similar body dimensions. Indeed, Engel et al. confirm that the model they used (i.e., the Dost/Beneke model) has been validated for men, but not for boys. Thus, in view of the known differences in transit times between children and adults, both Engel et al. and Beneke et al. should have taken these differences into account and employed at least a 3rather than a 2-component model when comparing boys to men. In looking at the descending (elimination) slopes in Engel et al.’s Figs. 2 and 3, there is no obvious boys–men rate difference. In 2003we showed thatwhen postexercise La concentrationswere similar, no difference in the rate of La elimination could be demonstrated (Dotan et al. 2003). This means that children do not possess any “secret weaponry” when it comes to metabolizing La and thus, this cannot be used to explain children’s faster recovery, in general, and fromhigh-intensity exercise, inparticular (Falk andDotan2006). The only detected boys–men difference in La-kinetics was a phase shift that saw the boys reach their peak La concentration some 2.6 min ahead of the men (very similar to the difference found by Engel et al.).Weattributed thisphase shift to theboys’ shorter transit times, although this might very well not be the only reason. We agree with Engel et al. that children likely possess higher type-I muscle-fibre composition. As we have previously suggested, we also believe that children recruit their type-II motor units to a lesser degree compared with men (e.g., Dotan et al. 2012). Certainly, these attributes could both be true. Whatever the exact reason, it is very likely that children’s functional muscle composition is skewed towards higher percentage of type-I motor units. This can clearly explain why children produce less La and lower power and hence have less to recover from (Falk and Dotan 2006). This, in turn, also explains children’s lower fatigability, as Engel et al. nicely demonstrated. The authors, however, failed to support their claim that type-I fibres release La to the blood faster than their type-II counterparts. We believe this is mainly a function of intra-muscular La concentration rather thanmuscle-fibre type. Jorfeldt et al. counter-intuitively concluded that “lactate formed in the muscle during exercise is released at low and moderate work loads but [is] partly accumulated in the tissue at heavy work loads when the rate of lactate formation is high” (Jorfeldt et al. 1978). That is, unlike the case with simple diffusion models, the relative rate of La release is, in essence, slowed down the more La is accumulated in the muscle (i.e., despite the increasing gradient). It is the fact that more La is produced and accumulated in type-II fibres that indirectly relates the differential release rate to fibre type. This, in addition to longer transit times, nicely explains why La concentration peaked 7.6 min postexercise in our men, while the corresponding delay for the boys was only 5.0 min (Dotan et al. 2003). It is noteworthy that although the boys–men difference in the time to peak La

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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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.013
GPT teacher head0.222
Teacher spread0.208 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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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Citations7
Published2015
Admission routes2
Has abstractyes

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