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?
Bibliographic record
Abstract
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".