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Enregistrement W2065492838 · doi:10.1097/jes.0b013e3181f4bb2a

Carbohydrate Availability and Training Adaptation

2010· review· en· W2065492838 sur OpenAlexaff
Martin J. Gibala

Notice bibliographique

RevueExercise and Sport Sciences Reviews · 2010
Typereview
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueMuscle metabolism and nutrition
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésGlycogenAthletesEndurance trainingStrength trainingAdaptive responseMedicineSports nutritionAdaptation (eye)Physical medicine and rehabilitationExercise physiologyPhysical therapyPsychologyInternal medicineBiology

Résumé

récupéré en direct d'OpenAlex

It is well known that nutrition can influence the acute and chronic response to exercise, including athletic performance and training-induced adaptations in skeletal muscle. One classic example is the positive relationship between carbohydrate intake, muscle glycogen content, and endurance exercise capacity. Leading sports nutrition experts continue to advocate that athletes who partake in activities that are heavily reliant on glycogen for energy ingest sufficient amounts of carbohydrate before, during, and after exercise (1). The general premise for this recommendation is that habitual training in a chronic high-carbohydrate state will maximize the training impulse and optimize performance. However, a long-standing unresolved question is whether it is a lack or a surplus of substrate that triggers the adaptive response to exercise. In this issue of Exercise and Sport Sciences Reviews, Drs. John A. Hawley and Louise M. Burke review the evidence from recent studies that have investigated whether selected markers of endurance training adaptation are enhanced to a greater extent when individuals commence periodic training sessions with low compared with normal or high carbohydrate availability (4). Several years ago, Danish researchers (3) published an intriguing study that reported greater increases in the maximal activities of oxidative enzymes and exercise time to fatigue when one leg was trained twice per day (with restricted carbohydrate intake between sessions) as compared with the contralateral leg that trained once daily. Given the unique experimental design, the original study authors were careful to note the limitations of their work, cautioning, "Coaches and athletes should be careful not to draw practical consequences of the present study with regard to training regimens." Nonetheless, the article coined the term "train low, compete high," a catchphrase borrowed from altitude physiology that Hawley and Burke (4) argue has become widely used in athletic circles (and scientific literature) to describe a range of practices other than the original protocol, with the potential for confusion owing to misunderstood terminology. For example, it is often overlooked that in the original study by Hansen et al. (3), only half of the training sessions were initiated with low muscle glycogen. Hawley and Burke (4) observe that there are many ways of manipulating carbohydrate availability, and their considerate review highlights subtle differences in various experimental designs that are important to properly evaluate the evidence for and against "training low." A periodic train-low approach may indeed offer a time-efficient method to augment adaptations, even in athletes who are already highly trained (5). Using a nutritional manipulation similar to Hansen et al. (3), but with a more applied research design that simulated the usual practice of competitive athletes, Yeo et al. (5) showed that cyclists who trained twice a day on alternate days experienced greater increases in mitochondrial enzymes and whole-body fat oxidation compared with a group that trained once daily. The mechanisms responsible for the enhanced skeletal muscle oxidative capacity under conditions of restricted carbohydrate intake remain largely elusive, but recent evidence points to several nutrient-sensitive signaling molecules that may be involved in the adaptive response (2). A conundrum highlighted by Hawley and Burke is that, despite the changes in "mechanistic" variables that should in theory enhance exercise performance (e.g., increased phosphorylation state of signaling molecules and/or the expression of proteins involved in mitochondrial biogenesis), there is a mismatch in many studies in that whole-body functional outcomes (e.g., changes in training capacity or measures of performance) remain unchanged. The authors propose several explanations for this disconnect, noting we have inadequate knowledge of underlying adaptive mechanisms as well as surprisingly little knowledge about glycogen utilization during the training sessions typically undertaken by competitive athletes or how their current real-world training and dietary practices interact to influence performance.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,015

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

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 Opus0,072
Tête enseignante GPT0,328
Écart entre enseignants0,256 · 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 source (Gemma direct ou Codex distillé), 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
GenreSynthèse

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

En bref

Citations2
Publié2010
Routes d'admission1
Résumé présentoui

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