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Record W2565236461 · doi:10.1113/jp273830

A high carbohydrate diet remains the evidence based choice for elite athletes to optimise performance

2017· letter· en· W2565236461 on OpenAlexaboutno aff
Jørn Wulff Helge

Bibliographic record

VenueThe Journal of Physiology · 2017
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsnot available
FundersTeam DanmarkKulturministeriet
KeywordsEndurance trainingGlycogenAthletesMicronutrientMedicineMuscle biopsyCarbohydrateExercise physiologyPhysical therapyInternal medicineEndocrinologyBiopsyPathology

Abstract

fetched live from OpenAlex

Fat loading or simply consuming a diet rich in fat has been used for more than 100 years to manipulate substrate oxidation both at rest and during exercise. Pioneering work by Christensen & Hansen in the late 1930s clearly demonstrated the link between exercise performance and substrate oxidation and provided early evidence that short term carbohydrate diet consumption enhanced endurance performance (Christensen & Hansen, 1939). The seminal studies performed in Stockholm in Sweden in the 60s by Bergström et al. (1967) reintroduced the needle muscle biopsy technique and very forcefully demonstrated the associations between depletion of muscle glycogen and the development of fatigue during endurance exercise. Founded on these studies major efforts were undertaken to study procedures that could optimise muscle glycogen storage and attenuate glycogen breakdown to increase endurance performance. Over time this led to the dietary recommendations for endurance athletes that are probably most common among endurance athletes today; a high carbohydrate, moderate protein, low fat diet optimised for micronutrients, dietary fibre and consumed in a timely fashion. To further optimise endurance training adaptations in muscle and whole body and to cater for individual differences, this dietary practice is often combined with the specific training practice including a number of procedures such as dietary cycling (fasting/fed and/or micro/macro cycles), sleep manipulation, timing of meals (before and after training), sequential exercise bouts (train low/high) to name the most common procedures. However, in recent years this approach has been challenged by applications of keto adaptation that uses very high fat and moderate protein diets almost devoid of carbohydrates to induce nutritional ketosis (Volek et al. 2015). Historically this diet is well tested and tolerated by our ancestors and in more recent times in the Arctic by Inuit and explorers during exercise under strenuous conditions. The underlying idea behind keto adaptation is obviously the almost non-limited availability of fatty acids during exercise that negates the muscle glycogen depletion and abolishes the need for carbohydrate supplementation during exercise. Yet although evidence has emerged that long term keto adapted ultra-endurance-athletes are capable of oxidising large amounts of fat during exercise, up to 1.5 g fat min−1, there is only anecdotal evidence linking this to endurance performance in elite athletes. However, this has not dampened the interest in and application of the keto adaptation regime to potentially optimise performance. In this issue of The Journal of Physiology Burke and colleagues strongly challenge the concept of a positive effect of keto adaptation on endurance performance in elite race walkers (Burke et al. 2017). The study applies three isoenergetic lightly hypocaloric diets during 3 weeks of controlled training: a ketogenic very low carbohydrate, moderate protein and high fat diet (LCHF) compared to a classic high carbohydrate diet (HCHO), and a diet with similar macronutrient composition (PCHO), but with alternating consumption before and after training. As expected peak oxygen uptake () during race walking was similarly increased in all three groups and LCHF had a markedly higher fat oxidation during 2 h exercise at 80% compared to HCHO and PCHO. However, the performance time for the 10 km race walk was only improved in HCHO and PCHO, and this occurred concomitantly with a reduced oxygen uptake at 20 km race pace only in HCHO and PCHO. Burke and colleagues elegantly conclude that 3 weeks of intensive training and (keto) adaptation to a ketogenic very low carbohydrate, moderate protein and high fat diet impairs exercise economy and attenuates the training induced performance improvements observed when comparing to the two high carbohydrate diets. Albeit not fully conclusive due to both the limited study duration of 3 weeks and application of slightly hypocaloric diets, the evidence presented by Burke and colleagues strongly suggests that, in elite athletes training and performing at intensities similar to elite sports competition, keto adaptation is not the optimal dietary choice. Almost 20 years ago we demonstrated that endurance performance was attenuated during submaximal exercise after 7 weeks of regular endurance training and adaptation to a high fat diet compared to a high carbohydrate diet in untrained males (Helge et al. 1996). Interestingly the rate of perceived exertion was higher after high fat compared to carbohydrate diet adaptation, a finding later demonstrated also in trained athletes (Stepto et al. 2002). However, the rate of perceived exertion is only a marker and be it high fat diet adaptation or keto adaptation there is still a need to fully understand the mechanisms in muscle and whole body that inevitably control the capacity to attain the full individual training induced endurance exercise performance potential, particularly in elite athletes. None declared.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.004

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 head0.029
GPT teacher head0.276
Teacher spread0.247 · 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 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".

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Citations24
Published2017
Admission routes1
Has abstractyes

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