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Whole Body Net Protein Balance Plateaus in Response to Varying Protein Intakes During Post‐Exercise Recovery: Identification of a Maximal Anabolic Intake

2017· article· en· W4389028416 on OpenAlexaff
Michael Mazzulla, Hiroyuki Kato, Jeff E. Packer, Denise J. Wooding, Daniel R. Moore

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAnabolismIngestionMealChemistryInternal medicineEndocrinologyCarbohydrateAnimal scienceFood scienceMedicineBiochemistryBiology

Abstract

fetched live from OpenAlex

Dietary protein is important for the repair and/or rebuilding of skeletal muscle and body proteins and is required to induce a net anabolic environment during recovery from exercise. While the majority of studies assess the effect of protein intake on protein synthesis (S), maximizing net balance [NB; the algebraic difference between S and breakdown (B)] would presumably optimize post‐exercise recovery for athletes. Given the suggestion by some that there is no practical limit to the anabolic response to mixed meal ingestion at rest, the present analysis sought to determine whether net protein balance plateaued in response to varying protein intakes within a mixed macronutrient meal during post‐exercise recovery. After a 2‐d controlled diet, 6 healthy, active males (23 ± 1 y; 82.3 ± 5.7 kg; 71.1 ± 5.1 kg fat‐free mass; mean ± 95% CI) and 7 females (21.4 ± 0.8 y; 68.5 ± 9.3 kg; 53.3 ± 8.6 kg fat‐free mass) consumed a liquid meal (1 g·kg −1 carbohydrate) prior to performing a variable intensity exercise protocol (Loughborough Intermittent Shuttle Test). During the 8‐h post‐exercise recovery period, participants consumed isoenergetic mixed meals providing 0.42 g·kg −1 ·h −1 of carbohydrate and a variable amount of fat (0.08–0.18 g·kg −1 ·h −1 ) and protein (0.02–0.22 g·kg −1 ·h −1 ). Protein was provided as crystalline amino acids modeled on the basis of egg protein with the exception of tyrosine (3.33 mg·kg −1 ·h −1 ) and phenylalanine (2.5 mg·kg −1 ·h −1 ), the latter of which contained 0.46 mg·kg −1 ·h −1 of l ‐[ 13 C]phenylalanine to model steady state phenylalanine kinetics. Breath and urine samples were taken at isotopic steady state to determine phenylalanine oxidation (O) and phenylalanine turnover (Q; corrected for phenylalanine intake), respectively, as an estimate of B. NB was determined from the difference between S (Q – O) and B. Mixed modeling bi‐phase linear regression (R 2 = 0.62; P < 0.05) explained a greater proportion of NB variance than linear regression (R 2 = 0.49; P < 0.05), indicating NB reached a plateau. NB was not different between males and females (P > 0.05) and increased up to a plateau at 0.13 ± 0.03 g·kg −1 ·h −1 (0.16 ± 0.02 g·kg −1 fat‐free mass·h −1 ) or the equivalent of 1.6 ± 0.32 g·kg −1 ·12h −1 when collapsed across sexes. The breakpoint in NB corresponded to a dietary protein:carbohydrate ratio of 1:3.2. Linear modeling revealed no discernible effect of protein intake on S (P = 0.79) or B (P = 0.17) suggesting the increase in NB was a combination of subtle changes in these kinetics. These data suggest there is a practical limit to the anabolic response to mixed meal ingestion during recovery from variable intensity exercise in active males and females. Athletes should consume ~1.6 g protein·kg −1 ·d −1 with adequate carbohydrate to maximize whole body NB and thus optimize post‐exercise recovery. Support or Funding Information Supported by the Ajinomoto Innovation Alliance Program

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.009
GPT teacher head0.243
Teacher spread0.234 · 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 designObservational
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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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