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Does Exclusive Consumption of Plant-based Dietary Protein Impair Resistance Training-induced Muscle Adaptations?

2019· article· en· W2954287672 on OpenAlexaff
Victoria Hevia-Larraín, Igor Longobardi, Rosa Maria Rodrigues Pereira, Guilherme Giannini Artioli, Stuart M. Phillips, Bruno Gualano, Hamilton Roschel

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAnabolismLean body massResistance trainingSarcopeniaSkeletal muscleLeucineMedicineInternal medicineMuscle strengthAnimal scienceStrength trainingBiologyEndocrinologyFood scienceAmino acidBody weightPhysical therapyBiochemistry

Abstract

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Dietary protein consumption maximizes the anabolic response during resistance training (RT) by triggering muscle protein synthesis and providing the indispensable amino acids for a net positive protein balance. Leucine is considered the key amino acid in this process, suggesting that differences in protein quality may influence RT-induced gains in muscle mass and strength. In this respect, despite acute evidence on lower anabolic properties of plant- vs. animal-based protein, the effects of an exclusive plant-based dietary protein diet on RT-induced adaptations are currently unknown. PURPOSE: To investigate the impact of dietary protein source (plant- vs. mixed diet-based protein) on RT-induced changes in muscle mass and strength in total protein-matched young healthy men. METHODS: Nineteen vegan (VEG 26±5 y; 72.7±7.1 kg, 1.78±0.05 m) and nineteen omnivorous (OMN 26±4 y; 73.3±7.8 kg, 1.76±0.06 m) physically active young men were enrolled in a 12-week, twice weekly, lower-limb RT program. Daily protein intake was adjusted to 1.6g/kg/day in both groups via supplementing either soy (VEG) or whey (OMN) protein. Leg lean mass (LLM, by DXA) and lower-limb maximal strength (leg-press one-repetition-maximum, 1-RM) were determined PRE and POST intervention. Six 24-hour dietary recalls were performed at baseline (for habitual protein intake determination) and three during the intervention, for monitoring purposes. RESULTS: Significant increases in LLM were observed in both VEG (PRE=18.9±2.2 kg and POST=20.1±2.2 kg, ∆%=6.4±5.8 %, p<0.0001) and OMN (PRE=19.1±2.4 kg and POST=20.3±2.7 kg, ∆%=6.1±3.9 %, p<0.0001). Similarly, 1-RM was significantly increased in both VEG (PRE=258±59 kg and POST=354±81 kg, ∆%=38.1±15.9 %, p<0.0001) and OMN (PRE=261±63 kg and POST=381±73 kg, ∆%=49.0±21.6 %, p<0.0001). No group by time interactions were found. Finally, total protein intake was similar between groups (VEG=1.68±0.14g/kg/d and OMN=1.72±0.10g/kg/d, p=0.30). CONCLUSION: A higher protein-content (~1.6g/kg/day) exclusive plant-based (including soy) protein diet is similarly effective as a mixed-diet in supporting RT-induced muscle adaptations, suggesting that total protein, rather than protein quality, may be more important for muscle adaptation in young individuals. Supported by FAPESP grant 2016/22083-3.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.000
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 head0.021
GPT teacher head0.268
Teacher spread0.248 · 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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