Plant-based food patterns to stimulate muscle protein synthesis and support muscle mass in humans: a narrative review
Bibliographic record
Abstract
The interest in a diet with a higher proportion of plant-based foods to animal-based foods is a global food pattern trend. However, there are concerns regarding adopting plants as the main dietary protein source to support muscle protein synthesis (MPS) and muscle mass. These concerns are centered on three issues: lower protein bioavailability due to antinutritional compounds in plants, lower per-serve scores of protein at similar energy intake, and amino acid scores of plants being lower than optimal. We aimed here to synthesize and discuss evidence around plant protein in human nutrition focusing on the capacity of these proteins to stimulate MPS as a key part of gaining or maintaining muscle mass. In this review, we addressed the issues of plant protein quality and provided evidence for how plant proteins can be made more effective to stimulate MPS and support muscle mass in partial or total replacement of consumption of products of animal origin. Novelty: Plant proteins are known, in general, to have lower protein quality scores than animal proteins, and this may have important implications, especially for those aiming to increase their skeletal muscle mass through exercise. A plant-based diet has been postulated to have lower protein quality limiting MPS responses and potentially compromising exercise-induced gains in muscle mass. Current evidence shows that plant proteins can stimulate MPS, as can whole foods, especially when combining food groups, increasing portion sizes, and optimizing amino acid bioavailability through processing or common preparation methods.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 |
| 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".