Dietary protein recommendations to support healthy muscle ageing in the 21st century and beyond: considerations and future directions
Bibliographic record
Abstract
This review explores the evolution of dietary protein intake requirements and recommendations, with a focus on skeletal muscle remodelling to support healthy ageing based on presentations at the 2023 Nutrition Society summer conference. In this review, we describe the role of dietary protein for metabolic health and ageing muscle, explain the origins of protein and amino acid (AA) requirements and discuss current recommendations for dietary protein intake, which currently sits at about 0⋅8 g/kg/d. We also critique existing (e.g. nitrogen balance) and contemporary (e.g. indicator AA oxidation) methods to determine protein/AA intake requirements and suggest that existing methods may underestimate requirements, with more contemporary assessments indicating protein recommendations may need to be increased to >1⋅0 g/kg/d. One example of evolution in dietary protein guidance is the transition from protein requirements to recommendations. Hence, we discuss the refinement of protein/AA requirements for skeletal muscle maintenance with advanced age beyond simply the dose (e.g. source, type, quality, timing, pattern, nutrient co-ingestion) and explore the efficacy and sustainability of alternative protein sources beyond animal-based proteins to facilitate skeletal muscle remodelling in older age. We conclude that, whilst a growing body of research has demonstrated that animal-free protein sources can effectively stimulate and support muscle remodelling in a manner that is comparable to animal-based proteins, food systems need to sustainably provide a diversity of both plant and animal source foods, not least for their protein content but other vital nutrients. Finally, we propose some priority research directions for the field of protein nutrition and healthy ageing.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".