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Record W2953455388 · doi:10.3389/fnut.2019.00083

Dietary Protein Quantity, Quality, and Exercise Are Key to Healthy Living: A Muscle-Centric Perspective Across the Lifespan

2019· review· en· W2953455388 on OpenAlexaff
Nicholas A. Burd, Colleen F. McKenna, Amadeo F. Salvador, Kevin J.M. Paulussen, Daniel R. Moore

Bibliographic record

VenueFrontiers in Nutrition · 2019
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLean body massSkeletal musclePopulationMuscle massGerontologyProtein qualityMedicineEnvironmental healthBiologyFood scienceEndocrinologyBody weight

Abstract

fetched live from OpenAlex

A healthy eating pattern, regardless of age, should consist of ingesting high quality protein preferably in adequate amounts across all meals throughout the day. Of particular relevance to overall health is the growth, development, and maintenance of skeletal muscle tissue. Skeletal muscle not only contributes to physical strength and performance, but also contributes to efficient macronutrient utilization and storage. Achieving an optimal amount of muscle mass begins early in life with transitions to "steady-state" maintenance as an adult, and then safeguarding against ultimate decline of muscle mass with age, all of which are influenced by physical activity and dietary (e.g., protein) factors. Current protein recommendations, as defined by recommended dietary allowances (RDA) for the US population or the population reference intakes (PRI) in Europe, are set to cover basic needs; however, it is thought that a higher protein intake might be necessary for optimizing muscle mass, especially for adults and individuals with an active lifestyle. It is necessary to balance the accurate assessment of protein quality (e.g., digestible indispensable amino acid score; DIAAS) with methods that provide a physiological correlate (e.g., established measures of protein synthesis, substrate oxidation, lean mass retention, or accrual, etc.) in order to accurately define protein requirements for these physiological outcomes. Moreover, current recommendations need to shift from single nutrient guidelines to whole food based guidelines in order to practically acknowledge food matrix interactions and other required nutrients for potentially optimizing the health effects of food. The aim of this paper is to discuss protein quality and amount that should be consumed with consideration to the presence of non-protein constituents within a food matrix and potential interactions with physical activity to maximize muscle mass throughout life.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.054
GPT teacher head0.359
Teacher spread0.306 · 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
GenreReview

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

Citations100
Published2019
Admission routes1
Has abstractyes

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