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Record W2508877659 · doi:10.3390/nu8080492

Protein Requirements during Aging

2016· review· en· W2508877659 on OpenAlexafffund
Glenda Courtney‐Martin, Ronald Ball, Paul B. Pencharz, Rajavel Elango

Bibliographic record

VenueNutrients · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of British ColumbiaUniversity of AlbertaHospital for Sick ChildrenBC Children's HospitalUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsNitrogen balanceAllowance (engineering)Dietary Reference IntakeProtein requirementBalance (ability)Reference Daily IntakeBody weightMedicineAnimal scienceBiologyEndocrinologyNitrogenPhysical therapyChemistryNutrientEnvironmental health

Abstract

fetched live from OpenAlex

Protein recommendations for elderly, both men and women, are based on nitrogen balance studies. They are set at 0.66 and 0.8 g/kg/day as the estimated average requirement (EAR) and recommended dietary allowance (RDA), respectively, similar to young adults. This recommendation is based on single linear regression of available nitrogen balance data obtained at test protein intakes close to or below zero balance. Using the indicator amino acid oxidation (IAAO) method, we estimated the protein requirement in young adults and in both elderly men and women to be 0.9 and 1.2 g/kg/day as the EAR and RDA, respectively. This suggests that there is no difference in requirement on a gender basis or on a per kg body weight basis between younger and older adults. The requirement estimates however are ~40% higher than the current protein recommendations on a body weight basis. They are also 40% higher than our estimates in young men when calculated on the basis of fat free mass. Thus, current recommendations may need to be re-assessed. Potential rationale for this difference includes a decreased sensitivity to dietary amino acids and increased insulin resistance in the elderly compared with younger individuals.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.985
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.030
GPT teacher head0.317
Teacher spread0.287 · 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 teacher head, 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

Citations57
Published2016
Admission routes2
Has abstractyes

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