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Record W148111281

Dietary animal protein intake: association with muscle mass index in older women.

2007· article· en· W148111281 on OpenAlexaff
Cameron Lord, Jean‐Philippe Chaput, M. Aubertin-Leheudre, Mélissa Labonté, I J Dionne

Bibliographic record

VenuePubMed · 2007
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsHealth and Social Services Centre University Institute of Geriatrics of Sherbrooke
Fundersnot available
KeywordsMedicineSarcopeniaBody mass indexInternal medicineEndocrinologyMuscle massDietary proteinCreatinineAnimal scienceBiology
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Aging is associated with reductions in muscle mass and strength, so-called sarcopenia, and is generally characterized using muscle mass index (MMI = FFM (kg)/height (m)2). It is believed that adequate nutrition especially regarding protein intake, can delay this progression and enhance the quality of life of elders. OBJECTIVES: We examined whether the predominant source of protein consumed (animal or vegetal) by older women was associated with MMI. DESIGN: Thirty-eight healthy, normal weight, sedentary women, aged between 57-75 years (mean age: 66 +/- 5 years old), and taking no medication that could influence metabolism were recruited. Body composition was measured by dual-energy X-ray absorptiometry; muscle protein content was measured by the use of creatinine excretion. Physical activity metabolism was obtained by the use of accelerometry, and indirect calorimetry. Finally, protein intake was measured with a 3-day dietary record. RESULTS: Significant correlations were observed between MMI and body mass index, fat-free mass, muscle protein content, total protein intake, animal protein intake, fat mass, visceral fat and daily energy expenditure. However, a stepwise regression analysis showed animal protein intake to be the only independent predictor of MMI (r2=0.19; p=0.008). CONCLUSIONS: Our results suggest that protein intake, especially from animal sources, may be associated with a better preservation of MMI. However, more research is needed to confirm our results.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.024
GPT teacher head0.265
Teacher spread0.241 · 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 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".

Quick stats

Citations81
Published2007
Admission routes1
Has abstractyes

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