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Record W2013916240 · doi:10.1017/s0007114511003886

Longitudinal changes in body composition associated with healthy ageing: men, aged 20–96 years

2011· article· en· W2013916240 on OpenAlexaff
Andrew S. Jackson, Ian Janssen, Xuemei Sui, Timothy S. Church, Steven N. Blair

Bibliographic record

VenueBritish Journal Of Nutrition · 2011
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsQueen's University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institute on AgingNational Institutes of Health
KeywordsAgeingComposition (language)GerontologyMedicineHealthy ageingPhysiologyInternal medicine

Abstract

fetched live from OpenAlex

Obesity and sarcopenia are health problems associated with ageing. The present study modelled the longitudinal changes in body composition of healthy men, aged from 20 to 96 years, and evaluated the fidelity of BMI to identify age-dependent changes in fat mass and fat-free mass. The data from 7265 men with multiple body composition determinations (total observations 38,328) were used to model the age-related changes in body mass, fat mass, fat-free mass, BMI and percentage of body fat. Changes in fat mass and fat-free mass were used to evaluate the fidelity of BMI and to detect body composition changes with ageing. Linear mixed regression models showed that all trajectories of body composition with healthy ageing were quadratic. Fat mass, BMI and percentage of body fat increased from age 20 years and levelled off at approximately 80 years. Fat-free mass increased slightly from age 20 to 47 years and then declined at a non-linear rate with ageing. Levels of aerobic exercise had a positive influence on fat mass and a slight negative effect on fat-free mass. BMI and percentage of body fat were sensitive in detecting the increase in fat mass that occurred with healthy ageing, but failed to identify the loss of fat-free mass that started at age 47 years.

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 score0.386
Threshold uncertainty score0.527

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 integrity0.0000.001
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 head0.059
GPT teacher head0.307
Teacher spread0.249 · 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

Citations189
Published2011
Admission routes1
Has abstractyes

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