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Cardiorespiratory Fitness is Associated with Lower Abdominal Fat Independent of Body Mass Index

2004· article· en· W2006109780 on OpenAlexaff
Suzy L Wong, Peter T. Katzmarzyk, Milton Z. Nichaman, Timothy S. Church, Steven N. Blair, Robert Ross

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2004
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsQueen's University
FundersNational Heart, Lung, and Blood InstituteNational Institute on Aging
KeywordsCardiorespiratory fitnessMedicineAbdominal obesityBody mass indexWaistInternal medicineAbdomenAdipose tissueObesityCardiologyEndocrinologySurgery

Abstract

fetched live from OpenAlex

WONG, S. L., P. T. KATZMARZYK, M. Z. NICHAMAN, T. S. CHURCH, S. N. BLAIR, and R. ROSS. Cardiorespiratory Fitness is Associated with Lower Abdominal Fat Independent of Body Mass Index. Med. Sci. Sports Exerc., Vol. 36, No. 2, pp. 286–291, 2004. Purpose To determine whether, for a given body mass index (BMI), men with high cardiorespiratory fitness (CRF) have lower waist circumference (WC) and less total abdominal, abdominal subcutaneous, and visceral adipose tissue (AT) compared with men with low CRF. Methods Subjects were categorized into HIGH CRF (N = 169) and LOW CRF (N = 124) groups based on age and CRF measured using a maximal treadmill test. Total abdominal, abdominal subcutaneous and visceral AT were measured by computerized tomography. Results For a given BMI, men in the HIGH CRF group had significantly lower WC (P < 0.001), total abdominal (P < 0.001), visceral AT (P < 0.001), and abdominal subcutaneous AT (P < 0.001) compared with men in the LOW CRF group. Conclusion These findings suggest that the ability of CRF to attenuate the health risks associated with BMI may be partially mediated through a reduction in abdominal AT. Accordingly, our observations reinforce the importance of regular physical activity in the prevention and reduction of obesity-related health risk independent of a corresponding reduction in body weight.

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 score0.006
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
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 head0.018
GPT teacher head0.294
Teacher spread0.275 · 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

Citations145
Published2004
Admission routes1
Has abstractyes

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