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Record W2111224791 · doi:10.1152/ajpendo.00196.2006

Sex-specific differences in leg fat uptake are revealed with a high-fat meal

2006· article· en· W2111224791 on OpenAlexaff
Susanne B. Votruba, Michael D. Jensen

Bibliographic record

VenueAmerican Journal of Physiology-Endocrinology and Metabolism · 2006
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsInstitute of Nutrition, Metabolism and Diabetes
FundersNational Center for Research ResourcesNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthMayo Clinic
KeywordsAdipose tissueMealTrioleinInternal medicineEndocrinologyLipoprotein lipaseChemistryTriglycerideFatty acidBiologyCholesterolMedicineLipaseBiochemistry

Abstract

fetched live from OpenAlex

The mechanism(s) by which sex specific differences in regional body fat distribution develop are not known. We assessed the effects of a high-fat (HF) meal on fatty acid oxidation and uptake into regional fat depots using isotopic tracers and adipose biopsies. Thirty men (BMI 23.6 +/- 0.3 kg/m(2)) and 29 women (BMI 22.4 +/- 0.3 kg/m(2)) received a meal containing [(3)H]triolein. Twelve of the men and 13 of the women received an additional 80 g of triolein in the meal (HF) and the remainder received a normal-fat (NF) meal. Adipose tissue lipoprotein lipase (LPL) activity was measured in the fed and fasted state. After 24 h, meal fatty acid uptake into subcutaneous adipose tissue was assessed. The efficiency of meal fat uptake into upper body subcutaneous fat was similar in both sexes, but women had a greater leg fat uptake, especially in response to a HF meal (P < 0.0001). A correlation between fed-state LPL activity and meal fat uptake was found in both upper and lower body fat (P < 0.0001, r = 0.69). These studies show that, in times of net fat storage, women preferentially increase uptake in leg adipose tissue, and this is likely mediated by fed-state LPL activity.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.014
GPT teacher head0.241
Teacher spread0.227 · 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.

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

Citations68
Published2006
Admission routes1
Has abstractyes

Explore more

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