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Record W2134001501 · doi:10.2337/db07-0439

Meal Fatty Acid Uptake in Visceral Fat in Women

2007· article· en· W2134001501 on OpenAlexaff
Susanne B. Votruba, Rebecca S. Mattison, Daniel A. Dumesic, Christina Koutsari, Michael D. Jensen

Bibliographic record

VenueDiabetes · 2007
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsInstitute of Nutrition, Metabolism and Diabetes
FundersDeutsches Krebsforschungszentrum
KeywordsAdipose tissueMealEndocrinologyInternal medicineFatty acidChemistryBiologyMedicineBiochemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: Differential meal fat uptake into adipose tissue depots may be a determinant of body fat distribution. RESEARCH DESIGN AND METHODS: We used the meal fat tracer/adipose tissue biopsy approach to compare the effects of meal fat content on the fat uptake into visceral and upper and lower body subcutaneous fat depots in 21 premenopausal women. [(3)H]triolein was used to trace the fate of fatty acids from a normal-fat or high-fat meal. RESULTS: The proportion of dietary fat uptake into the three depots did not differ between meals; visceral fat accounted for only approximately 5% of meal fat disposal irrespective of visceral fat mass. For the women consuming the normal-fat meal, the uptake of meal fatty acid into femoral fat (milligrams meal fat per gram lipid) increased as a function of leg fat mass (r = 0.68, P < 0.05), which we interpret as increased efficiency of uptake. The opposite pattern was seen in omental fat with the normal-fat meal and in all depots after the high-fat meal. For both meals, approximately 40% of meal fat was oxidized ((3)H(2)O production) after 24 h. CONCLUSIONS: We conclude that greater thigh adipose tissue in women is associated with greater efficiency of meal fat storage under conditions of energy balance, whereas the opposite is seen with visceral fat. These findings imply that different mechanisms may regulate fatty acid uptake in different depots, which may in turn impact on body fat distribution.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.833

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.011
GPT teacher head0.243
Teacher spread0.233 · 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

Citations71
Published2007
Admission routes1
Has abstractyes

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