Meal Fatty Acid Uptake in Visceral Fat in Women
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".