Enhanced dietary fat clearance in postobese women
Bibliographic record
Abstract
The objective of this study was to examine the postprandial response to an exogenous fat source in eight weight-stable postobese subjects (2;-3 years after gastric bypass) and eight matched control women, using a stable isotope, [13C]oleate. After a high fat breakfast meal (1,062 cal, 67% fat), [13C]oleate in triglyceride (TG)-rich lipoproteins (Sf >400 and Sf 20;-400) and nonesterified fatty acids (NEFA), and 13C in breath CO2, were monitored over 8 h. There were no differences in resting energy expenditure, thermic effect of food, carbohydrate/fat oxidation ratio, breath 13CO2 enrichment, or fecal fat content between postobese and control subjects. Postprandially, there was no difference in S(f) 20;-400 TG or NEFA, but postobese subjects had lower Sf >400 incremental area under the curve (AUC) (- 33%, P < 0.0025) and glucose [P < 0.01 by repeated measures analysis of variance (RM ANOVA)]. Postprandial 13C in Sf >400 TG returned to fasting levels 4 h earlier in postobese subjects and was lower than in control subjects at 4 and 6 h (P < 0.05 by RM ANOVA). The greatest difference was in the [13C]NEFA profiles. In control subjects [13C]NEFA increased markedly over 8 h; postobese subject [13C]NEFA remained close to fasting nonenriched values, and was strikingly lower than in control subjects (72% lower by AUC, P < 0.0001 by RM ANOVA). Finally, postobese subjects tended to have lower postprandial insulin (P < 0.01, 4 h), lower postprandial acylation-stimulating protein, and lower fasting leptin (-46%, P < 0.02). This study demonstrates clear metabolic differences in exogenous dietary fat partitioning in postobese women. These findings are compatible with an increased efficiency of dietary fat storage and suggest one possible mechanism for promotion of weight regain in postobese individuals.
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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 | 0.000 |
| 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) | 0.002 | 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".