Interaction of exercise training and<i>n</i>-3 fatty acid supplementation on postprandial lipemia
Bibliographic record
Abstract
The effect of combining omega-3 fatty acid (n-3 FA) supplementation and exercise training treatment on postprandial lipemia (PPL) has not been studied. The purpose of this study was to examine the interaction of n-3 FA and exercise training in attenuating PPL after a high-fat meal. Previously sedentary, overweight, subjects (n=22; 12 women, 10 men, BMI 26.6+/-0.7 kg/m2) were randomly assigned to one of two treatment groups: n-3 FA supplementation alone (FO, n=10) or n-3 FA supplementation plus exercise training (FO+ExTr, n=12). Both groups consumed 4 g/d n-3 FA, and one group also exercise trained for 45 min/d, 5d/week of brisk walking and (or) jogging at 60% VO2 max. Before and after 4 weeks of treatment, subjects performed a baseline PPL and a PPL following a single session of exercise (ExPPL). PPL was assessed by triglyceride (TG) area under the curve (AUC) and peak TG response (TGpeak). A two-way analysis of variance (ANOVA) with repeated measures was used to compare results from treatments for baseline and exercise trials. FO alone reduced PPL and Ex PPL, and FO+ExTr attenuated the ExPPL response measured as total AUC and TGpeak. There was no significant main effect for group or group by time interaction for baseline PPL or ExPPL. Fasting high-density lipoprotein cholesterol (HDL-C) and HDL2-C (i.e., subfraction 2) concentrations were significantly increased in the FO+ExTr group after the treatments. These results suggest that n-3 FA supplementation reduced PPL in sedentary subjects. Exercise training has no interference or additive effects with n-3 FA supplementation in attenuating PPL, but combined treatments may be additive in raising high-density lipoprotein cholesterol.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".