Effect of exercise intensity on postprandial lipemia, markers of oxidative stress, and endothelial function after a high-fat meal
Bibliographic record
Abstract
The aim of this study was to compare the effects of 2 different exercise intensities on postprandial lipemia, oxidative stress markers, and endothelial function after a high-fat meal (HFM). Eleven young men completed 2-day trials in 3 conditions: rest, moderate-intensity exercise (MI-Exercise) and heavy-intensity exercise (HI-Exercise). Subjects performed an exercise bout or no exercise (Rest) on the evening of day 1. On the morning of day 2, an HFM was provided. Blood was sampled at fasting (0 h) and every hour from 1 to 5 h during the postprandial period for triacylglycerol (TAG), thiobarbituric acid reactive substance (TBARS), and nitrite/nitrate (NOx) concentrations. Flow-mediated dilatation (FMD) was also analyzed. TAG concentrations were reduced in exercise conditions compared with Rest during the postprandial period (P < 0.004). TAG incremental area under the curve (iAUC) was smaller after HI-Exercise compared with Rest (P = 0.012). TBARS concentrations were reduced in MI-Exercise compared with Rest (P < 0.041). FMD was higher in exercise conditions than Rest at 0 h (P < 0.02) and NOx concentrations were enhanced in MI-Exercise compared with Rest at 0 h (P < 0.01). These results suggest that acute exercise can reduce lipemia after an HFM. However, HI-Exercise showed to be more effective in reducing iAUC TAG, which might suggest higher protection against postprandial TAG enhancement. Conversely, MI-Exercise can be beneficial to attenuate the susceptibility of oxidative damage induced by an HFM and to increase endothelial function in the fasted state compared with Rest.
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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) | 0.001 | 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".