Effect Of Exercise Intensity On Postprandial Lipemia And Oxidative Stress Markers After A High-fat Meal
Bibliographic record
Abstract
Exercise may attenuate postprandial lipemia and oxidative stress after the consumption of a high-fat meal. Still, it remains the debate about the most effective exercise intensity that can reduce lipemic curve and oxidative stress. PURPOSE: To compare the effect of two isocaloric sessions of exercise performed in different intensities on lipemic curve and markers of oxidative stress after a high-fat meal. METHODS: 11 young and physically active men participated in three randomized protocols with two-day trials each: Low Intensity exercise (LI); Moderate intensity exercise (MI); or Rest. On the evening of day 1, participants performed one of the three protocols. On day 2, participants arrived at the laboratory in a fasted state (12h) and a high-fat meal was provided. Blood collections for analysis of Triglycerides (TG), Thiobarbituric Acid Reactive Substances (TBARS), Nitrites and Nitrates (NOx) and Total Thiols were taken in fasted state and every post-prandial hour until complete 5 hours of the meal consumption. A mixed model ANOVA followed by Bonferroni correction was used to test the effect of the protocols in different times. RESULTS: Lower levels of TG were observed in LI compared to Rest at 4h (112.20 ± 25.08 vs. 152.17 ± 42.94mg.dl-1, p<0.05) and in MI compared to Rest at 3h (86.57 ± 33.91 vs. 160.01 ± 44.25, p<0.05); 4h (109.02 ± 34.88 vs. 152.17 ± 42.94mg.dl-1, p<0.05) and 5h (91.98 ± 13.23 vs. 124.34 ± 39.13, p<0.05). The total area under the curve (AUC) of TG was lower in LI and MI than Rest (21.13% and 29.03% lower than Rest, p<0.05). Levels of TBARS in LI and MI were lower than Rest at 1h (1.84 ± 0.55 and 2.57 ± 1.26 vs. 3.88 ± 0.14μM de MDA.L-1, p<0.01). Differences between LI and Rest were found in AUC of TBARS (26% lower than Rest, p=0.02) and NOx (48.3% higher than Rest, p = 0.01). At baseline, the concentrations of TBARS for LI were lower than MI (1.77 ± 0.70 vs. 2.78 ± 1.37μM de MDA.L-1, p=0.04) and concentrations of NOx were higher than Rest (12.12 ± 5.15 vs. 5.83 ± 5.8μM.L-1, p=0.012). There was no difference between the protocols for Total Thiols. CONCLUSIONS: Both exercise intensities were effective to reduce postprandial lipemia and markers of oxidative stress. Yet, there is an acute effect of LI exercise that can reduce baseline concentration of TBARS and increase NOx compared to MI and Rest. Supported by FAPERGS (Brazil).
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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".