Effects of Two Combined Exercise Designs Associated With High-Fat Meal Consumption on Postprandial Lipemia, Insulinemia, and Oxidative Stress
Bibliographic record
Abstract
Farinha, JB, Macedo, CEO, Rodrigues-Krause, J, Krüger, RL, Boeno, FP, Macedo, RCO, Queiroz, JN, Teixeira, BC, and Reischak-Oliveira, A. Effects of two combined exercise designs associated with high-fat meal consumption on postprandial lipemia, insulinemia, and oxidative stress. J Strength Cond Res 32(5): 1422-1430, 2018-Impaired postprandial lipemia (PPL) response after the consumption of a high-fat meal (HFM) is linked to diabetes, oxidative stress, and cardiovascular events. The aim of this study was to investigate lipid and glucose metabolism and oxidative stress responses of 2 different combined exercise designs associated with HFM consumption. Eleven healthy and physically active men (27.36 ± 5.04 years) participated in this study. After the pretrial visits, participants were randomly assigned to perform 2-day trials in 3 different conditions (interspaced by at least 1 week): resting (REST), circuit combined exercise (CIRC), or traditional combined exercise (COMB), on the evening of day 1. On the morning of day 2, an HFM was provided and blood samples were obtained before and after 1, 3, and 5 hours of HFM consumption. No differences were found with respect to glucose, thiobarbituric acid-reactive substances, or total thiol levels in between time points or conditions. One-way analysis of variance demonstrated a difference between REST and CIRC (p = 0.029; reduction of 35.29%) and between REST and COMB (p = 0.041; reduction of 33.41%) conditions with incremental area under the curve (iAUC) for triacylglycerol levels. A difference between REST and CIRC (p = 0.03; reduction of 34.22%) conditions in terms of iAUC for insulin was also found. Both CIRC and COMB exercise designs can reduce PPL associated with HFM consumption. Moreover, CIRC reduces the iAUC for insulin, suggesting additional benefits for prescribing this type of exercise.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".