Aerobic exercise improves postprandial inflammatory and hemostatic markers after a high-fat meal: a randomized crossover study
Bibliographic record
Abstract
Exercise intensity modulates postprandial lipemia. However, its effect on hemostatic and pro- and anti-inflammatory markers in the postprandial state is still unknown. Eleven young males performed a 2-day trial on different conditions: (i) REST: rest for 45 min; (ii) MIE: moderate-intensity exercise; and (iii) HIE: heavy-intensity exercise. Experimental conditions were performed in the evening. On the following morning, blood samples were taken in the fasted state (0 h) and at 1, 3, and 5 h after the consumption of a high-fat meal (HFM). Interleukin-10 (IL-10) levels were higher in the HIE vs. MIE trial at 0 and 1 h (p < 0.033) and IL-10 incremental area under the curve (iAUC) was greater in the MIE (p = 0.027) and HIE (p = 0.045) trials vs. REST. Lower levels of anti-coagulation factor VII (FVII) were observed at 1 h in the MIE condition vs. REST (p = 0.043). In comparison with REST, MIE improved hemostatic (FVII) and anti-inflammatory markers (IL-10 iAUC) whereas HIE enhanced IL-10 in the postprandial state. Regardless of the exercise intensity, aerobic exercise mitigates the deleterious consequences of an HFM. Novelty: Prior aerobic exercise at moderate-intensity attenuates next day’s postprandial FVII and IL-10 levels whereas exercise performed at heavy-intensity increases IL-10 levels. Moderate-intensity exercise may be more beneficial to improve hemostatic (FVII) and anti-inflammatory (IL-10) responses while heavy-intensity exercise may improve anti-inflammatory (IL-10) levels only.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".