Effects of an acute bout of moderate-intensity exercise on postprandial lipemia and airway inflammation
Bibliographic record
Abstract
A high-fat meal (HFM) induces an increase in blood lipids (postprandial lipemia; PPL), systemic inflammation, and acute airway inflammation. While acute exercise has been shown to have anti-inflammatory and lipid-lowering effects, it is unknown whether exercise prior to an HFM will translate to reduced airway inflammation post-HFM. Our purpose was to determine the effects of an acute bout of exercise on airway inflammation post-HFM and to identify whether any protective effect of exercise on airway inflammation was associated with a reduction in PPL or systemic inflammation. In a randomized cross-over study, 12 healthy, 18- to 29-year-old men (age, 23.0 ± 3.2 years; height, 178.9 ± 5.5 cm; weight, 78.5 ± 11.7 kg) consumed an HFM (1 g fat/1 kg body weight) 12 h following exercise (EX; 60 min at 60% maximal oxygen uptake) or without exercise (CON). Fractional exhaled nitric oxide (FENO; measure of airway inflammation), triglycerides (TG), and inflammatory markers (high-sensitivity C-reactive protein, tumor-necrosis factor-alpha, and interleukin-6) were measured while fasted at 2 h and 4 h post-HFM. FENO increased over time (2 h: CON, p = 0.001; EX, p = 0.002, but not by condition (p = 0.991). TG significantly increased 2 and 4 h post-HFM (p < 0.001), but was not significant between conditions (p = 0.256). Inflammatory markers did not significantly increase by time or condition (p > 0.05). There were no relationships between FENO and TG or systemic inflammatory markers for any time point or condition (p > 0.05). In summary, an acute bout of moderate-intensity exercise performed 12 h prior to an HFM did not change postprandial airway inflammation or lipemia in healthy, 18- to 29-year-old men.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".