Moderate-intensity, premeal cycling blunts postprandial increases in monocyte cell surface CD18 and CD11a and endothelial microparticles following a high-fat meal in young adults
Bibliographic record
Abstract
High-fat meals promote transient increases in proatherogenic factors, implicating the postprandial state in cardiovascular disease (CVD) progression. Although low-grade inflammation is associated with CVD, little research has assessed postprandial inflammation. Because of its anti-inflammatory properties, premeal exercise may counteract postprandial inflammation. The purpose of this study was to determine postprandial alterations in monocytes and circulating markers of endothelial stress and inflammation following a high-fat meal in young adults with or without premeal cycle exercise. Each subject completed two trials and was randomized to rest or cycle at a moderate intensity prior to eating a high-fat meal. Flow cytometry was used to assess monocyte cell surface receptor expression and concentration of endothelial microparticles (EMP). Plasma cytokines were assessed using Luminex MagPix. Statistical analysis was completed using separate linear mixed models analyses with first-order autoregressive (AR(1)) heterogeneous covariance structure. Significance was set at P ≤ 0.05. Percentage increases in classic monocyte CD11a and CD18 were greater overall in the postprandial period in the meal-only condition compared with the meal + exercise condition (P < 0.05). EMP concentration was 47% greater 3 h after the meal compared with premeal values in the meal-only condition (P < 0.05); no significant increase was observed in the meal + exercise condition. Premeal cycling blunted postprandial increases in EMP and CD11a and CD18. Acute, moderate-intensity exercise may help counteract possibly deleterious postprandial monocyte and endothelial cell activation.
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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) | 0.001 | 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 | 0.000 | 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".