Postprandial Variations of Plasma Inflammatory Markers in Abdominally Obese Men
Bibliographic record
Abstract
OBJECTIVE: Abdominal obesity is associated with a fasting proinflammatory condition. However, not much is known of the potential variations in circulating inflammatory markers after food intake. The purpose of the present study was to examine postprandial changes in plasma tumor necrosis factor (TNF)-alpha, interleukin (IL)-6, and C-reactive protein (CRP) concentrations in men and their potential associations with fat distribution and metabolic profile variables. RESEARCH METHODS AND PROCEDURES: Thirty-eight men were given a high-fat meal in the morning after an overnight fast, and TNF-alpha, IL-6, and CRP levels were measured in plasma at 0, 4, and 8 hours after the meal. Physical and metabolic profiles were also assessed for each participant. RESULTS: We observed a substantial increase in circulating IL-6 levels (p < 0.0001) after the meal. Although postprandial variations in circulating TNF-alpha levels across time failed to reach statistical significance (p = 0.02), we noted a significant decrease in plasma TNF-alpha concentrations 4 hours (-10%, p < 0.001 vs. 0 hours) after food intake. Plasma CRP levels were not affected by the fat load. We also noted that insulin-sensitive individuals displayed a less pronounced inflammatory response after food intake than insulin-resistant subjects. DISCUSSION: Results of the present study show that consumption of a high-fat meal leads to an increase in plasma IL-6 concentrations and transient decrease in circulating TNF-alpha levels in overweight men. Our results suggest a possible role of insulin resistance in the modulation of the postprandial inflammatory response, which could, in turn, contribute to worsen the state of insulin resistance.
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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 | 0.001 |
| 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".