The effects of chronic exercise on the inflammatory cytokines interleukin-6 and tumor necrosis factor-α are different with age
Bibliographic record
Abstract
Aging is associated with chronic low-grade inflammation, and interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) are key mediators of the inflammatory process. IL-6, especially muscle-derived IL-6, is expected to mediate the beneficial metabolic effects of exercise. There was no report that directly compares the effects of chronic endurance exercise on cytokine responses between old and young subjects in the same situation. Therefore, we compared the effects of endurance exercise on the expression of IL-6 and TNF-α in old and young rats. Young (3-month-old) and old (20-month-old) male Fisher rats were trained for 12 weeks on the treadmill. We measured serum TNF-α and IL-6 concentrations by enzyme-linked immunosorbent assay and examined mRNA expression of TNF-α and IL-6 in muscle, liver, and white adipose tissue using reverse transcription - polymerase chain reaction. We found that old rats had higher basal IL-6 levels in the liver, as well as in the serum and the muscle. After chronic endurance exercise, young rats exhibited significant decreases in serum TNF-α levels and hepatic IL-6 expression. However, old rats exhibited no significant changes in either serum or tissue cytokine levels after endurance exercise. These findings suggest that chronic endurance exercise could influence the inflammatory response of hepatic tissues, as well as muscle, and that the effects of chronic endurance exercise on inflammatory cytokine levels are different between old and young rats and an exercise program tailored for old subjects will be needed to obtain beneficial anti-inflammatory effects from 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 | 0.000 | 0.000 |
| 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".