Impact of aerobic exercise on levels of <scp>IL</scp>‐4 and <scp>IL</scp>‐10: results from two randomized intervention trials
Bibliographic record
Abstract
The mechanisms whereby regular exercise reduces chronic inflammation remain unclear. We investigated whether regular aerobic exercise alters basal levels of interleukin (IL)-10 and IL-4 in two randomized trials of physical activity. The Alberta Physical Activity and Breast Cancer Prevention Trial (ALPHA, n = 320) and the Breast Cancer and Exercise Trial in Alberta (BETA, n = 400) were two-center, two-armed randomized trials in inactive, healthy, postmenopausal women. Both trials included an exercise intervention prescribed five times/week and no dietary changes. In ALPHA, the exercise group was prescribed 225 min/week versus no activity in the controls. BETA examined dose-response effects comparing 300 (HIGH) versus 150 (MODERATE) min/week. Plasma concentrations of IL-10 and IL-4 were measured at baseline, 6, and 12 months. Intention-to-treat (ITT) analysis was performed using linear mixed models adjusted for baseline biomarker concentrations. Circulating anti-inflammatory cytokine levels decreased among all groups, with percent change ranging from -3.4% (controls) to -8.2% (HIGH) for IL-4 and -1.6% (controls) to -7.5% (HIGH) for IL-10. No significant group differences were found for IL-4 (ALPHA P = 0.54; BETA P = 0.32) or IL-10 (ALPHA P = 0.84; BETA P = 0.68). Some evidence for moderation of the effect of exercise by baseline characteristics was found for IL-10 but not for IL-4. Results from these two large randomized aerobic exercise intervention trials suggest that aerobic exercise does not alter IL-10 or IL-4 in a manner consistent with chronic disease and cancer prevention.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".