Inflammatory Marker Changes in a Yearlong Randomized Exercise Intervention Trial among Postmenopausal Women
Bibliographic record
Abstract
Chronic low-grade inflammation is a possible risk factor for cancer that may be modifiable with long-term exercise. Very few randomized controlled trials (RCT) have studied the isolated effects of exercise on low-grade inflammation exclusively in postmenopausal women. The Alberta Physical Activity and Breast Cancer Prevention Trial, a 2-armed RCT in healthy postmenopausal women, examined how 1 year of moderate to vigorous aerobic exercise, compared with usual inactivity, influenced circulating inflammatory markers. Baseline, 6-month, and 12-month serum was analyzed by direct chemiluminescent immunoassays to measure high sensitivity C-reactive protein (CRP) and ELISAs to measure interleukin 6 (IL-6) and TNF-α. Intention to treat analyses were conducted with linear mixed models. Statistically significant differences in CRP were observed over 12 months for exercisers versus controls (treatment effect ratio = 0.87, 95% CI = 0.79-0.96, P = 0.005), but not in IL-6 or TNF-α. A statistically significant trend (P(trend) = 0.021) of decreasing CRP with increasing exercise adherence and stronger intervention effects on CRP in women with higher baseline physical fitness (P(heterogeneity) = 0.040) was found. The intervention effect on CRP became statistically nonsignificant with adjustment for dietary fiber intake change and seemed to be mediated by fat loss. Low-grade inflammation may be lowered with exercise, but confounding by dietary intake occurred and should be considered in future studies. Further trials are needed to corroborate our findings about the optimal dose of exercise required to lower CRP levels and effect modification of CRP changes by levels of body fatness and fitness.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".