Effect of Exercise or Metformin on Biomarkers of Inflammation in Breast and Colorectal Cancer: A Randomized Trial
Bibliographic record
Abstract
Observational studies report that physical activity and metformin are associated with improved clinical outcome in patients with cancer. Inflammation is one biological mechanism hypothesized to mediate these associations. In this phase II, multicenter, 2 × 2 factorial trial, 139 patients with breast and colorectal cancer who completed standard therapy were randomized to one of four treatment groups for 12 weeks: exercise alone, metformin alone, exercise and metformin, or control. Inflammation outcomes included high-sensitivity C-reactive protein (hs-CRP), soluble tumor necrosis factor alpha receptor two (sTNFαR2), and IL6. The primary modeling strategy evaluated the trial product estimand that was quantified using a generalized linear mixed model. Compared with control, exercise alone reduced hs-CRP [-30.2%; 95% confidence interval (CI), -50.3, -1.0] and IL6 (-30.9%; 95% CI, -47.3, -9.5) but did not change sTNFαR2 (1.0%; 95% CI, -10.4, 13.9). Compared with control, metformin alone did not change hs-CRP (-13.9%; 95% CI, -40.0, 23.4), sTNFαR2 (-10.4%; 95% CI, -21.3, 2.0), or IL6 (-22.9%; 95% CI, -42.3, 2.0). Compared with control, exercise and metformin reduced sTNFαR2 (-13.1%; 95% CI, -22.9, -1.0) and IL6 (-38.7%; 95% CI, -52.3, -18.9) but did not change hs-CRP (-20.5%; 95% CI, -44.0, 12.7). The combination of exercise and metformin was not synergistic for hs-CRP, sTNFαR2, or IL6. In survivors of breast and colorectal cancer with low baseline physical activity and without type 2 diabetes, exercise and metformin reduced measures of inflammation that are associated with cancer recurrence and mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".