Inflammation Is a Mediating Factor in the Association between Lifestyle and Fatigue in Colorectal Cancer Patients
Bibliographic record
Abstract
Fatigue is very common among colorectal cancer (CRC) patients. We examined the association between adherence to the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) lifestyle recommendations and fatigue among stage I-III CRC patients, and whether inflammation mediated this association. Data from two prospective cohort studies were used. Adherence to the WCRF/AICR recommendations was expressed as a score ranging from 0–7, and assessed shortly after diagnosis. Six months post-diagnosis, fatigue was assessed with the European Organization for Research and Treatment of Cancer quality of life questionnaire C30 (EORTC QLQ-C30), and in a subpopulation, the plasma levels of inflammation markers (IL6, IL8, TNFα, and hsCRP) were assessed. Multiple linear regression analyses were performed to investigate the association between adherence to the WCRF/AICR recommendations and fatigue. To test mediation by inflammation, the PROCESS analytic tool developed by Hayes was used. A higher WCRF/AICR adherence score was associated with less fatigue six months after diagnosis (n = 1417, β −2.22, 95%CI −3.65; −0.78). In the population of analysis for the mediation analyses (n = 551), the total association between lifestyle and fatigue was (β −2.17, 95% CI −4.60; 0.25). A statistically significant indirect association via inflammation was observed (β −0.97, 95% CI −1.92; −0.21), explaining 45% of the total association between lifestyle and fatigue (−0.97/−2.17 × 100). Thus, inflammation is probably one of the underlying mechanisms linking lifestyle to fatigue.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".