Healthy lifestyle and the risk of lymphoma in the European Prospective Investigation into Cancer and Nutrition study
Bibliographic record
Abstract
Limited evidence exists on the role of modifiable lifestyle factors on the risk of lymphoma. In this work, the associations between adherence to healthy lifestyles and risks of Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL) were evaluated in a large-scale European prospective cohort. Within the European Prospective Investigation into Cancer and Nutrition (EPIC), 2,999 incident lymphoma cases (132 HL and 2,746 NHL) were diagnosed among 453,808 participants after 15 years (median) of follow-up. The healthy lifestyle index (HLI) score combined information on smoking, alcohol intake, diet, physical activity and BMI, with large values of HLI expressing adherence to healthy behavior. Cox proportional hazards models were used to estimate lymphoma hazard ratios (HR) and 95% confidence interval (CI). Sensitivity analyses were conducted by excluding, in turn, each lifestyle factor from the HLI score. The HLI was inversely associated with HL, with HR for a 1-standard deviation (SD) increment in the score equal to 0.78 (95% CI: 0.66, 0.94). Sensitivity analyses showed that the association was mainly driven by smoking and marginally by diet. NHL risk was not associated with the HLI, with HRs for a 1-SD increment equal to 0.99 (0.95, 1.03), with no evidence for heterogeneity in the association across NHL subtypes. In the EPIC study, adherence to healthy lifestyles was not associated with overall lymphoma or NHL risk, while an inverse association was observed for HL, although this was largely attributable to smoking. These findings suggest a limited role of lifestyle factors in the etiology of lymphoma subtypes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".