Association between Five Lifestyle Habits and Cancer Risk: Results from the E3N Cohort
Bibliographic record
Abstract
Although some modifiable lifestyle characteristics have been associated with decreased cancer risk, little is known about their combined effect or about the proportion of cancer cases that could be prevented by improving lifestyle behaviors. We aimed to quantify the association between lifestyle habits and all-site and site-specific cancer risk in middle-aged women. The study included 64,732 women from the French E3N prospective cohort, ages 43 to 68 years at baseline. During a 15-year follow-up period, 6,938 cases of invasive cancer were diagnosed. We defined an index that aggregated five lifestyle characteristics: smoking, body mass index, alcohol consumption, fruit and vegetable consumption, and physical activity. Proportional hazard Cox regressions were performed to evaluate the association between lifestyle and cancer risk and to estimate multivariate HRs and their 95% confidence intervals (CI). In addition, population-attributable fractions were used to estimate the proportion of cancer cases that could be prevented by healthier behaviors. A significant decrease in all-site cancer risk was observed and was associated with a healthy lifestyle (HR, 0.81; 95% CI, 0.73-0.89 when comparing the highest with the lowest health index category; Ptrend across categories < 0.01). Combining all five characteristics would have prevented 6.3% (2.2%-10.3%) of any-site, 6.3% (0.5%-12.1%) of postmenopausal breast, and 47.5% (26.8%-64.1%) of lung cancers. In conclusion, compliance with only five modifiable lifestyle behaviors could prevent a significant number of cancers, notably postmenopausal breast and lung cancers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".