Diet and Breast Cancer: Evidence That Extremes in Diet Are Associated With Poor Survival
Bibliographic record
Abstract
PURPOSE: Diet has been postulated to influence breast cancer prognosis; however, existing evidence is weak and inconsistent. Previous studies have sought evidence of a linear relationship between diet and breast cancer outcomes. Because of a U-shaped association of body mass index (BMI) with survival in breast cancer, we hypothesized that a nonlinear association also existed for dietary variables. PATIENTS AND METHODS: Four hundred seventy-seven women with surgically resected T1 to T3, N0/1, M0 breast cancer completed the Block Food Frequency Questionnaire 9.3 +/- 4.6 weeks (mean +/- standard deviation) after diagnosis, reporting intake over the preceding 12 months. Data on tumor-related factors, treatment, and outcomes were obtained prospectively from medical records. A series of Cox models was performed, modeling the association of dietary factors with breast cancer survival linearly and quadratically, adjusting for total energy intake, tumor- and treatment-related variables, and BMI. RESULTS: Significant nonlinear survival associations were found for protein, oleic acid, cholesterol, polyunsaturated-saturated fat ratio, and for percentage of calories from fat and percentage of calories from carbohydrates in multivariate models. The shape of the survival associations varied across nutrients. Hazard ratios for highest risk quintiles ranged from 2.1 to 6.5. For total fat, adjustment for BMI reduced the multivariate P value obtained from nonlinear Cox models from.05 to.10. No significant linear associations were identified. CONCLUSION: The association of key dietary variables with breast cancer survival may be U-shaped rather than linear. Our data suggest that midrange intake of most major energy sources is associated with the most favorable outcomes, and extremes are associated with less favorable outcomes.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".