Abstract B25: Mammographic density and risk of breast cancer by body weight: A combined analysis of four case-control studies
Bibliographic record
Abstract
Abstract Background. Breast density assessed from mammography is a strong predictor of breast cancer risk, but the strength of the association may vary with adiposity. To examine effect modification by adiposity, we combined data from four case-control studies on breast density that represented an ethnically diverse population with a wide variation in level of adiposity as measured by body mass index (BMI, kg/m2). Methods. We combined data from four case-control studies representing different locations: California, Hawaii and Minnesota in the United States, and Gifu in Japan. All studies included incident breast cancer cases diagnosed between 1994 and 2002 and matched controls representing the underlying case population. One mammographic image per subject was selected, specifically the mammogram at diagnosis for the studies from California, Minnesota, and Japan and the closest prediagnostic mammogram for Hawaii. Percent density was measured by one reader, who was blinded to case status, using a computer-assisted method. Self-reported anthropometric measures were used to classify women as normal, overweight, and obese according to ethnic-specific BMI cut points (<23, 23-27.4, and ≥27.5 for Asian women and <25, 25-29.9, and ≥30 for other ethnic groups). Logistic regression was used to estimate odds ratios (OR) and 95% confidence intervals (95% CI) and to evaluate interactions using the likelihood ratio while adjusting for potential confounders, including age and ethnicity. Heterogeneity across studies was marginally significant as assessed by examining density-by-study interaction (P = 0.09) and, therefore, we also adjusted for study-site. Results. The study included 1,699 cases and 2,422 controls of diverse ethnicity: 45% Caucasian, 40% Asian, 9% African-American, and 7% Other. Of these women, we classified 34% as overweight and19% as obese. Age-adjusted mean percent density was significantly greater for cases than for controls: 31.7% versus 28.9%, respectively (P < 0.001). BMI was inversely associated with breast density; the estimated age-adjusted mean percent density was 36.5%, 26.8%, and 19.3% for normal, overweight, and obese, respectively (Ptrend < 0.001). The overall OR for a 10% higher percent density was 1.15 (95% CI: 1.05, 1.18) with higher estimates in overweight (OR: 1.19, 95% CI: 1.09, 1.29) and obese (OR: 1.25, 95% CI: 1.11, 1.41) than normal BMI (OR: 1.11, 95% CI: 1.05, 1.18). The effect modification by BMI was statistically significant (Pinteraction = 0.01). Conclusions. Our findings confirm that the elevated risk of breast cancer associated with breast density differs by level of adiposity, with a higher risk for overweight and obese than normal BMI. Further research is needed to understand the underlying biological reasons for these noted differences in association by adiposity. Citation Information: Cancer Prev Res 2010;3(12 Suppl):B25.
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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 | 0.026 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.015 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".