Body shape throughout life and the risk for breast cancer at adulthood in the French E3N cohort
Bibliographic record
Abstract
Although earlier studies have indicated an inverse association between corpulence during childhood and the risk for breast cancer in adulthood, no study had evaluated body shape at different ages or body shape evolution in relation to the risk for breast cancer defined by menopausal status and hormone receptor status. Hazard ratios (HR) were estimated using multivariate Cox models in 81,089 women from the French E3N cohort to evaluate the risk for breast cancer associated with body shape (Sørensen's scale) at ages 8, menarche, 20-25, and 35-40 years. Six lifetime body shape trajectories were also estimated according to Nagin's approach to group-based trajectory modeling, and were analyzed in relation to the risk for breast cancer. During follow-up, 3573 breast cancer cases were diagnosed. At age 8 and at menarche, an increasing size of the body was associated with a significantly negative risk for estrogen receptor (ER)+/progesterone receptor (PR)+ postmenopausal breast cancer [both had a P(trend)=0.001; HR=0.80 (0.67-0.94) and HR=0.74 (0.64-0.86), respectively], for the largest body shapes compared with the first body shape. No significant association with body shape was found at the other ages. In terms of body shape evolution throughout life, women with a large body shape at menarche, irrespective of their body shape before or after menarche, tended to have a decreased risk for ER+/PR+ postmenopausal breast cancer compared with women who were constantly lean. Adjustment for current BMI or age at menarche did not modify any associations. Our study found an inverse association between adiposity at menarche and the risk for ER+/PR+ postmenopausal breast cancer, independent of corpulence at other ages. This work should encourage longitudinal studies with hormonal information to examine the underlying biological mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 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".