Weight Gain and the Risk of Ovarian Cancer in <i>BRCA1</i> and <i>BRCA2</i> Mutation Carriers
Bibliographic record
Abstract
Abstract Background: Weight gain and other anthropometric measures on the risk of ovarian cancer for women with BRCA mutations are not known. We conducted a prospective analysis of weight change since age 18, height, body mass index (BMI) at age 18, and current BMI and the risk of developing ovarian cancer among BRCA1 and BRCA2 mutation carriers. Methods: In this prospective cohort study, height, weight, and weight at age 18 were collected at study enrollment. Weight was updated biennially. Cox proportional hazards models were used to estimate the hazard ratio (HR) and 95% confidence intervals (CI) for ovarian cancer. Results: This study followed 4,340 women prospectively. There were 121 incident cases of ovarian cancer. Weight gain of more than 20 kg since age 18 was associated with a 2-fold increased risk of ovarian cancer, compared with women who maintained a stable weight (HR, 2.00; 95% CI, 1.13–3.54; P = 0.02). Current BMI of 26.5 kg/m2 or greater was associated with an increased risk of ovarian cancer in BRCA1 mutation carriers, compared with those with a BMI less than 20.8 kg/m2 (Q4 vs. Q1 HR, 2.13; 95% CI, 1.04–4.36; P = 0.04). There were no significant associations between height or BMI at age 18 and risk of ovarian cancer. Conclusions: Adult weight gain is a risk factor for ovarian cancer in women with a BRCA1 or BRCA2 mutation. Impact: These findings emphasize the importance of maintaining a healthy body weight throughout adulthood in women at high risk for ovarian cancer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".