Sustained Weight Loss and Risk of Breast Cancer in Women 50 Years and Older: A Pooled Analysis of Prospective Data
Bibliographic record
Abstract
BACKGROUND: Excess body weight is an established cause of postmenopausal breast cancer, but it is unknown if weight loss reduces risk. METHODS: Associations between weight change and risk of breast cancer were examined among women aged 50 years and older in the Pooling Project of Prospective Studies of Diet and Cancer. In 10 cohorts, weight assessed on three surveys was used to examine weight change patterns over approximately 10 years (interval 1 median = 5.2 years; interval 2 median = 4.0 years). Sustained weight loss was defined as no less than 2 kg lost in interval 1 that was not regained in interval 2. Among 180 885 women, 6930 invasive breast cancers were identified during follow-up. RESULTS: Compared with women with stable weight (±2 kg), women with sustained weight loss had a lower risk of breast cancer. This risk reduction was linear and specific to women not using postmenopausal hormones (>2-4.5 kg lost: hazard ratio [HR] = 0.82, 95% confidence interval [CI] = 0.70 to 0.96; >4.5-<9 kg lost: HR = 0.75, 95% CI = 0.63 to 0.90; ≥9 kg lost: HR = 0.68, 95% CI = 0.50 to 0.93). Women who lost at least 9 kg and gained back some (but not all) of it were also at a lower risk of breast cancer. Other patterns of weight loss and gain over the two intervals had a similar risk of breast cancer to women with stable weight. CONCLUSIONS: These results suggest that sustained weight loss, even modest amounts, is associated with lower breast cancer risk for women aged 50 years and older. Breast cancer prevention may be a strong weight-loss motivator for the two-thirds of American women who are overweight or obese.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.014 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".