Premenopausal levels of circulating insulin‐like growth factor I and the risk of postmenopausal breast cancer
Bibliographic record
Abstract
Increased levels of insulin-like growth factor I (IGF-I) may directly stimulate breast cell proliferation and promote growth and survival of transformed cells. Higher levels of IGF-I have been associated with increased risk of premenopausal breast cancer but not postmenopausal breast cancer. We investigated whether circulating levels of IGF-I prior to menopause are associated with breast cancer diagnosed after menopause in a population-based nested case-control study. Female cohort participants were enrolled in 1974 (n = 15,192) and 1989 (n = 18,724) and blood was drawn. Cases were women diagnosed with primary breast cancer at ages > or =50, of whom 152 were premenopausal at blood draw. One control was matched to each case on cohort participation, age, ethnic group, menopausal status and date of blood draw. Levels of IGF-I and IGF binding protein 3 (IGFBP-3) were measured using enzyme-linked immunoabsorbent assays. The association between IGF-I and breast cancer was determined using conditional logistic regression, adjusting for IGFBP-3. IGF-I levels decreased with age (p = 0.0001). Prior to age-stratification, IGF-I levels neither measured before nor after menopause were associated with postmenopausal breast cancer. After age-stratification, associations were suggested in the youngest premenopausal age group (upper vs. lowest third: odds ratio (OR) = 5.31, 95% confidence intervals (CI) = 0.85-33.13; p trend = 0.06) and oldest postmenopausal age group (upper vs. lowest third: OR = 3.41, 95% CI = 0.66-17.71; p trend = 0.13). The association between circulating levels of IGF-I and postmenopausal breast cancer risk may be modified by age. Increased levels of circulating IGF-I may be of particular interest in the younger premenopausal women and older postmenopausal women. Age-stratification should be undertaken in larger investigations of IGF-I levels as predictors of postmenopausal breast 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".