A sequence repeat in the insulin‐like growth factor‐1 gene and risk of breast cancer
Bibliographic record
Abstract
Insulin-like growth factor-1 (IGF-I), a potent mitogen, is hypothesized to influence breast cancer risk. In 3 previous studies, a polymorphism in the IGF-1 gene (sequence repeat length) was associated with plasma IGF-I level. We evaluated prospectively the relationships among a (CA)(n) repeat polymorphism in the IGF-1 gene, IGF-I level and breast cancer risk in a nested case-control study conducted within the Nurses' Health Study. Blood samples were collected in 1989-1990; up to June 1994, we identified 463 cases of breast cancer. One to 2 controls were selected per case, matched by age, menopausal status, postmenopausal hormone use, month and time of day of blood collection and fasting status, for a total of 622 controls. Although no significant trend was observed, plasma IGF-I levels were significantly lower among controls, with no copy of the 19 allele, compared with those homozygous for the 19 (CA)(n) repeat length (146 and 173 ng/ml, respectively; p-value for pairwise mean comparison = 0.005). In conditional logistic regression, controlling for established breast cancer risk factors, we observed no significant association between (CA)(n) repeat length genotype and risk of breast cancer [compared with repeat genotype 19/19-18/19 genotype relative risk (RR) = 0.96, 95% confidence interval (CI) = 0.56-1.64; 18/20 genotype RR = 0.92, 95% CI = 0.39-2.19; 19/20 genotype RR = 1.16, 95% CI = 0.82-1.64; 19/21 genotype RR = 0.69, 95% CI = 0.42-1.14; 20/20 genotype RR = 0.55, 95% CI = 0.28-1.10; 20/21 genotype RR = 0.72, 95% CI = 0.29-1.79]. Results did not vary substantially when evaluated according to menopausal status, tumor receptor status or category of other breast cancer risk factors. Although a modest association cannot be excluded, our data do not support an important relation between this IGF-1 gene polymorphism and breast cancer risk.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".