Genetic Polymorphisms Involved in Insulin-like Growth Factor (IGF) Pathway in Relation to Mammographic Breast Density and IGF Levels
Bibliographic record
Abstract
The insulin-like growth factor (IGF) pathway is believed to play a role in carcinogenesis of the mammary gland. Single nucleotide polymorphisms (SNPs) of IGF-I, IGF-binding protein-3 (IGFBP-3), IGF receptor 1, insulin receptor substrate 1, and phosphoinositide-3-kinase, catalytic, beta polypeptide genes, which are members of the IGF pathway, have been associated with risk of common cancers, breast density, and/or IGF levels but results remain inconclusive. Thus, we evaluated the association of 11 targeted IGF pathway SNPs with circulating IGF levels and mammographic breast density. Among 741 white premenopausal women, blood samples were collected at time of screening mammography, and plasma IGF-I and IGFBP-3 levels were measured by ELISA. Percent and absolute breast density were estimated using a computer-assisted method. Multivariate linear models were used to examine the associations. Women carrying increasing number of copies of the rare allele of IGF-I rs1520220 and rs6220 SNPs had increased percent breast density (P(trend) = 0.04 and 0.06, respectively). Carriers of increasing number of copies of the rare allele of phosphoinositide-3-kinase, catalytic, beta polypeptide rs361072 SNP had decreased percent (P(trend) = 0.04) and absolute (P(trend) = 0.02) breast density. An association of insulin receptor substrate 1 rs1801278 SNP with absolute density (P(trend) = 0.03) was also observed. All four IGFBP-3 SNPs (including rs2854744) were associated with IGF-I and IGFBP-3 levels. This study shows that several components of the IGF pathway are associated with breast density or IGF levels. Our findings provide additional support for the idea that several components of the IGF pathway may affect breast cancer risk and that this effect on breast cancer development may be mediated, at least in part, through its influence on the morphogenesis of breast tissue.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".