Genetic variation and circulating levels of IGF‐I and IGFBP‐3 in relation to risk of proliferative benign breast disease
Bibliographic record
Abstract
Insulin-like growth factor-I (IGF-I) and its major binding protein IGFBP-3 have been implicated in breast carcinogenesis. We examined the associations between genetic variants and circulating levels of IGF-I and IGFBP-3 with proliferative benign breast disease (BBD), a marker of increased breast cancer risk, in the Nurses' Health Study II (NHSII). Participants were 359 pathology-confirmed proliferative BBD cases and 359 matched controls. Circulating IGF-I and IGFBP-3 levels were measured in blood samples collected between 1996 and 1999. Thirty single nucleotide polymorphisms (SNPs) in IGF-I, IGFBP-1, and IGFBP-3 genes were selected using a haplotype tagging approach and genotyped in cases and controls. Circulating IGF-I levels were not associated with proliferative BBD risk. Higher circulating IGFBP-3 levels were significantly associated with increased risk of proliferative BBD (highest vs. lowest quartile odds ratio (OR) [95% confidence interval (CI)], 1.70 (1.06-2.72); p-trend = 0.03). The minor alleles of 2 IGFBP-3 SNPs were associated with lower proliferative BBD risk (homozygous variant vs. homozygous wild-type OR (95% CI): rs3110697: 0.6 (0.4-0.9), p-trend = 0.02; rs2132570: 0.2 (0.1-0.6), p-trend = 0.02). Three other IGFBP-3 SNPs (rs2854744, rs2960436 and rs2854746) were significantly associated with circulating IGFBP-3 levels (p < 0.01). Although these SNPs were not significantly associated with proliferative BBD risk, there was suggestive evidence that the alleles associated with higher circulating IGFBP-3 levels were also associated with higher risk of proliferative BBD. These results suggest that genetic variants and circulating levels of IGFBP-3 may play a role in the early stage of breast carcinogenesis.
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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 | 0.001 |
| 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) | 0.001 | 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".