Abstract PR04: Exogenous estrogen as a mediator of racial differences in insulin-like growth factor-I levels among postmenopausal women
Bibliographic record
Abstract
Abstract Purpose: The role of exogenous estrogen use in explaining racial/ethnic differences in insulin-like growth factor-I (IGF-I) levels in relation to cancer risk is not clear. We investigated whether the relationship between race and circulating bioactive IGF-I proteins is mediated by exogenous estrogen and the extent to which exogenous estrogen explained the race–IGF-I relationship in postmenopausal women. Methods: This cross-sectional study included 636 white and 133 black postmenopausal women enrolled in an ancillary study of the Women's Health Initiative Observational Study between February 1995 and July 1998. The race–IGF-I relationship was analyzed using ordinal regression, and quartiles of molar ratios of IGF-I/IGF binding protein-3 were used as a proxy of bioactive IGF-I outcomes. To assess exogenous estrogen as a mediator of the race–IGF-I relationship, we used the Baron-Kenny method and an estimation of the proportional change in the odds ratios for race on IGF-I levels plus a bootstrapping test for the significance of the mediation effect. Results: Compared with white women, black women were more likely to have high IGF-I levels and less likely to use exogenous estrogen. After accounting for race, estrogen nonusers had higher IGF-I levels than estrogen users did. When women were stratified by oral contraceptive (OC) ever use, among OC ever users, exogenous estrogen showed a strong mediation effect (67%; P=0.018) in the race–IGF-I relationship. Moreover, when women were classified by a history of hypertension, the IGF-I levels of women with a history of hypertension were higher than those of women with no history of hypertension. Of the women with a history of hypertension, exogenous estrogen explained differences in IGF-I levels between white and black women to a modest degree (23%; P=0.029). Conclusions: Exogenous estrogen use has a potentially important role in racial/ethnic disparities in cancer risk among postmenopausal women. This abstract is also presented as Poster A38. Citation Format: Su Yon Jung, Jennifer Hays-Grudo, Electra Paskett, Stephen D. Hursting, Jenifer Fenton, Michael Pollak, Mara Vitolins, Shine Chang. Exogenous estrogen as a mediator of racial differences in insulin-like growth factor-I levels among postmenopausal women. [abstract]. In: Proceedings of the Twelfth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2013 Oct 27-30; National Harbor, MD. Philadelphia (PA): AACR; Can Prev Res 2013;6(11 Suppl): Abstract nr PR04.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".