Dataset Information

Association of Anti-Mullerian Hormone, Follicle-Stimulating Hormone, and Inhibin B with Risk of Ovarian Cancer in the Janus Serum Bank.

ABSTRACT: BACKGROUND:Reproductive factors, including parity, breastfeeding, and contraceptive use, affect lifetime ovulatory cycles and cumulative exposure to gonadotropins and are associated with ovarian cancer. To understand the role of ovulation-regulating hormones in the etiology of ovarian cancer, we prospectively analyzed the association of anti-Mullerian hormone (AMH), follicle-stimulating hormone (FSH), and inhibin B with ovarian cancer risk. METHODS:Our study included 370 women from the Janus Serum Bank, including 54 type I and 82 type II invasive epithelial ovarian cancers, 49 borderline tumors, and 185 age-matched controls. We used conditional logistic regression to assess the relationship between hormones and risk of ovarian cancer overall and by subtype (types I and II). RESULTS:Inhibin B was associated with increased risk of ovarian cancer overall [OR, 1.97; 95% confidence interval (CI), 1.14-3.39; P trend = 0.05] and with type I ovarian (OR, 3.10; 95% CI, 1.04-9.23; P trend = 0.06). FSH was not associated with ovarian cancer risk overall, but higher FSH was associated with type II ovarian cancers (OR, 2.78; 95% CI, 1.05-7.38). AMH was not associated with ovarian cancer risk. CONCLUSIONS:FSH and inhibin B may be associated with increased risk in different ovarian cancer subtypes, suggesting that gonadotropin exposure may influence risk of ovarian cancer differently across subtypes. IMPACT:Associations between prospectively collected AMH, FSH, and inhibin B levels with risk of ovarian cancer provide novel insight on the influence of premenopausal markers of ovarian reserve and gonadotropin signaling. Heterogeneity of inhibin B and FSH effects in different tumor types may be informative of tumor etiology.

SUBMITTER: Irvin SR

PROVIDER: S-EPMC7060092 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Association of Anti-Mullerian Hormone, Follicle-Stimulating Hormone, and Inhibin B with Risk of Ovarian Cancer in the Janus Serum Bank.

Irvin Sarah R SR Weiderpass Elisabete E Stanczyk Frank Z FZ Brinton Louise A LA Trabert Britton B Langseth Hilde H Wentzensen Nicolas N

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20200113 3

<h4>Background</h4>Reproductive factors, including parity, breastfeeding, and contraceptive use, affect lifetime ovulatory cycles and cumulative exposure to gonadotropins and are associated with ovarian cancer. To understand the role of ovulation-regulating hormones in the etiology of ovarian cancer, we prospectively analyzed the association of anti-Mullerian hormone (AMH), follicle-stimulating hormone (FSH), and inhibin B with ovarian cancer risk.<h4>Methods</h4>Our study included 370 women fro ...[more]

PMID: 31932414

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Project description:BackgroundPreviously, we reported a model for assessing ovarian reserves using 4 predictors: anti-Müllerian hormone (AMH) level, antral follicle count (AFC), follicle-stimulating hormone (FSH) level, and female age. This model is referred as the AAFA (anti-Müllerian hormone level-antral follicle count-follicle-stimulating hormone level-age) model.ObjectiveThis study aims to explore the possibility of establishing a model for predicting ovarian reserves using only 3 factors: AMH level, FSH level, and age. The proposed model is referred to as the AFA (anti-Müllerian hormone level-follicle-stimulating hormone level-age) model.MethodsOocytes from ovarian cycles stimulated by gonadotropin-releasing hormone antagonist were collected retrospectively at our reproductive center. Poor ovarian response (<5 oocytes retrieved) was defined as an outcome variable. The AFA model was built using a multivariable logistic regression analysis on data from 2017; data from 2018 were used to validate the performance of AFA model. Measurements of the area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predicative value were used to evaluate the performance of the model. To rank the ovarian reserves of the whole population, we ranked the subgroups according to the predicted probability of poor ovarian response and further divided the 60 subgroups into 4 clusters, A-D, according to cut-off values consistent with the AAFA model.ResultsThe AUCs of the AFA and AAFA models were similar for the same validation set, with values of 0.853 (95% CI 0.841-0.865) and 0.850 (95% CI 0.838-0.862), respectively. We further ranked the ovarian reserves according to their predicted probability of poor ovarian response, which was calculated using our AFA model. The actual incidences of poor ovarian response in groups from A-D in the AFA model were 0.037 (95% CI 0.029-0.046), 0.128 (95% CI 0.099-0.165), 0.294 (95% CI 0.250-0.341), and 0.624 (95% CI 0.577-0.669), respectively. The order of ovarian reserve from adequate to poor followed the order from A to D. The clinical pregnancy rate, live-birth rate, and specific differences in groups A-D were similar when predicted using the AFA and AAFA models.ConclusionsThis AFA model for assessing the true ovarian reserve was more convenient, cost-effective, and objective than our original AAFA model.

Dataset Information

Association of Anti-Mullerian Hormone, Follicle-Stimulating Hormone, and Inhibin B with Risk of Ovarian Cancer in the Janus Serum Bank.

Publications

Association of Anti-Mullerian Hormone, Follicle-Stimulating Hormone, and Inhibin B with Risk of Ovarian Cancer in the Janus Serum Bank.

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