Search

Previously developed models for predicting absolute risk of invasive epithelial ovarian cancer have included a limited number of risk factors and have had low discriminatory power (area under the receiver operating characteristic curve (AUC) < 0.60). Because of this, we developed and internally validated a relative risk prediction model that incorporates 17 established epidemiologic risk factors and 17 genome-wide significant single nucleotide polymorphisms (SNPs) using data from 11 case-control studies in the United States (5,793 cases; 9,512 controls) from the Ovarian Cancer Association Consortium (data accrued from 1992 to 2010). We developed a hierarchical logistic regression model for predicting case-control status that included imputation of missing data. We randomly divided the data into an 80% training sample and used the remaining 20% for model evaluation. The AUC for the full model was 0.664. A reduced model without SNPs performed similarly (AUC = 0.649). Both models performed better than a baseline model that included age and study site only (AUC = 0.563). The best predictive power was obtained in the full model among women younger than 50 years of age (AUC = 0.714); however, the addition of SNPs increased the AUC the most for women older than 50 years of age (AUC = 0.638 vs. 0.616). Adapting this improved model to estimate absolute risk and evaluating it in prospective data sets is warranted., (© The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: The Total Worker Health (TWH) program of the National Institute for Occupational Safety and Health aims to advance worker well-being by integrating injury and illness prevention efforts with work-related safety and health hazard efforts., Purpose: To evaluate evidence on the benefits and harms of integrated TWH interventions., Data Sources: MEDLINE, Cochrane Library, and PsycINFO (January 1990 through September 2015); clinical trial registries; and reference lists., Study Selection: English-language studies that enrolled employed adults and compared integrated interventions with usual work practice, no intervention, or another intervention., Data Extraction: Dual abstraction and risk-of-bias (ROB) assessment., Data Synthesis: Ten of the 15 included studies had high ROB, primarily because of selection and attrition bias. Findings graded as having low strength of evidence (SOE) supported the effectiveness of TWH interventions for improving smoking cessation, as measured by self-reported 7-day abstinence over 22 to 26 weeks (2 randomized, controlled trials [RCTs]; n = 737), and increasing consumption of fruits and vegetables over 26 to 104 weeks (3 RCTs; n = 6056); results apply to populations of blue-collar manufacturing and construction workers. Findings graded as having low SOE supported the effectiveness of TWH interventions for reducing sedentary work behavior in office workers over 16 to 52 weeks (2 RCTs; n = 262). Evidence was insufficient or lacking for other outcomes of interest, such as rates of work injuries, quality of life, and harms., Limitation: Small, diverse body of evidence with many methodological limitations; possible publication bias., Conclusion: Integrated TWH interventions might improve health behaviors (for example, reduce tobacco use and sedentary behavior and improve diet) of workers, but effects of these interventions on injuries and overall quality of life are not known., Primary Funding Source: Agency for Healthcare Research and Quality.

Disruption in circadian gene expression, whether due to genetic variation or environmental factors (e.g., light at night, shiftwork), is associated with increased incidence of breast, prostate, gastrointestinal and hematologic cancers and gliomas. Circadian genes are highly expressed in the ovaries where they regulate ovulation; circadian disruption is associated with several ovarian cancer risk factors (e.g., endometriosis). However, no studies have examined variation in germline circadian genes as predictors of ovarian cancer risk and invasiveness. The goal of the current study was to examine single nucleotide polymorphisms (SNPs) in circadian genes BMAL1, CRY2, CSNK1E, NPAS2, PER3, REV1 and TIMELESS and downstream transcription factors KLF10 and SENP3 as predictors of risk of epithelial ovarian cancer (EOC) and histopathologic subtypes. The study included a test set of 3,761 EOC cases and 2,722 controls and a validation set of 44,308 samples including 18,174 (10,316 serous) cases and 26,134 controls from 43 studies participating in the Ovarian Cancer Association Consortium (OCAC). Analysis of genotype data from 36 genotyped SNPs and 4600 imputed SNPs indicated that the most significant association was rs117104877 in BMAL1 (OR = 0.79, 95% CI = 0.68-0.90, p = 5.59 × 10 -4 ]. Functional analysis revealed a significant down regulation of BMAL1 expression following cMYC overexpression and increasing transformation in ovarian surface epithelial (OSE) cells as well as alternative splicing of BMAL1 exons in ovarian and granulosa cells. These results suggest that variation in circadian genes, and specifically BMAL1 , may be associated with risk of ovarian cancer, likely through disruption of hormonal pathways.

Epithelial ovarian cancer (EOC) has a heritable component that remains to be fully characterized. Most identified common susceptibility variants lie in non-protein-coding sequences. We hypothesized that variants in the 3' untranslated region at putative microRNA (miRNA)-binding sites represent functional targets that influence EOC susceptibility. Here, we evaluate the association between 767 miRNA-related single-nucleotide polymorphisms (miRSNPs) and EOC risk in 18,174 EOC cases and 26,134 controls from 43 studies genotyped through the Collaborative Oncological Gene-environment Study. We identify several miRSNPs associated with invasive serous EOC risk (odds ratio=1.12, P=10(-8)) mapping to an inversion polymorphism at 17q21.31. Additional genotyping of non-miRSNPs at 17q21.31 reveals stronger signals outside the inversion (P=10(-10)). Variation at 17q21.31 is associated with neurological diseases, and our collaboration is the first to report an association with EOC susceptibility. An integrated molecular analysis in this region provides evidence for ARHGAP27 and PLEKHM1 as candidate EOC susceptibility genes.