Risk Prediction for Epithelial Ovarian Cancer in 11 United States-Based Case-Control Studies: Incorporation of Epidemiologic Risk Factors and 17 Confirmed Genetic Loci.

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.

He isotope, U/Th isotope, Calcium carbonate, biogenic opal, rare earth element concentrations, and grain size distribution measured on core-top sediments during SONNE cruise SO202 (INOPEX)

Eolian dust is a significant source of iron and other nutrients that are essential for the health of marine ecosystems and potentially a controlling factor of the high nutrient-low chlorophyll status of the Subarctic North Pacific. We map the spatial distribution of dust input using three different geochemical tracers of eolian dust, 4He, 232Th and rare earth elements, in combination with grain size distribution data, from a set of core-top sediments covering the entire Subarctic North Pacific. Using the suite of geochemical proxies to fingerprint different lithogenic components, we deconvolve eolian dust input from other lithogenic inputs such as volcanic ash, ice-rafted debris, riverine and hemipelagic input. While the open ocean sites far away from the volcanic arcs are dominantly composed of pure eolian dust, lithogenic components other than eolian dust play a more crucial role along the arcs. In sites dominated by dust, eolian dust input appears to be characterized by a nearly uniform grain size mode at ~4 µm. Applying the 230Th-normalization technique, our proxies yield a consistent pattern of uniform dust fluxes of 1-2 g/m**2/yr across the Subarctic North Pacific. Elevated eolian dust fluxes of 2-4 g/m**2/yr characterize the westernmost region off Japan and the southern Kurile Islands south of 45° N and west of 165° E along the main pathway of the westerly winds. The core-top based dust flux reconstruction is consistent with recent estimates based on dissolved thorium isotope concentrations in seawater from the Subarctic North Pacific. The dust flux pattern compares well with state-of-the-art dust model predictions in the western and central Subarctic North Pacific, but we find that dust fluxes are higher than modeled fluxes by 0.5-1 g/m**2/yr in the northwest, northeast and eastern Subarctic North Pacific. Our results provide an important benchmark for biogeochemical models and a robust approach for downcore studies testing dust-induced iron fertilization of past changes in biological productivity in the Subarctic North Pacific.