New Malignancies after Squamous Cell Carcinoma and Melanomas: A Population-Based Study from Norway

Skin cancer survivors experience an increased risk for subsequent malignancies but the associated risk factors are poorly understood. This study examined the risk of a new primary cancer following an initial skin cancer and assessed risk factors associated with second primary cancers

Workgroup Report: Biomonitoring Study Design, Interpretation, and Communication—Lessons Learned and Path Forward

Human biomonitoring investigations have provided data on a wide array of chemicals in blood and urine and in other tissues and fluids such as hair and human milk. These data have prompted questions such as a) What is the relationship between levels of environmental chemicals in humans and external exposures? b) What is the baseline or “background” level against which individual levels should be compared? and c) How can internal levels be used to draw conclusions about individual and/or population health? An interdisciplinary panel was convened for a 1-day workshop in November 2004 with the charge of focusing on three specific aspects of biomonitoring: characteristics of scientifically robust biomonitoring studies, interpretation of human biomonitoring data for potential risks to human health, and communication of results, uncertainties, and limitations of biomonitoring studies. In this report we describe the recommendations of the panel

Functional Dyadicity and Heterophilicity of Gene-Gene Interactions in Statistical Epistasis Networks

Background: The interaction effect among multiple genetic factors, i.e. epistasis, plays an important role in explaining susceptibility on common human diseases and phenotypic traits. The uncertainty over the number of genetic attributes involved in interactions poses great challenges in genetic association studies and calls for advanced bioinformatics methodologies. Network science has gained popularity in modeling genetic interactions thanks to its structural characterization of large numbers of entities and their complex relationships. However, little has been done on functionally interpreting statistically inferred epistatic interactions using networks. Results: In this study, we propose to characterize gene functional properties in the context of interaction network structure. We used Gene Ontology (GO) to functionally annotate genes as vertices in a statistical epistasis network, and quantitatively characterize the correlation between the distribution of gene functional properties and the network structure by measuring dyadicity and heterophilicity of each functional category in the network. These two parameters quantify whether genetic interactions tend to occur more frequently for genes from the same functional category, i.e. dyadic effect, or more frequently for genes from across different functional categories, i.e. heterophilic effect. Conclusions: By applying this framework to a population-based bladder cancer dataset, we were able to identify several GO categories that have significant dyadicity or heterophilicity associated with bladder cancer susceptibility. Thus, our informatics framework suggests a new methodology for embedding functional analysis in network modeling of statistical epistasis in genetic association studies

Risk of Death from Cardiovascular Disease Associated with Low-level Arsenic Exposure Among Long-term Smokers in a US Population-based Study

High levels of arsenic exposure have been associated with increases in cardiovascular disease risk. However, studies of arsenic’s effects at lower exposure levels are limited and few prospective studies exist in the United States using long-term arsenic exposure biomarkers. We conducted a prospective analysis of the association between toenail arsenic and cardiovascular disease mortality using longitudinal data collected on 3939 participants in the New Hampshire Skin Cancer Study. Using Cox proportional hazard models adjusted for potential confounders, we estimated hazard ratios and 95% confidence intervals associated with the risk of death from any cardiovascular disease, ischemic heart disease, and stroke, in relation to natural-log transformed toenail arsenic concentrations. In this US population, although we observed no overall association, arsenic exposure measured from toenail clipping samples was related to an increased risk of ischemic heart disease mortality among long-term smokers (as reported at baseline), with increased hazard ratios among individuals with ≥ 31 total smoking years (HR: 1.52, 95% CI: 1.02, 2.27), ≥ 30 pack-years (HR: 1.66, 95% CI: 1.12, 2.45), and among current smokers (HR: 1.69, 95% CI: 1.04, 2.75). These results are consistent with evidence from more highly exposed populations suggesting a synergistic relationship between arsenic exposure and smoking on health outcomes and support a role for lower-level arsenic exposure in ischemic heart disease mortality

MedZIM: Mediation analysis for Zero-Inflated Mediators with applications to microbiome data

The human microbiome can contribute to the pathogenesis of many complex diseases such as cancer and Alzheimer's disease by mediating disease-leading causal pathways. However, standard mediation analysis is not adequate in the context of microbiome data due to the excessive number of zero values in the data. Zero-valued sequencing reads, commonly observed in microbiome studies, arise for technical and/or biological reasons. Mediation analysis approaches for analyzing zero-inflated mediators are still lacking largely because of challenges raised by the zero-inflated data structure: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are actually not zero (i.e., false zeros). We develop a novel mediation analysis method under the potential-outcomes framework to fill this gap. We show that the mediation effect of the microbiome can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. The first component corresponds to the mediation effect attributable to a unit-change over the positive relative abundance and the second component corresponds to the mediation effect attributable to discrete binary change of the mediator from zero to a non-zero state. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the applications in two real microbiome studies demonstrate that our approach outperforms existing mediation analysis approaches.Comment: Corresponding: Zhigang L