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

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

Diabetes Pharmacotherapies and Bladder Cancer: A Medicare Epidemiologic Study

Objective: Patients with type II diabetes have an increased risk of bladder cancer and are commonly treated with thiazolidinediones and angiotensin receptor blockers (ARBs), which have been linked to cancer risk. We explored the relationship between use of one or both of these medication types and incident bladder cancer among diabetic patients (diabetics) enrolled in Medicare. Research Design and Methods: We constructed both a prevalent and incident retrospective cohort of pharmacologically treated prevalent diabetics enrolled in a Medicare fee-for-service plan using inpatient, outpatient (2003–2011) and prescription (2006–2011) administrative data. The association of incident bladder cancer with exposure to pioglitazone, rosiglitazone and ARBs was studied using muitivariable Cox’s hazard models with time-dependent covariates in each of the two cohorts. Results: We identified 1,161,443 prevalent and 320,090 incident pharmacologically treated diabetics, among whom 4433 and 1159, respectively, developed incident bladder cancers. In the prevalent cohort mean age was 75.1 years, mean follow-up time was 38.0 months, 20.2% filled a prescription for pioglitazone during follow-up, 10.4% received rosiglitazone, 31.6% received an ARB and 8.0% received combined therapy with pioglitazone + ARB. We found a positive association between bladder cancer and duration of pioglitazone use in the prevalent cohort (P for trend = 0.008), with ≥24 months of pioglitazone exposure corresponding to a 16% (95% confidence interval 0–35%) increase in the incidence of bladder cancer compared to no use. There was a positive association between bladder cancer and rosiglitazone use for \u3c24 months in the prevalent cohort, but no association with ARB use. There were no significant associations in the incident cohort. Conclusions: We found that the incidence of bladder cancer increased with duration of pioglitazone use in a prevalent cohort of diabetics aged 65+ years residing in the USA, but not an incident cohort

Design of an epidemiologic study of drinking water arsenic exposure and skin and bladder cancer risk in a U.S. population

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155818/1/Karagas_et_al_1998_Design_of_an_epidemiologic.pd

Exposure to a Mixture of Metals and Growth Indicators in 6–11‑Year‑Old Children from the 2013–2016 NHANES

Lead (Pb), mercury (Hg), and fuoride (F) exposure during childhood is of concern owing to their toxicity. Also, evidence suggests that high and low exposure levels to manganese (Mn) and selenium (Se) during this vulnerable period are associated with an increased risk of adverse health efects. A reduced growth is associated with high Pb and F exposure; however, little is known about their impact on children’s body size, and there is a lack of consensus on the efects of Hg, Mn, and Se exposure on children’s anthropometric measures. This is particularly true for childhood metal co-exposures at levels relevant to the general population. We investigated the joint efects of exposure to a metal mixture (Pb, Mn, Hg, and Se in blood and F in plasma) on 6–11-year-old US children’s anthropometry (n=1634). Median F, Pb, Mn, Hg, and Se concentrations were 0.3 µmol/L, 0.5 µg/dL, 10.2 µg/L, 0.3 µg/L, and 178.0 µg/L, respectively. The joint efects of the fve metals were modeled using Bayesian kernel machine and linear regressions. Pb and Mn showed opposite directions of associations with all outcome measured, where Pb was inversely associated with anthropometry. For body mass index and waist circumference, the efect estimates for Pb and Mn appeared stronger at high and low concentrations of the other metals of the mixture, respectively. Our fndings suggest that metal co-exposures may infuence children’s body mass and linear growth indicators, and that such relations may difer by the exposure levels of the components of the metal mixture

Detecting Gene-Gene Interactions Using a Permutation-Based Random Forest Method

Identifying gene-gene interactions is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Here, we aimed at developing a permutation-based methodology relying on a machine learning method, random forest (RF), to detect gene-gene interactions. Our approach called permuted random forest (pRF) which identified the top interacting single nucleotide polymorphism (SNP) pairs by estimating how much the power of a random forest classification model is influenced by removing pairwise interactions