Arsenic exposure and the seroprevalence of total hepatitis A antibodies in the US population: NHANES, 2003-2012.

We evaluated the association between urinary arsenic and the seroprevalence of total hepatitis A antibodies (total anti-HAV: IgG and IgM) in 11 092 participants aged ⩾6 years using information collected in the US National Health and Nutrition Examination Survey (2003-2012). Multivariate logistic regression models evaluated associations between total anti-HAV and total urinary arsenic defined as the sum of arsenite, arsenate, monomethylarsonate and dimethylarsinate (TUA1). Effect modification by self-reported HAV immunization status was evaluated. Total anti-HAV seroprevalence was 35·1% [95% confidence interval (CI) 33·3-36·9]. Seropositive status was associated with higher arsenic levels and this association was modified by immunization status (P = 0·03). For participants that received ⩾2 vaccine doses or did not know if they had received any doses, a positive dose-response association was observed between increasing TUA1 and odds of total anti-HAV [odds ratio (OR) 1·42, 95% CI 1·11-1·81; and OR 1·75, 95% CI 1·22-2·52], respectively. A positive but not statistically significant association was observed in those who received <2 doses (OR 1·46, 95% CI 0·83-2·59) or no dose (OR 1·12, 95% CI 0·98-1·30). Our analysis indicates that prevalent arsenic exposure was associated with positive total anti-HAV seroprevalence. Further studies are needed to determine if arsenic increases the risk for incident hepatitis A infection or HAV seroconversion.

Review of processing and analysis methods for DNA methylation array data.

The promise of epigenome-wide association studies and cancer-specific somatic DNA methylation changes in improving our understanding of cancer, coupled with the decreasing cost and increasing coverage of DNA methylation microarrays, has brought about a surge in the use of these technologies. Here, we aim to provide both a review of issues encountered in the processing and analysis of array-based DNA methylation data and a summary of the advantages of recent approaches proposed for handling those issues, focusing on approaches publicly available in open-source environments such as R and Bioconductor. We hope that the processing tools and analysis flowchart described herein will facilitate researchers to effectively use these powerful DNA methylation array-based platforms, thereby advancing our understanding of human health and disease.

Source apportionment of indoor residential fine particulate matter using land use regression and constrained factor analysis.

UNLABELLED: Source contributions to urban fine particulate matter (PM(2.5) ) have been modelled using land use regression (LUR) and factor analysis (FA). However, people spend more time indoors, where these methods are less explored. We collected 3-4- day samples of nitrogen dioxide and PM(2.5) inside and outside of 43 homes in summer and winter, 2003-2005, in and around Boston, Massachusetts. Particle filters were analysed for black carbon and trace element concentrations using reflectometry, X-ray fluorescence (XRF), and high-resolution inductively coupled mass spectrometry (ICP-MS). We regressed indoor against outdoor concentrations modified by ventilation, isolating the indoor-attributable fraction, and then applied constrained FA to identify source factors in indoor concentrations and residuals. Finally, we developed LUR predictive models using GIS-based outdoor source indicators and questionnaire data on indoor sources. FA using concentrations and residuals reasonably separated outdoor (long-range transport/meteorology, fuel oil/diesel, road dust) from indoor sources (combustion, smoking, cleaning). Multivariate LUR regression models for factors from concentrations and indoor residuals showed limited predictive power, but corroborated some indoor and outdoor factor interpretations. Our approach to validating source interpretations using LUR methods provides direction for studies characterizing indoor and outdoor source contributions to indoor cocentrations. PRACTICAL IMPLICATIONS: By merging indoor-outdoor modeling, factor analysis, and LUR-style predictive regression modeling, we have added to previous source apportionment studies by attempting to corroborate factor interpretations. Our methods and results support the possibility that indoor exposures may be modeled for epidemiologic studies, provided adequate sample size and variability to identify indoor and outdoor source contributions. Using these techniques, epidemiologic studies can more clearly examine exposures to indoor sources and indoor penetration of source-specific components, reduce exposure misclassification, and improve the characterization of the relationship between particle constituents and health effects.

DNA methylation profiles delineate etiologic heterogeneity and clinically important subgroups of bladder cancer.

DNA methylation profiles can be used to define molecular cancer subtypes that may better inform disease etiology and clinical decision-making. This investigation aimed to create DNA methylation profiles of bladder cancer based on CpG methylation from almost 800 cancer-related genes and to then examine the relationship of those profiles with exposures related to risk and clinical characteristics. DNA, derived from formalin-fixed paraffin-embedded tumor samples obtained from incident cases involved in a population-based case-control study of bladder cancer in New Hampshire, was used for methylation profiling on the Illumina GoldenGate Methylation Bead Array. Unsupervised clustering of those loci with the greatest change in methylation between tumor and non-diseased tissue was performed to defined molecular subgroups of disease, and univariate tests of association followed by multinomial logistic regression was used to examine the association between these classes, bladder cancer risk factors and clinical phenotypes. Membership in the two most methylated classes was significantly associated with invasive disease (P < 0.001 for both class 3 and 4). Male gender (P = 0.04) and age >70 years (P = 0.05) was associated with membership in one of the most methylated classes. Finally, average water arsenic levels in the highest percentile predicted membership in an intermediately methylated class of tumors (P = 0.02 for both classes). Exposures and demographic associated with increased risk of bladder cancer specifically associate with particular subgroups of tumors defined by DNA methylation profiling and these subgroups may define more aggressive disease.

Residual-based diagnostics for structural equation models.

Classical diagnostics for structural equation models are based on aggregate forms of the data and are ill suited for checking distributional or linearity assumptions. We extend recently developed goodness-of-fit tests for correlated data based on subject-specific residuals to structural equation models with latent variables. The proposed tests lend themselves to graphical displays and are designed to detect misspecified distributional or linearity assumptions. To complement graphical displays, test statistics are defined; the null distributions of the test statistics are approximated using computationally efficient simulation techniques. The properties of the proposed tests are examined via simulation studies. We illustrate the methods using data from a study of in utero lead exposure.

Airborne influenza virus detection with four aerosol samplers using molecular and infectivity assays: considerations for a new infectious virus aerosol sampler.

As a first step in conducting studies of airborne influenza transmission, we compared the collection performance of an SKC Biosampler, a compact cascade impactor (CCI), Teflon filters, and gelatin filters by collecting aerosolized influenza virus in a one-pass aerosol chamber. Influenza virus infectivity was determined using a fluorescent focus assay and influenza virus nucleic acid (originating from viable and non-viable viruses) was measured using quantitative PCR. The results showed that the SKC Biosampler recovered and preserved influenza virus infectivity much better than the other samplers - the CCI, Teflon, and gelatin filters recovered only 7-22% of infectious viruses compared with the Biosampler. Total virus collection was not significantly different among the SKC Biosampler, the gelatin, and Teflon filters, but was significantly lower in the CCI. Results from this study show that a new sampler is needed for virus aerosol sampling, as commercially available samplers do not efficiently collect and conserve virus infectivity. Applications for a new sampler include studies of airborne disease transmission and bioterrorism monitoring. Design parameters for a new sampler include high collection efficiency for fine particles and liquid sampling media to preserve infectivity. Practical Implications New air samplers are needed to study infectious airborne viruses and learn about airborne disease transmission. As a first step in designing a new air sampler to collect influenza virus we evaluated four commercial samplers and determined necessary design parameters for a new collector.

Asbestos exposure predicts cell cycle control gene promoter methylation in pleural mesothelioma.

Malignant pleural mesothelioma (MPM) is a rapidly fatal tumor with increasing incidence worldwide responsible for many thousands of deaths annually. Although there is a clear link between exposure to asbestos and mesothelioma, and asbestos is known to be both clastogenic and cytotoxic to mesothelial cells, the mechanisms of causation of MPM remain largely unknown. However, there is a rapidly emerging literature that describes inactivation of a diverse array of tumor suppressor genes (TSGs) via promoter DNA CpG methylation in MPM, although the etiology of these alterations remains unclear. We studied the relationships among promoter methylation silencing, asbestos exposure, patient demographics and tumor histology using a directed approach; examining six cell cycle control pathway TSGs in an incident case series of 70 MPMs. Promoter hypermethylation of APC, CCND2, CDKN2A, CDKN2B, HPPBP1 and RASSF1 were assessed. We observed significantly higher lung asbestos body burden if any of these cell cycle genes were methylated (P < 0.02), and there was a significant trend of increasing asbestos body counts as the number of methylated cell cycle pathway genes increased from 0 to 1 to >1 (P < 0.005). This trend of increasing asbestos body count and increasing number of methylated cell cycle pathway genes remained significant (P < 0.05) after controlling for age, gender and tumor histology. These data suggest a novel tumorigenic mechanism of action of asbestos and may contribute to the understanding of precisely how asbestos exposure influences the etiology and clinical course of malignant mesothelioma.

Fine particulate matter and polycyclic aromatic hydrocarbon concentration patterns in Roxbury, Massachusetts: a community-based GIS analysis.

Given an elevated prevalence of respiratory disease and density of pollution sources, residents of Roxbury, Massachusetts, have been interested in better understanding their exposures to air pollution. To determine whether local transportation sources contribute significantly to exposures, we conducted a community-based pilot investigation to measure concentrations of fine particulate matter (particulate matter < 2.5 microm; PM(2.5)) and particle-bound polycyclic aromatic hydrocarbons (PAHs) in Roxbury in the summer of 1999. Community members carried portable monitors on the streets in a 1-mile radius around a large bus terminal to create a geographic information system (GIS) map of concentrations and gathered data on site characteristics that could predict ambient concentrations. Both PM(2.5) and PAH concentrations were greater during morning rush hours and on weekdays. In linear mixed-effects regressions controlling for temporal autocorrelation, PAH concentrations were significantly higher with closer proximity to the bus terminal (p < 0.05), and both pollutants were elevated, but not statistically significantly so, on bus routes. Regressions on a subset of measurements for which detailed site characteristics were gathered showed higher concentrations of both pollutants on roads reported to have heavy bus traffic. Although a more comprehensive monitoring protocol would be needed to develop robust predictive functions for air pollution, our study demonstrates that pollution patterns in an urban area can be characterized with limited monitoring equipment and that university-community partnerships can yield relevant exposure information.

Particle concentrations in urban microenvironments.

Although ambient particulate matter has been associated with a range of health outcomes, the health risks for individuals depend in part on their daily activities. Information about particle mass concentrations and size distributions in indoor and outdoor microenvironments can help identify high-risk individuals and the significant contributors to personal exposure. To address these issues in an urban setting, we measured particle count concentrations in four size ranges and particulate matter (3/4) 10 microm (PM(10)) concentrations outdoors and in seven indoor microenvironments in Boston, Massachusetts. Particle counts and PM(10) concentrations were continuously measured with two light-scattering devices. Because of the autocorrelation between sequential measurements, we used linear mixed effects models with an AR-1 autoregressive correlation structure to evaluate whether differences between microenvironments were statistically significant. In general, larger particles were elevated in the vicinity of significant human activity, and smaller particles were elevated in the vicinity of combustion sources, with indoor PM(10) concentrations significantly higher than the outdoors on buses and trolleys. Statistical models demonstrated significant variability among some indoor microenvironments, with greater variability for smaller particles. These findings imply that personal exposures can depend on activity patterns and that microenvironmental concentration information can improve the accuracy of personal exposure estimation.

Epigenetic profiles distinguish pleural mesothelioma from normal pleura and predict lung asbestos burden and clinical outcome.

Mechanisms of action of nonmutagenic carcinogens such as asbestos remain poorly characterized. As pleural mesothelioma is known to have limited numbers of genetic mutations, we aimed to characterize the relationships among gene-locus-specific methylation alterations, disease status, asbestos burden, and survival in this rapidly fatal asbestos-associated tumor. Methylation of 1505 CpG loci associated with 803 cancer-related genes were studied in 158 pleural mesotheliomas and 18 normal pleura. After false-discovery rate correction, 969 CpG loci were independently associated with disease status (Q < 0.05). Classifying samples based on CpG methylation profile with a mixture model approach, methylation classes discriminated tumor from normal pleura (permutation P < 0.0001). In a random forests classification, the overall misclassification error rate was 3.4%, with <1% (n = 1) of tumors misclassified as normal (P < 0.0001). Among tumors, methylation class membership was significantly associated with lung tissue asbestos body burden (P < 0.03), and significantly predicted survival (likelihood ratio P < 0.01). Consistent with prior work, asbestos burden was associated with an increased risk of death (hazard ratio, 1.4; 95% confidence interval, 1.1-1.8). Our results have shown that methylation profiles powerfully differentiate diseased pleura from nontumor pleura and that asbestos burden and methylation profiles are independent predictors of mesothelioma patient survival. We have added to the growing body of evidence that cellular epigenetic dysregulation is a critical mode of action for asbestos in the induction of pleural mesothelioma. Importantly, these findings hold great promise for using epigenetic profiling in the diagnosis and prognosis of human cancers.

Susceptibility to arsenic-induced skin lesions from polymorphisms in base excision repair genes.

Genetic polymorphisms in the base excision DNA repair pathway may influence individual susceptibility to arsenic and the development of arsenic-induced skin lesions. Data from a case-control study of 792 cases and 792 matched controls conducted in Bangladesh from 2001 to 2003 were analyzed using conditional logistic regression to assess the associations between four common base excision repair (BER) genetic polymorphisms X-ray repair cross-complementing group 1 (XRCC1) Arg399Gln, XRCC1 Arg194Trp, human 8-oxoguanine DNA glycosylase (hOGG1) Ser326Cys and apurinic/apyrimidinic endonuclease (APE1) Asp148Glu and arsenic-induced skin lesions including melanosis and keratosis. Adjusted for toenail arsenic, body mass index, education, smoking and betel nut use, individuals with the APE1 148Glu/Glu polymorphism had a 2-fold increased odds of skin lesions compared with individuals with the 148Asp/Asp genotype (1.93; 95% confidence interval 1.15, 3.19). Gene-environment interactions between toenail arsenic and XRCC1 Arg194Trp and APE1 Asp148Glu were observed. Within the lowest arsenic tertile, APE1 148Glu/Glu had 2.5 times the odds ratio compared with wild-type, whereas within the highest tertile of arsenic the odds ratios for skin lesions did not differ. In contrast, at low arsenic levels, the odds ratios for skin lesions did not differ much by XRCC1 Arg194Trp genotype. However, at the highest tertile of arsenic, the XRCC1 194Arg/Arg polymorphism conferred a 3-fold larger odds ratio for skin lesions compared with XRCC1 194Trp/Trp. Individuals may have different odds for developing skin lesions based in part on their genetic profile for BER and their arsenic exposure history. Future research on arsenic-induced skin lesions should consider the impact of genetic variation to individual susceptibility to arsenic toxicity.