Dioxin(-like)-Related Biological Effects through Integrated Chemical-wide and Metabolome-wide Analyses.

Dioxin(-like) exposures are linked to adverse health effects, including cancer. However, metabolic alterations induced by these chemicals remain largely unknown. Beyond known dioxin(-like) compounds, we leveraged a chemical-wide approach to assess chlorinated co-exposures and parent compound products [termed dioxin(-like)-related compounds] among 137 occupational workers. Endogenous metabolites were profiled by untargeted metabolomics, namely, reversed-phase chromatography with negative electrospray ionization (C18-negative) and hydrophilic interaction liquid chromatography with positive electrospray ionization (HILIC-positive). We performed a metabolome-wide association study to select dioxin(-like) associated metabolic features using a 20% false discovery rate threshold. Metabolic features were then characterized by pathway enrichment analyses. There are no significant features associated with polychlorinated dibenzo-p-dioxins (PCDDs), a subgroup of known dioxin(-like) compounds. However, 3,110 C18-negative and 2,894 HILIC-positive features were associated with at least one of the PCDD-related compounds. Abundant metabolic changes were also observed for polychlorinated dibenzofuran-related and polychlorinated biphenyl-related compounds. These metabolic features were primarily enriched in pathways of amino acids, lipid and fatty acids, carbohydrates, cofactors, and nucleotides. Our study highlights the potential of chemical-wide analysis for comprehensive exposure assessment beyond targeted chemicals. Coupled with advanced endogenous metabolomics, this approach allows for an in-depth exploration of metabolic alterations induced by environmental chemicals.

Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding.

Through investigating the combined impact of the environmental exposures experienced by an individual throughout their lifetime, exposome research provides opportunities to understand and mitigate negative health outcomes. While current exposome research is driven by epidemiological studies that identify associations between exposures and effects, new frameworks integrating more substantial population-level metadata, including electronic health and administrative records, will shed further light on characterizing environmental exposure risks. Molecular biology offers methods and concepts to study the biological and health impacts of exposomes in experimental and computational systems. Of particular importance is the growing use of omics readouts in epidemiological and clinical studies. This paper calls for the adoption of mechanistic molecular biology approaches in exposome research as an essential step in understanding the genotype and exposure interactions underlying human phenotypes. A series of recommendations are presented to make the necessary and appropriate steps to move from exposure association to causation, with a huge potential to inform precision medicine and population health. This includes establishing hypothesis-driven laboratory testing within the exposome field, supported by appropriate methods to read across from model systems research to human.

Multiomic Signatures of Traffic-Related Air Pollution in London Reveal Potential Short-Term Perturbations in Gut Microbiome-Related Pathways.

This randomized crossover study investigated the metabolic and mRNA alterations associated with exposure to high and low traffic-related air pollution (TRAP) in 50 participants who were either healthy or were diagnosed with chronic pulmonary obstructive disease (COPD) or ischemic heart disease (IHD). For the first time, this study combined transcriptomics and serum metabolomics measured in the same participants over multiple time points (2 h before, and 2 and 24 h after exposure) and over two contrasted exposure regimes to identify potential multiomic modifications linked to TRAP exposure. With a multivariate normal model, we identified 78 metabolic features and 53 mRNA features associated with at least one TRAP exposure. Nitrogen dioxide (NO2) emerged as the dominant pollutant, with 67 unique associated metabolomic features. Pathway analysis and annotation of metabolic features consistently indicated perturbations in the tryptophan metabolism associated with NO2 exposure, particularly in the gut-microbiome-associated indole pathway. Conditional multiomics networks revealed complex and intricate mechanisms associated with TRAP exposure, with some effects persisting 24 h after exposure. Our findings indicate that exposure to TRAP can alter important physiological mechanisms even after a short-term exposure of a 2 h walk. We describe for the first time a potential link between NO2 exposure and perturbation of the microbiome-related pathways.

Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study.

Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) and untargeted metabolomics are increasingly used in exposome studies to study the interactions between nongenetic factors and the blood metabolome. To reliably and efficiently link detected compounds to exposures and health phenotypes in such studies, it is important to understand the variability in metabolome measures. We assessed the within- and between-subject variability of untargeted LC-HRMS measurements in 298 nonfasting human serum samples collected on two occasions from 157 subjects. Samples were collected ca. 107 (IQR: 34) days apart as part of the multicenter EXPOsOMICS Personal Exposure Monitoring study. In total, 4294 metabolic features were detected, and 184 unique compounds could be identified with high confidence. The median intraclass correlation coefficient (ICC) across all metabolic features was 0.51 (IQR: 0.29) and 0.64 (IQR: 0.25) for the 184 uniquely identified compounds. For this group, the median ICC marginally changed (0.63) when we included common confounders (age, sex, and body mass index) in the regression model. When grouping compounds by compound class, the ICC was largest among glycerophospholipids (median ICC 0.70) and steroids (0.67), and lowest for amino acids (0.61) and the O-acylcarnitine class (0.44). ICCs varied substantially within chemical classes. Our results suggest that the metabolome as measured with untargeted LC-HRMS is fairly stable (ICC > 0.5) over 100 days for more than half of the features monitored in our study, to reflect average levels across this time period. Variance across the metabolome will result in differential measurement error across the metabolome, which needs to be considered in the interpretation of metabolome results.

Impact of PFAS exposure on prevalence of immune-mediated diseases in adults in the Czech Republic.

BACKGROUND: Per- and polyfluoroalkyl substances (PFASs) are emerging environmental contaminants with multiple hazardous properties including immunomodulation potency. Human exposure to PFASs has been associated with various immune-mediated diseases and outcomes. This study aimed to investigate the association between PFAS exposure and immune-mediated diseases such as allergies, eczemas, and autoimmune diseases in a population of adults in the Czech Republic. METHODS: This study included 309 adults from the Central European Longitudinal Study of Parents and Children: Young Adults (CELSPAC: YA). 12 PFASs were measured in participants' serum by HPLC-MS/MS, 3 PFASs were removed from the subsequent analyses due to low detection frequency. The associations of 9 PFASs with 9 immune-mediated diseases were assessed by logistic regression. Furthermore, Bayesian kernel machine regression (BKMR) was used to estimate the effect of the PFAS mixture on immune-mediated diseases. All analyses were adjusted for sex, age, BMI, smoking, education, and family history of immune-mediated diseases. In cases of a statistically significant interaction of PFASs and sex, stratified analyses were performed for men and women. RESULTS: Perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS) were negatively associated with both atopic eczema (OR per IQR increase 0.58 (95% CI 0.37-0.90) for PFOA and 0.56 (0.32-0.95) for PFOS) and contact dermatitis (0.37 (0.16-0.85) for PFOA and 0.33 (0.11-0.94) for PFOS). Perfluoroundecanoate (PFUnDA) was negatively associated with pollen, dust, and mite allergy (0.62 (0.43-0.89)). BKMR modelling showed a negative tendency in the overall effect of PFAS mixture on immune-health outcomes. Based on the stratified analysis, sex was suggested to be an effect modifier in the association of PFOS and atopic eczema. CONCLUSION: Our results contribute to the body of literature that observes the immunosuppressive effect of PFAS exposure during eczemas and allergies, both for PFASs individually and as a mixture.

Effects of pesticide exposure on oxidative stress and DNA methylation urinary biomarkers in Czech adults and children from the CELSPAC-SPECIMEn cohort.

Current-use pesticide (CUP) exposure occurs mainly through diet and environmental application in both agricultural and urban settings. While pesticide exposure has been associated with many adverse health outcomes, the intermediary molecular mechanisms are still not completely elucidated. Among others, their roles in epigenetics (DNA methylation) and DNA damage due to oxidative stress are presumed. Scientific evidence on urinary biomarkers of such body response in general population is limited, especially in children. A total of 440 urine samples (n = 110 parent-child pairs) were collected during the winter and summer seasons in order to describe levels of overall DNA methylation (5-mC, 5-mdC, 5-hmdC, 7-mG, 3-mA) and oxidative stress (8-OHdG) biomarkers and investigate their possible associations with metabolites of pyrethroids (3-PBA, t/c-DCCA), chlorpyrifos (TCPY), and tebuconazole (TEB-OH). Linear mixed-effects models accounting for intraindividual and intrahousehold correlations were utilized. We applied false discovery rate procedure to account for multiplicity and adjusted for potential confounding variables. Higher urinary levels of most biological response biomarkers were measured in winter samples. In adjusted repeated measures models, interquartile range (IQR) increases in pyrethroid metabolites were associated with higher oxidative stress. t/c-DCCA and TCPY were associated with higher urinary levels of cytosine methylation biomarkers (5-mC and/or 5-mdC). The most robust association was observed for tebuconazole metabolite with 3-mA (-15.1% change per IQR increase, 95% CI = -23.6, -5.69) suggesting a role of this pesticide in reduced demethylation processes through possible DNA glycosylase inhibition. Our results indicate an urgent need to extend the range of analyzed environmental chemicals such as azole pesticides (e.g. prothioconazole) in human biomonitoring studies. This is the first study to report urinary DNA methylation biomarkers in children and associations between CUP metabolites and a comprehensive set of biomarkers including methylated and oxidized DNA alterations. Observed associations warrant further large-scale research of these biomarkers and environmental pollutants including CUPs.

Environmental risk factors of type 2 diabetes-an exposome approach.

Type 2 diabetes is one of the major chronic diseases accounting for a substantial proportion of disease burden in Western countries. The majority of the burden of type 2 diabetes is attributed to environmental risks and modifiable risk factors such as lifestyle. The environment we live in, and changes to it, can thus contribute substantially to the prevention of type 2 diabetes at a population level. The 'exposome' represents the (measurable) totality of environmental, i.e. nongenetic, drivers of health and disease. The external exposome comprises aspects of the built environment, the social environment, the physico-chemical environment and the lifestyle/food environment. The internal exposome comprises measurements at the epigenetic, transcript, proteome, microbiome or metabolome level to study either the exposures directly, the imprints these exposures leave in the biological system, the potential of the body to combat environmental insults and/or the biology itself. In this review, we describe the evidence for environmental risk factors of type 2 diabetes, focusing on both the general external exposome and imprints of this on the internal exposome. Studies provided established associations of air pollution, residential noise and area-level socioeconomic deprivation with an increased risk of type 2 diabetes, while neighbourhood walkability and green space are consistently associated with a reduced risk of type 2 diabetes. There is little or inconsistent evidence on the contribution of the food environment, other aspects of the social environment and outdoor temperature. These environmental factors are thought to affect type 2 diabetes risk mainly through mechanisms incorporating lifestyle factors such as physical activity or diet, the microbiome, inflammation or chronic stress. To further assess causality of these associations, future studies should focus on investigating the longitudinal effects of our environment (and changes to it) in relation to type 2 diabetes risk and whether these associations are explained by these proposed mechanisms.

High-Performance Data Processing Workflow Incorporating Effect-Directed Analysis for Feature Prioritization in Suspect and Nontarget Screening.

Effect-directed analysis (EDA) aims at the detection of bioactive chemicals of emerging concern (CECs) by combining toxicity testing and high-resolution mass spectrometry (HRMS). However, consolidation of toxicological and chemical analysis techniques to identify bioactive CECs remains challenging and laborious. In this study, we incorporate state-of-the-art identification approaches in EDA and propose a robust workflow for the high-throughput screening of CECs in environmental and human samples. Three different sample types were extracted and chemically analyzed using a single high-performance liquid chromatography HRMS method. Chemical features were annotated by suspect screening with several reference databases. Annotation quality was assessed using an automated scoring system. In parallel, the extracts were fractionated into 80 micro-fractions each covering a couple of seconds from the chromatogram run and tested for bioactivity in two bioassays. The EDA workflow prioritized and identified chemical features related to bioactive fractions with varying levels of confidence. Confidence levels were improved with the in silico software tools MetFrag and the retention time indices platform. The toxicological and chemical data quality was comparable between the use of single and multiple technical replicates. The proposed workflow incorporating EDA for feature prioritization in suspect and nontarget screening paves the way for the routine identification of CECs in a high-throughput manner.

Short-term personal and outdoor exposure to ultrafine and fine particulate air pollution in association with blood pressure and lung function in healthy adults.

Studies reporting on associations between short-term exposure to outdoor fine (PM2.5), and ultrafine particles (UFP) and blood pressure and lung function have been inconsistent. Few studies have characterized exposure by personal monitoring, which especially for UFP may have resulted in substantial exposure measurement error. We investigated the association between 24-h average personal UFP, PM2.5, and soot exposure and dose and the health parameters blood pressure and lung function. We further assessed the short-term associations between outdoor concentrations measured at a central monitoring site and near the residences and these health outcomes. We performed three 24-h personal exposure measurements for UFP, PM2.5, and soot in 132 healthy adults from Basel (Switzerland), Amsterdam and Utrecht (the Netherlands), and Turin (Italy). Monitoring of each subject was conducted in different seasons in a one-year study period. Subject's activity levels and associated ventilation rates were measured using actigraphy to calculate the inhaled dose. After each 24-h monitoring session, blood pressure and lung function were measured. Contemporaneously with personal measurements, UFP, PM2.5 and soot were measured outdoor at the subject's residential address and at a central site in the research area. Associations between short-term personal and outdoor exposure and dose to UFP, PM2.5, and soot and health outcomes were tested using linear mixed effect models. The 24-h mean personal, residential and central site outdoor UFP exposures were not associated with blood pressure or lung function. UFP mean exposures in the 2-h prior to the health test was also not associated with blood pressure and lung function. Personal, central site and residential PM2.5 exposure were positively associated with systolic blood pressure (about 1.4 mmHg increase per Interquartile range). Personal soot exposure and dose were positively associated with diastolic blood pressure (1.2 and 0.9 mmHg increase per Interquartile range). No consistent associations between PM2.5 or soot exposure and lung function were observed. Short-term personal, residential outdoor or central site exposure to UFP was not associated with blood pressure or lung function. Short-term personal PM2.5 and soot exposures were associated with blood pressure, but not lung function.

Effect modification of the association between total cigarette smoking and ALS risk by intensity, duration and time-since-quitting: Euro-MOTOR.

BACKGROUND: We investigated the association between cigarette smoking and risk of amyotrophic lateral sclerosis (ALS) in a pooled analysis of population-based case-control studies and explored the independent effects of intensity, duration and time-since-quitting. METHODS: ALS cases and controls, matched by age, sex and region, were recruited in the Netherlands, Italy and Ireland (*Euro-MOTOR project). Demographics and detailed lifetime smoking histories were collected through questionnaires. Effects of smoking status, intensity (cigarettes/day), duration (years), pack-years and time-since-quitting (years) on ALS risk were estimated using logistic regression models, adjusting for age, sex, alcohol, education and centre. We further investigated effect modification of the linear effects of pack-years by intensity, duration and time-since-quitting using excess OR (eOR) models. RESULTS: Analyses were performed on 1410 cases and 2616 controls. Pack-years were positively associated with ALS risk; OR=1.26 (95% CI: 1.03 to 1.54) for the highest quartile compared with never smokers. This association appeared to be predominantly driven by smoking duration (ptrend=0.001) rather than intensity (ptrend=0.86), although the trend for duration disappeared after adjustment for time-since-quitting. Time-since-quitting was inversely related to ALS (ptrend<0.0001). The eOR decreased with time-since-quitting smoking, until about 10 years prior to disease onset. High intensity smoking with shorter duration appeared more deleterious than lower intensity for a longer duration. CONCLUSIONS: Our findings provide further support for the association between smoking and ALS. Pack-years alone may be insufficient to capture effects of different smoking patterns. Time-since-quitting appeared to be an important factor, suggesting that smoking may be an early disease trigger.

Modeling the Complex Exposure History of Smoking in Predicting Bladder Cancer: A Pooled Analysis of 15 Case-Control Studies.

BACKGROUND: Few studies have modeled smoking histories by combining smoking intensity and duration to show what profile of smoking behavior is associated with highest risk of bladder cancer. This study aims to provide insight into the association between smoking exposure history and bladder cancer risk by modeling both smoking intensity and duration in a pooled analysis. METHODS: We used data from 15 case-control studies included in the bladder cancer epidemiology and nutritional determinants study, including a total of 6,874 cases and 17,727 controls. To jointly interpret the effects of intensity and duration of smoking, we modeled excess odds ratios per pack-year by intensity continuously to estimate the risk difference between smokers with long duration/low intensity and short duration/high intensity. RESULTS: The pattern observed from the pooled excess odds ratios model indicated that for a fixed number of pack-years, smoking for a longer duration at lower intensity was more deleterious for bladder cancer risk than smoking more cigarettes/day for a shorter duration. We observed similar patterns within individual study samples. CONCLUSIONS: This pooled analysis shows that long duration/low intensity smoking is associated with a greater increase in bladder cancer risk than short duration/high intensity smoking within equal pack-year categories, thus confirming studies in other smoking-related cancers and demonstrating that reducing exposure history to a single metric such as pack-years was too restrictive.

Night shift work and abnormal liver function: is non-alcohol fatty liver a necessary mediator?

OBJECTIVES: Accumulated evidence implies that night shift work may trigger liver dysfunction. Non-alcoholic fatty liver (NAFL) is suggested to be a necessary mediator in this process. This study aimed to examine the relationship between night shift work and elevated level of alanine transaminase (e-ALT) of workers and investigate the potential mediation effect of NAFL. METHODS: This study included all male workers from the baseline survey of a cohort of night shift workers. Information on demographics, lifestyle and lifetime working schedule was collected by face-to-face interview. Liver sonography was used to identify NAFL cases. Serum ALT level was detected by an automatic biochemical analyser. e-ALT was defined as ALT >40 U/L. Logistic regression models were used to evaluate ORs, and mediation analysis was employed to examine the mediation effect. RESULTS: Among 4740 male workers, 39.5% were night shift workers. Night shift workers had an increased risk of e-ALT (OR, 1.19, 95% CI 1.00 to 1.42). With the increase in night shift years, the OR of e-ALT increased from 1.03 (95% CI 0.77 to 1.36) to 1.60 (95% CI 1.08 to 2.39) among workers without NAFL. A similar trend was not found among workers with NAFL. In addition, no significant mediation effect of NAFL in the association between night shift work and e-ALT was found. CONCLUSIONS: Night shift work is positively associated with abnormal liver function, in particular among workers without NAFL. Shift work involving circadian disruption is likely to exert a direct effect on liver dysfunction rather than rely on the mediation effect of NAFL.

Cardiovascular effects among workers exposed to multiwalled carbon nanotubes.

OBJECTIVES: The increase in production of multiwalled carbon nanotubes (MWCNTs) has led to growing concerns about health risks. In this study, we assessed the association between occupational exposure to MWCNTs and cardiovascular biomarkers. METHODS: A cross-sectional study was performed among 22 workers of a company commercially producing MWCNTs (subdivided into lab personnel with low or high exposure and operators), and a gender and age-matched unexposed population (n=42). Exposure to MWCNTs and 12 cardiovascular markers were measured in participants' blood (phase I). In a subpopulation of 13 exposed workers and six unexposed workers, these measures were repeated after 5 months (phase II). We analysed associations between MWCNT exposure and biomarkers of cardiovascular risk, adjusted for age, body mass index, sex and smoking. RESULTS: We observed an upward trend in the concentration of endothelial damage marker intercellular adhesion molecule-1 (ICAM-1), with increasing exposure to MWCNTs in both phases. The operator category showed significantly elevated ICAM-1 geometric mean ratios (GMRs) compared with the controls (phase I: GMR=1.40, P=1.30E-3; phase II: GMR=1.37, P=0.03). The trends were significant both across worker categories (phase I: P=1.50E-3; phase II: P=0.01) and across measured GM MWCNT concentrations (phase I: P=3.00E-3; phase II: P=0.01). No consistent significant associations were found for the other cardiovascular markers. CONCLUSION: The associations between MWCNT exposure and ICAM-1 indicate endothelial activation and an increased inflammatory state in workers with MWCNT exposure.

EXPOsOMICS: final policy workshop and stakeholder consultation.

The final meeting of the EXPOsOMICS project "Final Policy Workshop and Stakeholder Consultation" took place 28-29 March 2017 to present the main results of the project and discuss their implications both for future research and for regulatory and policy activities. This paper summarizes presentations and discussions at the meeting related with the main results and advances in exposome research achieved through the EXPOsOMICS project; on other parallel research initiatives on the study of the exposome in Europe and in the United States and their complementarity to EXPOsOMICS; lessons learned from these early studies on the exposome and how they may shape the future of research on environmental exposure assessment; and finally the broader implications of exposome research for risk assessment and policy development on environmental exposures. The main results of EXPOsOMICS in relation to studies of the external exposome and internal exposome in relation to both air pollution and water contaminants were presented as well as new technologies for environmental health research (adductomics) and advances in statistical methods. Although exposome research strengthens the scientific basis for policy development, there is a need in terms of showing added value for public health to: improve communication of research results to non-scientific audiences; target research to the broader landscape of societal challenges; and draw applicable conclusions. Priorities for future work include the development and standardization of methodologies and technologies for assessing the external and internal exposome, improved data sharing and integration, and the demonstration of the added value of exposome science over conventional approaches in answering priority policy questions.

Exploring the nature of prediagnostic blood transcriptome markers of chronic lymphocytic leukemia by assessing their overlap with the transcriptome at the clinical stage.

BACKGROUND: We recently identified 700 genes whose expression levels were predictive of chronic lymphocytic leukemia (CLL) in a genome-wide gene expression analysis of prediagnostic blood from future cases and matched controls. We hypothesized that a large fraction of these markers were likely related to early disease manifestations. Here we aim to gain a better understanding of the natural history of the identified markers by comparing results from our prediagnostic analysis, the only prediagnostic analysis to date, to results obtained from a meta-analysis of a series of publically available transcriptomics profiles obtained in incident CLL cases and controls. RESULTS: We observed considerable overlap between the results from our prediagnostic study and the clinical CLL signals (p-value for overlap Bonferroni significant markers 0.01; p-value for overlap nominal significant markers < 2.20e-16). We observed similar patterns with time to diagnosis and similar functional annotations for the markers that were identified in both settings compared to the markers that were only identified in the prediagnostic study. These results suggest that both gene sets operate in similar pathways. CONCLUSION: An overlap exists between expression levels of genes predictive of CLL identified in prediagnostic blood and expression levels of genes associated to CLL at the clinical stage. Our analysis provides insight in a set of genes for which expression levels can be used to follow the time-course of the disease; providing an opportunity to study CLL progression in more detail in future studies.

Associations Between Genome-wide Gene Expression and Ambient Nitrogen Oxides.

BACKGROUND: We hypothesize that biological perturbations due to exposure to ambient air pollution are reflected in gene expression levels in peripheral blood mononuclear cells. METHODS: We assessed the association between exposure to ambient air pollution and genome-wide gene expression levels in peripheral blood mononuclear cells collected from 550 healthy subjects participating in cohorts from Italy and Sweden. Annual air pollution estimates of nitrogen oxides (NOx) at time of blood collection (1990-2006) were available from the ESCAPE study. In addition to univariate analysis and two variable selection methods to investigate the association between expression and exposure to NOx, we applied gene set enrichment analysis to assess overlap between our most perturbed genes and gene sets hypothesized to be related to air pollution and cigarette smoking. Finally, we assessed associations between NOx and CpG island methylation at the identified genes. RESULTS: Annual average NOx exposure in the Italian and Swedish cohorts was 94.2 and 6.7 µg/m, respectively. Long-term exposure to NOx was associated with seven probes in the Italian cohort and one probe in the Swedish (and combined) cohorts. For genes AHCYL2 and MTMR2, changes were also seen in the methylome. Genes hypothesized to be downregulated due to cigarette smoking were enriched among the most strongly downregulated genes from our study. CONCLUSION: This study provides evidence of subtle changes in gene expression related to exposure to long-term NOx. On a global level, the observed changes in the transcriptome may indicate similarities between air pollution and tobacco induced changes in the transcriptome.

Exposure-Response Analyses of Asbestos and Lung Cancer Subtypes in a Pooled Analysis of Case-Control Studies.

BACKGROUND: Evidence is limited regarding risk and the shape of the exposure-response curve at low asbestos exposure levels. We estimated the exposure-response for occupational asbestos exposure and assessed the joint effect of asbestos exposure and smoking by sex and lung cancer subtype in general population studies. METHODS: We pooled 14 case-control studies conducted in 1985-2010 in Europe and Canada, including 17,705 lung cancer cases and 21,813 controls with detailed information on tobacco habits and lifetime occupations. We developed a quantitative job-exposure-matrix to estimate job-, time period-, and region-specific exposure levels. Fiber-years (ff/ml-years) were calculated for each subject by linking the matrix with individual occupational histories. We fit unconditional logistic regression models to estimate odds ratios (ORs), 95% confidence intervals (CIs), and trends. RESULTS: The fully adjusted OR for ever-exposure to asbestos was 1.24 (95% CI, 1.18, 1.31) in men and 1.12 (95% CI, 0.95, 1.31) in women. In men, increasing lung cancer risk was observed with increasing exposure in all smoking categories and for all three major lung cancer subtypes. In women, lung cancer risk for all subtypes was increased in current smokers (ORs ~two-fold). The joint effect of asbestos exposure and smoking did not deviate from multiplicativity among men, and was more than additive among women. CONCLUSIONS: Our results in men showed an excess risk of lung cancer and its subtypes at low cumulative exposure levels, with a steeper exposure-response slope in this exposure range than at higher, previously studied levels. (See video abstract at, http://links.lww.com/EDE/B161.).

Land Use Regression Models for Ultrafine Particles in Six European Areas.

Long-term ultrafine particle (UFP) exposure estimates at a fine spatial scale are needed for epidemiological studies. Land use regression (LUR) models were developed and evaluated for six European areas based on repeated 30 min monitoring following standardized protocols. In each area; Basel (Switzerland), Heraklion (Greece), Amsterdam, Maastricht, and Utrecht ("The Netherlands"), Norwich (United Kingdom), Sabadell (Spain), and Turin (Italy), 160-240 sites were monitored to develop LUR models by supervised stepwise selection of GIS predictors. For each area and all areas combined, 10 models were developed in stratified random selections of 90% of sites. UFP prediction robustness was evaluated with the intraclass correlation coefficient (ICC) at 31-50 external sites per area. Models from Basel and The Netherlands were validated against repeated 24 h outdoor measurements. Structure and model R2 of local models were similar within, but varied between areas (e.g., 38-43% Turin; 25-31% Sabadell). Robustness of predictions within areas was high (ICC 0.73-0.98). External validation R2 was 53% in Basel and 50% in The Netherlands. Combined area models were robust (ICC 0.93-1.00) and explained UFP variation almost equally well as local models. In conclusion, robust UFP LUR models could be developed on short-term monitoring, explaining around 50% of spatial variance in longer-term measurements.

Robustness of intra urban land-use regression models for ultrafine particles and black carbon based on mobile monitoring.

Land-use regression (LUR) models for ultrafine particles (UFP) and Black Carbon (BC) in urban areas have been developed using short-term stationary monitoring or mobile platforms in order to capture the high variability of these pollutants. However, little is known about the comparability of predictions of mobile and short-term stationary models and especially the validity of these models for assessing residential exposures and the robustness of model predictions developed in different campaigns. We used an electric car to collect mobile measurements (n = 5236 unique road segments) and short-term stationary measurements (3 × 30min, n = 240) of UFP and BC in three Dutch cities (Amsterdam, Utrecht, Maastricht) in 2014-2015. Predictions of LUR models based on mobile measurements were compared to (i) measured concentrations at the short-term stationary sites, (ii) LUR model predictions based on short-term stationary measurements at 1500 random addresses in the three cities, (iii) externally obtained home outdoor measurements (3 × 24h samples; n = 42) and (iv) predictions of a LUR model developed based upon a 2013 mobile campaign in two cities (Amsterdam, Rotterdam). Despite the poor model R2 of 15%, the ability of mobile UFP models to predict measurements with longer averaging time increased substantially from 36% for short-term stationary measurements to 57% for home outdoor measurements. In contrast, the mobile BC model only predicted 14% of the variation in the short-term stationary sites and also 14% of the home outdoor sites. Models based upon mobile and short-term stationary monitoring provided fairly high correlated predictions of UFP concentrations at 1500 randomly selected addresses in the three Dutch cities (R2 = 0.64). We found higher UFP predictions (of about 30%) based on mobile models opposed to short-term model predictions and home outdoor measurements with no clear geospatial patterns. The mobile model for UFP was stable over different settings as the model predicted concentration levels highly correlated to predictions made by a previously developed LUR model with another spatial extent and in a different year at the 1500 random addresses (R2 = 0.80). In conclusion, mobile monitoring provided robust LUR models for UFP, valid to use in epidemiological studies.

A systematic comparison of statistical methods to detect interactions in exposome-health associations.

BACKGROUND: There is growing interest in examining the simultaneous effects of multiple exposures and, more generally, the effects of mixtures of exposures, as part of the exposome concept (being defined as the totality of human environmental exposures from conception onwards). Uncovering such combined effects is challenging owing to the large number of exposures, several of them being highly correlated. We performed a simulation study in an exposome context to compare the performance of several statistical methods that have been proposed to detect statistical interactions. METHODS: Simulations were based on an exposome including 237 exposures with a realistic correlation structure. We considered several statistical regression-based methods, including two-step Environment-Wide Association Study (EWAS2), the Deletion/Substitution/Addition (DSA) algorithm, the Least Absolute Shrinkage and Selection Operator (LASSO), Group-Lasso INTERaction-NET (GLINTERNET), a three-step method based on regression trees and finally Boosted Regression Trees (BRT). We assessed the performance of each method in terms of model size, predictive ability, sensitivity and false discovery rate. RESULTS: GLINTERNET and DSA had better overall performance than the other methods, with GLINTERNET having better properties in terms of selecting the true predictors (sensitivity) and of predictive ability, while DSA had a lower number of false positives. In terms of ability to capture interaction terms, GLINTERNET and DSA had again the best performances, with the same trade-off between sensitivity and false discovery proportion. When GLINTERNET and DSA failed to select an exposure truly associated with the outcome, they tended to select a highly correlated one. When interactions were not present in the data, using variable selection methods that allowed for interactions had only slight costs in performance compared to methods that only searched for main effects. CONCLUSIONS: GLINTERNET and DSA provided better performance in detecting two-way interactions, compared to other existing methods.