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 R(2) 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 (R(2) = 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 (R(2) = 0.80). In conclusion, mobile monitoring provided robust LUR models for UFP, valid to use in epidemiological studies

Plastic leachate exposure drives antibiotic resistance and virulence in marine bacterial communities

Plastic pollution is a serious global problem, with more than 12 million tonnes of plastic waste entering the oceans every year. Plastic debris can have considerable impacts on microbial community structure and functions in marine environments, and has been associated with an enrichment in pathogenic bacteria and antimicrobial resistance (AMR) genes. However, our understanding of these impacts is largely restricted to microbial assemblages on plastic surfaces. It is therefore unclear whether these effects are driven by the surface properties of plastics, providing an additional niche for certain microbes residing in biofilms, and/or chemicals leached from plastics, the effects of which could extend to surrounding planktonic bacteria. Here, we examine the effects of polyvinyl chloride (PVC) plastic leachate exposure on the relative abundance of genes associated with bacterial pathogenicity and AMR within a seawater microcosm community. We show that PVC leachate, in the absence of plastic surfaces, drives an enrichment in AMR and virulence genes. In particular, leachate exposure significantly enriches AMR genes that confer multidrug, aminoglycoside and peptide antibiotic resistance. Additionally, enrichment of genes involved in the extracellular secretion of virulence proteins was observed among pathogens of marine organisms. This study provides the first evidence that chemicals leached from plastic particles alone can enrich genes related to microbial pathogenesis within a bacterial community, expanding our knowledge of the environmental impacts of plastic pollution with potential consequences for human and ecosystem health

Impact of long-term exposure to PM2.5 on peripheral blood gene expression pathways involved in cell signaling and immune response

Background: Exposure to ambient air pollution, even at low levels, is a major environmental health risk. The peripheral blood transcriptome provides a potential avenue for the elucidation of ambient air pollution related biological perturbations. We assessed the association between long-term estimates for seven priority air pollutants and perturbations in peripheral blood transcriptomics data collected in the Dutch National Twin Register (NTR) and Netherlands Study of Depression and Anxiety (NESDA) cohorts. Methods: In both the discovery (n = 2438) and replication (n = 1567) cohort, outdoor concentration of 7 air pollutants (NO2, NOx, particulate matter (PM2.5, PM2.5abs, PM10, PMcoarse), and ultrafine particles) was predicted with land use regression models. Gene expression was assessed by Affymetrix U219 arrays. Multi-variable univariate mixed-effect models were applied to test for an association between the air pollutants and the transcriptome. Functional analysis was conducted in DAVID. Results: In the discovery cohort, we observed for 335 genes (374 probes with FDR < 5 %) a perturbation in peripheral blood gene expression that was associated with long-term average levels of PM2.5. For 69 genes pooled effect estimates from the NTR and NESDA cohorts were significant. Identified genes play a role in biological pathways related to cell signaling and immune response. Sixty-two out of 69 genes had a similar direction of effect in an analysis in which we regressed the probes on differential PM2.5 exposure within monozygotic twin pairs, indicating that the observed differences in gene expression were likely driven by differences in air pollution, rather than by confounding by genetic factors. Conclusion: Our results indicate that PM2.5 can elicit a response in cell signaling and the immune system, both hallmarks of environmental diseases. The differential effect that we observed between air pollutants may aid in the understanding of differential health effects that have been observed with these exposures

Urinary pesticide mixture patterns and exposure determinants in the adult population from the Netherlands and Switzerland : Application of a suspect screening approach

INTRODUCTION: Non-occupational sources of pesticide exposure may include domestic pesticide usage, diet, occupational exposure of household members, and agricultural activities in the residential area. We conducted a study with the ambition to characterize pesticide mixture patterns in a sample of the adult population of the Netherlands and Switzerland, using a suspect screening approach and to identify related exposure determinants. METHODS: A total of 105 and 295 adults participated in the Dutch and Swiss studies, respectively. First morning void urine samples were collected and analyzed in the same laboratory. Harmonized questionnaires about personal characteristics, pesticide-related activities, and diet were administered. Detection rates and co-occurrence patterns were calculated to explore internal pesticide exposure patterns. Censored linear and logistic regression models were constructed to investigate the association between exposure and domestic pesticide usage, consumption of homegrown and organic foods, household members' exposure, and distance to agricultural and forest areas. RESULTS: From the 37 detected biomarkers, 3 (acetamiprid (-CH2), chlorpropham (4-HSA), and flonicamid (-C2HN)) were detected in ≥40% of samples. The most frequent combination of biomarkers (acetamiprid-flonicamid) was detected in 22 (5.5%) samples. Regression models revealed an inverse association between high organic vegetable and fruit consumption and exposure to acetamiprid, chlorpropham, propamocarb (+O), and pyrimethanil (+O + SO3). Within-individual correlations in repeated samples (summer/winter) from the Netherlands were low (≤0.3), and no seasonal differences in average exposures were observed in Switzerland. CONCLUSION: High consumption of organic fruit and vegetables was associated with lower pesticide exposure. In the two countries, detection rates and co-occurrence were typically low, and within-person variability was high. Our study results provide an indication for target biomarkers to include in future studies aimed at quantifying urinary exposure levels in European adult populations

A physiologically-based kinetic (PBK) model for work-related diisocyanate exposure: Relevance for the design and reporting of biomonitoring studies

Diisocyanates are highly reactive substances and known causes of occupational asthma. Exposure occurs mainly in the occupational setting and can be assessed through biomonitoring which accounts for inhalation and dermal exposure and potential effects of protective equipment. However the interpretation of biomonitoring data can be challenging for chemicals with complex kinetic behavior and multiple exposure routes, as is the case for diisocyanates. To better understand the relation between external exposure and urinary concentrations of metabolites of diisocyanates, we developed a physiologically based kinetic (PBK) model for methylene bisphenyl isocyanate (MDI) and toluene di-isocyanate (TDI). The PBK model covers both inhalation and dermal exposure, and can be used to estimate biomarker levels after either single or chronic exposures. Key parameters such as absorption and elimination rates of diisocyanates were based on results from human controlled exposure studies. A global sensitivity analysis was performed on model predictions after assigning distributions reflecting a mixture of parameter uncertainty and population variability. Although model-based predictions of urinary concentrations of the degradation products of MDI and TDI for longer-term exposure scenarios compared relatively well to empirical results for a limited set of biomonitoring studies in the peer-reviewed literature, validation of model predictions was difficult because of the many uncertainties regarding the precise exposure scenarios that were used. Sensitivity analyses indicated that parameters with a relatively large impact on model estimates included the fraction of diisocyanates absorbed and the binding rate of diisocyanates to albumin relative to other macro molecules.We additionally investigated the effects of timing of exposure and intermittent urination, and found that both had a considerable impact on estimated urinary biomarker levels. This suggests that these factors should be taken into account when interpreting biomonitoring data and included in the standard reporting of isocyanate biomonitoring studies

Flexible Meta-Regression to Assess the Shape of the Benzene–Leukemia Exposure–Response Curve

Ba c k g r o u n d: Previous evaluations of the shape of the benzene–leukemia exposure–response curve (ERC) were based on a single set or on small sets of human occupational studies. Integrating evidence from all available studies that are of sufficient quality combined with flexible meta-regression models is likely to provide better insight into the functional relation between benzene exposure and risk of leukemia. Objectives: We used natural splines in a flexible meta-regression method to assess the shape of the benzene–leukemia ERC. Met h o d s: We fitted meta-regression models to 30 aggregated risk estimates extracted from nine human observational studies and performed sensitivity analyses to assess the impact of a priori assessed study characteristics on the predicted ERC. Re s u l t s: The natural spline showed a supralinear shape at cumulative exposures less than 100 ppmyears, although this model fitted the data only marginally better than a linear model (p = 0.06). Stratification based on study design and jackknifing indicated that the cohort studies had a considerable impact on the shape of the ERC at high exposure levels (&gt; 100 ppm-years) but that predicted risks for the low exposure range (&lt; 50 ppm-years) were robust. Co n c l u s i o n s: Although limited by the small number of studies and the large heterogeneity between studies, the inclusion of all studies of sufficient quality combined with a flexible meta-regression method provides the most comprehensive evaluation of the benzene–leukemia ERC to date. The natural spline based on all data indicates a significantly increased risk of leukemia [relative risk (RR) = 1.14; 95 % confidence interval (CI), 1.04–1.26] at an exposure level as low as 10 ppm-years. Key w o r d s: benzene, epidemiology, leukemia, meta-regression, quantitative risk assessment. Environ Health Perspect 118:526–532 (2010). doi:10.1289/ehp.0901127 available vi

Evolving DNA methylation and gene expression markers of B-cell chronic lymphocytic leukemia are present in pre-diagnostic blood samples more than 10 years prior to diagnosis

Background B-cell chronic lymphocytic leukemia (CLL) is a common type of adult leukemia. It often follows an indolent course and is preceded by monoclonal B-cell lymphocytosis, an asymptomatic condition, however it is not known what causes subjects with this condition to progress to CLL. Hence the discovery of prediagnostic markers has the potential to improve the identification of subjects likely to develop CLL and may also provide insights into the pathogenesis of the disease of potential clinical relevance. Results We employed peripheral blood buffy coats of 347 apparently healthy subjects, of whom 28 were diagnosed with CLL 2.0–15.7 years after enrollment, to derive for the first time genome-wide DNA methylation, as well as gene and miRNA expression, profiles associated with the risk of future disease. After adjustment for white blood cell composition, we identified 722 differentially methylated CpG sites and 15 differentially expressed genes (Bonferroni-corrected p < 0.05) as well as 2 miRNAs (FDR < 0.05) which were associated with the risk of future CLL. The majority of these signals have also been observed in clinical CLL, suggesting the presence in prediagnostic blood of CLL-like cells. Future CLL cases who, at enrollment, had a relatively low B-cell fraction (<10%), and were therefore less likely to have been suffering from undiagnosed CLL or a precursor condition, showed profiles involving smaller numbers of the same differential signals with intensities, after adjusting for B-cell content, generally smaller than those observed in the full set of cases. A similar picture was obtained when the differential profiles of cases with time-to-diagnosis above the overall median period of 7.4 years were compared with those with shorted time-to-disease. Differentially methylated genes of major functional significance include numerous genes that encode for transcription factors, especially members of the homeobox family, while differentially expressed genes include, among others, multiple genes related to WNT signaling as well as the miRNAs miR-150-5p and miR-155-5p. Conclusions Our findings demonstrate the presence in prediagnostic blood of future CLL patients, more than 10 years before diagnosis, of CLL-like cells which evolve as preclinical disease progresses, and point to early molecular alterations with a pathogenetic potential

A systematic comparison of linear regression-based statistical methods to assess exposome-health associations

BACKGROUND: The exposome constitutes a promising framework to better understand the effect of environmental exposures on health by explicitly considering multiple testing and avoiding selective reporting. However, exposome studies are challenged by the simultaneous consideration of many correlated exposures. OBJECTIVES: We compared the performances of linear regression-based statistical methods in assessing exposome-health associations. METHODS: In a simulation study, we generated 237 exposure covariates with a realistic correlation structure, and a health outcome linearly related to 0 to 25 of these covariates. Statistical methods were compared primarily in terms of false discovery proportion (FDP) and sensitivity. RESULTS: On average over all simulation settings, the elastic net and sparse partial least-squares regression showed a sensitivity of 76% and a FDP of 44%; Graphical Unit Evolutionary Stochastic Search (GUESS) and the deletion/substitution/addition (DSA) algorithm a sensitivity of 80% and a FDP of 33%. The environment-wide association study (EWAS) underperformed these methods in terms of FDP (average FDP, 86%), despite a higher sensitivity. Performances decreased considerably when assuming an exposome exposure matrix with high levels of correlation between covariates. CONCLUSIONS: Correlation between exposures is a challenge for exposome research, and the statistical methods investigated in this study are limited in their ability to efficiently differentiate true predictors from correlated covariates in a realistic exposome context. While GUESS and DSA provided a marginally better balance between sensitivity and FDP, they did not outperform the other multivariate methods across all scenarios and properties examined, and computational complexity and flexibility should also be considered when choosing between these methods