Narrative review of occupational exposures and noncommunicable diseases.

OBJECTIVE: Within the scope of the Exposome Project for Health and Occupational Research on applying the exposome concept to working life health, we aimed to provide a broad overview of the status of knowledge on occupational exposures and associated health effects across multiple noncommunicable diseases (NCDs) to help inform research priorities. METHODS: We conducted a narrative review of occupational risk factors that can be considered to have "consistent evidence for an association," or where there is "limited/inadequate evidence for an association" for 6 NCD groups: nonmalignant respiratory diseases; neurodegenerative diseases; cardiovascular/metabolic diseases; mental disorders; musculoskeletal diseases; and cancer. The assessment was done in expert sessions, primarily based on systematic reviews, supplemented with narrative reviews, reports, and original studies. Subsequently, knowledge gaps were identified, e.g. based on missing information on exposure-response relationships, gender differences, critical time-windows, interactions, and inadequate study quality. RESULTS: We identified over 200 occupational exposures with consistent or limited/inadequate evidence for associations with one or more of 60+ NCDs. Various exposures were identified as possible risk factors for multiple outcomes. Examples are diesel engine exhaust and cadmium, with consistent evidence for lung cancer, but limited/inadequate evidence for other cancer sites, respiratory, neurodegenerative, and cardiovascular diseases. Other examples are physically heavy work, shift work, and decision latitude/job control. For associations with limited/inadequate evidence, new studies are needed to confirm the association. For risk factors with consistent evidence, improvements in study design, exposure assessment, and case definition could lead to a better understanding of the association and help inform health-based threshold levels. CONCLUSIONS: By providing an overview of knowledge gaps in the associations between occupational exposures and their health effects, our narrative review will help setting priorities in occupational health research. Future epidemiological studies should prioritize to include large sample sizes, assess exposures prior to disease onset, and quantify exposures. Potential sources of biases and confounding need to be identified and accounted for in both original studies and systematic reviews.

Validation of a COVID-19 Job Exposure Matrix (COVID-19-JEM) for Occupational Risk of a SARS-CoV-2 Infection at Work: Using Data of Dutch Workers.

OBJECTIVES: A COVID-19 Job Exposure Matrix (COVID-19-JEM) has been developed, consisting of four dimensions on transmission, two on mitigation measures, and two on precarious work. This study aims to validate the COVID-19-JEM by (i) comparing risk scores assigned by the COVID-19-JEM with self-reported data, and (ii) estimating the associations between the COVID-19-JEM risk scores and self-reported COVID-19. METHODS: Data from measurements 2 (July 2020, n = 7690) and 4 (March 2021, n = 6794) of the Netherlands Working Conditions Survey-COVID-19 (NWCS-COVID-19) cohort study were used. Responses to questions related to the transmission risks and mitigation measures of Measurement 2 were used to calculate self-reported risk scores. These scores were compared with the COVID-19-JEM attributed risk scores, by assessing the percentage agreement and weighted kappa (κ). Based on Measurement 4, logistic regression analyses were conducted to estimate the associations between all COVID-19-JEM risk scores and self-reported COVID-19 (infection in general and infected at work). RESULTS: The agreement between the COVID-19-JEM and questionnaire-based risk scores was good (κ ≥ 0.70) for most dimensions, except work location (κ = 0.56), and face covering (κ = 0.41). Apart from the precarious work dimensions, higher COVID-19-JEM assigned risk scores had higher odds ratios (ORs; ranging between 1.28 and 1.80) on having had COVID-19. Associations were stronger when the infection were thought to have happened at work (ORs between 2.33 and 11.62). CONCLUSIONS: Generally, the COVID-19-JEM showed a good agreement with self-reported infection risks and infection rates at work. The next step is to validate the COVID-19-JEM with objective data in the Netherlands and beyond.

Automated Network Assembly of Mechanistic Literature for Informed Evidence Identification to Support Cancer Risk Assessment.

BACKGROUND: Mechanistic data is increasingly used in hazard identification of chemicals. However, the volume of data is large, challenging the efficient identification and clustering of relevant data. OBJECTIVES: We investigated whether evidence identification for hazard assessment can become more efficient and informed through an automated approach that combines machine reading of publications with network visualization tools. METHODS: We chose 13 chemicals that were evaluated by the International Agency for Research on Cancer (IARC) Monographs program incorporating the key characteristics of carcinogens (KCCs) approach. Using established literature search terms for KCCs, we retrieved and analyzed literature using Integrated Network and Dynamical Reasoning Assembler (INDRA). INDRA combines large-scale literature processing with pathway databases and extracts relationships between biomolecules, bioprocesses, and chemicals into statements (e.g., "benzene activates DNA damage"). These statements were subsequently assembled into networks and compared with the KCC evaluation by the IARC, to evaluate the informativeness of our approach. RESULTS: We found, in general, larger networks for those chemicals which the IARC has evaluated the evidence to be strong for KCC induction. Larger networks were not directly linked to publication count, given that we retrieved small networks for several chemicals with little support for KCC activation according to the IARC, despite the significant volume of literature for these specific chemicals. In addition, interpreting networks for genotoxicity and DNA repair showed concordance with the IARC KCC evaluation. DISCUSSION: Our method is an automated approach to condense mechanistic literature into searchable and interpretable networks based on an a priori ontology. The approach is no replacement of expert evaluation but, instead, provides an informed structure for experts to quickly identify which statements are made in which papers and how these could connect. We focused on the KCCs because these are supported by well-described search terms. The method needs to be tested in other frameworks as well to demonstrate its generalizability. https://doi.org/10.1289/EHP9112.

Estimation of the Exposure-Response Relation between Benzene and Acute Myeloid Leukemia by Combining Epidemiologic, Human Biomarker, and Animal Data.

BACKGROUND: Chemical risk assessment can benefit from integrating data across multiple evidence bases, especially in exposure-response curve (ERC) modeling when data across the exposure range are sparse. METHODS: We estimated the ERC for benzene and acute myeloid leukemia (AML), by fitting linear and spline-based Bayesian meta-regression models that included summary risk estimates from non-AML and nonhuman studies as prior information. Our complete dataset included six human AML studies, three human leukemia studies, 10 human biomarker studies, and four experimental animal studies. RESULTS: A linear meta-regression model with intercept best predicted AML risks after cross-validation, both for the full dataset and AML studies only. Risk estimates in the low exposure range [<40 parts per million (ppm)-years] from this model were comparable, but more precise when the ERC was derived using all available data than when using AML data only. Allowing for between-study heterogeneity, RRs and 95% prediction intervals (95% PI) at 5 ppm-years were 1.58 (95% PI, 1.01-3.22) and 1.44 (95% PI, 0.85-3.42), respectively. CONCLUSIONS: Integrating the available epidemiologic, biomarker, and animal data resulted in more precise risk estimates for benzene exposure and AML, although the large between-study heterogeneity hampers interpretation of these results. The harmonization steps required to fit the Bayesian meta-regression model involve a range of assumptions that need to be critically evaluated, as they seem crucial for successful implementation. IMPACT: By describing a framework for data integration and explicitly describing the necessary data harmonization steps, we hope to enable risk assessors to better understand the advantages and assumptions underlying a data integration approach.See related commentary by Keil, p. 695.

A Scoping Review of Technologies and Their Applicability for Exposome-Based Risk Assessment in the Oil and Gas Industry.

INTRODUCTION: Oil and gas workers have been shown to be at increased risk of chronic diseases including cancer, asthma, chronic obstructive pulmonary disease, and hearing loss, among others. Technological advances may be used to assess the external (e.g. personal sensors, smartphone apps and online platforms, exposure models) and internal exposome (e.g. physiologically based kinetic modeling (PBK), biomonitoring, omics), offering numerous possibilities for chronic disease prevention strategies and risk management measures. The objective of this study was to review the literature on these technologies, by focusing on: (i) evaluating their applicability for exposome research in the oil and gas industry, and (ii) identifying key challenges that may hamper the successful application of such technologies in the oil and gas industry. METHOD: A scoping review was conducted by identifying peer-reviewed literature with searches in MEDLINE/PubMed and SciVerse Scopus. Two assessors trained on the search strategy screened retrieved articles on title and abstract. The inclusion criteria used for this review were: application of the aforementioned technologies at a workplace in the oil and gas industry or, application of these technologies for an exposure relevant to the oil and gas industry but in another occupational sector, English language and publication period 2005-end of 2019. RESULTS: In total, 72 articles were included in this scoping review with most articles focused on omics and bioinformatics (N = 22), followed by biomonitoring and biomarkers (N = 20), external exposure modeling (N = 11), PBK modeling (N = 10), and personal sensors (N = 9). Several studies were identified in the oil and gas industry on the application of PBK models and biomarkers, mainly focusing on workers exposed to benzene. The application of personal sensors, new types of exposure models, and omics technology are still in their infancy with respect to the oil and gas industry. Nevertheless, applications of these technologies in other occupational sectors showed the potential for application in this sector. DISCUSSION AND CONCLUSION: New exposome technologies offer great promise for personal monitoring of workers in the oil and gas industry, but more applied research is needed in collaboration with the industry. Current challenges hindering a successful application of such technologies include (i) the technological readiness of sensors, (ii) the availability of data, (iii) the absence of standardized and validated methods, and (iv) the need for new study designs to study the development of disease during working life.

Future Prospects of Occupational Exposure Modelling of Substances in the Context of Time-Resolved Sensor Data.

This commentary explores the use of high-resolution data from new, miniature sensors to enrich models that predict exposures to chemical substances in the workplace. To optimally apply these sensors, one can expect an increased need for new models that will facilitate the interpretation and extrapolation of the acquired time-resolved data. We identified three key modelling approaches in the context of sensor data, namely (i) enrichment of existing time-integrated exposure models, (ii) (new) high-resolution (in time and space) empirical models, and (iii) new 'occupational dispersion' models. Each approach was evaluated in terms of their application in research, practice, and for policy purposes. It is expected that substance-specific sensor data will have the potential to transform workplace modelling by re-calibrating, refining, and validating existing (time-integrated) models. An increased shift towards 'sensor-driven' models is expected. It will allow for high-resolution modelling in time and space to identify peak exposures and will be beneficial for more individualized exposure assessment and real-time risk management. New 'occupational dispersion models' such as interpolation, computational fluid dynamic models, and assimilation techniques, together with sensor data, will be specifically useful. These techniques can be applied to develop site-specific concentration maps which calculate personal exposures and mitigate worker exposure through early warning systems, source finding and improved control design and control strategies. Critical development and investment needs for sensor data linked to (new) model development were identified such as (i) the generation of more sensor data with reliable sensor technologies (achieved by improved specificity, sensitivity, and accuracy of sensors), (ii) investing in statistical and new model developments, (iii) ensuring that we comply with privacy and security issues of concern, and (iv) acceptance by relevant target groups (such as employers and employees) and stimulation of these new technologies by policymakers and technology developers.

Exploring Evaluation Variables for Low-Cost Particulate Matter Monitors to Assess Occupational Exposure.

(1) Background: Small, lightweight, low-cost optical particulate matter (PM) monitors are becoming popular in the field of occupational exposure monitoring, because these devices allow for real-time static measurements to be collected at multiple locations throughout a work site as well as being used as wearables providing personal exposure estimates. Prior to deployment, devices should be evaluated to optimize and quantify measurement accuracy. However, this can turn out to be difficult, as no standardized methods are yet available and different deployments may require different evaluation procedures. To gain insight in the relevance of different variables that may affect the monitor readings, six PM monitors were selected based on current availability and evaluated in the laboratory; (2) Methods: Existing strategies that were judged appropriate for the evaluation of PM monitors were reviewed and seven evaluation variables were selected, namely the type of dust, within- and between-device variations, nature of the power supply, temperature, relative humidity, and exposure pattern (peak and constant). Each variable was tested and analyzed individually and, if found to affect the readings significantly, included in a final correction model specific to each monitor. Finally, the accuracy for each monitor after correction was calculated; (3) Results: The reference materials and exposure patterns were found to be main factors needing correction for most monitors. One PM monitor was found to be sufficiently accurate at concentrations up to 2000 µg/m3 PM2.5, with other monitors appropriate at lower concentrations. The average accuracy increased by up to three-fold compared to when the correction model did not include evaluation variables; (4) Conclusions: Laboratory evaluation and readings correction can greatly increase the accuracy of PM monitors and set boundaries for appropriate use. However, this requires identifying the relevant evaluation variables, which are heavily reliant on how the monitors are used in the workplace. This, together with the lack of current consensus on standardized procedures, shows the need for harmonized PM monitor evaluation methods for occupational exposure monitoring.

Prenatal exposure to organophosphate pesticides and brain morphology and white matter microstructure in preadolescents.

BACKGROUND: Prenatal exposure to organophosphate (OP) pesticides associate with impaired neurodevelopment in humans and animal models. However, much uncertainty exists about the brain structural alterations underlying these associations. The objective of this study was to determine whether maternal OP pesticide metabolite concentrations in urine repeatedly measured during gestation are associated with brain morphology and white matter microstructure in 518 preadolescents aged 9-12 years. METHOD: Data came from 518 mother-child pairs participating in the Generation R Study, a population-based birth cohort from Rotterdam, the Netherlands. Maternal urine concentrations were determined for 6 dialkylphosphates (DAPs) including 3 dimethyl (DM) and 3 diethyl (DE) alkyl phosphate metabolites, collected at early, mid, and late pregnancy. At child's age 9-12 years, magnetic resonance imaging was performed to obtain T1-weighted images for brain volumes and surface-based cortical thickness and cortical surface area, and diffusion tensor imaging was used to measure white matter microstructure through fractional anisotropy (FA) and mean diffusivity (MD). Linear regression models were fit for the averaged prenatal exposure across pregnancy. RESULTS: DM and DE metabolite concentrations were not associated with brain volumes, cortical thickness, and cortical surface area. However, a 10-fold increase in averaged DM metabolite concentrations across pregnancy was associated with lower FA (B = -1.00, 95%CI = -1.80, -0.20) and higher MD (B = 0.13, 95%CI = 0.04, 0.21). Similar associations were observed for DE concentrations. CONCLUSIONS: This study provides the first evidence that OP pesticides may alter normal white matter microstructure in children, which could have consequences for normal neurodevelopment. No associations were observed with structural brain morphology, including brain volumes, cortical thickness, and cortical surface area.

A Quantitative Meta-Analysis of the Relation between Occupational Benzene Exposure and Biomarkers of Cytogenetic Damage.

BACKGROUND: The genotoxicity of benzene has been investigated in dozens of biomonitoring studies, mainly by studying (classical) chromosomal aberrations (CAs) or micronuclei (MN) as markers of DNA damage. Both have been shown to be predictive of future cancer risk in cohort studies and could, therefore, potentially be used for risk assessment of genotoxicity-mediated cancers. OBJECTIVES: We sought to estimate an exposure-response curve (ERC) and quantify between-study heterogeneity using all available quantitative evidence on the cytogenetic effects of benzene exposure on CAs and MN respectively. METHODS: We carried out a systematic literature review and summarized all available data of sufficient quality using meta-analyses. We assessed the heterogeneity in slope estimates between studies and conducted additional sensitivity analyses to assess how various study characteristics impacted the estimated ERC. RESULTS: Sixteen CA (1,356 individuals) and 13 MN studies (2,097 individuals) were found to be eligible for inclusion in a meta-analysis. Studies where benzene was the primary genotoxic exposure and that had adequate assessment of both exposure and outcomes were used for the primary analysis. Estimated slope estimates were an increase of 0.27% CA [(95% CI: 0.08%, 0.47%); based on the results from 4 studies] and 0.27% MN [(95% CI: -0.23%, 0.76%); based on the results from 7 studies] per parts-per-million benzene exposure. We observed considerable between-study heterogeneity for both end points (I2>90%). DISCUSSION: Our study provides a systematic, transparent, and quantitative summary of the literature describing the strong association between benzene exposure and accepted markers of genotoxicity in humans. The derived consensus slope can be used as a best estimate of the quantitative relationship between real-life benzene exposure and genetic damage in future risk assessment. We also quantitate the large between-study heterogeneity that exists in this literature, a factor which is crucial for the interpretation of single-study or consensus slopes. https://doi.org/10.1289/EHP6404.

Biomonitoring for Occupational Exposure to Diisocyanates: A Systematic Review.

Diisocyanates are a group of chemicals that are widely used in occupational settings. They are known to induce various health effects, including skin- and respiratory tract sensitization resulting in allergic dermatitis and asthma. Exposure to diisocyanates has been studied in the past decades by using different types of biomonitoring markers and matrices. The aim of this review as part of the HBM4EU project was to assess: (i) which biomarkers and matrices have been used for biomonitoring diisocyanates and what are their strengths and limitations; (ii) what are (current) biomonitoring levels of the major diisocyanates (and metabolites) in workers; and (iii) to characterize potential research gaps. For this purpose we conducted a systematic literature search for the time period 2000-end 2018, thereby focussing on three types of diisocyanates which account for the vast majority of the total isocyanate market volume: hexamethylene diisocyanate (HDI), toluene diisocyanate (TDI), and 4,4'-methylenediphenyl diisocyanate (MDI). A total of 28 publications were identified which fulfilled the review inclusion criteria. The majority of these studies (93%) investigated the corresponding diamines in either urine or plasma, but adducts have also been investigated by several research groups. Studies on HDI were mostly in the motor vehicle repair industry [with urinary hexamethylene diamine result ranging from 0.03 to 146.5 µmol mol-1 creatinine]. For TDI, there is mostly data on foam production [results for urinary toluene diamine ranging from ~0.01 to 97 µmol mol-1 creatinine] whereas the available MDI data are mainly from the polyurethane industry (results for methylenediphenyl diamine range from 0.01 to 32.7 µmol mol-1 creatinine). About half of the studies published were prior to 2010 hence might not reflect current workplace exposure. There is large variability within and between studies and across sectors which could be potentially explained by several factors including worker or workplace variability, short half-lives of biomarkers, and differences in sampling strategies and analytical techniques. We identified several research gaps which could further be taken into account when studying diisocyanates biomonitoring levels: (i) the development of specific biomarkers is promising (e.g. to study oligomers of HDI which have been largely neglected to date) but needs more research before they can be widely applied, (ii) since analytical methods differ between studies a more uniform approach would make comparisons between studies easier, and (iii) dermal absorption seems a possible exposure route and needs to be further investigated. The use of MDI, TDI, and HDI has been recently proposed to be restricted in the European Union unless specific conditions for workers' training and risk management measures apply. This review has highlighted the need for a harmonized approach to establishing a baseline against which the success of the restriction can be evaluated.

Increased telomere length and mtDNA copy number induced by multi-walled carbon nanotube exposure in the workplace.

Carbon nanotubes (CNTs) except MWCNT-7 have been classified as Group 3 ["Not classifiable as to its carcinogenicity to humans"] by the IARC. Despite considerable mechanistic evidence in vitro/in vivo, the classification highlights a general lack of data, especially among humans. In our previous study, we reported epigenetic changes in the MWCNT exposed workers. Here, we evaluated whether MWCNT can also cause alterations in aging related features including relative telomere length (TL) and/or mitochondrial copy number (mtDNAcn). Relative TL and mtDNAcn were measured on extracted DNA from peripheral blood from MWCNT exposed workers (N = 24) and non-exposed controls (N = 43) using a qPCR method. A higher mtDNAcn and longer TL were observed in MWCNT exposed workers when compared to controls. Independent of age, sex, smoking behavior, alcohol consumption and BMI, MWCNT-exposure was associated with an 18.30 % increase in blood TL (95 % CI: 7.15-30.62 %; p = 0.001) and 35.21 % increase in mtDNAcn (95 % CI: 19.12-53.46 %). Our results suggest that exposure to MWCNT can induce an increase in the mtDNAcn and TL; however, the mechanistic basis or consequence of such change requires further experimental studies.

Organophosphate pesticide metabolite concentrations in urine during pregnancy and offspring attention-deficit hyperactivity disorder and autistic traits.

BACKGROUND: Prenatal exposure to organophosphate (OP) pesticides has been associated with altered neuronal cell development and behavioral changes in animal offspring. However, the few studies investigating the association between prenatal OP pesticide exposure and neurodevelopmental outcomes such as Attention-Deficit Hyperactivity Disorder (ADHD) and autistic traits in children produced mixed findings. OBJECTIVE: The objective of the present study was to examine whether maternal urinary concentrations of OP pesticide metabolites are associated with ADHD and autistic traits in children participating in the Generation R Study, a population-based birth cohort from Rotterdam, the Netherlands. METHOD: Maternal concentrations of 6 dialkylphosphates (DAPs) were measured using gas chromatography coupled with tandem mass spectrometry in urine samples collected at <18 weeks, 18-25 weeks, and > 25 weeks of gestation in 784 mother-child pairs. DAP metabolite concentrations were expressed as molar concentrations divided by creatinine levels and log10 transformed. ADHD traits were measured at ages 3, 6, and 10 years using the Child Behavior Checklist (CBCL) (n = 781) and autistic traits were measured at age 6 years using the Social Responsiveness Scale (SRS) (n = 622). First, regression models were fit for the averaged prenatal exposure across pregnancy. Second, we investigated associations for each collection phase separately, and applied a mutually adjusted model in which the effect of prenatal DAP concentrations from each time period on ADHD and autistic traits were jointly estimated. All associations were adjusted for relevant confounders. RESULTS: Median DAP metabolite concentration was 309 nmol/g creatinine at <18 weeks, 316 nmol/g creatinine at 18-25 weeks, and 308 nmol/g creatinine at >25 weeks of gestation. Overall, DAP metabolite concentrations were not associated with ADHD traits. For instance, a log10 increase in averaged total DAP concentrations across gestation was not associated with a lower ADHD score (-0.03 per SD 95 CI: -0.28 to 0.23). Similarly, no associations between maternal DAP concentrations and autistic traits were detected. CONCLUSIONS: In this study of maternal urinary DAP metabolite concentrations during pregnancy, we did not observe associations with ADHD and autistic traits in children. These are important null observations because of the relatively high background DAP concentrations across pregnancy, the relatively large sample size, and the 10-year follow-up of the offspring. Given the measurement error inherent in our OP pesticide exposure biomarkers, future studies using more urine samples are needed to accurately measure OP pesticide exposure over pregnancy in relation to ADHD and autistic traits.

Determinants of organophosphate pesticide exposure in pregnant women: A population-based cohort study in the Netherlands.

BACKGROUND: In the Netherlands organophosphate (OP) pesticides are frequently used for pest control in agricultural settings. Despite concerns about the potential health impacts of low-level OP pesticides exposure, particularly in vulnerable populations, the primary sources of exposure remain unclear. The present study was designed to investigate the levels of DAP metabolites concentrations across pregnancy and to examine various determinants of DAP metabolite concentrations among an urban population of women in the Netherlands. METHOD: Urinary concentrations of six dialkyl phosphate (DAP) metabolites, the main urinary metabolites of OP pesticides, were determined at <18, 18-25, and >25 weeks of pregnancy in 784 pregnant women participating in the Generation R Study (between 2004 and 2006), a large population-based birth cohort in Rotterdam, the Netherlands. Questionnaires administered prenatally assessed demographic and lifestyle characteristics and maternal diet. Linear mixed models, with adjustment for relevant covariates, were used to estimate associations between the potential exposure determinants and DAP metabolite concentrations expressed as molar concentrations divided by creatinine levels. RESULTS: The median DAP metabolite concentration was 311 nmol/g creatinine for the first trimester, 317 nmol/g creatinine for the second trimester, and 310 nmol/g creatinine for the third trimester. Higher maternal age, married/living with a partner, underweight or normal weight (BMI of <18.5 and 18.5-<25), high education, high income, and non-smoking were associated with higher DAP metabolite concentrations, and DAP metabolite concentrations tended to be higher during the summer. Furthermore, fruit intake was associated with increased DAP metabolite concentrations. Each 100 g/d difference in fruit consumption was associated with a 7% higher total DAP metabolite concentration across pregnancy. Other food groups were not associated with higher DAP metabolite concentrations. CONCLUSIONS: The DAP metabolite concentrations measured in the urine of pregnant women in the Netherlands were higher than those in most other studies previously conducted. Fruit intake was the main dietary source of exposure to OP pesticides in young urban women in the Netherlands. The extent to which DAP metabolite concentrations reflect exposure to the active parent pesticide rather than to less toxic metabolites remains unclear. Further research will be undertaken to investigate the possible effects of this relatively high level OP pesticides exposure on offspring health.

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.

Changes in DNA methylation induced by multi-walled carbon nanotube exposure in the workplace.

This study was designed to assess the epigenetic alterations in blood cells, induced by occupational exposure to multi-wall carbon nanotubes (MWCNT). The study population comprised of MWCNT-exposed workers (n=24) and unexposed controls (n=43) from the same workplace. We measured global DNA methylation/hydroxymethylation levels on the 5th cytosine residues using a validated liquid chromatography tandem-mass spectrometry (LC-MS/MS) method. Sequence-specific methylation of LINE1 retrotransposable element 1 (L1RE1) elements, and promoter regions of functionally important genes associated with epigenetic regulation [DNA methyltransferase-1 (DNMT1) and histone deacetylase 4 (HDAC4)], DNA damage/repair and cell cycle pathways [nuclear protein, coactivator of histone transcription/ATM serine/threonine kinase (NPAT/ATM)], and a potential transforming growth factor beta (TGF-ß) repressor [SKI proto-oncogene (SKI)] were studied using bisulfite pyrosequencing. Analysis of global DNA methylation levels and hydroxymethylation did not reveal significant difference between the MWCNT-exposed and control groups. No significant changes in Cytosine-phosphate-Guanine (CpG) site methylation were observed for the LINE1 (L1RE1) elements. Further analysis of gene-specific DNA methylation showed a significant change in methylation for DNMT1, ATM, SKI, and HDAC4 promoter CpGs in MWCNT-exposed workers. Since DNA methylation plays an important role in silencing/regulation of the genes, and many of these genes have been associated with occupational and smoking-induced diseases and cancer (risk), aberrant methylation of these genes might have a potential effect in MWCNT-exposed workers.

A cross-sectional study of changes in markers of immunological effects and lung health due to exposure to multi-walled carbon nanotubes.

BACKGROUND: Multi-wall carbon nanotubes (MWCNTs) are manufactured nanomaterials to which workers and the general population will be increasingly exposed in coming years. Little is known about potential human health effects of exposure to MWCNTs, but effects on the lung and the immune system have been reported in animal and mechanistic studies. OBJECTIVES: We conducted a cross-sectional study to assess the association between occupational exposure to MWCNTs and effects on lung health and the immune system. METHODS: We assessed 51 immune markers and three pneumoproteins in serum, complete blood cell counts (CBC), fractional exhaled nitric oxide (FENO), and lung function among 22 workers of a MWCNT producing facility and 39 age- and gender-matched, unexposed controls. Measurements were repeated four months later among 16 workers also included in the first phase of the study. Regression analyses were adjusted for potentially confounding parameters age, body mass index, smoking, and sex, and we explored potential confounding by other factors in sensitivity analyses. RESULTS: We observed significant upward trends for immune markers C-C motif ligand 20 (p = .005), basic fibroblast growth factor (p = .05), and soluble IL-1 receptor II (p = .0004) with increasing exposure to MWCNT. These effects were replicated in the second phase of the study and were robust to sensitivity analyses. We also observed differences in FENO and several CBC parameters between exposed and non-exposed, but no difference in lung function or the pneumoproteins. CONCLUSIONS: We observed indications of early effects of occupational exposure to MWCNTs on lung health and the immune system.

Comparison of industrial emissions and carpet dust concentrations of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans in a multi-center U.S. study.

Proximity to facilities emitting polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/F) has been associated with increased risk of non-Hodgkin lymphoma (NHL). There is limited information about whether proximity to industrial sources leads to indoor PCDD/F contamination of homes. We measured carpet dust concentrations (pg/g) of 17 toxic PCDD/F congeners and calculated their toxic equivalence (TEQ) in 100 homes in a population-based case-control study of NHL in Detroit, Los Angeles, Seattle, and Iowa (1998-2000). We took global positioning system readings at residences and obtained coordinates and PCDD/F emissions (ng TEQ/yr) from an Environmental Protection Agency database for 6 facility types: coal-fired electricity generating plants, cement kilns burning non-hazardous waste, hazardous waste incinerators, medical waste incinerators, municipal solid waste incinerators, and sewage sludge incinerators. For each residence, we computed an inverse distance-squared weighted average emission index (AEI [pg TEQ/km2/yr]) for all facilities within 5km from 1983 to 2000. We also computed AEIs for each of the 6 facility types. We evaluated relationships between PCDD/F dust concentrations and the all-facility AEI or categories of facility-type AEIs using multivariable linear regression, adjusting for study center, demographics, and home characteristics. A doubling of the all-facility AEI was associated with a 4-8% increase in PCDD/F dust concentrations of 7 of 17 PCDD/F congeners and the TEQ (p-value<0.1). We also observed positive associations between PCDD/F dust concentrations and facility-type AEIs (highest vs. lowest exposure category) for municipal solid waste incinerators (9 PCDD/F, TEQ), and medical waste incinerators (7 PCDD/F, TEQ) (p<0.1). Our results from diverse geographical areas suggest that industrial PCDD/F emission sources contribute to residential PCDD/F dust concentrations. Our emissions index could be improved by incorporating local meteorological data and terrain characteristics. Future research is needed to better understand the links between nearby emission sources, human exposure pathways, and health risks.

Combining Decision Rules from Classification Tree Models and Expert Assessment to Estimate Occupational Exposure to Diesel Exhaust for a Case-Control Study.

OBJECTIVES: To efficiently and reproducibly assess occupational diesel exhaust exposure in a Spanish case-control study, we examined the utility of applying decision rules that had been extracted from expert estimates and questionnaire response patterns using classification tree (CT) models from a similar US study. METHODS: First, previously extracted CT decision rules were used to obtain initial ordinal (0-3) estimates of the probability, intensity, and frequency of occupational exposure to diesel exhaust for the 10 182 jobs reported in a Spanish case-control study of bladder cancer. Second, two experts reviewed the CT estimates for 350 jobs randomly selected from strata based on each CT rule's agreement with the expert ratings in the original study [agreement rate, from 0 (no agreement) to 1 (perfect agreement)]. Their agreement with each other and with the CT estimates was calculated using weighted kappa (κ w) and guided our choice of jobs for subsequent expert review. Third, an expert review comprised all jobs with lower confidence (low-to-moderate agreement rates or discordant assignments, n = 931) and a subset of jobs with a moderate to high CT probability rating and with moderately high agreement rates (n = 511). Logistic regression was used to examine the likelihood that an expert provided a different estimate than the CT estimate based on the CT rule agreement rates, the CT ordinal rating, and the availability of a module with diesel-related questions. RESULTS: Agreement between estimates made by two experts and between estimates made by each of the experts and the CT estimates was very high for jobs with estimates that were determined by rules with high CT agreement rates (κ w: 0.81-0.90). For jobs with estimates based on rules with lower agreement rates, moderate agreement was observed between the two experts (κ w: 0.42-0.67) and poor-to-moderate agreement was observed between the experts and the CT estimates (κ w: 0.09-0.57). In total, the expert review of 1442 jobs changed 156 probability estimates, 128 intensity estimates, and 614 frequency estimates. The expert was more likely to provide a different estimate when the CT rule agreement rate was <0.8, when the CT ordinal ratings were low to moderate, or when a module with diesel questions was available. CONCLUSIONS: Our reliability assessment provided important insight into where to prioritize additional expert review; as a result, only 14% of the jobs underwent expert review, substantially reducing the exposure assessment burden. Overall, we found that we could efficiently, reproducibly, and reliably apply CT decision rules from one study to assess exposure in another study.

Occupational Exposure to Multi-Walled Carbon Nanotubes During Commercial Production Synthesis and Handling.

The world-wide production of carbon nanotubes (CNTs) has increased substantially in the last decade, leading to occupational exposures. There is a paucity of exposure data of workers involved in the commercial production of CNTs. The goals of this study were to assess personal exposure to multi-walled carbon nanotubes (MWCNTs) during the synthesis and handling of MWCNTs in a commercial production facility and to link these exposure levels to specific activities. Personal full-shift filter-based samples were collected, during commercial production and handling of MWCNTs, R&D activities, and office work. The concentrations of MWCNT were evaluated on the basis of EC concentrations. Associations were studied between observed MWCNT exposure levels and location and activities. SEM analyses showed MWCNTs, present as agglomerates ranging between 200 nm and 100 µm. Exposure levels of MWCNTs observed in the production area during the full scale synthesis of MWCNTs (N = 23) were comparable to levels observed during further handling of MWCNTs (N = 19): (GM (95% lower confidence limit-95% upper confidence limit)) 41 µg m(-3) (20-88) versus 43 µg m(-3) (22-86), respectively. In the R&D area (N = 11) and the office (N = 5), exposure levels of MWCNTs were significantly (P < 0.05) lower: 5 µg m(-3) (2-11) and 7 µg m(-3) (2-28), respectively. Bagging, maintenance of the reactor, and powder conditioning were associated with higher exposure levels in the production area, whereas increased exposure levels in the R&D area were related to handling of MWCNTs powder.