The exposome - a new approach for risk assessment.

Complementing the human genome with an exposome reflects the increasingly obvious impact of environmental exposure, which far exceeds the role of genetics, on human health. Considering the complexity of exposures and, in addition, the reactions of the body to exposures - i.e., the exposome - reverses classical exposure science where the precise measurement of single or few exposures is associated with specific health or environmental effects. The complete description of an individual's exposome is impossible; even less so is that of a population. We can, however, cast a wider net by foregoing some rigor in assessment and compensating with the statistical power of rich datasets. The advent of omics technologies enables a relatively cheap, high-content description of the biological effects of substances, especially in tissues and biofluids. They can be combined with many other rich data-streams, creating big data of exposure and effect. Computational methods increasingly allow data integration, discerning the signal from the noise and formulating hypotheses of exposure-effect relationships. These can be followed up in a targeted way. With a better exposure element in the risk equation, exposomics - new kid on the block of risk assessment - promises to identify novel exposure (interactions) and health/environment effect associations. This may also create opportunities to prioritize the more relevant chemicals for risk assessment, thereby lowering the burden on hazard assessment in an expo-sure-driven approach. Technological developments and synergies between approaches, quality assurance (ultimately as Good Exposome Practices), and the integration of mechanistic thinking will advance this approach.

Monte Carlo methods for nonparametric regression with heteroscedastic measurement error.

Nonparametric regression is a fundamental problem in statistics but challenging when the independent variable is measured with error. Among the first approaches was an extension of deconvoluting kernel density estimators for homescedastic measurement error. The main contribution of this article is to propose a new simulation-based nonparametric regression estimator for the heteroscedastic measurement error case. Similar to some earlier proposals, our estimator is built on principles underlying deconvoluting kernel density estimators. However, the proposed estimation procedure uses Monte Carlo methods for estimating nonlinear functions of a normal mean, which is different than any previous estimator. We show that the estimator has desirable operating characteristics in both large and small samples and apply the method to a study of benzene exposure in Chinese factory workers.

The Saliva Exposome for Monitoring of Individuals' Health Trajectories.

BACKGROUND: There is increasing evidence that environmental, rather than genetic, factors are the major causes of most chronic diseases. By measuring entire classes of chemicals in archived biospecimens, exposome-wide association studies (EWAS) are being conducted to investigate associations between a myriad of exposures received during life and chronic diseases. OBJECTIVES: Because the intraindividual variability in biomarker levels, arising from changes in environmental exposures from conception onwards, leads to attenuation of exposure-disease associations, we posit that saliva can be collected repeatedly in longitudinal studies to reduce exposure-measurement errors in EWAS. METHODS: From the literature and an open-source saliva-metabolome database, we obtained concentrations of 1,233 chemicals that had been detected in saliva. We connected salivary metabolites with human metabolic pathways and PubMed Medical Subject Heading (MeSH) terms, and performed pathway enrichment and pathway topology analyses. RESULTS: One hundred ninety-six salivary metabolites were mapped into 49 metabolic pathways and connected with human metabolic diseases, central nervous system diseases, and neoplasms. We found that the saliva exposome represents at least 14 metabolic pathways, including amino acid metabolism, TCA cycle, gluconeogenesis, glutathione metabolism, pantothenate and CoA biosynthesis, and butanoate metabolism. CONCLUSIONS: Saliva contains molecular information worthy of interrogation via EWAS. The simplicity of specimen collection suggests that saliva offers a practical alternative to blood for measurements that can be used to characterize individual exposomes. https://doi.org/10.1289/EHP1011.

Persistent organic pollutants in dust from older homes: learning from lead.

OBJECTIVES: We aimed to (1) evaluate the relation between home age and concentrations of multiple chemical contaminants in settled dust and (2) discuss the feasibility of using lead hazard controls to reduce children's exposure to persistent organic pollutants. METHODS: As part of the California Childhood Leukemia Study, from 2001 to 2007, we used a high-volume small surface sampler and household vacuum cleaners to collect dust samples from 583 homes and analyzed the samples for 94 chemicals with gas chromatography-mass spectrometry and inductively coupled plasma mass spectrometry. We evaluated relations between chemical concentrations in dust and home age with Spearman rank correlation coefficients. RESULTS: Dust concentrations of lead, polychlorinated biphenyls, organochlorine insecticides, and polycyclic aromatic hydrocarbons were correlated with home age (ρ > 0.2; P < .001), whereas concentrations of pyrethroid insecticides and polybrominated diphenyl ethers were not. CONCLUSIONS: Dust in older homes contains higher levels of multiple, persistent chemicals than does dust in newer homes. Further development of strategies to reduce chemical exposures for children living in older homes is warranted.

Determinants of polycyclic aromatic hydrocarbon levels in house dust.

Estimation of human exposures to polycyclic aromatic hydrocarbons (PAHs) is often desired for the epidemiological studies of cancer. One way to obtain information about indoor levels of PAHs is to measure these chemicals in house dust. In this study, we evaluated the predictive value of self-reported and geographic data for estimating measured levels of nine PAHs in house dust from 583 households in the Northern California Childhood Leukemia Study (NCCLS). Using multivariable linear regression models, we evaluated the effects on house-dust PAH concentrations from the following covariates: residential heating sources, smoking habits, house characteristics, and outdoor emission sources. House dust was collected from 2001 to 2007, using both high-volume surface samplers and household vacuum cleaners, and was analyzed for nine PAHs using gas chromatography-mass spectrometry. All nine PAHs were detected in more than 93% of dust samples, with median concentrations ranging from 14 to 94 ng/g dust. Statistically significant effects on PAH concentrations in house dust were found for gas heating, outdoor PAH concentrations, and residence age. Yet, the optimal regression model only explained 15% of the variation in PAH levels in house dust. As self-reported data and outdoor PAH sources were only marginally predictive of observed PAH levels, we recommend that PAH concentrations be measured directly in dust samples for use in epidemiological studies.

Is house-dust nicotine a good surrogate for household smoking?

The literature is inconsistent regarding associations between parental smoking and childhood leukemia, possibly because previous studies used self-reported smoking habits as surrogates for children's true exposures to cigarette smoke. Here, the authors investigated the use of nicotine concentrations in house dust as measures of children's exposure to cigarette smoke in 469 households from the Northern California Childhood Leukemia Study (1999-2007). House dust was collected by using high-volume surface samplers and household vacuum cleaners and was analyzed for nicotine via gas chromatography-mass spectrometry. Using multivariable linear regression, the authors evaluated the effects of self-reported parental smoking, parental demographics, house characteristics, and other covariates on house-dust nicotine concentrations. They observed that nicotine concentrations in house dust were associated with self-reported smoking for periods of months and years before dust collection. Furthermore, the authors found that the relation between nicotine dust levels and self-reported smoking varied by parental age and socioeconomic status. These findings suggest that house-dust nicotine concentrations reflect long-term exposures to cigarette smoke in the home and that they may be less biased surrogates for children's exposures to cigarette smoke than self-reported smoking habits.

Assessment of dermal exposure to benzene and toluene in shoe manufacturing by activated carbon cloth patches.

OBJECTIVES: The aim of this investigation was to use activated carbon cloth (ACC) patches to study the probability and extent of dermal exposure to benzene and toluene in a shoe factory. METHODS: Inhalation and dermal exposure loading were measured simultaneously in 70 subjects on multiple days resulting in 113 observations. Dermal exposure loading was assessed by ACC patches attached to likely exposed skin areas (e.g. the palm of the hand and abdomen). A control patch at the chest and an organic vapor monitor (OVM) were used to adjust the hand and abdomen patches for the contribution from the air through passive absorption of benzene and toluene on the ACC patches. Systemic exposure was assessed by quantification of unmetabolized benzene (UBz) and toluene (UTol) in urine. RESULTS: Mean air concentrations for the study population were 1.5 and 7.5 ppm for benzene and toluene, respectively. Iterative regression analyses between the control patch, OVM and the dermal patches showed that only a small proportion of the ACC patches at the hand had likely benzene (n = 4; mean 133 microg cm(-2) h(-1)) or toluene (n = 5; mean 256 microg cm(-2) h(-1)) contamination. Positive patches were exclusively observed among subjects performing the task of gluing. Significant dermal exposure loading to the abdomen was detected only for toluene (n = 2; mean 235 microg cm(-2) h(-1)). No relation was found between having a positive hand or abdomen ACC patch and UBz or UTol levels. In contrast a strong association was found between air levels of benzene (p = 0.0016) and toluene (p < 0.0001) and their respective urinary levels. CONCLUSIONS: ACC patches are shown to be a useful technique for quantifying the probability of dermal exposure to organic solvents and to provide estimates of the potential contribution of the dermal pathway to systemic exposure. Using ACC patches we show that dermal exposure to benzene and toluene in a shoe manufacturing factory is probably rare, and when it occurred exposures were relatively low and did not significantly contribute to systemic exposure.

Detailed exposure assessment for a molecular epidemiology study of benzene in two shoe factories in China.

OBJECTIVES: We carried out a detailed exposure assessment of benzene and toluene in two shoe factories in Tianjin, China. Our goal was to identify workers with a broad range of benzene exposures, for an epidemiologic study relating exposure to early biologic effects. METHODS: A comprehensive exposure survey program was initiated. Over a period of 16 months, 2783 personal solvent exposure samples were collected in two workplaces from 250 workers. Mixed-effects models were used to identify factors affecting exposure. Principal component analyses (PCA) and subsequent regression analyses on the scores of the identified principal components were used to relate potential co-exposures to various exposure sources present in the workplace. RESULTS: The mean benzene exposure level was 21.86 p.p.m. (10th-90th percentiles 5.23-50.63 p.p.m.) in the smaller shoe factory (factory A) and 3.46 p.p.m. (10th-90th percentiles 0.20-7.00 p.p.m.) in the larger shoe factory (factory B). Within-factory exposure levels differed among job titles and were higher for subjects directly involved in handling glues. In contrast, mean toluene levels were relatively similar in the two factories (factory A, 9.52 p.p.m.; factory B, 15.88 p.p.m.). A seasonal trend was identified for both benzene and toluene in factory B. This could be explained in part by changes in air movement and ventilation patterns occurring during the year. A seasonal trend was not present in the smaller shoe factory, where general ventilation was absent. Supplemental analysis showed that exposure levels to other hydrocarbons were low (< or =5 p.p.m.), less than 5% of their respective ACGIH threshold limit values, and generally comparable in the two factories. PCA showed that co-exposures in factory B could largely be explained by glue sources that were used in distinct areas in the workplace. CONCLUSIONS: We demonstrated the occurrence of a broad range of benzene exposure levels in two shoe manufacturing factories in Tianjin, China. Benzene and toluene exposures were determined in part by the degree of contact with glues, the benzene and toluene content of each glue, air movement and ventilation patterns. The availability of long-term monthly personal monitoring data provides an excellent opportunity to estimate individual exposures at different times during the 1 yr period of observation.

Variability of environmental exposures to volatile organic compounds.

Although studies of occupational exposure to volatile organic compounds (VOCs) often partition variability across groups, and between and within persons, those of environmental exposure to VOCs have not involved such partitioning. Using data from the Environmental Protection Agency's total exposure assessment methodology (TEAM) studies, we partitioned exposure variability across cities, and between and within persons for nine VOCs. The estimated variance components decreased in the order: within-person > between-person > across city. Despite their smaller magnitudes, estimates of between-person and across-city variance components were sufficiently large to provide reasonable contrast for informative epidemiology studies of most VOCs. Estimates of between-person variance components for environmental VOCs were similar to those published for occupational VOCs (groups defined by job and factory). However, estimates of within-person variance components were much greater for environmental VOCs, probably due to the greater diversity of locations (including the workplace) visited by the general public over time. For benzene and perchloroethylene, we used a simple model to calculate numbers of personal measurements required to relate the exposure level to health outcome statistically. About 10 times more personal measurements would be required to investigate perchloroethylene exposure as compared to benzene exposure; this disparity reflects the greater within-subject variability of perchloroethylene data compared to benzene data. We conclude that variability should be partitioned for environmental VOC exposures in much the same manner as for occupational exposures. There should be sufficient variability in the levels of most VOCs across cities and between subjects to provide reasonable contrast for informative epidemiology studies, as we illustrate for exposures to benzene. Yet, epidemiologists should be wary of investigating environmental VOCs without preliminary data with which to estimate the variance structure of exposure variables.