Empirical evidence suggests that human occupants indoors, through their presence and activities, can influence the dynamics of semivolatile organic compounds (SVOCs). To better understand these dynamics, a transient multimedia human exposure model was developed (Activity-Based Indoor Chemical Assessment Model (ABICAM)). This model parametrizes massbalance equations as functions of time-dependent human activities. As a case study, ABICAM simulated exposures of an archetypal adult and toddler over 24 h to diethyl phthalate (DEP), butyl benzyl phthalate (BBzP), and di-2-ethylhexyl phthalate (DEHP) that span a wide range of gas-particle partitioning tendencies. Under baseline (no activities beyond respiration), the toddler's time-average internal doses were three to four times higher than the adult's, due to differences in physical human attributes (e.g., inhalation rate). When time-dependent activities were considered, interindividual (e.g., adult vs toddler) variability was accentuated by up to a factor of 3 for BBzP. Activities with the greatest influence on time-average internal dose were showering (−71% for BBzP), cooking (+27% for DEHP), and sleeping (−26% for DEHP). Overall, the results support the hypotheses that (1) transient indoor activities can give rise to intraindividual variability in estimated internal doses of SVOCs, and (2) interindividual variability in such exposure can result from differences in activity patterns and physical human attributes, according to a compound's physical−chemical properties.

BackgroundComplex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets.ResultsA multi-step decision tree-based method is described for defining endotypes based on gene expression, clinical covariates, and disease indicators using childhood asthma as a case study. We attempted to use alternative approaches such as the Student’s t-test, single data domain clustering and the Modk-prototypes algorithm, which incorporates multiple data domains into a single analysis and none performed as well as the novel multi-step decision tree method. This new method gave the best segregation of asthmatics and non-asthmatics, and it provides easy access to all genes and clinical covariates that distinguish the groups.ConclusionsThe multi-step decision tree method described here will lead to better understanding of complex disease in general by allowing purely data-driven disease endotypes to facilitate the discovery of new mechanisms underlying these diseases. This application should be considered a complement to ongoing efforts to better define and diagnose known endotypes. When coupled with existing methods developed to determine the genetics of gene expression, these methods provide a mechanism for linking genetics and exposomics data and thereby accounting for both major determinants of disease.

BackgroundAsthma and allergy represent complex phenotypes, which disproportionately burden ethnic minorities in the United States. Strong evidence for genomic factors predisposing subjects to asthma/allergy is available. However, methods to utilize this information to identify high risk groups are variable and replication of genetic associations in African Americans is warranted.MethodsWe evaluated 41 single nucleotide polymorphisms (SNP) and a deletion corresponding to 11 genes demonstrating association with asthma in the literature, for association with asthma, atopy, testing positive for food allergens, eosinophilia, and total serum IgE among 141 African American children living in Detroit, Michigan. Independent SNP and haplotype associations were investigated for association with each trait, and subsequently assessed in concert using a genetic risk score (GRS).ResultsStatistically significant associations with asthma were observed for SNPs in GSTM1, MS4A2, and GSTP1 genes, after correction for multiple testing. Chromosome 11 haplotype CTACGAGGCC (corresponding to MS4A2 rs574700, rs1441586, rs556917, rs502581, rs502419 and GSTP1 rs6591256, rs17593068, rs1695, rs1871042, rs947895) was associated with a nearly five-fold increase in the odds of asthma (Odds Ratio (OR) = 4.8, p = 0.007). The GRS was significantly associated with a higher odds of asthma (OR = 1.61, 95% Confidence Interval = 1.21, 2.13; p = 0.001).ConclusionsVariation in genes associated with asthma in predominantly non-African ethnic groups contributed to increased odds of asthma in this African American study population. Evaluating all significant variants in concert helped to identify the highest risk subset of this group.

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BackgroundAsthma is a common complex disease responsible for considerable morbidity and mortality, particularly in urban minority populations. The Mechanistic Indicators of Childhood Asthma study was designed to pilot an integrative approach in children's health research. The study incorporates exposure metrics, internal dose measures, and clinical indicators to decipher the biological complexity inherent in diseases such as asthma and cardiovascular disease with etiology related to gene-environment interactions.Methods/Design205 non-asthmatic and asthmatic children, (9-12 years of age) from Detroit, Michigan were recruited. The study includes environmental measures (indoor and outdoor air, vacuum dust), biomarkers of exposure (cotinine, metals, total and allergen specific Immunoglobulin E, polycyclic aromatic hydrocarbons, volatile organic carbon metabolites) and clinical indicators of health outcome (immunological, cardiovascular and respiratory). In addition, blood gene expression and candidate SNP analyses were conducted.DiscussionBased on an integrative design, the MICA study provides an opportunity to evaluate complex relationships between environmental factors, physiological biomarkers, genetic susceptibility and health outcomes.Project approvalIRB Number 05-EPA-2637: The human subjects' research protocol was reviewed by the Institutional Review Board (IRB) of the University of North Carolina; the IRB of Westat, Inc., the IRB of the Henry Ford Health System; and EPA's Human Subjects' Research Review Official.