Derived from the term exposure, the exposome is an omic-scale characterization of the nongenetic drivers of health and disease. With the genome, it defines the phenome of an individual. The measurement of complex environmental factors that exert pressure on our health has not kept pace with genomics and historically has not provided a similar level of resolution. Emerging technologies make it possible to obtain detailed information on drugs, toxicants, pollutants, nutrients, and physical and psychological stressors on an omic scale. These forces can also be assessed at systems and network levels, providing a framework for advances in pharmacology and toxicology. The exposome paradigm can improve the analysis of drug interactions and detection of adverse effects of drugs and toxicants and provide data on biological responses to exposures. The comprehensive model can provide data at the individual level for precision medicine, group level for clinical trials, and population level for public health.

The Exposome: Molecules to Populations.

Background Recent studies suggest that exposure to traffic-related air pollutants, including particulate matter (PM), is associated with autism spectrum disorder (autism). Methods Children with autism were identified by records-based surveillance (n = 645 born in North Carolina in 1994, 1996, 1998, or 2000, and n = 334 born in the San Francisco Bay Area in California in 1996). They were compared with randomly sampled children born in the same counties and years identified from birth records (n = 12,434 in North Carolina and n = 2,232 in California). Exposure to PM less than 10 μm (PM10) at the birth address was assigned to each child by a geostatistical interpolation method using daily concentrations from air pollution regulatory monitors. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) for a 10 μg/m increase in PM10 within 3-month periods from preconception through the child's first birthday, adjusting for year, state, maternal education and age, race/ethnicity, and neighborhood-level urbanization and median household income, and including a nonparametric term for week of birth to account for seasonal trends. Results Temporal patterns in PM10 were pronounced, leading to an inverse correlation between the first- and third-trimester concentrations (r = -0.7). Adjusted ORs were, for the first trimester, 0.86 (95% CI = 0.74-0.99), second trimester, 0.97 (0.83-1.15), and third trimester, 1.36 (1.13-1.63); and, after simultaneously including first- and third-trimester concentrations to account for the inverse correlation, were: first trimester, 1.01 (0.81-1.27) and third trimester, 1.38 (1.03-1.84). Conclusions Our study adds to previous work in California showing a relation between traffic-related air pollution and autism, and adds similar findings in an eastern US state, with results consistent with increased susceptibility in the third-trimester.

Particulate matter exposure, prenatal and postnatal windows of susceptibility, and autism spectrum disorders.

https://biblio.ugent.be/publication/8604875/file/8695600.pdf

Is the hematocrit still an issue in quantitative dried blood spot analysis

Recent studies suggest that exposure to traffic-related air pollutants, including particulate matter, is associated with autism spectrum disorder. This study adds to previous work showing a relation between traffic-related air pollution and autism.

Particulate matter exposure, prenatal and postnatal windows of susceptibility, and autism spectrum disorders

Untargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients. Using metabolomics datasets that were generated in our laboratory from samples of human blood, as well as two public LC-MS datasets, we compared our data-adaptive filtering method with traditional methods that rely on non-method specific thresholds. The data-adaptive approach outperformed traditional approaches in terms of removing noisy features and retaining high quality, biologically informative ones. The R code for running the data-adaptive filtering method is provided at https://github.com/courtneyschiffman/Metabolomics-Filtering
 . Our proposed data-adaptive filtering pipeline is intuitive and effectively removes uninformative features from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens.

/pdf/filtering-procedures-for-untargeted-lc-ms-metabolomics-data-2453lytakt.pdf

Filtering procedures for untargeted LC-MS metabolomics data

For pediatric diseases like childhood leukemia, a short latency period points to in-utero exposures as potentially important risk factors. Untargeted metabolomics of small molecules in archived newborn dried blood spots (DBS) offers an avenue for discovering early-life exposures that contribute to disease risks. The purpose of this study was to develop a quantitative method for untargeted analysis of archived newborn DBS for use in an epidemiological study (California Childhood Leukemia Study, CCLS). Using experimental DBS from the blood of an adult volunteer, we optimized extraction of small molecules and integrated measurement of potassium as a proxy for blood hematocrit. We then applied this extraction method to 4.7-mm punches from 106 control DBS samples from the CCLS. Sample extracts were analyzed with liquid chromatography—high resolution mass spectrometry (LC-HRMS) and an untargeted workflow was used to screen for metabolites that discriminate population characteristics such as sex, ethnicity, and birth weight. Thousands of small molecules were measured in extracts of archived DBS. Normalizing for potassium levels removed variability related to varying hematocrit across DBS punches. Of the roughly 1000 prevalent small molecules that were tested, multivariate linear regression detected significant associations with ethnicity (three metabolites) and birth weight (15 metabolites) after adjusting for multiple testing. This untargeted workflow can be used for analysis of small molecules in archived DBS to discover novel biomarkers, to provide insights into the initiation and progression of diseases, and to provide guidance for disease prevention.

/pdf/an-untargeted-metabolomics-method-for-archived-newborn-dried-bm73lcm2a1.pdf

An untargeted metabolomics method for archived newborn dried blood spots in epidemiologic studies

This work proposes a space/time estimation method for atmospheric PM2.5 components by modelling the mass fraction at a selection of space/time locations where the component is measured and applying the model to the extensive PM2.5 monitoring network. The method we developed utilizes the nonlinear Bayesian maximum entropy framework to perform the geostatistical estimation. We implemented this approach using data from nine carcinogenic, particle-bound polycyclic aromatic hydrocarbons (PAHs) measured from archived PM2.5 samples collected at four locations around the World Trade Center (WTC) from September 22, 2001 to March 27, 2002. The mass fraction model developed at these four sites was used to estimate PAH concentrations at additional PM2.5 monitors. Even with limited PAH data, a spatial validation showed the application of the mass fraction model reduced the mean squared error (MSE) by 7–22%, while in the temporal validation there was an exponential improvement in MSE positively associated with the number of days of PAH data removed. Our results include space/time maps of atmospheric PAH concentrations in the New York area after 9/11.

Mass fraction spatiotemporal geostatistics and its application to map atmospheric polycyclic aromatic hydrocarbons after 9/11

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An untargeted metabolomics method for archived newborn dried blood spots in epidemiologic studies

Mass fraction spatiotemporal geostatistics and its application to map atmospheric polycyclic aromatic hydrocarbons after 9/11