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. 2017 Dec 6;12(1):016008.
doi: 10.1088/1752-7163/aa863c.

Metabolomic profiles of plasma, exhaled breath condensate, and saliva are correlated with potential for air toxics detection

Chandresh Nanji Ladva  1 , Rachel Golan, Roby Greenwald, Tianwei Yu, Stefanie Ebelt Sarnat, W Dana Flanders, Karan Uppal, Douglas I Walker, ViLinh Tran, Donghai Liang, Dean P Jones, Jeremy A Sarnat
Affiliations

Metabolomic profiles of plasma, exhaled breath condensate, and saliva are correlated with potential for air toxics detection

Chandresh Nanji Ladva et al. J Breath Res. .

Abstract

Introduction: Advances in the development of high-resolution metabolomics (HRM) have provided new opportunities for their use in characterizing exposures to environmental air pollutants and air pollution-related disease etiologies. Exposure assessment studies have considered blood, breath, and saliva as biological matrices suitable for measuring responses to air pollution exposures. The current study examines comparability among these three matrices using HRM and explores their potential for measuring mobile-source air toxics.

Methods: Four participants provided saliva, exhaled breath concentrate (EBC), and plasma before and after a 2 h road traffic exposure. Samples were analyzed on a Thermo Scientific QExactive MS system in positive electrospray ionization mode and resolution of 70 000 full-width at half-maximum with C18 chromatography. Data were processed using an apLCMS and xMSanalyzer on the R statistical platform.

Results: The analysis yielded 7110, 6019, and 7747 reproducible features in plasma, EBC, and saliva, respectively. Correlations were moderate-to-strong (R = 0.41-0.80) across all pairwise comparisons of feature intensity within profiles, with the strongest between EBC and saliva. The associations of mean intensities between matrix pairs were positive and significant, controlling for subject and sampling time effects. Six out of 20 features shared in all three matrices putatively matched a list of known mobile-source air toxics.

Conclusions: Plasma, saliva, and EBC have largely comparable metabolic profiles measurable through HRM. These matrices have the potential to be used in identification and measurement of exposures to mobile-source air toxics, though further, targeted study is needed.

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Figures

Figure 1
Figure 1
Venn Diagram of Feature Counts. Counts are from features detected at any sampling time. Features in one matrix were matched to features in another if they were within 10ppm of their respective m/z and within 30s of retention time. The numbers in the parentheses indicate the counts of features from the USEPA Master List of Mobile Source Air Toxics.
Figure 2
Figure 2
Scatterplots of Mean Intensities of Shared Features across Subjects between Matrix Pairs at the Pre-Exposure Sampling Time. Red dots indicate those features that also match the Master List of Mobile Source Air Toxics. Sample sizes (from top to bottom) range from 1,862, 2,039, and 1,875 features for Plasma vs. EBC, Plasma vs. Saliva, and EBC vs. Saliva, respectively.
Figure 3
Figure 3
Estimates of the Spearman’s Rank Correlations between Matrices Stratified by Time. Three sets of the data were considered in turn. The All set (far left) is the complete list of shared, reproducible features. The <10% CoV set (middle) is a subset of All with a very high standard for chemical measurement. The EPA-matched (far right) is the subset of features that match the Master List of Mobile Source Air Toxics. All feature matching here was within m/z of 10ppm and RT of 300s.
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References

    1. Miller GW, Jones DP. The Nature of Nurture: Refining the Definition of the Exposome. Toxicological Sciences. 2014;137(1):1–+. - PMC - PubMed
    1. Wild CP. Complementing the genome with an “exposome”: The outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiology Biomarkers & Prevention. 2005;14(8):1847–50. - PubMed
    1. Walker DI, Go Y-M, Liu K, Pennell KD, Jones DP. Metabolic Phenotyping in Personalized and Public Healthcare. Boston: Academic Press; 2016. Chapter 7 – Population Screening for Biological and Environmental Properties of the Human Metabolic Phenotype: Implications for Personalized Medicine; pp. 167–211.
    1. Rappaport SM. Implications of the exposome for exposure science. Journal of Exposure Science and Environmental Epidemiology. 2011;21(1):5–9. - PubMed
    1. Park YH, Lee K, Soltow QA, Strobel FH, Brigham KL, Parker RE, et al. High-performance metabolic profiling of plasma from seven mammalian species for simultaneous environmental chemical surveillance and bioeffect monitoring. Toxicology. 2012;295(1–3):47–55. - PMC - PubMed

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