Influence of Home Indoor Dampness Exposure on Volatile Organic Compounds in Exhaled Breath of Mothers and Their Infants: The NELA Birth Cohort

Abstract

Currently, the effect of exposure to indoor air contaminants and the presence of dampness at home on respiratory/atopic health is of particular concern to physicians. The measurement of volatile organic compounds (VOCs) in exhaled breath is a useful approach for monitoring environmental exposures. A great advantage of this strategy is that it allows the study of the impact of pollutants on the metabolism through a non-invasive method. In this paper, the levels of nine VOCs (acetone, isoprene, toluene, p/m-xylene, o-xylene, styrene, benzaldehyde, naphthalene, and 2-ethyl-1-hexanol) in the exhaled breath of subjects exposed and not exposed to home dampness were assessed. Exhaled breath samples were collected from 337 mother–child pairs of a birth cohort and analysed by gas-chromatography–mass-spectrometry. It was observed that the levels of 2-ethyl-1-hexanol in the exhaled breath of the mothers were significantly influenced by exposure to household humidity. In the case of the infants, differences in some of the VOC levels related to home dampness exposure; however, they did not reach statistical significance. In addition, it was also found that the eosinophil counts of the mothers exposed to home dampness were significantly elevated compared to those of the non-exposed mothers. To our knowledge, these findings show, for the first time, that exposure to home dampness may influence VOC patterns in exhaled breath.

Application of breathomics in pediatric asthma: a review

“…When assimilating findings into a clinical context, comprehensive investigations necessitate large subject cohorts, environmental impact assessment on breath samples, implementation of reproducible and transparent data pre-processing workflows, and rigorous data analysis protocols. 61 The identification of VOCs as potential disease biomarkers necessitates comprehensive data analysis for the development of predictive models using clinical data. However, many of the results reported in the existing literature have not been validated in an independent data pool to check the interpretability and generalizability of the models.…”