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190523_XenO_val_paper_ACS_template_final_incl_SI.pdf (2.25 MB)
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A Generic LC-MS/MS Exposome Method for the Determination of Xenoestrogens in Biological Matrices

preprint
submitted on 23.05.2019 and posted on 23.05.2019 by Karin Preindl, Dominik Braun, Georg Aichinger, Sabina Sieri, Mingliang Fang, Doris Marko, Benedikt Warth
We are constantly exposed to a variety of environmental contaminants and hormones including those mimicking endogenous estrogens. These highly heterogeneous molecules are collectively referred to as xenoestrogens and hold the potential to affect and alter the delicate hormonal balance of the human body. To monitor exposure and investigate potential health implications, comprehensive analytical methods covering all major xenoestrogen classes are urgently needed but still not available. Herein, we describe an LC-MS/MS method for the simultaneous determination of multiple classes of endogenous as well as exogenous estrogens in human urine, serum and breast milk to enable proper exposure and risk assessment. In total, 75 analytes were included, whereof a majority was successfully in-house validated in the three matrices. Extraction recoveries of validated analytes ranged from 71% to 110% and limits of quantification from 0.015 to 5 µg/L, 0.03 to 14 µg/L, and 0.03 to 4.6 µg/L in urine, serum and breast milk, respec-tively. The applicability of the novel method was demonstrated in proof of principle experiments by analyzing urine from Austrian, and breast milk from Austrian and Nigerian individuals. Thereby, we proved the methods’ feasibility to identify and quantify different classes of xenoestrogens simultaneously. The results illustrate the general importance of multi-class exposure assessment in the context of the exposome paradigm. Specifically, they highlight the need for estimating total estrogenic burden rather than single analyte or chemical class measurements and its potential impact in endocrine disruption and hormone related diseases including cancers.

Funding

University of Vienna

History

Email Address of Submitting Author

benedikt.warth@univie.ac.at

Institution

University of Vienna

Country

Austria

ORCID For Submitting Author

0000-0002-6104-0706

Declaration of Conflict of Interest

Authors declare no conflict of interest

Version Notes

Version_1_190523

Exports