Hair Metabolomics in Animal Studies and Clinical Settings release_rev_69d29cb6-def7-4e37-989a-c1e25b385f70

by Won-Jun Jang, Jae Yoon Choi, Byoungduck Park, Ji Hae Seo, Young Ho Seo, Sangkil Lee, Chul-Ho Jeong, Sooyeun Lee

Published in Molecules by MDPI AG.

2019   Volume 24, Issue 12, p2195

Abstract

Metabolomics is a powerful tool used to understand comprehensive changes in the metabolic response and to study the phenotype of an organism by instrumental analysis. It most commonly involves mass spectrometry followed by data mining and metabolite assignment. For the last few decades, hair has been used as a valuable analytical sample to investigate retrospective xenobiotic exposure as it provides a wider window of detection than other biological samples such as saliva, plasma, and urine. Hair contains functional metabolomes such as amino acids and lipids. Moreover, segmental analysis of hair based on its growth rate can provide information on metabolic changes over time. Therefore, it has great potential as a metabolomics sample to monitor chronic diseases, including drug addiction or abnormal conditions. In the current review, the latest applications of hair metabolomics in animal studies and clinical settings are highlighted. For this purpose, we review and discuss the characteristics of hair as a metabolomics sample, the analytical techniques employed in hair metabolomics and the consequence of hair metabolome alterations in recent studies. Through this, the value of hair as an alternative biological sample in metabolomics is highlighted.
In application/xml+jats format

Type  article-journal
Stage   published
Date   2019-06-12
Language   en ?
Container Metadata
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:  1420-3049
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Revision

This is a specific, static metadata record, not necessarily linked to any current entity in the catalog.

Catalog Record
Revision: 69d29cb6-def7-4e37-989a-c1e25b385f70
API URL: JSON