Hair Metabolomics: Identification of Fetal Compromise Provides Proof of Concept for Biomarker Discovery

Abstract

Analysis of the human metabolome has yielded valuable insights into health, disease and toxicity. However, the metabolic profile of complex biological fluids such as blood is highly dynamic and this has limited the discovery of robust biomarkers. Hair grows relatively slowly, and both endogenous compounds and environmental exposures are incorporated from blood into hair during growth, which reflects the average chemical composition over several months.We used hair samples to study the metabolite profiles of women with pregnancies complicated by fetal growth restriction (FGR) and healthy matched controls. We report the use of GC-MS metabolite profiling of hair samples for biomarker discovery. Unsupervised statistical analysis showed complete discrimination of FGR from controls based on hair composition alone. A predictive model combining 5 metabolites produced an area under the receiver-operating curve of 0.998.This is the first study of the metabolome of human hair and demonstrates that this biological material contains robust biomarkers, which may lead to the development of a sensitive diagnostic tool for FGR, and perhaps more importantly, to stable biomarkers for a range of other diseases.

Novel insights, challenges and practical implications of DOHaD‐omics research

“…The decision on sampling material will be specific to the research question and critical to the interpretation of results. For example, blood samples reflect highly dynamic responses, but hair samples reflect prolonged exposure and can therefore provide a more stable phenotype 18 . The large volume of data generated by such techniques can provide insight into interactions between metabolites, genes, transcripts and proteins 19 .…”

“…The potential for metabolomics platforms to be used to identify biomarkers predicting pregnancy outcome is already becoming apparent. These platforms include observations of differences in the neonatal blood metabolome across gestational ages (differences that are dependent on postnatal age at sampling 21 ) and specific pathology and illness severity; 22 a study linking the maternal hair metabolome with fetal growth restriction; 18 and an ongoing prospective study for early prediction of pre‐eclampsia 23 (trial http://ctgov:NCT01891240).…”

“…For example, blood samples reflect highly dynamic responses, but hair samples reflect prolonged exposure and can therefore provide a more stable phenotype. 18 The large volume of data generated by such techniques can provide insight into interactions between metabolites, genes, transcripts and proteins. 19 These data can be highly informative about mechanisms leading to disease and the impact of environmental exposures on system physiology, such as the developmental impact of prenatal exposure to the endocrine disruptor, bisphenol A.…”

In search of hair damage using metabolomics?

“…Metabolomics is most commonly applied to body/tissue fluids; however, recently the concept of metabolomic analysis of hair was successfully introduced by Sulek et al . to predict foetal growth restriction (FGR) by examination of maternal hair samples, and they found a distinct metabolite profile that distinguished FGR from normal pregnancy upon statistical comparison.…”

“…Fibre morphology can also tell a story of hair damage and more rarely, genetics, as recently described by van Steensel et al8 who discovered polymorphisms in two hair keratin genes KRT83 and KRT86 in monilethrix. However, in cosmetic science, the relationship of fibre Commentary | 551 morphology to function/behaviour is often a very subtle one, requiring ever more sophisticated methods for differentiating products and their benefits for the consumer.Metabolomics is most commonly applied to body/tissue fluids; however, recently the concept of metabolomic analysis of hair was successfully introduced by Sulek et al9 to predict foetal growthrestriction (FGR) by examination of maternal hair samples, and they found a distinct metabolite profile that distinguished FGR from normal pregnancy upon statistical comparison. Both proteomic analysis and metabolomics generate complex data sets, and all studies require considerable understanding and rigour in the statistical analysis applied.…”