Building a human hair metabolome atlas for clinical diagnostics

Promovendus/a: Maria van de Lavoir

Promotor(en): Prof. Adrian Covaci, Prof. Alexander van Nuijs

Metabolites (small molecules <2000 Da) play an important role in metabolic processes and are influenced by lifestyle, diet, genetics, and disease states. Metabolomics, the study of these metabolites in clinical samples such as blood, urine, and hair, enables the detection of metabolic changes associated with disease. These metabolites act as signaling molecules, or biomarkers.
Hair offers unique advantages as a clinical sample due to its chemical stability and ability to preserve a long-term biochemical profile. Unlike blood or urine, which reflect short-term metabolic fluctuations, months. Segmental hair analysis is therefore a promising, non-invasive tool for monitoring chronic diseases and detecting disease at an early stage.
Despite its potential, hair metabolomics remains underexplored, particularly regarding the biochemical composition of hair and variations in metabolite concentrations along the hair strand.
This study presents the Human Hair Atlas, a comprehensive reference database of the chemical composition of human hair. To achieve this, advanced analytical techniques, such as liquid chromatography-high-resolution mass spectrometry and ion mobility spectrometry, were applied to precisely map the molecular structure of hair.

Key findings and contributions
A hair-specific, systematic 10-step analytical workflow was developed to optimize the extraction and identification of metabolites in hair with increased efficiency and accuracy. This improved method enabled the creation of a large-scale dataset: the Human Hair Atlas, which mapped over 1,200 metabolites in healthy individuals. This dataset serves as a reference profile for future comparisons, such as with patients with specific diseases.
Analysis of consecutive 1 cm hair segments revealed that metabolite concentrations varied by up to 50%, highlighting the need for correction of baseline variations in hair-based studies, particularly in chronic disease research.
Additionally, 122 metabolites were identified that are potentially exogenous, mainly originating from personal care products. By identifying and categorizing exogenous compounds, the Human Hair Atlas supports the interpretation of hair metabolomics data, preventing misattribution of these compounds as disease-related metabolic variations.
By detecting a wide range of metabolites, the Human Hair Atlas highlights both the potential of hair metabolomics and the value of hair as a clinical matrix. The Human Hair Atlas is publicly accessible (https://metabolomics.cloud/hair/) and serves as a reference for research in hair metabolomics, lipidomics, and exposomics.