Finger sweat analysis enables short interval metabolic biomonitoring in humans

Julia Brunmair, Mathias Gotsmy, Laura Niederstaetter, Benjamin Neuditschko, Andrea Bileck, Astrid Slany, Max Lennart Feuerstein, Clemens Langbauer, Lukas Janker, Jürgen Zanghellini, Samuel M. Meier-Menches and Christopher Gerner ()
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Julia Brunmair: Faculty of Chemistry, University of Vienna
Mathias Gotsmy: Faculty of Chemistry, University of Vienna
Laura Niederstaetter: Faculty of Chemistry, University of Vienna
Benjamin Neuditschko: Faculty of Chemistry, University of Vienna
Andrea Bileck: Faculty of Chemistry, University of Vienna
Astrid Slany: Faculty of Chemistry, University of Vienna
Max Lennart Feuerstein: Faculty of Chemistry, University of Vienna
Clemens Langbauer: Faculty of Chemistry, University of Vienna
Lukas Janker: Faculty of Chemistry, University of Vienna
Jürgen Zanghellini: Faculty of Chemistry, University of Vienna
Samuel M. Meier-Menches: Faculty of Chemistry, University of Vienna
Christopher Gerner: Faculty of Chemistry, University of Vienna

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues, which are impeding time-course studies. Here, we show that the metabolic profiling of the minute amounts of sweat sampled from fingertips addresses this challenge. Sweat sampling from fingertips is non-invasive, robust and can be accomplished repeatedly by untrained personnel. The sweat matrix represents a rich source for metabolic phenotyping. We confirm the feasibility of short interval sampling of sweat from the fingertips in time-course studies involving the consumption of coffee or the ingestion of a caffeine capsule after a fasting interval, in which we successfully monitor all known caffeine metabolites as well as endogenous metabolic responses. Fluctuations in the rate of sweat production are accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. To conclude, metabotyping using sweat from fingertips combined with mathematical network modelling shows promise for broad applications in precision medicine by enabling the assessment of dynamic metabolic patterns, which may overcome the limitations of purely compositional biomarkers.

Date: 2021
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DOI: 10.1038/s41467-021-26245-4

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