Typically, clinical metabolome analysis is performed on blood samples. However, drawing blood is not only a cumbersome procedure for patients but requires qualified personnel which impairs measurement during real-life settings. A promising alternative is the analysis of the metabolome from finger sweat where sampling is as simple as holding filter paper between fingertips. High-resolution orbitrap MS/MS hyphenated with UHPLC then enabled metabolomic phenotyping from minute amounts of the collected sweat. This method drastically simplifies sampling at short intervals which is valuable for time-course studies.
However, a major obstacle to finger sweat analysis is the inability to control or measure the amount of sweat produced by the sweat glands at any given time. Even conservative estimates put the variability of the sweat flux on fingertips between 0.05 and 0.62 mg cm^(-2) min^(-1), depending on multiple endo- and exogenous factors. Not addressing this problem prevents a reliable quantification of metabolites in finger sweat. Here we present a computational method based on the identification of metabolic pairs in the sweat metabolome that allows us to quantify sweat volumes and enables an individualized, accurate quantitative finger sweat analysis for clinical applications.
In a proof-of-principal application, we use short interval sampling of sweat from fingertips to monitor the dynamic response of 43 individuals after caffeine consumption. We not only identified corresponding xenobiotics but extracted individualized kinetic parameters of caffeine metabolites from sweat and show the long-time stability of these parameters. Moreover, based on the computationally recovered sweat volumes we identified marker metabolites that are correlated to the sweat volume, which in turn allows us to predict sweat volumes of future metabolome measurements.
In conclusion, this work highlights the feasibility of individualized and reliable biomonitoring using sweat samples from fingertips which may have far-reaching implications for personalized medical diagnostics and biomarker discovery.