Identification of end-stage renal disease metabolic signatures from human perspiration

End-stage renal disease (ESRD), characterized by cessation in kidney function, has been linked to severe metabolic disturbances, caused by the buildup of toxic solutes in blood. To remove these solutes, ESRD patients undergo dialysis. As a proof of concept, we tested whether ESRD-related metabolic signatures can be detected in perspiration samples using a combined methodology. Our rapid methodology involves swabbing a glass slide across the patient's forehead, detecting the metabolites in the imprint using desorption electrospray ionization mass spectrometry, and identifying the key differences using machine learning methods. Based on collecting 42 healthy and 27 ESRD samples, we find saturated fatty acids are consistently suppressed in ESRD patients, with little change after dialysis. Moreover, our method enables the detection of uremic solutes, where we find elevated levels of uric acid (6.7 fold higher on average) that sharply decrease after dialysis. Beyond the study of individual metabolites, we find that a lasso model, which selects for 8 m/z fragments from 24,602 detected analytes, achieves area under the curve performance of 0.85 and 0.87 on training (n = 52) and validation sets (n = 17), respectively. Together, these results suggest that this methodology is promising for detecting signatures relevant for precision health.Key pointsCombines physical measurements with statistical analysis based on machine learning.Uses results to make medical predictions.Learns about biological pathways. After rapidly swabbing a glass slide across an individual's forehead to capture the sweat profile, the combination of desorption electrospray ionization mass-spectrometry and machine learning are used to identify metabolic signatures that differentiate healthy and end-stage renal disease patients, with uric acid (elevated 6.7x in end-stage patients) shown as an example.image