Sweat as a diagnostic biofluid: analytical advances and future directions

The use of sweat as an alternative specimen for biological analyses is well recognised and has been successfully applied in forensic toxicology and clinical health assessments. Compared to conventional matrices such as urine and blood, sweat samples are easy to obtain, are less invasive and present a reduced number of endogenous interferents. Advanced analytical techniques in sweat analysis have considerably expanded capabilities in the isolation of drugs, detection of complex metabolites and identification of biomarkers. The ability to detect a broader range of substances with greater sensitivity and accuracy, improved speed and efficiency and enhanced reliability present opportunities for non-invasive chemical sensing in sweat for a much broader scope of metabolites than traditionally recognised. The year 2023 was marked by an evolutionary step in artificial intelligence (AI), opening the door for improved pattern analysis and classification algorithms to improve diagnostic precision and therapeutic accuracy. It is anticipated that when combined with advances in the performance and miniaturization of integrated circuits, stretchable electronics, wireless connectivity and longer battery life, a substantial impact is expected on the development of wearable biosensor devices to provide meaningful information to the physiology of end-users, expanding its role in personalised medicine and health monitoring. Therefore, the aim of this review is to provide an integrative overview of sweat as a diagnostic and monitoring biofluid by first discussing traditional methods in clinical and forensic applications. Recent advancements in sweat biomarker detection are then highlighted, followed by an evaluation of sweat's potential for real-time physiological monitoring. • Sweat is an accessible source of information for diagnostic & health monitoring • Advanced analytical techniques are expanding capabilities in biomarker discovery • A key challenge is the identification of biomarkers of clinical signification • Improvements in AI are anticipated to enable advancements in diagnostic accuracy