Blood sampling has long been the standard for monitoring internal health, but it is invasive, costly, and unsuitable for continuous tracking. Sweat, by contrast, is non-invasive and abundant, but conventional label-based and label-free sensors have faced challenges in collecting and controlling it effectively. A new approach now demonstrates how sweat can be used to precisely track changes in metabolites, paving the way for more accessible and accurate health monitoring.

Researchers at the Korea Advanced Institute of Science and Technology (KAIST, Daejeon, South Korea) have developed a smart wearable patch that enables real-time, label-free analysis of multiple metabolites in sweat. The patch combines nanophotonics, which manipulates light at the nanoscale to read molecular properties, with microfluidics, which guides sweat through ultrathin channels. The device integrates six to seventeen chambers, where sweat flows during exercise, allowing continuous monitoring of changes in metabolite concentrations.

The patch was tested on human subjects and successfully tracked sweat composition during exercise. For the first time, researchers were able to simultaneously quantify three metabolites—uric acid, lactic acid, and tyrosine—without staining or labels. Artificial intelligence analysis was used to distinguish signals from complex sweat components, showing significantly improved precision compared to previous label-free methods. The results were published in Nature Communications.

This wearable platform offers new possibilities for continuous health monitoring without the need for blood tests. It could transform chronic disease management, drug response tracking, and environmental exposure monitoring. Researchers also envision its use in discovering biomarkers for metabolic diseases, providing clinicians with a simple, non-invasive, and scalable diagnostic tool. Future work will aim to expand the device’s scope across diverse healthcare applications.

“This research lays the foundation for precisely monitoring internal metabolic changes over time without blood sampling by combining nanophotonics and microfluidics technologies,” said KAIST Professor Ki-Hun Jeong, who led the research team. “In the future, it can be expanded to diverse fields such as chronic disease management, drug response tracking, environmental exposure monitoring, and the discovery of next-generation biomarkers for metabolic diseases.”

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