Project Summary This proposal aims to enable precision nutrition by creating a wearable technology that can be scaled across the general population to non-invasively track the diurnal profiles of a panel of putative circulating nutrients and biomarkers. Accordingly, we will address fundamental and intermeshed engineering bottlenecks and scientific questions at sensor, device, and data analytics levels to realize a sweat-based wearable bioanalytical technology, equipped with autonomous sweat secretion modulation, biofluid management, and analysis capabilities. To illustrate our technology’s transformative potential, we will particularly position it to monitor a panel of nutrients and indicators of the metabolic and disease state that are relevant in cystic fibrosis (CF, the most common inherited multisystemic disorder), in order to enable individualized nutritional support, which is central to the CF treatment. Accordingly, in the first phase (R21), we will develop microsensor arrays targeting glucose, triglyceride, and β- hydroxybutyrate. We will incorporate our readily developed auxiliary sensing modalities (sweat sodium, chloride, pH, and sweat secretion rate sensing interfaces) to enable the in-situ characterization of the secretion profile (which is useful for the normalization of sweat readings and tracking of the CF progression). In parallel to these engineering efforts, we will conduct sweat characterization experiments to study the effect of the secretion rate on analyte partitioning from blood into sweat. These datasets will be augmented with state-of-art machine learning algorithms to formulate a dedicated analytical framework that accounts for sweat secretion variabilities and determines optimal sweat secretion condition(s) to provide undistorted and physiologically meaningful sweat readings. In the second phase (R33), we will establish the clinical utility of our technology by demonstrating the ability to non-invasively track the target nutrients’ temporal profiles in relation to their circulating levels in blood (in both healthy subjects and CF patients and through simple/mixed meal-modulated studies). Accordingly, we will first measure the sweat and blood analytes’ excursion profiles after controlled single/binary combinations of nutrients intake and develop a machine-learning based algorithm to correlate the sweat analyte readouts to their circulating concentrations. Then we will assess and characterize the predictive utility of our solution in the context of complex nutritional supplement studies. Upon its validation, we will recruit a cohort of CF patients and perform a longitudinal randomized nutritional support study to demonstrate the enabling remote patient monitoring capabilities rendered by our solution. The success of this work will represent a groundbreaking contribution towards the development of strategies to enable precision nutrition and personalized medicine.