# An Autonomous, Non-invasive, and Bioanalytics-enabled Wearable Platform for Precision Nutrition and Personalized Medicine

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $210,796

## Abstract

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.

## Key facts

- **NIH application ID:** 10198604
- **Project number:** 1R21DK128711-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** SAM EMAMINEJAD
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $210,796
- **Award type:** 1
- **Project period:** 2021-05-24 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10198604

## Citation

> US National Institutes of Health, RePORTER application 10198604, An Autonomous, Non-invasive, and Bioanalytics-enabled Wearable Platform for Precision Nutrition and Personalized Medicine (1R21DK128711-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10198604. Licensed CC0.

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