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

用于精准营养和个性化医疗的自主、非侵入性且支持生物分析的可穿戴平台

• 批准号: 10888746
• 负责人: SAM EMAMINEJAD
• 金额: $ 64.16万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-24 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10888746
• 关键词: Algorithms; Biological Markers; Blood; Chlorides; Clinical; Clinical Research; Complex; Cystic Fibrosis; Data Analytics; Data Set; Dedications; Development; Devices; Dietary Intervention; Disease; Disease Management; Engineering; General Population; Glucose; Hour; In Situ; Individual; Influentials; Inherited; Intake; Iontophoresis; Machine Learning; Measures; Metabolic; Methodology; Microfluidics; Mission; Modality; Modeling; Monitor; Nutrient; Nutritional Support; Nutritional status; Periodicals; Personal Satisfaction; Phase; Physiological; Physiology; Positioning Attribute; Randomized; Sampling; Sodium; Study Subject; Sweat Glands; Sweat test; System; Technology; Triglycerides; Validation; Work; beta-Hydroxybutyrate; cofactor; cohort; cystic fibrosis patients; dietary supplements; effective therapy; experimental study; fabrication; human subject; machine learning algorithm; microsensor; multidisciplinary; operation; personalized medicine; precision nutrition; predictive modeling; recruit; remote patient monitoring; response; sensor; success; targeted biomarker; translational applications; wearable device; wearable monitor; wearable platform

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.

项目摘要 该建议旨在通过创建可穿戴技术来缩放的可穿戴技术来实现精确营养 普通人群非侵入性跟踪一系列假定的循环营养素和 生物标志物。彼此之间，我们将讨论基本和相互结合的工程瓶颈和科学 传感器，设备和数据分析水平的问题，以实现基于汗水的可穿戴生物分析 技术，配备了自主汗水分泌调制，生物流体管理和分析 功能。为了说明我们技术的变革性潜力，我们将特别定位它以监视 与囊性纤维化相关的代谢和疾病状态的养分和指标（CF， 最常见的遗传多系统疾病），以实现个性化的营养支持，这是 CF治疗的中心。 彼此之间，在第一阶段（R21），我们将开发针对葡萄糖，甘油三酸酯和β-的微传感器阵列 羟基丁酸。我们将结合易于发展的辅助感应方式（汗液，氯化物， pH和汗水分泌率感测界面），以实现分泌曲线的原位表征 （这对于汗水读数的归一化和CF进展的跟踪非常有用）。与这些平行 工程工作，我们将进行汗水表征实验以研究分泌率的影响 分析物从血液分配到汗水。这些数据集将使用最先进的机器增强 学习算法以制定专门的分析框架，以说明汗水的分泌能力 并确定最佳的汗水分泌条件，以提供未分布和身体有意义的甜味 读数。 在第二阶段（R33）中，我们将通过证明能力来确定技术的临床实用性 非侵入性地跟踪靶营养物质与血液循环水平有关的临时概况（在 健康受试者和CF患者以及简单/混合餐调节研究）。根据，我们将首先 在营养的单个/二元组合后，测量汗水和血液分析物的游览曲线 摄入并开发基于机器学习的算法，以将汗水分析物读数与他们的 循环浓度。然后，我们将评估和表征解决方案在上下文中的预测效用 复杂的营养补充研究。通过验证，我们将招募一组CF患者并进行 一项纵向随机营养支持研究，以证明启用远程患者监测 我们的解决方案提供的功能。 这项工作的成功将代表对制定战略的开创性贡献 启用精确的营养和个性化医学。