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: 10198604
• 负责人: SAM EMAMINEJAD
• 金额: $ 21.08万
• 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 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10198604
• 关键词: Accounting; Address; Algorithms; Biological Markers; Blood; Clinical; Clinical Engineering; Clinical Research; Complex; Cystic Fibrosis; Data Analytics; Data Set; Development; Devices; Dietary Intervention; Disease; Disease Management; Engineering; General Population; Glucose; Health; Hour; In Situ; Individual; Influentials; Inherited; Intake; Iontophoresis; Machine Learning; Measures; Metabolic Diseases; Methodology; Microfluidics; Mission; Modality; Modeling; Monitor; Nutrient; Nutritional Support; Nutritional status; Periodicity; Personal Satisfaction; Phase; Physiological; Physiology; Positioning Attribute; Randomized; Reading; Sampling; Sodium Chloride; Study Subject; Sweat Glands; Sweat test; System; Technology; Triglycerides; Validation; Work; base; beta-Hydroxybutyrate; cofactor; cohort; cystic fibrosis patients; dietary supplements; effective therapy; experimental study; human subject; machine learning algorithm; microsensor; multidisciplinary; operation; personalized medicine; precision nutrition; predictive modeling; recruit; remote patient monitoring; response; sensor; success; targeted biomarker; wearable device

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 该提案旨在通过创建一种可扩展的可穿戴技术来实现精准营养 一般人群非侵入性地跟踪一组假定的循环营养素的昼夜分布， 生物标志物。因此，我们将解决基本的和相互交织的工程瓶颈， 传感器、设备和数据分析层面的问题，以实现基于汗液的可穿戴生物分析 技术，配备自主汗液分泌调节、生物流体管理和分析 能力的为了说明我们的技术的变革潜力，我们将特别将其定位为监测 与囊性纤维化相关的一组营养素和代谢和疾病状态指标（CF， 最常见的遗传性多系统疾病），以实现个性化的营养支持， CF治疗的核心。 因此，在第一阶段（R21），我们将开发针对葡萄糖、甘油三酯和β-葡萄糖的微传感器阵列。 羟基丁酸盐。我们将结合我们容易开发的辅助传感模式（汗液钠，氯化物， pH和汗液分泌速率感测接口），以实现分泌概况的原位表征 （这对于汗液读数的标准化和CF进展的跟踪是有用的）。与此同时， 工程的努力，我们将进行汗液表征实验，研究分泌率的影响， 分析物从血液中分离到汗液中。这些数据集将通过最先进的机器 学习算法以制定考虑汗液分泌变化的专用分析框架 并确定最佳汗液分泌条件以提供不失真的和生理上有意义的汗液 读数。 在第二阶段（R33），我们将通过展示以下能力来建立我们技术的临床实用性： 非侵入性地跟踪目标营养素的时间分布与它们在血液中的循环水平（在两种情况下） 健康受试者和CF患者以及通过简单/混合膳食调节研究）。因此，我们将首先 在营养素的受控单一/二元组合之后测量汗液和血液分析物的偏移曲线 摄入并开发基于机器学习的算法，以将汗液分析物读数与其 循环浓度。然后，我们将评估和描述我们的解决方案在上下文中的预测效用 复杂的营养补充研究。经过验证，我们将招募一组CF患者， 一项纵向随机营养支持研究，以证明远程患者监测 我们的解决方案提供的能力。 这项工作的成功将是对制定战略的一个突破性贡献 以实现精准营养和个性化医疗。