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: 10408784
• 负责人: SAM EMAMINEJAD
• 金额: $ 19.59万
• 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/10408784
• 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; translational applications; wearable device; 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.

项目概要 该提案旨在通过创建可扩展的可穿戴技术来实现精准营养 普通人群以非侵入性方式追踪一组假定的循环营养素的昼夜分布， 因此，我们将解决基本的、相互交织的工程瓶颈和科学问题。 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 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控 与囊性纤维化（CF，the 最常见的遗传性多系统疾病），以便实现个体化营养支持，即 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-的微传感器阵列。 我们将结合我们现成的辅助传感方式（汗钠、氯化物、 pH 值和汗液分泌率传感接口），以实现分泌曲线的原位表征 （这对于汗液读数的标准化和 CF 进展的跟踪非常有用）。 工程上，我们会进行汗液表征努力实验来研究分泌率的影响 这些数据集将通过最先进的机器得到增强。 学习算法来制定专用的分析框架来解释汗液分泌变量 并确定最佳的汗液分泌条件，以提供不失真且具有生理意义的汗液 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： 非侵入性地跟踪与血液中循环水平相关的目标营养素的时间分布（在两种情况下） 健康受试者和 CF 患者并通过简单/混合膳食调节研究）。 在受控的单一/二元营养组合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，将汗液分析物读数与其相关联 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。 复杂的营养补充剂研究经过验证后，我们将招募一组 CF 患者并进行研究。 一项纵向随机营养支持研究，旨在证明远程患者监测的可行性 我们的解决方案提供的功能。 这项工作的成功将为战略的制定做出开创性的贡献 实现精准营养和个性化医疗。

项目成果

Skin-interfaced electronics: A promising and intelligent paradigm for personalized healthcare.

皮肤接口电子产品：个性化医疗保健的有前途的智能范例。

• DOI: 10.1016/j.biomaterials.2023.122075
• 发表时间: 2023-03-01
• 期刊: Biomaterials
• 影响因子: 14
• 作者: Yangzhi Zhu;Jinghang Li;Jinjoo Kim;Shaopei Li;Yichao Zhao;Jamaluddin Bahari;Payam Eliahoo;Guangh
• 通讯作者: Guangh

Autonomous wearable sweat rate monitoring based on digitized microbubble detection.

基于数字化微泡检测的自主可穿戴出汗率监测。

• 发表时间: 2022-11-08
• 影响因子: 6.1
• 作者: Lin, Haisong;Yu, Wenzhuo;Suarez, Jorge Emiliano De Dios;Athavan, Harish;Wang, Yibo;Yeung, Christopher;Lin, Shuyu;Sankararaman, Sriram;Milla, Carlos;Emaminejad, Sam
• 通讯作者: Emaminejad, Sam