Minimally-invasive technology for personalized nutritional monitoring

用于个性化营养监测的微创技术

• 批准号: 10693521
• 负责人: Nicolaas E Deutz
• 金额: $ 65.9万
• 依托单位: TEXAS ENGINEERING EXPERIMENT STATION
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10693521
• 关键词: Address; Affect; Aging; Algorithms; American; Amino Acids; Biological Markers; Biomedical Engineering; Blood; Blood specimen; Body mass index; Calibration; Carbohydrates; Catheters; Chronic; Clinical Treatment; Collaborations; Communities; Complex; Computational algorithm; Computer Models; Computers; Computing Methodologies; Consumption; Continuous Glucose Monitor; Controlled Study; Data; Development; Devices; Diabetes Mellitus; Diet; Diet Monitoring; Dietary intake; Digestion; Elements; Engineering; Evaluation; Exhibits; Fatty acid glycerol esters; Freezing; Glucose; Glucose Plasma Concentration; Glycosylated hemoglobin A; Health; Hour; Hydrogels; Individual; Injectable; Intake; Intercellular Fluid; Kidney; Knowledge; Lead; Liquid substance; Liver Failure; Low income; Macronutrients Nutrition; Measurement; Measures; Metabolism; Methods; Microdialysis; Monitor; Muscle function; Muscular Atrophy; Nutrition Monitoring; Nutritional; Obesity; Optical Readers; Optical reporter; Optics; Oxidases; Oxygen; Participant; Performance; Periodicals; Physical activity; Plasma; Proteins; Proxy; Reader; Reporting; Research; Research Personnel; Sampling; Scientist; Solid; Supermarket; System; Technology; Testing; Work; data fusion; data streams; design; glucose oxidase; glucose sensor; human data; human subject; innovation; insight; instrumentation; machine learning algorithm; machine learning method; machine learning model; minimally invasive; muscle form; novel; novel strategies; nutrition; precision nutrition; predictive modeling; protein intake; prototype; sensor; sensor technology; sex; skin color; small molecule; success; tool; trend; wearable device; wearable sensor technology

Project Summary/Abstract Monitoring and optimizing dietary intake are important for precision nutrition in the treatment of clinical conditions (diabetes, obesity, kidney/liver failure, etc) as well as attenuating loss of muscle function and mass during aging. However, current methods for personal health monitoring do not provide reliable measurement of macronutrient intake or availability of metabolites after digestion. Thus, this project aims to address this gap by developing multi-analyte sensing devices based on an innovative combination of insertable optical reporters, wearable readers, and advanced computational methods. These devices will provide continuous/on-demand measurement of metabolites in interstitial fluid (ISF), then use those measures to predict macronutrient intake. The project is organized into three specific aims: Aim 1 will involve collecting blood samples and ISF samples (via microdialysis) from healthy human subjects consuming fresh meals and then using machine learning methods to establish a quantitative relationship between macronutrient intake and the resulting blood and ISF levels of metabolites. Fresh meals will be designed by the investigators to have three different levels of protein and carbohydrates. Data will be analyzed to establish quantitative relationships between compartmental concentrations, identifying proportionality coefficients and temporal lag/lead parameters, and develop machine- learning models to predict macronutrients in meals from the plasma and ISF concentrations of metabolites. Aim 2 will proceed in parallel with Aim 1, focusing on modifying current oxygen and glucose-sensing devices to measure amino acids. Extensive benchtop testing will be used to verify accuracy and precision of calibration based on fusion of sensor data streams. Aim 3 will then focus on deploying and testing a removable version of the multi-analyte sensing system in human subjects. Participants will be fitted with the sensors, a microdialysis catheter, and a continuous glucose monitor. Subjects will consume fresh meals as well as macronutrient- matched frozen meals, matched for contents, to verify that the system (sensors and algorithms) also work for meals that are more representative of the diet for many Americans, especially in low-income communities. The computational algorithms developed in Aim 1 will be used to predict macronutrient availability from sensor data as well as microdialysis. Sex, skin color, and health indicators (BMI, HbA1c, etc) will be factored into analyses to assess whether they substantially affect performance of instrumentation and algorithms. This project will bring together a team of biomedical engineers, computer scientists, and nutrition & metabolism researchers to develop instrumentation and collect data from human subjects that will yield: new knowledge and insight on the relationship between nutritional intake and systemic metabolite levels (Aim 1); advances in technology for sensing metabolites as nutritional biomarkers (Aim 2); evaluation of the sensing technology and computational models in diverse human subjects consuming common meals. (Aim 3). Success in this project will advance research on metabolism and support development of new approaches to personalized nutrition.

项目概要/摘要 监测和优化膳食摄入对于临床治疗中的精准营养非常重要 疾病（糖尿病、肥胖、肾/肝衰竭等）以及减轻肌肉功能和质量的损失 老化期间。然而，目前的个人健康监测方法并不能提供可靠的测量 大量营养素的摄入量或消化后代谢物的利用率。因此，该项目旨在通过以下方式解决这一差距 开发基于可插入光学报告器的创新组合的多分析物传感装置， 可穿戴阅读器和先进的计算方法。这些设备将提供连续/按需 测量间质液 (ISF) 中的代谢物，然后使用这些测量值来预测大量营养素的摄入量。 该项目分为三个具体目标： 目标 1 将涉及收集血液样本和 ISF 样本 （通过微透析）来自健康人类受试者食用新鲜食物，然后使用机器学习 建立常量营养素摄入量与所得血液和 ISF 之间定量关系的方法 代谢物水平。研究人员将设计新鲜膳食，使其含有三种不同水平的蛋白质 和碳水化合物。将分析数据以建立分区之间的定量关系 浓度，确定比例系数和时间滞后/超前参数，并开发机器 学习模型根据代谢物的血浆和 ISF 浓度来预测膳食中的常量营养素。 目标 2 将与目标 1 并行进行，重点是修改现有的氧气和葡萄糖传感设备，以 测量氨基酸。将使用广泛的台式测试来验证校准的准确性和精度 基于传感器数据流的融合。然后，目标 3 将重点部署和测试可移动版本 人类受试者的多分析物传感系统。参与者将配备传感器、微透析器 导管和连续血糖监测仪。受试者将食用新鲜膳食以及常量营养素- 匹配冷冻食品，匹配内容，以验证系统（传感器和算法）也适用于 这些膳食更能代表许多美国人的饮食，尤其是在低收入社区。这 目标 1 中开发的计算算法将用于根据传感器数据预测常量营养素的可用性 以及微透析。性别、肤色和健康指标（BMI、HbA1c 等）将被纳入分析中 评估它们是否会严重影响仪器和算法的性能。 该项目将汇集生物医学工程师、计算机科学家以及营养与代谢专家团队 研究人员开发仪器并收集人类受试者的数据，这将产生： 新知识 以及对营养摄入和全身代谢水平之间关系的深入了解（目标 1）；取得进展 将代谢物作为营养生物标志物的传感技术（目标 2）；传感技术的评估和 不同人类受试者食用共同膳食的计算模型。 （目标 3）。这个项目的成功 将推进新陈代谢研究并支持个性化营养新方法的开发。