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.

项目总结/摘要 监测和优化膳食摄入对于临床治疗中的精确营养至关重要 条件（糖尿病，肥胖，肾/肝功能衰竭等）以及减弱肌肉功能和质量的损失 在老化过程中。然而，目前的个人健康监测方法不能提供可靠的测量， 大量营养素摄入或消化后代谢物的可用性。因此，本项目旨在通过以下方式弥补这一差距： 开发基于可插入光学报告器的创新组合的多分析物感测装置， 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，然后使用这些测量来预测大量营养素的摄入。 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 (via微透析），然后使用机器学习 建立大量营养素摄入量与血液和ISF之间定量关系的方法 代谢物水平。新鲜的膳食将由研究人员设计，有三种不同的蛋白质水平 和碳水化合物。将分析数据，以建立房室模型之间的定量关系 浓度，确定比例系数和时间滞后/领先参数，并开发机器- 学习模型，从代谢物的血浆和ISF浓度预测膳食中的常量营养素。 目标2将与目标1平行进行，重点是修改当前的氧气和葡萄糖传感设备， 测量氨基酸。将使用广泛的台架测试来验证校准的准确度和精密度 基于传感器数据流的融合。Aim 3将专注于部署和测试可移动版本的 多分析物传感系统。参与者将配备传感器，微透析 导管和连续血糖监测仪受试者将食用新鲜食物以及大量营养素- 匹配的冷冻餐，匹配的内容，以验证系统（传感器和算法）也适用于 对许多美国人来说，尤其是在低收入社区，更能代表饮食的膳食。的 在目标1中开发的计算算法将用于根据传感器数据预测大量营养素的可用性 以及微透析。性别、肤色和健康指标（BMI、HbA 1c等）将纳入分析 以评估它们是否实质上影响仪器和算法的性能。 这个项目将汇集一个团队的生物医学工程师，计算机科学家，营养和新陈代谢 研究人员开发仪器，并从人类受试者中收集数据，这些数据将产生： 以及对营养摄入和全身代谢物水平之间关系的认识（目标1）; 用于感测作为营养生物标志物的代谢物的技术（目标2）;感测技术的评价和 计算模型在不同的人类受试者消费共同的膳食。(Aim 3）。在这个项目中取得成功 将推进对新陈代谢的研究，并支持开发个性化营养的新方法。