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.

项目摘要/摘要 监测和优化膳食摄入量对临床治疗中的精确营养具有重要意义 病情(糖尿病、肥胖、肾/肝衰竭等)以及减轻肌肉功能和质量的损失 在衰老过程中。然而，目前用于个人健康监测的方法不能提供可靠的测量 消化后大量营养素的摄入量或代谢物的利用率。因此，该项目旨在通过以下方式解决这一差距 开发基于可插入式光学记者的创新组合的多分析物传感设备， 可穿戴阅读器，以及先进的计算方法。这些设备将提供连续/按需 测量间质液体(ISF)中的代谢物，然后使用这些测量来预测常量营养素的摄入量。 该项目分为三个具体目标：目标1将涉及采集血液样本和ISF样本 (通过微透析)来自健康受试者的食用新鲜食物，然后使用机器学习 方法建立大量营养素摄入量与由此产生的血液和ISF之间的定量关系 代谢物的水平。研究人员设计的新鲜食物含有三种不同水平的蛋白质 和碳水化合物。将对数据进行分析，以建立各隔室之间的定量关系 集中，确定比例系数和时间滞后/领先参数，并开发机器- 从血浆和ISF代谢物浓度预测膳食中的常量营养素的学习模型。 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密度 基于传感器数据流的融合。然后，AIM 3将专注于部署和测试可移动版本的 人类受试者的多分析物传感系统。参与者将被安装传感器，微透析器 导管和连续血糖监测仪。受试者将食用新鲜食物和常量营养素- 匹配的冷冻餐，匹配的内容物，以验证系统(传感器和算法)是否也适用于 对于许多美国人来说，饮食更具代表性，特别是在低收入社区。这个 在AIM 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 以评估它们是否会对仪器和算法的性能产生重大影响。 这个项目将汇集一个由生物医学工程师、计算机科学家和营养与新陈代谢组成的团队 研究人员开发仪器并从人类受试者那里收集数据将产生：新知识 和对营养摄入量和全身代谢物水平之间关系的洞察(目标1)； 作为营养生物标记物的代谢物传感技术(目标2)；传感技术和 食用普通膳食的不同人类受试者的计算模型。(目标3)。在这个项目中取得成功 将推进新陈代谢研究，并支持个性化营养新方法的开发。