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/文摘