BCCMA: VA-iMITIGATE Project 3: Integrating Metabolic and Vascular Sensors to Monitor Cardiometabolic Disorders after Nutrition Interventions

About This Grant

ABSTRACT – Project 3 Despite advances in prevention and intervention, cardiometabolic disorders (CMDs) disparities are persistent in our veterans and their families. While both genetic and epigenetic factors contribute to the pathogenesis of CMDs (including type 2 diabetes mellitus [T2D], hyperlipidemia, hypertension, obesity, and fatty liver), non-alcoholic fatty liver disease (NAFLD) is an early predictor of metabolic dysregulation associated with impaired insulin action, and increased risks for CMDs. A recent liver biopsy study revealed the prevalence of NAFLD as 94.82% in patients with metabolic syndrome and 100% in patients with T2D. However, identifying these individuals responsive to early intervention remain a bottleneck due to the limited non-invasive strategies to monitor CMD risk factors and vascular dysfunction. To this end, we seek to demonstrate a wearable sensor for both metabolic and vascular monitoring of CMD biomarkers and vascular dysfunction for personalized prevention and intervention. Recent advances in wearable technology has enabled monitoring changes in physiologically relevant metabolites from perspiration (sweat) and detecting the characteristics of vascular function or known as pulse wave velocity (PWV) for arterial stiffness. These advances allow for developing the multimodal wearable system to monitor a panel of CMD biomarkers, including glucose, uric acid (UA), cholesterol, triglycerides (TG), and nitric oxide (NO·) in response to nutrition intervention. In collaboration with the Clinical Core (Center for Human Nutrition and Obesity) (Li, Project 1 PI), we propose to continuously monitor CMD participants in response to three nutrition interventions: Mediterranean diet (MED), high-fat/low carb diet (HF/LC), and low-fat diet (LF). Our general hypothesis is that multimodal detection of metabolic biomarkers (i.e., glucose, insulin, TG, and NO) and vascular dysfunction would accelerate our capacity to predict veterans responsive to personalized nutrition intervention for early prevention of CMD risk. Project 3 PI (Hsiai) and Co-Investigators (Gao from Caltech and Li from Human Nutrition) have demonstrated the feasibility to implement the wearable prototypes to monitor biomarkers, including uric acid and insulin (Nat Biotech and Nat Biomed Eng) at the Center for Human Nutrition (Project 1: Li).10, 11 Project 2 (Chen) has collaborated with Project 1 to integrate diabetes-mediated biomarkers and vasculopathy, and recapitulated the biomarkers and endothelial dysfunction with Project 3. To test our general hypothesis, we have 3 aims. In Aim 1, we plan to demonstrate wearable metabolic sweat sensors for continuous monitoring of CMD biomarkers. In Aim 2, we plan to demonstrate wearable vascular sensors to detect basal levels and changes in vascular function. In Aim 3, we plan to integrate the metabolic and vascular monitoring to identify CMD veterans responsive to the specific nutrition interventions. In collaboration with data scientists to embrace social determinants of health (SDoH), we have developed artificial Intelligence (AI)/Machine Learning (ML) algorithms to analyze basal levels and changes in these CMD markers after nutrition interventions and to classify veterans who develop reduced risk factors for CMD-realted NAFLD, and to better identify the responders for personalized intervention. The success of this project will result in three key products: 1) a wearable multimodal system for seamless monitoring of metabolic biomarkers and vascular dysfunction; 2) multimodal detection of metabolic biomarkers (i.e., glucose, insulin, UA, TG, and NOx) along with vascular functional phenotypes (HR, HRV and PWV) to enhance prediction of CMD risk; and 3) a low-cost and remote sensor to improve access for our socioeconomically disadvantaged veterans to mitigate their CMD risk.