Zsolt P. Ori

By Zsolt Peter Ori

We want to demonstrate the feasibility of the concept of a cyber-physical system (CPS) by showing good correlation of insulin resistance by HOMA-IR with changes of state variables (SVs) such as R-ratio, Rw-ratio, calculated 24 h nonprotein respiratory quotient, and fat-burning fraction from serial measurements of weight and fat mass. We utilize principles of indirect calorimetry. We calculate SVs from published data of an energy perturbation study. We perform correlation analysis between changes of insulin resistance measured with HOMA-IR and selected SVs. The result of this correlation analysis confirms a highly significant correlation between HOMA-IR and the selected SVs. The implication of these results is that CPS is a suitable concept to indirectly measure and predict the otherwise very-difficult- or impossible-to-measure slow changes of SVs and capture them for the first time noninvasively. Serial fat and weight measurements and energy calculations can help unmask changes of insulin resistance in response to user’s diet and exercise habits, creating the necessary environment to measure metabolic flexibility. Further, CPS has the potential to estimate cardiorespiratory fitness by indirectly estimating maximum oxygen uptake from measuring heart rate reserve, heart rate variability, and pulse oximetry changes with exercise.

Part of the book: Type 2 Diabetes

By Zsolt P. Ori

The goal of this paper is to demonstrate feasibility of a concept of mobile computing to help users to reach and maintain metabolic health. For this purpose, we analyze data from 12 clinical studies with a total of 39 study arms from the international literature to show that insulin resistance measured by HOMA-IR could be followed and its changes could be predicted using our weight-fat mass-energy balance calculations taking advantage of the significant and strong correlation between changes of HOMA-IR and state variables of the energy metabolism like changes of weight, fat mass, R-ratio, Rw-ratio, and fat burning fraction of the energy production. We introduce here our extended weight-fat mass-energy balance calculation to assess de novo lipogenesis, adaptive thermogenesis, and the 24-hour nonprotein respiratory quotient. We show how we can analyze and predict individualized state variables of the metabolism, which serve as metrics for the quantification of the interrelationship between energy metabolism and insulin resistance facilitating management and self-management of insulin-resistance related conditions including obesity, fatty liver, prediabetes, metabolic syndrome, and type 2 diabetes. The feedback of individualized metrics using tools of the digital health era may amount to channeling focus also to patient-centered individualized care and to accelerating nutrition research.

Part of the book: Mobile Computing

By Zsolt P. Ori

Dietary and exercise interventions are the mainstay of prevention, and they constitute important part in the treatment of type 2 diabetes (DM2) and its complications. Automated, continuous, individualized non-invasive measurement of pathological processes leading to DM2 and complications are needed in terms of self-explaining metrics for improved individualized lifestyle management. Our company, the Ori Diagnostic Instruments, LLC is using tools of Medical Cybernetics (MC) to monitor non-invasive indicators of insulin resistance, exercise capacity, and autonomic dysfunction. The MC approach utilizes mathematical process and measurement models which are connected to a wearable sensor system. This chapter has the purpose to show how already widely available information technologies like smart phones, cloud computing, and sensor devices of the fitness industry could be put together into an integrated cyber-physical system (ICPS) to support fitness goals like fighting cardiometabolic conditions including high insulin resistance and low level of cardiorespiratory fitness and help building resilience with improved physiological reserve capacity. We want to demonstrate also how ICPS can be not only used for fitness self-management but can be extended to become a platform of noninvasive monitoring devices and become a medical software to support person-centered, outcome driven treatments for DM2 and complications in primary care.

Part of the book: Type 2 Diabetes