7^th International Conference on
Global Healthcare

Biography:

Zsolt P. Ori is a practicing internist, primary physician and hospitalist with previous training in Bio-Medical Cybernetics (Ilmenau University of Technology, Germany) before entering medical school (Albert Szent-Györgyi Medical School , Szeged, Hungary) and post-doctoral research fellowship in non-invasive cardiology (Northwestern University, Chicago) .  His engineering training together with his experiences in primary care in academic and non-academic environments have inspired his vision for using Cybernetics to improve cardiometabolic health with result driven predictive feedback control. His patented inventions can build a bridge between sensory data from wearables and plug the data into physiological process models and make the otherwise undetectable slow changes of the energy metabolism observable and derive easily readable trend indicators allowing for stepwise dynamic behavior control for reaching cardiometabolic health and mental resilience. CPS derived data can provide metrics facilitating education about metabolic health as well as to reach community health, corporate health and public health goals.

Abstract:

There is a need to facilitate efforts to reduce cardiovascular risks such as insulin resistance with healthy lifestyle and to improve cardiometabolic fitness, resilience and longevity with both self-management and guided therapy. To facilitate this process, we recently introduced a Cyber-Physical System (CPS) [4-5]. CPS is a mobile technology integrating sensory data from various mobile devices into individualized dynamic mathematical models of physiological processes allowing for analysis, prediction and maximizing control by user and potentially supported also by machine learning. We invented self-adjusting mathematical models [1-3], allowing for the noninvasive observation of insulin resistance changes by R- or Rw-ratio. We were able to prove the feasibility of our modelling concept of the insulin resistance by finding strong correlation ρ= -0.6745, P=0.000024  between changes of Rw-ratio and insulin resistance HOMA-IR in 12 clinical studies with 39 clinical study arms as in Figure 1. Based on these results we found that CPS is a suitable concept to indirectly measure and predict the otherwise very difficult- or impossible-to-measure slow changes of state variables (SV’s) of the metabolism such as 24h nonprotein respiratory quotient, utilized macronutrient energies, fat oxidation rate, carbohydrate oxidation rates, de novo lipogenesis, and adaptive thermogenesis and capture them for the first time noninvasively in the user’s natural environment. 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. 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. In conclusion, CPS can serve as an appropriate real-time tool to monitor and optimally adjust modifiable risk factors, allowing for planning and executing dynamic changes of behavior for optimization and control of metabolic SV’s.