Using GH-Method: Math-Physical Medicine to Investigate the Risk Probability of Metabolic Disorders Induced Cardiovascular Diseases, Stroke, and Renal Complications

Abstract

The author uses GH-Method: math-physical medicine (MPM) 
approach to investigate two clinical cases A and B of risk probability on metabolic disorders induced cardiovascular diseases (CVD) or stroke (“Risk”). He addresses the multiple correlations among three metabolic bio markers, i.e. body weight, glucose, and blood pressure (BP), which are also closely related to both CVD and stroke. He further examines Case A’s bladder and renal complications due to diabetes and hypertension. engineering technique, and nonlinear algebra operations to develop a mathematical metabolism model which contains four output categories (weight, glucose, BP, other lab-tested data), six input categories (food, water drinking, exercise, sleep, stress, routine life patterns and safety measures), and approximately 500 detailed elements. He further defined a new parameter, metabolism index (MI), as the combined score of the above 10 metabolism categories and 500 elements. Since 2012, he has collected and stored ~1.5 million data of his own body and personal lifestyle. It should be noted that through his developed predicted weight and glucose (FPG, PPG, A1C) models, he has successfully reduced his glucose level from 280 mg/dL (A1C 10%) in 2010 to 116 mg/dL (A1C 
6.4%) during his “no medication” period from 2015 through 2019. In addition, his ACR has dropped from 116 in 2010 down to 8 in 2018.