Impact on Cardiovascular Disease Risks from Measured Versus Predicted Body Weight, FPG, PPG over 8.5 years: Analysis Using GH-Method's Math-Physical Medicine Model (No. 1001, VMT #399, 12/16-17/2023)

Abstract

Gerald C. Hsu

After 18 years of studying at seven universities and pursuing various careers in professional engineering, founding seven start-ups, and managing high-tech businesses, the 63-year-old author faced severe, lifethreatening health issues in 2010. This experience prompted him to undertake a self-study of internal medicine and food nutrition in order to save his own life. Individuals with chronic diseases often face challenges in managing their body weight and glucose levels, which fluctuate constantly and over extended periods. Drawing on his 30 years of industrial experience in engineering design and the electronics semiconductor business, the author recognized the significance of "prediction" in averting failures in machines, structures, and business operations. He applied predictive analysis techniques from engineering and enterprise systems to enhance damage prevention and reduce the likelihood of breakdowns or failures. The author initially directed his medical research towards developing predictive equations for three key biomarkers: body weight, fasting morning glucose, and post-meal glucose. His prediction formulas for these three biomarkers demonstrated exceptionally high accuracy and very high waveform similarities, both exceeding 99%, In this study, the space-domain viscoplastic medicine energy theory (SD-VMT) was utilized to analyze the dynamic relationships between his risks of having cardiovascular diseases (CVD) or strokes and three key biomarkers: body weight (BW), fasting plasma glucose (FPG), and postprandial plasma glucose (PPG). His CVD risk values are calculated using his developed model of metabolism index (MI) in combination with certain special considerations, such as blockage and rupture of arteries with structural damages of blood vessels resulted from diabetes.

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