Viscoelastic or Viscoplastic Glucose Theory (VGT #59): Using the VGT Energy and Frequency Energy to Estimate the Relative Energy Associated with Cardiovascular Disease (CVD) Risk Probability Percentages under the Influence of the Lab-Tested A1C (L.A1C) and Metabolism Index (MI) over Two 6-Year Periods, Y2010-Y2015 Versus Y2016-Y2021 Based on GH-Method: Math-Physical Medicine (No. 647)

Abstract

The author was a professional engineer working in the fields of the space shuttle, naval battleships, nuclear power plants, computer hardware and software, artificial intelligence, and semiconductor chips. After his retirement, he started to self-study and research internal medicine, emphasizing biomarker relationships exploration and disease prevention. Since 2010, he has utilized the disciplines learned from 7 different universities along with various work experiences to formulate his current medical research work during the past 13 years. In engineering and medicine, he discovered that we frequently seek answers, illustrations, or explanations for the relationships between the input variable (force applied on a structure or cause of a disease) and output variable (deformation of a structure or symptom of a disease). However, the multiple relationships between input and output could be expressed with many different matrix formats of 1 x 1, 1 x n, m x 1, or m x n (m or n means different multiple variables). In addition to these described mathematical complications, the output resulting from one or more inputs can also become an input of another output, which is a symptom of certain causes that can become a cause of another different symptom. This phenomenon is a complex scenario with “chain effects”. In fact, engineering and biomedical complications are fundamentally mathematical problems that correlate or conform with many inherent physical laws or principles. Over the past 13 years, in his medical research, he has encountered more than 100 different sets of biomarkers with almost equal amounts of cause/input variables versus symptom/output variables. For example, food and exercise influence both body weight and glucose level, where persistent high glucose can result in diabetes. When diabetes combines with hypertension (high blood pressure) and hyperlipidemia (high blood lipids), it can cause cardiovascular disease (CVD), chronic kidney disease (CKD), or diabetic retinopathy (DR). Furthermore, obesity and diabetes are also linked with various kinds of cancers. The author has researched these multiple sets of biomedical input versus output using different tools he has learned from academic fields of mathematics, physics, computer science, and engineering. For example, he has applied a signal processing technique to separate 19 components from the combined postprandial plasma glucose (PPG) wave. He identified the carbs/sugar intake amount and post-meal exercise (around 40% of contribution) as the two most important contributing factors to PPG formation. Based on these findings, he then applied the theory of elasticity to develop a linear elastic glucose theory (LEGT) to predict PPG value with high prediction accuracy, using fasting plasma glucose (FPG), carbs/sugar intake grams, and post-meal walking k-steps as three major input components of predicted PPG formation.