Viscoelastic or Viscoplastic Glucose Theory (VGT #50): Studying the Role and Impact of the Viscosity Factors (Body Weight and Body Temperature) on Sensor Collected FPG (Symptom or Strain) using a Stress-Strain Diagram from the Viscoelasticity/Plasticity Theory with Measured and Normalized Data over 18 Months from 10/1/2020 to 3/27/2022 Based on the GH-Method: Math-Physical Medicine (No. 635)

Abstract

The author was a professional engineer working in the fields of the space shuttle, naval battleships, nuclear power plant, computer hardware and software, artificial intelligence, and semiconductor chips. After retiring from his engineering job, he initiated his self-study and research on internal medicine with an emphasis on biomarker relationship exploration and disease prevention. Since 2010, he has utilized the academic disciplines learned from 7 different universities along with various work experiences to formulate his current medical research work over the past 13 years. One thing he has learned is that in engineering or medicine, people are frequently seeking answers, illustrations, or explanations for the relationships between the input variable (force on a structure or cause of a disease) and output variable (deformation on a structure or symptom of a disease). However, the 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 mathematical complications, the output resulting from one or more inputs can also become an input of another output i.e., a symptom of certain causes can become a cause of another different symptom. This phenomenon is a complex scenario for a “chain effect”. In fact, engineering and biomedical complications are fundamentally mathematical problems which correlate with many inherent physical laws or principles. Over the past 13 years, in his medical research work, he has encountered more than 100 different biomarkers with various relationships of causes (input variables) versus symptoms (output variables). For example, food and exercise influence both body weight (BW) and postprandial plasma glucose (PPG), while the pancreatic health state, BW, body temperature (BT), and sleep conditions affect the fasting plasma glucose (FPG) in the early morning. A persistent high glucose condition, including FPG and PPG, can result in diabetes. When diabetes combines with hypertension (high blood pressure) and hyperlipidemia (high blood lipids), they would likely cause various cardiovascular diseases (heart attacks) or strokes where blood pressure (BP) and lipids are tightly connected with food. Furthermore, obesity and diabetes are also linked with various cancers. These multiple sets of lifestyle and biomedical input versus disease output have been researched by the author using different tools he has learned from mathematics, physics, computer science, and engineering.