Applying Linear Elastic Glucose Theory (LEGT) and Viscoelastic or Viscoplastic Glucose Theory (VGT) While using Data Collected over an ~7-Year Period, from 7/1/2015 to 3/30/2022, Organized into 8 Annual Data Points, 81 Monthly Data Points, and 2,463 Daily Data Points, along with a Biomedical Explanation of 8 Sub-Cellular Pathology Pathways of Chronic Diseases to Study the Relationship among Pancreatic Beta

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 retiring from his work, he initiated self-study and research on internal medicine with an emphasis on biomarker relationship exploration and disease prevention. Since 2010, he has utilized these disciplines learned from 7 different universities along with various work experiences to formulate his current medical research work during the past 13 years. One thing he has learned is that in engineering or medicine, we are frequently seeking 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, i.e. a symptom of certain causes can become a cause of another different symptom. This phenomenon is a complex scenario with “chain effects”. 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 work, 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 diseases. Furthermore, obesity and diabetes are also linked with various kinds of cancers. These multiple sets of biomedical input versus output have been researched by the author using different tools he has learned from the fields of mathematics, physics, computer science, and engineering.