Viscoelastic or Viscoplastic Glucose Theory (VGT #85): A Study on the Influences of Cardiovascular Disease Risk, Estimated Daily Average Glucose, and Body Weight from a Type 2 Diabetes Patient’s Psychological Reactions and Lifestyle Changes During 9-Quarters of the COVID-19 Pandemic Period from Y2020Q1 to Y2022Q1 Based on GH-Method: Math-Physical Medicine, Especially the VGT Energy Tool (No. 675)

Abstract

The author is a mathematician and engineer who has conducted medical research work over the past 13 years. Thus far, he has written 670 medical research papers. Beginning with paper No. 578 dated 1/8/2022, he wrote a total of 80 medical research articles using the viscoelasticity and viscoplasticity theories (VGT) from physics and engineering disciplines on 80 different medical problems with their associated data. These papers aim to explore some hidden biophysical behaviors and provide a quantitative understanding of the inter-relationships of a selected medical output (symptom) versus either singular input or multiple inputs (root causes, risk factors, or influential inputs). The hidden biophysical behaviors and possible inter-relationships exist among lifestyle details, medical conditions, chronic diseases, and certain severe medical complications, such as heart attacks, stroke, cancers, dementia, and even longevity concerns. The chosen medical subjects with their asso- ciated data, multiple symptoms, and influential factors are “time-dependent” which means that all biomedical variables change from time to time because body living cells are dynamically changing. This is what Profes- sor Norman Jones, the author’s adviser at MIT, suggested to him in December 2021 and why he utilizes the VGT tools from physics and engineering to conduct his medical research work since then. During his recent medical research work using the tool of viscoelastic or viscoplastic behavior theory, he sud- denly realized a strong similarity between medicine and economics. The behaviors and patterns of economics variables (inputs and outputs) he observed are comparable to the behaviors and patterns of medical variables he studied and researched (causes and symptoms) in terms of their curve shape & waveforms, fluctuation patterns, moving trends, physical behaviors, etc. For example, he has applied the candlestick chart or K-line diagram from Wall Street as an effective glucose representation tool in medicine. Most importantly, variables in both medicine and economics possessed the common “time-dependent” characteristics. The recent COVID-19 pandemic is a severe and unique experience to worldwide people that is comparable to the Spanish Flu that happened over a century ago. He wondered what type of biomedical impact or inter-relationship from this COVID pandemic had on some of his collected (eAG and BW) or calculated (CVD Risk) biomarkers. Therefore, in this article, he selects a chain of mutually influenced biomarkers of the cardiovascular disease risk % (CVD risk), estimated daily average glucose (eAG), and body weight (BW) along with the COVID-19 infection cases in the USA as the key input variable to conduct a series of inter-connected VGT analysis research work.