Investigation of two glucose coefficients of GH.f-modulus and GH.p-modulus based on the data of three clinical cases during the COVID-19 period using linear elastic glucose theory of GH-Method: math-physical medicine, Part 8 (No. 360)

Abstract

This article is Part 8 of the author’s linear elastic glucose behavior study, which focuses on the deeper understanding of these two newly defined glucose coefficients, GH.f-modulus and GH.p-modulus. Findings have shown the sensitive relationships with health conditions such as obesity and diabetes and certain lifestyle components, e.g. carbs/sugar intake amount and post-meal walking steps. I hope that in the near future, he will be able to develop reasonable and applicable numerical ranges of these two glucose coefficients. He used his measured glucose data and predicted glucose models for both fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) for three patients with chronic diseases over the COVID-19 quarantined period, from 3/1/2020 to 11/10/2020. The consolidated diagram below shows a complicated presentation of multiple components and a variety of results. It is a four-dimensional chart that illustrates the PPG prediction via carbs/sugar and post-meal walking under the influence of the glucose coefficient, GH.p-modulus, for three clinical cases during this timeframe. This single diagram demonstrates the complete results of this study. The research of the linear elastic glucose behaviors theory summarizes all of his previous findings from Parts 1 through 7. This article utilizes the clinical cases with different severities of obesity and diabetes. Through different weights along with differing FPG, the author calculates the GH.f-modulus values for each patient, which indicates the relative severity between obesity and diabetes. Furthermore, through the linearity relationship or the linear indication of GH.p-modulus between incremental PPG and carbs/ sugar intake amount, he can connect the complicated components of cars/sugar, exercise, weight or FPG into his final predicted PPG equation. His developed linear elastic glucose theory inspired by his previously acquired knowledge of engineering theory of elasticity, which has been proven useful for diabetes control. These research results from the biomedical linearity situations probably are applicable for ~80% of worldwide diabetes patients. However, for a smaller amount of diabetes patients’ hyperglycemic situations, ~20% of the total population whose glucose frequently exceed 200 mg/dL level, a nonlinear plastic theory may need to be developed and then applied to these extreme situations.