Linearized Conversion Ratio Analysis of FPG Versus Body Weight and F.PPG Versus Carbohydrates Intake Grams Using Finger-Pierced Glucose Data and Viscoplastic Energy Model in GH-Method: Math-Physical Medicine (No. 1022, Viscoelastic Medicine Theory #420)

Abstract

Gerald C. Hsu

Individuals with type 2 diabetes (T2D) encounter difficulties in managing their condition, primarily due to the underlying issue of insulin resistance (IR) in their pancreatic beta cells, which is closely linked to their fasting glucose (FPG) levels in the morning.FPG also serves as a baseline for postprandial glucose (PPG) levels.In addition to IR, the grams of carbohydrates and sugar consumed in meals is the key contributing factor to both PPG and daily estimated average glucose (eAG) levels.

The author, diagnosed with T2D since 1996, previously relied solely on medications to manage his T2D. Following five cardio episodes and chronic kidney disease, in 2010, the author began self-studying T2D and focusing on lifestyle adjustments.Since December 7, 2015, he has completely ceased his dependency on diabetes medications.

From January 1, 2010, to April 30, 2015, the author monitored his glucoses four times daily using finger- pierced tests (finger).After May 1, 2018, he additionally utilized a continuous glucose monitoring (CGM) sensor device, which provided 96 data points daily.He further developed an AI-based software to assess and analyze the carbohydrate and sugar amount of the food he consumed, using meal photo provided information.

This article details his use of finger glucose data points and body weights in the morning, along with the carbohydrate amounts in individual meals, to calculate linearized conversion ratios from body weight to F.FPG and carbs to F.PPG. Additionally, the author applied the space-domain viscoplastic energy method (SD-VMT) to calculate the associated energies of these four inputs and eAG.

In summary, based on the author collected data over an 8-years period from 1/1/2016 to 12/31/2023, the author's simple and strait-forward statistical analysis has yielded the following two conversion ratios:

For every one pound of weight reduction, there was an average reduction of 4.9 mg/dL in his fasting plasma glucose (FPG) levels.

For every gram of reduction in carbohydrate and sugar intake, there was an average reduction of 2.4 mg/ dL in his postprandial plasma glucose (PPG) levels. The subsequent part of this study will focus on the viscoplastic energy ratios.

F.FPG = 27%

F.PPG = 24%

BW = 26%

Carbs = 23%

The body weight affects FPG which serves as a measure of insulin resistance from pancreatic beta cells and also forms a baseline for PPG levels.The combined influence of BW, FPG, and PPG contributes to approximately 77% of daily eAG, with choices of carbs consumption for the remaining 23%.It should be noted that part of PPG contribution is related to exercise, stress, ambient temperature, etc.