Investigation of GH Modulus on the Linear Elastic Glucose Behavior Based On Three Diabetes Patients’ Data using the GH-Method: Math-Physical Medicine, Part 3 (No. 350)

Abstract

This article is Part 3 of the linear elastic glucose research work. It is also the continuation of the simple linear equation for the predicted postprandial plasma glucose (PPG) in References 2, 3, and 4. Here is the formula Predicted PPG= (FPG* 0.97) + (carbs/sugar grams * M2) - (post-meal waking K-steps * 5) The author connected his biomedical equation with a basic concept of linear elasticity which includes stress and strain, along with the Young’s modulus of strength of materials in structural & mechanical engineering. By using the collected data of glucose, food, and exercise from three different type 2 diabetes (T2D) patients, he demonstrated once again that a “pseudo-linear” relationship existed in all three clinical cases, between the carbs/sugar intake amount and incremental PPG amount, with a newly defined coefficient of “GH modulus” (same as the M2 multiplier) cited in References 7 and 8. The linear elastic glucose behavior is similar to the Young’s modules (E) linking stress and strain of engineering strength of materials. He selected three T2D patients with different levels of severity. For data consistency purposes, he has chosen data from 7-monthly sub-periods of equal length from 3/18/2020 - 10/18/2020. The main objective of this study is to prove that GH-modulus (M2) indeed vary with the severity of diabetes for these three clinical cases. The 7-month average value of each monthly M2 variables (i.e., GH-modulus) are 3.7, 2.6, and 1.0, and with an average measured PPG values at 122 mg/dL, 114 md/dL, and 109 mg/dL, for Case A, Case B, and Case C, respectively, which are ranked according to the severity of their diabetes conditions. In summary, the higher the M2, the higher values of both x (carbs/sugar intake amount) and y (incremental PPG amount) become, and the higher predicted and measured PPG values are. The key conclusion from these three clinical observations is that the M2 values are varying based on patients’ body conditions (liver and pancreas), especially their diabetes severity. This is similar to the different inorganic materials having the different Young’s modules values, such as nylon ~3 versus steel ~200. The article represents the author’s special interest in using math-physical and engineering modeling methodologies to investigate various biomedical problems. The methodology and approach are a result of his specific academic background and various professional experiences prior to the start of his medical research work in 2010. Therefore, he has been trying to link his newly acquired biomedical knowledge over the past decade with his previously acquired mathematics, physics, computer science, and engineering knowledge over 40 years. The human body is the most complex system he has dealt with, which includes aerospace, navy defense, nuclear power, computers, and semiconductors. By applying his previous acquired knowledge to his newly found interest of medicine, he can discover many hidden facts or truths inside the biomedical systems. Many basic concepts, theoretical frame of thoughts, and practical modeling techniques from his fundamental disciplines in the past can be appliedto his medical research endeavor. After all, science is based on theory via creation and proof via evidence, and as long as we can discover hidden truths, it does not matter which method we use and which option we take. This is the foundation of the GH-Method: math-physics medicine. The author has spent four decades as a practical engineer and understands the importance of basic concepts, sophisticated theories, and practical equations which serve as the necessary background of all kinds of applications. Therefore, he spent his time and energy to investigate glucose related subjects using variety of methods he studied in the past, including this particular interesting stress-strain approach. On the other hand, he also realizes the importance and urgency on helping diabetes patients to control their glucoses. That is why, over the past few years, he has continuously simplified his findings about diabetes and derive more useful formulas or practical tools for meeting the general public’s interest on controlling chronic diseases and their complications to reduce their pain and death threat probability.