Viscoelastic and viscoplastic Glucose Theory (VGT #26): Investigation of the Relationships between an Overall Metabolism Index and type 2 Diabetes Utilizing HbA1C, Diabetic Nephropathy via Urinary Albumin-to-Creatinine Ratio, along with Diabetic Retinopathy with Hypothyroidism through Thyroid Stimulating Hormone using Statistical Correlation Coefficient and VGT of GH-Method: Math-Physical Medicine (No. 607)

Abstract

Over the past two years, the author self-studied certain biomarkers and their relationships with certain diabetic complications, such as albumin-to-creatinine ratio (ACR) for diabetic nephropathy, thyroid stimulating hormone (TSH) for hypothyroidism and diabetic retinopathy (DR). The medical papers he reviewed utilized statistical tools, including regression analysis, with input data collected from hundreds of different patients in hospitals (References 1-14). In this article, he uses his own collected data from the past ~9 years, where he applies a combined math-physical medicine tools, such as correlations (a statistics tool), viscoelastic and viscoplastic glucose theory (VGT, a physics tool) from engineering, and perturbation theory from quantum mechanics to investigate certain hidden behaviors of selected biomarkers, such as MI, HbA1C, ACR, TSH, and their associated diseases. Initially, he attempts to find relationships between two biomarkers, ACR and TSH. Although these two biomarkers belong to output variables of the human body, he tries to alter their roles with the following two assumptions: TSH influences ACR and ACR influences TSH. He then selects the metabolism index (MI) as the sole input variable of all outputs for endocrinological diseases, including diabetes, chronic kidney disease (CKD), and diabetic retinopathy (DR). The MI is an integrated value from 10 selected categories: weight, glucose, blood pressure, blood lipid, diet, exercise, stress, sleep, water intake, and daily life routines. Next, he utilizes the general health status unit (GHSU), which is the 90-days moving average value of MI for this analysis. The logic behind this decision is that MI, chronic disease biomarkers and lifestyle details, influences type 2 diabetes (T2D) which in turn impacts various diabetic complications, including CKD (ACR as biomarker), DR and hypothyroidism (TSH as biomarker). In summary, the research path from GHSU (or MI) to T2D, then from GHSU to ACR or CKD, and GHSU to TSH or DR. Along this biomedical pathway, the following six key findings are observed: (1) The correlation between GHSU and Daily A1C is 82% within a period from 5/29/2018 to 2/15/2022. But, the correlation between Daily A1C and lab-tested A1C is 74% over 16 discrete dates. Both correlations are quite high which indicates that the overall metabolism is indeed strongly influencing T2D conditions.