Viscoelastic or Viscoplastic Glucose Theory (VGT #41): Applying VGT to Study the Relationship of CVD Risk Probability % Versus Sensor Daily Average Glucose (eAG) and its Calculated HbA1C, while using the Viscoelastic Perturbation Model to Predict Two CVD Risk % Values over a 15-Month Period from October of Y2020 to February of Y2022 Based on the GH-Method: Math-Physical Medicine (No.

Abstract

Since 2012, the author has been collecting his body weight (m1) and finger-piercing glucose values (m2) each day. In addition, he accumulates medical conditions data including a combination of data for blood pressure (BP) and heart rate (HR), and blood lipids along with lifestyle details of diet, exercise, sleep, stress, water intake and daily routine details. Based on the collected big data, he further organized them into two main groups. The first group is medical conditions (MC) with 4 categories: weight, glucose, BP, and blood lipids. The second group is lifestyle details (LD) with 6 categories: food & diet, exercise, water intake, sleep, stress, and daily routines. He collects his daily data and then calculates a unique combined score for each MC and LD with their 10 categories. The combined scores of the 2 groups, 10 categories, and 500+ elements constitute an overall “metabolism index (MI) model”. This MI model includes the root causes of 6 lifestyle inputs and 4 symptoms of the disease including the rudimentary chronic diseases: obesity, diabetes, hypertension, and hyperlipidemia. As we know, lifestyle details cause rudimentary chronic diseases which further influence more complicated diseases, such as heart problems (CVD & CHD), chronic kidney disease (CKD), stroke, diabetic retinopathy (DR), neuropathy, hypothyroidism, and others. However, in addition to the lifestyle-induced chronic disease and complications, environmental factors, such as radiation, air and water pollution, food poison and pollution, toxic chemicals, and hormonal therapy, can contribute to the causes for a variety of cancer. Some genetic conditions and lifetime unhealthy habits, such as smoking, alcohol consumption, illicit drug use would account for approximately 15% to 25% of the root cause for rudimentary chronic diseases, complications, and cancer. All of the above-described diseases fall into the “symptoms” category which are the “root-causes” due to poor and unhealthy lifestyles. In this article, the author applies the viscoelasticity and viscoplasticity theories to conduct his research to discover some hidden behavior or possible relationship among 3 key biomarkers, CVD risk % (a symptom disease), daily average sensor glucose (eAG), and its related sensor HbA1C (A1C). The hidden behaviors and possible inter-relationships among the three biomarkers are “time-dependent” which change from time to time. This is why he applies viscoelastic & viscoplastic theories (VGT) to conduct his recent research work. The author previously conducted similar analyses for this set of biomarkers but used a traditional statistical regression method. Generally speaking, statistical methods only deal with numerical characteristics of collected datasets and do not influence the internal physical characteristics. Incidentally, the accuracy and applicability of using any statistical analysis results are heavily dependent on characteristics of the data sample, data sample size, and the time-window of the dataset. Therefore, we must be careful in selecting appropriate statistical methods and treat their analysis conclusions cautiously.