Applying the Distributional Data Analysis Tool, Glucose Density, with Collected Daily Finger Glucose Data from the Past 10-Years Combined with the Continuous Glucose Monitoring Sensor Fasting Plasma Glucose Data from the Past 3-Years of a Patient with Chronic Diseases to Investigate the Close Relationship Between Weight and Glucose Based on GH-Method: Math-Physical Medicine (No. 513)

Abstract

Gerald C Hsu

Recently, the author conducted a series of medical research projects by applying a distributional data density analysis tool on his glucose, weight, blood pressure, and heart conditions by using the collected big data regarding certain biomarkers over the past multiple years. In this article, he only utilizes the collected biomarker data from himself, where the data covers two timespans of 10 years and 3 years for this particular study. Moreover, he can interpret the results and explore additional information since he is most familiar with his own health conditions. The main purpose of writing this series of research articles is to demonstrate the applicability and power of using this specific distributional data weight density (WD) analysis tool. In the past, when he researched certain biomarkers and their relationships with other factors, such as fast plasma glucose (FPG) and food consumption quantity, he mostly used the average values of those biomarkers, including body weight. However, we know that biomarkers like body weight and glucoses would fluctuate along the time scale in the form of a “wave” which has one key factor for the “amplitude” of the biomarker, where the other two key factors are frequency and wavelength. Therefore, without focusing on the wave shape of the biomarker and only depending on its mean value, we would lose many vital, interesting, and useful hidden information. These types of mean values, such as HbA1C, or sparsely collected blood lipid data from quarterly testing can only provide partial views of health conditions. However, these biomarkers still have some missing information carrying certain hidden internal turmoil or vital signs, e.g. biomarker variations or its severe stimulations due to all types of external and/or internal stimulators. By applying this basic knowledge of distributional data analysis, he has defined new term known as the “general biomarker density or Bio-density (BMD)” in order to explore additional, different, deeper and useful hidden information in the collected biomarker data and their associated waveforms

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