Applying the Distributional Data Analysis Tool, Biomarker Density, with the Collected Daily Data from 2 Types of Glucoses over the Past 3.5 Years of a Patient with Chronic Diseases to Investigate his Glucose Conditions Based on GH-Method: Math-Physical Medicine (No. 518)

Abstract

Gerald C Hsu

Recently, the author conducted a series of medical research projects by applying a distributional data density analysis tool on his weight, glucose, blood pressure (BP), and heart conditions, while using his collected big data regarding certain biomarker’s density distribution for the selected years. In this article, he investigates his collected glucose data density via two different collection methods, finger-piercing and continuous glucose monitoring (CGM) sensor device, within a time span of 3.5 years (5/8/2018 - 9/13/2021). With this data, he can interpret the results and explore additional and in-depth information since he is most familiar with his own health conditions. The findings from his own data is applicable to other patients of type 2 diabetes (T2D). The main purpose of writing this series of research articles is to further demonstrate the applicability and power of the specific distributional data density analysis tool. When he previously researched certain biomarkers and their relationships with other influential factors, he generally used the average values of those biomarkers. We know that most biomarkers, including glucoses, could fluctuate along the time scale in the form of a “wave”. Each wave has its own unique amplitude and specific measuring unit which are associated with this particular biomarker. However, there are two other key factors, frequency and wavelength, to be considered as well. Particularly, the frequency component is associated with energy and excessive energy which causes damages to the internal organs. Therefore, without focusing on waveform of a biomarker and depending only on its mean value, we would lose many vital, interesting, and useful hidden information. This type of mean value, such as HbA1C, can only provide partial views of our overall diabetic conditions. These biomarkers still have missing information which carry certain hidden internal turmoil or vital signs, e.g. biomarker variation or its severe stimulation due to all types of external and/or internal stimulators. Therefore, by applying this basic knowledge of distributional data analysis by defining another term known as the “general biomarker density or Bio-density%” (BMD%), he can explore additional, different, in-depth, and useful hidden information from collected biomarker data and their associated waveforms.

PDF