Using Distributional Data Analysis Tools to Investigate the Glucose Density Distribution of the Mean Daily Glucose Values (eAG) for Three Type 2 Diabetes Patients Over an 18-Month Period Based on GH-Method: Math-Physical Medicine (No. 510)

Abstract

Gerald C Hsu

The author read an article recently, “Glucodensities: a new representation of glucose profiles using distributional data analysis,” dated August 19, 2020, from arxiv.org (see Reference 1). Incidentally, he has also made two further improvements on his glucose data analysis with his collected big data of sensor glucoses via a continuous glucose monitoring sensor device (CGM). First, in addition to using the HbA1C, which is the mean value of the past 115 days of red blood cells carried glucoses, of a patient is the golden standard in evaluating diabetes conditions. He investigates the glucose fluctuation or GF (glucose excursion or glycemic variability) and then transforms the GF values from a wave’s time-space into an energy’s frequency-space via Fourier transform operations. Using this approach, he can then guesstimate the degree of damage on internal organs caused by the energies associated with glucose fluctuations. Although the GF research is one step deeper compared to the study of mean value of glucoses, such as HbA1C, it is still not deep enough to provide additional details and useful information hidden within the glucose waves. Second, he realized that the average values or mean values of glucoses defined by the American Diabetes Association (ADA) such as the HbA1C or Time in Range (average glucose within a range) can only provide partial overviews of diabetes conditions. However, these basic biomarkers are still missing some hidden internal turmoil, i.e. glucose vibrations or severe stimulations, throughout certain selected timeframes due to all types of external and/or internal stimulators. Therefore, he has defined another term known as the glucose density (GD) in order to explore more and different information hidden within the glucose data and their waveforms. GD is defined as the occurrence frequency at a specific glucose value, for example 2.1% occurrence rate at 110 mg/dL glucose value over a selected time period of collected sensor glucoses. In this way, he can then calculate and examine each glucose value’s occurrence rate within a glucose range that is suitable to a specific patient.

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