Using Regression Analysis Model to Explore the Relationship Between the CGM Sensor Measured Fasting Plasma Glucose from Sleeping Hours and Measured Body Temperature in the Early Morning Over a One-Year Period for a Type 2 Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 555)

Abstract

Gerald C Hsu

The author began measuring his finger-piercing fasting plasma glucose (FPG) at the wakeup moment starting on the morning of 1/1/2012. In addition, he started measuring his FPG using a continuous glucose monitoring (CGM) device at 15-minute time intervals beginning on 5/8/2018. His sensor FPG uses the average glucose value between 12:00 midnight and 07:00 AM for a total of 29 glucose values. Incidentally, the difference between his average finger FPG and average sensor FPG is a mere 1%. Since 10/1/2020, he has been measuring his daily body temperature (BT) and blood oxygen levels at the wakeup moment in the early morning as an additional daily biomarker to monitor for possible COVID-19 infection. Currently, he has over one year’s worth of data on his BT. He wondered which primary biomarkers would have a connection with BT. Through a quick and easy time-domain analysis, he identified that his FPG has an extremely high correlation with BT, using the 90-days moving average data, finger FPG vs. BT at 73%, and using the 90-days moving average data, sensor FPG vs. BT at 85%, for the one-year period from 11/21/2020 to 11/21/2021. Therefore, he decided to use his CGM sensor FPG as the dependent variable Y and his BT as the independent variable X to conduct a space-domain regression analysis.

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