Predicting the Fasting Plasma Glucose Level Using Two Linear Regression Equations from Body Temperature and Weight in the Early Morning as Inputs over a 14-Month Period for a type 2 Diabetes Patient Based on GH-Method: Math-Physical Medicine (No. 558)

Abstract

Gerald C Hsu

The author is a 27-year type 2 diabetes (T2D) patient, who has self-studied and researched diabetes, endocrinology, and chronic disease induced complications since 2010. He is a mathematician and engineer but not a medical doctor; therefore, he does his best to derive some mathematical equations or formulas with sufficient accuracy to describe the observed biomedical or biophysical phenomena. His medical research work started with the task of collecting big data on his own biomarker values and lifestyle details. To date, he has collected and processed nearly 3 million data related to his health. The data in this article covers a few categories. Since 1/1/2012, he has accumulated data on his body weight in the early morning. Beginning on 1/2/2013, he measures his finger-piercing fasting plasma glucose (FPG) at the wakeup moment in the morning. In addition, starting on 5/8/2018, he measures his FPG using a continuous glucose monitoring (CGM) sensor device at 15-minute time intervals. 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 (104.6 mg/dL) and average sensor FPG (106.8 mg/dL) over the 3.5-year period from 5/8/2018 to 11/27/2018 is a mere 2%.

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