GH-METHODS

Math-Physical Medicine

NO. 030

Differences Between Fasting Plasma Glucose and Postprandial Plasma Glucose

Corresponding Author: Gerald C. Hsu, eclaireMD Foundation, USA.

Introduction
This paper describes the quantitative and qualitative differences between fasting plasma glucose (FPG) and postprandial plasma glucose (PPG).

Method
The author has been researching type 2 diabetes (T2D) since 2012.

Here are the three stages:

  1. Collected a total of 9,490 glucose data, including 7,665 PPG (1/1/2012-12/31/2018) and 1,825 FPG (1/1/2014-12/31/2018);
  2. Studied and analyzed glucose to determine their major influential factors with respective contributing margins;
  3. Developed predicted glucose models and then calculated the predicted hemoglobin A1C value.

Not only is glucose a medical indicator, but it also involves lifestyle factors.  Some healthcare professionals do not have a comprehensive understanding of this simple term: Glucose.

Results
Table 1 shows glucose differences, analysis methods, and conclusions by using a big data analytics (~1.5 million data).

Table 1: Comparison between FPG and PPG

Most people define glucose as blood sugar level and nothing more; however, FPG and PPG are quite different because their influential factors and contribution percentages are diverse.  In addition, their behaviors are different in terms of changing speed, normal peak, sensitivity, etc.  The prediction methodologies are also very different. The author believes in preventive medicine, including prediction of glucose, and controlling T2D via lifestyle management.  The better you can predict the glucose behavior, the better chance you can reduce their damage.  Since 6/1/2015, he spent three years to develop five prediction models to achieve approximately 99% of linear accuracy (see Figure 1) with high correlations (waveform similarity) between two biomedical signal waves, predicted glucose and measured glucose.

Conclusion
Currently, the patient’s T2D is completely under control by using his developed methodology and five AI-based prediction tools.  A deep understanding of quantitative sense and trend prediction of both FPG and PPG will benefit the task of effectively controlling diabetes.

Figure 1: Glucose Prediction Models’ results
Figure 2: A1C prediction vs. Lab-tested A1C and waveforms of FOG & PPG
Figure 3: Contribution % of Weight, FPG, PPG, A1C