Math-Physical Medicine

NO. 118

A1C variance study and PPG prediction methodology over six periods using GH-Method: math-physical medicine

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

In this case study, the author analyzed, predicted, and interpreted a type 2 diabetes (T2D) patient’s hemoglobin A1C variances based on six periods data utilizing the GH-Method: math-physical medicine approach by applying mathematics, physics, engineering modeling, and computer science (big data analytics and AI).  He believes in “prediction” and has developed five models, including metabolism index, weight, fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and hemoglobin A1C.  All prediction models have reached to 95% to 99% accuracy.  His focus is on preventive medicine, especially on diabetes control via lifestyle management.

As shown in Figure 1, there are six hemoglobin A1C checkup results at the same hospital:

  • 7% on 4/9/2017
  • 1% on 9/12/2017
  • 9% on 1/26/2018
  • 5% on 6/29/2018
  • 6% on 10/22/2018
  • 8% on 4/4/2019
  • 6% on 9/25/2019
Figure 1: Daily predicted A1C and Lab-tested A1C of 6 periods

The author selected six periods of almost equal length with about five months each and then observed their measured A1C changes (variances) against previous period as follows:

  • Period A (4/1/2017 – 8/31/2017): -0.6%
  • Period B (9/1/2017 – 1/31/2018): +0.8%
  • Period C (2/1/2018 – 6/30/2018): -0.4%
  • Period D (6/29/2018 – 10/22/2018): +0.1%
  • Period E (10/22/2018 – 4/4/2019): +0.2%
  • Period F (4/4/2019 – 9/25/2019): -0.2%

He applied his developed GH-Method: math-physical medicine (MPM approach) to analyze the contribution of 7 A1C variances:

  • A1C variances contributed by FPG
  • FPG variance due to weight change
  • Colder weather impact on FPG
  • A1C variances contributed by PPG
  • PPG variance due to carbs/sugar intake
  • PPG variance due to post-meal walking
  • Warm weather impact on PPG

Based on the author’s numerous publications of HbA1C contributions by FPG and PPG, along with the prediction models of two glucoses and A1C, Figure 2 shows his FPG and PPG during the most recent period F and Table 1 displays a step-by-step calculation on how to derive and interpret the causes of the these A1C variances.

Figure 2: FPG, PPG, Carbs/sugar intake, and post-meal walking (4/4/2019 - 9/25/2019)

As shown in Table 1, his mathematically predicted A1C variances completely match the test results from the laboratory.  

Table 1: Calculation of A1C Variances (6 periods)

The A1C case study focused on six periods within 908 days.  It contains 2,724 meals data, including key contribution factors such as carbs/sugar intake, post-meal exercise, weather, etc.  This study has demonstrated a high degree of accuracy on calculating and predicting the patient’s forthcoming A1C value by using the GH-Method: math-physical medicine (MPM) approach.  Once the healthcare professionals and T2D patients understand and learn this skill for the HbA1C prediction method, the patient’s overall T2D condition can then be more easily under control. The purpose of this research paper is to help T2D patients to prevent further damage to their internal organs caused by high HbA1C, while waiting for the laboratory test results.


  1. Hsu, Gerald C. (2018). Using Math-Physical Medicine to Control T2D via Metabolism Monitoring and Glucose Predictions. Journal of Endocrinology and Diabetes, 1(1), 1-6.
  2. Hsu, Gerald C. (2018). Using Signal Processing Techniques to Predict PPG for T2D. International Journal of Diabetes & Metabolic Disorders, 3(2),1-3.
  3. Hsu, Gerald C. (2018). Using Math-Physical Medicine and Artificial Intelligence Technology to Manage Lifestyle and Control Metabolic Conditions of T2D. International Journal of Diabetes & Its Complications, 2(3),1-7.
  4. Hsu, Gerald C. (2018). Using Math-Physical Medicine to Study the Risk Probability of having a Heart Attack or Stroke Based on Three Approaches, Medical Conditions, Lifestyle Management Details, and Metabolic Index. EC Cardiology, 5(12), 1-9.