Math-Physical Medicine

NO. 026

A Case Study of Investigation and Prediction of A1C Variances 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 three periods utilizing GH-Method: math-physical medicine approach.


There are five hemoglobin A1C checkup results at the same hospital:

  • 6.7% on 4/9/2017
  • 6.1% on 9/12/2017
  • 6.9% on 1/26/2018
  • 6.5% on 6/29/2018
  • 6.6% on 10/22/2018

The author selected three equal-length periods of five months plus one more recent period of four months, and observed four measured A1C changes 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%

He applied GH-Method: math-physical medicine techniques to analyze four A1C variances contributed by fasting plasma glucose (FPG) via weight change (gain or loss) and colder weather effect on FPG as well as A1C variances contributed by postprandial glucose (PPG) via changes of carbs/sugar intake, post-meal walking, and warmer weather temperature on PPG.

Figure 1 and 2 show various time-series analysis results on weight, glucose, food, and exercise during these four periods.  Based on the author’s previous publications of adjusted A1C contributions by FPG and PPG along with the prediction models of glucose and A1C, Table 1 displays a step-by-step detailed calculation on how to derive the patient’s A1C variances.

Figure 1: Three Periods of Weight, FPG, PPG, Daily Glucose, Carbs/Sugar Intake, Post-Meal Walking
Figure 2: Period D (6/29/2018 - 10/22/2018) EclaireMD predicted A1C, Daily Glucose, FPG, PPG, Carbs/Sugar, Post-meal Walking

As shown, his EclaireMD predicted A1C variances completely match the lab test results from the hospital.

The A1C case study focused on four periods of approximately 514 days, which contained about 1,542 meals carbs/sugar data plus other big data including exercise, weather, traveling, sickness, etc.  This study demonstrates the degree of precision on predicting and interpreting the patient’s A1C variance using GH-Method: math-physical medicine.  Once patients master the skill of understanding and predicting the forthcoming A1C lab test results, their overall T2D condition can then be under control easily.

Table 1: Calculation of A1C Variances (4 periods)