Math-Physical Medicine

NO. 156

A Case Study on the Investigation and Prediction of A1C Variances Over Seven 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 seven periods data utilizing the GH-Method: math-physical medicine approach.

As shown in Figure 1, there are eight hemoglobin A1C checkup results:

  • 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
  • 6.8% on 4/4/2019
  • 6.6% on 9/25/2019
  • 6.6% on 12/20/2019

The author selected seven periods of almost equal length with about five months each and then observed their measured A1C changes (variances) against the 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%
  • Period G (9/25/2019 – 12/20/2019): +0.0%

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

  1. A1C variances contributed by fasting plasma glucose (FPG)
  2. FPG variance due to weight change
  3. Colder weather impact on FPG
  4. A1C variances contributed by postprandial plasma glucose (PPG)
  5. PPG variance due to carbs/sugar intake
  6. PPG variance due to post-meal walking
  7. 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 these two glucoses and HbA1C, Figure 1 shows a summarized chart of these A1C values over 7-periods.  Figure 2 and 3 illustrate weight, carbs intake, exercise, FPG, PPG, daily glucose, and A1C of Period G (9/25/2019-12/20/2019). Figure 4 depicts the step-by-step calculation table of all the periods on how to derive and interpret these A1C variances.

Figure 1: HbA1C during a long period of 4/1/2017 through 12/20/2019
Figure 2: Weight, Carbs intake, and Post-meal walking
(9/25/2019 - 12/20/2019)
Figure 3: FPG, PPG, Daily Glucose, and A1C (9/25/2019 - 12/20/2019)

As shown in Figure 4, his predicted A1C variances completely match the test results from the laboratory for Period A through Period F.  The reason for the 100% match of eclaireMD predicted A1C and Lab-tested A1C for periods A through F is that the lab-tested A1C has a single decimal, whereas the eclaireMD predicted A1C has two decimals.

Figure 4: HbA1C step-by-step calculation table during 7-periods (4/1/2017 - 12/20/2019)

For Period G (from 9/25/2019 to 12/20/2019), the Lab-tested A1C is 6.6%, while the predicted A1C is 6.7% (actually 6.66%).  The eclaireMD predicted A1C has only achieved 99% accuracy.  During this Period G, the A1C incremental amount of 0.06 mainly resulted from the FPG increase.  As indicated in Figure 5, the usual high correlation (77%) existed between Weight and FPG (1/1/2014-12/20/2019) was not found for this particular Period G, because it was a duration of 3-months instead of 5-months.  In addition, it was tested at a different hospital which may have different operating procedures.  Therefore, the author will review this predicted A1C against the newly tested A1C around 2/25/2019.  This issue warrants a deeper investigation by the author.

Figure 5: FPG vs. Weight for long period (4/1/2017-12/20/2019) and period G (9/25/2019-12/20/2019)

The A1C case study focused on seven periods within 994 days.  It contains 2,982 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 these 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, June). Using Math-Physical Medicine to Analyze Metabolism and Improve Health Conditions. Video presented at the meeting of the 3rd International Conference on Endocrinology and Metabolic Syndrome 2018, Amsterdam, Netherlands.
  2. 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.
  3. Hsu, Gerald C. (2018). Using Signal Processing Techniques to Predict PPG for T2D. International Journal of Diabetes & Metabolic Disorders, 3(2),1-3.
  4. Hsu, Gerald C. (2018). A Clinic Case of Using Math-Physical Medicine to Study the Probability of Having a Heart Attack or Stroke Based on Combination of Metabolic Conditions, Lifestyle, and Metabolism Index. Journal of Clinical Review & Case Reports, 3(5), 1-2.
  5. Hsu, Gerald C. (2019). Using Wave and Energy Theories on Quantitative Control of Postprandial Plasma Glucose via Optimized Combination of Food and Exercise (Math-Physical Medicine). International Journal of Research Studies in Medical and Health Sciences, 5(4), 1-7.