Math-Physical Medicine

NO. 279

Investigation of HbA1C variances and predictions over eight sub-periods using GH-Method: math-physical medicine

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

In this study, the author analyzed, predicted, and interpreted a type 2 diabetes (T2D) patient’s hemoglobin A1C variances and predictions based on eight sub-periods within a time period of 4/1/2017 through 6/20/2020 using the GH-Method: math-physical medicine approach.

As shown in Figure 1, there are nine hemoglobin A1C lab-tested results:

  • 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
  • 6% on 12/20/2019
  • 3% on 6/20/2020
Figure 1: HbA1C history during 8 periods between 4/1/2017 and 6/20/2020

The author selected eight sub-periods of almost equal length of approximately 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%
  • Period H (12/20/2019 – 6/20/2020): -0.3%

By applying his developed GH-Method: math-physical medicine (MPM approach), he analyzed the following seven contribution factors of A1C:

  • 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

He further developed a sophisticated mathematical model to predict his HbA1C value.  This model contains the following key conceptual formulas:

  • FPG is proportional to weight change
  • PPG is proportional to changes of carbs/sugar intake (~39%) and post-meal walking steps (~41%)
  • FPG contributes about 20%-30% of HbA1C
  • PPG contributes about 70%-80% of HbA1C

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 (References 1 through 8), Figure 1 show a summarized chart of these HbA1C values over 8 sub-periods.  Figure 2, 3, and 4 depict weight, FPG, PPG, carbs/sugar intake, and post-meal walking steps of sub-period H from 12/20/2019 to 6/20/2020.

Figure 5 demonstrates the step-by-step calculation table of all parameters in the 8 sub-periods on how to derive and interpret these HbA1C variances and predictions.

Figure 2: Weight change between 9/25/2019-12/20/2019 & 12/20/2019-6/20/2920
Figure 3: FPG ad PPG changes between 9/25/2019-12/20/2019 & 12/20/2019-6/20/2920)
Figure 4: cars/sugar amount and walking steps during period of 12/20/2019-6/20/2920
Figure 5: HbA1C step-by-step calculation table during 8 periods (4/1/2017 - 6/20/2020)

As shown in Figure 6, his predicted Finger A1C values (red line) almost match his lab-tested A1C values (blue line), except for two small variances in 2 sub-periods.  The sensor based A1C values (gray line) illustrates larger variance amounts from both finger and lab data.  These results are due to two primary reasons.  First, his A1C prediction model was originally developed using the finger glucoses as its base and has been refined for the past 8-years.  Secondly, the continuous glucose monitor (CGM) sensor method collects ~80 glucoses per day versus the finger method which only collects 4 glucoses per day.  

The most recent sub-period H, from 12/20/2019 to 6/20/2020, happens to fall within the COVID-19 quarantine period.  For the author, this “isolation” sub-period has certain unique characteristic phenomena, such as all home-cooked meals, no travel, relatively peaceful and stressless lifestyle.  Therefore, both of his overall heath state, the “metabolism index”, and his overall glucose control have reached to their respective “best conditions” within the last 10 years.  As a result, his most-recent HbA1C value has reached to a record low” level, between 6.3% to 6.4% without any medication or insulin.  

Figure 6: HbA1C comparison and differences of Lab-tested, Finger predicted, and Sensor predicted

This HbA1C study focuses on eight sub-periods (~40 months).  This long period contains thousands of different meals, including key contribution factors such as carbs/sugar intake, post-meal exercise, and weather.  The results again demonstrate a high degree of accuracy on calculating and predicting the patient’s forthcoming A1C value by using his developed MPM approach.  Once the healthcare professionals and other T2D patients understand and learn this skill for the HbA1C prediction method, a patient’s overall T2D condition can then be easier to control.  The purpose of this research paper is to help other T2D patients predict their HbA1C in order to prevent further damage to their internal organs caused by hyperglycemia, while waiting for their next laboratory test results in three or four months.


  1. Hsu, Gerald C., eclaireMD Foundation, USA. March 2020. No. 156: “A Case Study on the Investigation and Prediction of A1C Variances Over Seven Periods Using GH-Method: math-physical medicine.”
  2. Hsu, Gerald C., eclaireMD Foundation, USA. May 2020. No. 262: “A Case Study on the Prediction of A1C Variances over Seven Periods with guidelines Using GH-Method: math-physical medicine.”
  3. Hsu, Gerald C., eclaireMD Foundation, USA. April 2020. No. 248: “Comparison of HbA1C values among Lab-tested, Finger-piercing, CGM-collected  (GH-Method: math-physical medicine).”
  4. Hsu, Gerald C., eclaireMD Foundation, USA. May 2019. No. 85: “Using GH-Method: Math-Physical Medicine to investigate different contribution margins of FPG vs. PPG on HbA1C.”
  5. Hsu, Gerald C., eclaireMD Foundation, USA. April 2019. No. 65: “A Case Study of Investigation and Prediction of A1C Variances Over 5 Periods Using GH-Method: math-physical medicine.”
  6. Hsu, Gerald C., eclaireMD Foundation, USA. February 2019. No. 55: “Using GH-Method: math-physical medicine to investigate the role of HbA1C in the triangular relationship with Weight and Blood Pressure.”
  7. Hsu, Gerald C., eclaireMD Foundation, USA. June 2019. No. 26: “A Case Study of Investigation and Prediction of A1C Variances Using GH-Method: math-physical medicine”
  8. Hsu, Gerald C., eclaireMD Foundation, USA. January 2019. No. 10: “A Case Study of Analyzing and Predicting A1C Changes Using Math-Physics Medicine.”