GH-METHODS

Math-Physical Medicine

NO. 329

A Case Study on the Prediction of A1C Variances over Eight Periods using GH-Method: math-physical medicine

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

Abstract
In this case study, the author analyzed, predicted, and interpreted a type 2 diabetes (T2D) patient’s hemoglobin A1C variance or A1C based on eight time periods, ~5 months each, utilizing the GH-Method: math-physical medicine (MPM) approach.  He utilized the same method and calculation formulas since 1/1/2014.  This particular article emphasizes the eight periods from 12/21/2019 to 5/20/2020.

During these periods, his predicted A1C was 6.32%, while the lab-tested A1C was 6.4%.  The difference is a mere 0.08%.  

The author focused on eight periods over 1,145 days.  It contains 3,435 meal data, including key contribution factors such as carbs/sugar intake, post-meal exercise, weather, and more.  This study demonstrated a high degree of accuracy for the calculation and prediction of the patient’s forthcoming HbA1C value by using the MPM approach.  Once the healthcare professionals and T2D patients understand the HbA1C mathematical prediction method, then the overall diabetes condition for the patient can be easier to control. The purpose for this research paper is to help people with T2D by preventing further damage to their internal organs caused by elevated A1C values, while waiting to take the laboratory test.

If healthcare professionals and diabetes patients have an interest to delve deeper regarding the formation of tested glucose and mathematical predicted A1C, they should focus on the influential factors and their respective weighted contribution percentages described in the author’s previous papers.

Here is the summary:

  • The most important month which contributes to the A1C is the month prior to the lab test.
  • PPG controls HbA1C (> 2/3) .
  • Body weight controls ~77% or more of the fasting plasma glucose (FPG); therefore, it is important to keep BMI below 25.
  • Carbs/sugar amount contributes ~39% to postprandial plasma glucose (PPG).  For T2D patients, it is safe to keep carbs/sugar intake amount below 15 grams per meal.
  • Post-meal walking steps contributes ~41% to PPG.  It is safe to maintain post-meal walking exercise around 4,000 steps after each meal.
  • A combined effort of diet and exercise controls ~80% to PPG formation.

Introduction
In this case study, the author analyzed, predicted, and interpreted a type 2 diabetes (T2D) patient’s hemoglobin A1C variance or A1C based on eight time periods, ~5 months each, utilizing the GH-Method: math-physical medicine (MPM) approach.  He utilized the same method and calculation formulas since 1/1/2014.  This particular article emphasizes the eight periods from 12/21/2019 to 5/20/2020.

Method
As shown in Figure 1, there are nine hemoglobin A1C lab-checkup 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
  • 4% on 5/20/2019
Figure 1: HbA1C history during a long period of 4/1/2017 through 5/20/2020

The author selected eight periods of almost equal length with ~5 months each, 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 – 5/20/2020): -0.2%

He applied his developed MPM approach to analyze the following seven contribution factors of HbA1C:

  • A1C variances contributed by FPG between 15% to 35%, which he used 25% in his calculation for this article.
  • FPG variance due to weight change with ~77% contribution.
  • Colder weather impact on FPG with a decrease of each Fahrenheit degree caused 0.3 mg/dL decrease of FPG.
  • A1C variances contributed by PPG between 65% to 85%, which he used 75% in his calculation for this article.
  • PPG variance due to carbs/sugar intake with ~39% weighted contribution on PPG.
  • PPG variance due to post-meal walking with ~41% weighted contribution on PPG.
  • Warm weather impact on PPG with an increase of each Fahrenheit degree caused 0.9 mg/dL increase of PPG.

It should be noted that his developed mathematical HbA1C prediction model is based on different weighted ratio for the previous 4-month glucose data, instead of the standard concept of the three-month average glucose for A1C.  He choose 120 days for his HbA1C calculation is based on the fact that average human red blood cells (RBC), after differentiating from erythroblasts in the bone marrow, are released into the blood and survive in the circulation for approximately 115 days.

Please note, there are no other complicated or sophisticated mathematical tools being used in this analysis.

Results
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, a summarized chart of these HbA1C values over eight periods are observed in Figure 1.

Figure 2 and 3 depict weight, carbs/sugar intake, exercise (post-meal walking), FPG, PPG, daily glucose, and HbA1C in Period H from 12/21/2019 to 5/20/2020.

Figure 2: Weight, carbs/sugar intake, post-meal walking
(12/21/2019 - 5/20/2020)
Figure 3: FPG & PPG, Daily Glucose & HbA1C (12/21/2019 - 5/20/2020)

A step-by-step background calculation table of all periods detail how to derive and interpret these A1C variances (Figure 4).

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

Figure 4: HbA1C step-by-step calculation table during 8 periods (4/1/2017 - 5/20/2020)

For Period G, from 9/25/2019 to 12/20/2019, the Lab A1C is 6.6%, while the predicted A1C is 6.7% (actually 6.66%). The eclaireMD predicted A1C has achieved up to 99% accuracy. During Period G, the A1C incremental amount of 0.06% mainly resulted from an increase in FPG.  

For Period H, from 10/21/2019 to 5/20/2020, the Lab A1C is 6.4%, while the predicted A1C is 6.3% (actually 6.32%). The eclaireMD predicted A1C has also achieved up to 99% accuracy.  During Period H, the A1C amount difference of 0.08% mainly resulted from a change in FPG.  During period H, the total reduction of A1C is 0.28% from the 6.6% lab tested on 12/20/2019 to the 6.32% lab tested on 5/20/2020.  This 0.28% difference on HbA1C stemmed from the 0.20% of FPG contribution and 0.08% of PPG contribution.  

Nevertheless, for Periods G and H along with both HbA1C differences are less than 0.1% (actually 0.06% and 0.08%), which is acceptable for the accuracy of predicted HbA1C value.  

Figure 5: FPG vs. Weight for short period (no correlation during 12/21/2019 - 5/20/2020) and long period (76% correlation during 1/1/2014 - 5/20/2020)

Conclusion
The author focused on eight periods over 1,145 days.  It contains 3,435 meal data, including key contribution factors such as carbs/sugar intake, post-meal exercise, weather, and more.  This study demonstrated a high degree of accuracy for the calculation and prediction of the patient’s forthcoming HbA1C value by using the MPM approach.  Once the healthcare professionals and T2D patients understand the HbA1C mathematical prediction method, then the overall diabetes condition for the patient can be easier to control. The purpose for this research paper is to help people with T2D by preventing further damage to their internal organs caused by elevated A1C values, while waiting to take the laboratory test.

If healthcare professionals and diabetes patients have an interest to delve deeper regarding the formation of tested glucose and mathematical predicted A1C, they should focus on the influential factors and their respective weighted contribution percentages described in the author’s previous papers.

Here is the summary:

  • The most important month which contributes to the A1C is the month prior to the lab test.
  • PPG controls HbA1C (> 2/3) .
  • Body weight controls ~77% or more of the fasting plasma glucose (FPG); therefore, it is important to keep BMI below 25.
  • Carbs/sugar amount contributes ~39% to postprandial plasma glucose (PPG).  For T2D patients, it is safe to keep carbs/sugar intake amount below 15 grams per meal.
  • Post-meal walking steps contributes ~41% to PPG.  It is safe to maintain post-meal walking exercise around 4,000 steps after each meal.
  • A combined effort of diet and exercise controls ~80% to PPG formation.

References

  1. Hsu, Gerald C., eclaireMD Foundation, USA, No. 310: “Biomedical research methodology based on GH-Method: math-physical medicine”
  2. Hsu, Gerald C., eclaireMD Foundation, USA, 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, No. 116: “A Case Study on the Investigation and Prediction of A1C Variances Over Six Periods Using GH-Method: math-physical medicine”
  4. Hsu, Gerald C., eclaireMD Foundation, USA, No. 65: “A Case Study of Investigation and Prediction of A1C Variances Over 5 Periods Using GH-Method: math-physical medicine”
  5. Hsu, Gerald C., eclaireMD Foundation, USA, No. 326: “Segmentation and pattern analyses for three meals of postprandial plasma glucose values and associated carbs/sugar amounts using GH-Method: math-physical medicine”
  6. Hsu, Gerald C., eclaireMD Foundation, USA, No. 68: “Using GH-Method: math-physical medicine to Conduct Segmentation Analysis to Investigate the Impact of both Weight and Weather Temperatures on Fasting Plasma Glucose (FPG)”