GH-METHODS

Math-Physical Medicine

NO. 149

Applying segmentation pattern analysis to investigate postprandial plasma glucose characteristics and behaviors of the carbs/sugar intake amounts in different nations (GH Method: math-physical medicine)

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

Introduction
In this paper, the author presents the results of his national segmentation pattern analysis of the sensor PPG data based on both high-carb and low-carb intake amounts.  It also verified his earlier findings on the communication model between the brain and internal organs such as the stomach, liver, and pancreas.

Methods
First, he studies the overall combined PPG waveforms associated with various meals in different nations.  Secondly, he defines low-carb intake as 0-14.9 grams carbs/sugar intake amount per meal and high-carb intake as 15-150 grams carbs/sugar intake amount per meal, and then analyze PPG accordingly.  He generates the PPG sub-waveforms associated with these two different carbs/sugar intake ranges.  Thirdly, he calculates the data and plots the graphs of PPG differences between high-carbs and low-carbs.  He utilized this third step to reverify his earlier findings of communication model between the brain along with stomach, liver, and pancreas.

Results
(1) National overall PPG combined waveform data:  Final results are shown in Figures 1 and 2 and conclusions are listed below in the format of (averaged carbs; finger PPG; sensor PPG; and walking steps)

  • USA:  (12.5; 114.4; 136.2; 4,222)
  • Taiwan:  (14.7; 113.7; 132.1; 4,745)
  • Japan:  (16.3; 117.6; 135.9; 4,658)
  • Others:  (18.7; 119.6; 140.2; 4,301)
  • Total:  (14.6; 115.8; 136.3; 4,252)
Figure 1: Summary data of national combined PPG
Figure 2: High-carb and low-carb national segmented PPG waveforms

In summary, both USA and Taiwan are better than the overall average number.  These are due to more home cooked meals by the author, who has deep knowledge regarding the relationship between glucose behaviors and food nutritional input.  Japan is also remarkably close to the average, but worse than Taiwan and USA.  This is due to higher sugar content and higher carb-based dishes in Japan.  Other nations fall into the last category as being the worst because the author stayed in the hotels and ate at restaurants exclusively.  Lastly, all of the national exercise amounts are comparable.

It should be pointed out that the averaged sensor PPG is 18% higher than the averaged finger PPG.

(2) National low-carb vs. high-carb segmented data:  Final results are shown in Figures 3, 4, 5, 6 and conclusions are listed below in the format of (low-carbs; high-carbs; averaged carbs; low finger PPG; high finger PPG, averaged finger PPG)

  • USA:  (7.9; 25.8; 12.5; 111; 126; 114)
  • Taiwan:  (8.5; 27.8; 14.7; 110; 124; 114)
  • Japan:  (9.1; 30.2; 16.3; 112; 124; 118)
  • Others:  (10.4; 26.1; 18.7; 112; 126; 120)
  • Total:  (8.5; 27.1; 14.6; 111; 126; 116)
Figure 3: Summary data of national carbs segmented PPG
Figure 6: Waveforms of national carbs segmented PPG
Figure 4: Summary data table of national combined PPG and carbs segmented PPG
Figure 5: Detailed data table of national carbs segmented PPG

It should be noted that all of the low-carb intakes are in the range of 8-10 grams and high-carbs are in the range of 26-30 grams.  The high carb grams are ~3x of the low-carb grams.  All of the low-carb finger PPG are in a narrow range of 110-112 mg/dL and high-carb finger PPG are in a narrow range of 124-126 mg/dL.  These national carb segmented analysis results are dissimilar to the national overall combined PPG results, which have many noticeable and significant varying conclusions.  

(3) Differences between high-carb PPG and low-carb PPG for these four national subgroups are quite similar to the national overall combined PPG difference.  

Observing from Figures 7 and 8, the author has summarized his findings as follows.  At 0-minute, the PPG gaps are extremely small.  At 30-minutes, initial digestion stage, the PPG gaps become larger (2x to 6x, overall around 4x).  At 60 to 75-minutes, full digestion stage, the PPG gaps become huge (6x to 10x, overall around 8x).  However, after the full digestion stage, the PPG gaps become even slightly bigger, but largely due to the different decaying speeds of two PPG waves, and partially also due to continuously burn off of the remaining energies associated with high carbs.  At 120 to 135-minutes, low-carb waves almost completely decease, while high-carb waves still have an excessive amount of left-over energy (~10% of opening glucose and ~20% of leftover energy) inside the blood system.  

The above descriptions were derived from careful physical observations of complex biochemical behaviors of glucoses.  They have reconfirmed the author’s previous hypothesis regarding the communication model between the heart along with stomach, liver, and pancreas.  

Figure 7: Detailed data table and waveforms of national combined PPG for verification of brain’s function
Figure 8: Detailed national PPG differences from carbs segmented PPG for verification of brain’s function

Conclusions
The conclusions derived from this analysis are based on sensor data exclusively during a period of about 20 months (5/5/2018 – 12/13/2019), which are quite similar to some of his previously published conclusions derived from finger PPG data analysis.

However, the “waveform” created by massive sensor data have indeed offered more insights regarding the PPG characteristics and behaviors.  For example, the verification of his hypothesis using carb-segmented analysis related to communication model between the brain and some internal organs are remarkably similar to his previously published findings based on the overall PPG waveform study.

References

  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, 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.
  5. 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.