GH-METHODS

Math-Physical Medicine

NO. 332

Traveling impact on glucose, metabolism, and cardiovascular risk percentage based on 9-years data using GH-Method: math-physical medicine

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

Abstract
In this article, the author conducted an impact study of his average glucose and overall metabolism based on a period of approximately nine years from 1/1/2012 to 9/19/2020.  He separated his 242 travel days during this 9-year period into 134 short travel trips (< 3 hours of flying time) and 108 long travel trips (>3 hours of flying time).  He purposely chose glucose and metabolism for this investigation because he has had type-2 diabetes (T2D) for over 25 years and his main medical research work is based on lifestyle and metabolism.

Here is a summary of the results in the format of glucose / MI score, where MI has a break-even line of 73.5%.

  • Glucose / MI
    Short:   126 / 76.6%
    Long:   127 / 79.7%
    Total:   123 / 68.5%

By using the total period as the baseline, we can further calculate how much excessive percentages of both glucose and MI associated with two different types of traveling.  

  • Glucose / MI
    Short:   103% / 112%
    Long:   104% / 116%
    Total:   100% / 100%

During the short travel trips, his glucose was 3% higher and his MI was 12% higher than the total average. For the long travel trips, his glucose was 4% higher and his MI was 16% higher than the total average.

This article quantitatively demonstrates the impact on glucose, metabolism, and cardiovascular (CVD) risk while traveling especially based on the results of the long-flight trips.  The author understands the necessity of having some inevitable traveling;  however, by observing the impact levels resulting from different types of trips, he can do better to avoid hectic travel schedules.  There are many T2D patients who need to travel, hopefully, they can use this practical information to guide them.

Introduction
In this article, the author conducted an impact study of his average glucose and overall metabolism based on a period of approximately nine years from 1/1/2012 to 9/19/2020.  He separated his 242 travel days during this 9-year period into 134 short travel trips (< 3 hours of flying time) and 108 long travel trips (>3 hours of flying time).  He purposely chose glucose and metabolism for this investigation because he has had type-2 diabetes (T2D) for over 25 years and his main medical research work is based on lifestyle and metabolism.

Method
1. Background
To learn more about the GH-Method: math-physical medicine (MPM) research methodology, readers can review his article, Biomedical research methodology based on GH-Method: math-physical medicine (No. 310), to understand his MPM analysis method.

2. Data Collection
The author started measuring his daily glucose since 1/1/2012 by using traditional finger-piercing and test strip (Finger glucose) 4 times each day, once in early morning as his fasting plasma glucose (FPG) value when he wakes up from sleeping, and three times at two-hours after each meal as his postprandial plasma glucose (PPG) values.  He then takes the average value of these four-glucose data as his daily glucose.

3. Metabolism
After the first 4 years (2010-2013) of self-studying endocrinology and food nutrition, he spent the entire year of 2014 to develop a complex mathematical model of metabolism.  This model contains four easily measured biomarkers of medical conditions such as body weight, glucose, blood pressure, and lipids, along with six lifestyle details including food portion quantity & nutritional quality balance, water intake, appropriate exercise, sleep amount & quality, stress reduction, and daily life routine regularity.  He applied the concept of topology from mathematics and the modeling technique of finite element method from engineering to develop this mathematical model of metabolism which became the cornerstone of his future medical research work.

In 2014, he also defined two specific output parameters of his metabolism model as metabolism index (MI) and general health status unit (GHSU). MI is the combined score of the four medical conditions and six lifestyle details which can be calculated on one specific day, a time instant, or over a period of time.  GHSU is defined as the 90-days moving average MI values.  He has also identified a “break-even line” at 0.735 (73.5%) to separate his metabolic conditions between the healthy state (below 0.735) and unhealthy state (above 0.735).  His MI was above 73.5% prior to 2014 and below 73.5% after 2014; therefore, his health “turning-point” year was 2014.  To date, he has collected about two million data regarding his own medical conditions and lifestyle details.

During 2018-2019, he further developed a complex mathematical model to estimate a set of his risk probability percentages of having a CVD or stroke based on medical conditions, lifestyle details, and overall MI scores.

Results
When he traveled between two cities with a total flying time less than three hours, this type of short trip only affected one meal and he did not suffer any jet lag.  On the contrary, when his flying time is more than 3 hours, this kind of long trip affected two meals and he would suffer jet lag most of the time.  As we know, jet lag can impact many categories of metabolism, including sleep, stress, daily routine of life, and other factors.

In his previous research reports, he identified air-travel related food and meals are unhealthy for glucose control.  Other options include home-cooked meals, chain restaurant food, individual restaurant food, and supermarket prepared meals.

As a result, based on the meal information above, he separated his travel category into short trips versus long trips.  During this 3,182 days of ~9-year period, he had a total of 242 air trips, where he traveled every 13 days, which is approximately 7.6%.  In summary, he had 134 short trips (4.2%) and 108 long trips (3.4%).

Figure 1 shows the background data table of this study.  Here is a summary of the results in the format of glucose / MI score, where MI has a break-even line at 73.5%.

  • Glucose / MI
    Short:   126 / 76.6%
    Long:   127 / 79.7%
    Total:   123 / 68.5%
Figure 1: Background data tables of Long trips vs. Short trips (2012 - 2020)

By using the total period as the baseline, we can further calculate how much excessive percentages of both glucose and MI associated with two different types of trips.  

  • Glucose / MI
    Short:   103% / 112%
    Long:   104% / 116%
    Total:   100% / 100%

In other words, during short travel trips, his glucose was 3% higher than the total average and his MI was 12% higher than the total average.  However, during longer travel trips, his glucose was 4% higher than the total average and his MI was 16% higher than the total average.  The findings confirm the impact on metabolism between the air travel food-glucose relationship and jet lag.

The author obtained the knowledge and tried extremely hard to control his carbs/sugar intake amount during air flights that resulted into a smaller difference of 3% to 4% compared to his average glucoses.  There are still some uncontrollable factors, such as limited meal choices for in-flight, at airline lounges, and airport restaurants.  On the other hand, the impact on metabolism, while traveling, is more severe than the impact on glucose alone because the metabolic impact lasts the entire day, sometimes a few days, rather than just during flight hours, e.g. jet lag.

The bar charts in Figure 2 reflect the excessive difference percentage of both glucose and MI, showing a much clearer comparison than a simple table.

The impact on glucose and metabolism also induce further influences on his risk probability of having a CVD or stroke.  In Figure 3, it shows his CVD/Stroke risk percentage during a period from 2014 through 2020.  It is obvious that the years 2018 and 2019 have higher risk percentage than 2017 and 2020.  From 2018 to 2019, he traveled to ~50 international cities where he attended 65+ medical conferences and delivered ~120 oral presentations.  This hectic travel schedule not only elevated his glucose and metabolism index, but also increased his risk percentage of having CVD/Stroke.  The enclosed higher CVD risk percentages of 2018 and 2019 illustrated his viewpoints and analysis results.  It should be highlighted that 2020 has the lowest risk percentage due to the COVID-19 quarantined life because of no traveling.

Figure 2: Bart chart of excessive glucose and MI, comparison of long trips
and short trips versus total period (2012-2020)
Figure 3: CVD/Stroke risk probability % due to traveling (2012 - 2020)

Conclusion
This article quantitatively demonstrates the impact on glucose, metabolism, and CVD risk while traveling especially based on the results of the long-flight trips.  The author understands the necessity of having some inevitable traveling; however, by observing the impact levels resulting from different types of trips, he can do better to avoid hectic travel schedules.  There are many T2D patients who need to travel, hopefully, they can use this practical information to guide them.

References

  1. Hsu, Gerald C. eclaireMD Foundation, USA. “Biomedical research methodology based on GH-Method: math-physical medicine (No. 310)”
  2. Hsu, Gerald C. eclaireMD Foundation, USA. “The Impact of Traveling on Glucose and Metabolism (003)”
  3. Hsu, Gerald C. eclaireMD Foundation, USA. “The Impact of Traveling on Glucose and Metabolism (031)”
  4. Hsu, Gerald C. eclaireMD Foundation, USA. “Using Math-Physical Medicine to Study Traveling vs. Metabolism and Glucose, Weather Temperature vs. FPG and PPG, Food & Meals vs. PPG (234)”
  5. Hsu, Gerald C., eclaireMD Foundation, USA; “Using Math-Physics Medicine to Analyze Metabolism and Improve Health Conditions (005)”
  6. Hsu, Gerald C., eclaireMD Foundation, USA; “Using Math-Physical Medicine to Control T2D via Metabolism Monitoring and Glucose Predictions (009)”
  7. Hsu, Gerald C., eclaireMD Foundation, USA; “Detailed contribution analysis of metabolism index (MI) categories on risk probability percentage of having a cardiovascular disease or stroke using GH-Method: math-physical medicine (No. 316)”
  8. Hsu, Gerald C., eclaireMD Foundation, USA; “Risk Probability of Atherosclerosis, Cardiovascular Disease, and Stroke during the COVID-19 period using GH-Method: math-physical medicine (No. 289)”
  9. Hsu, Gerald C., eclaireMD Foundation, USA; “Using glucose and its associated energy to study the risk probability percentage of having a stroke or cardiovascular diseases from 2018 through 2020 (GH-Method: math-physical medicine) No. 273”