GH-METHODS

Math-Physical Medicine

NO. 336

Influences from medication and lifestyle on glucose and metabolism during two equal length of 4-years but different sub-periods, one with-medication and the other without-medication using GH-Method: math-physical medicine

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

Abstract
The author conducted a special investigation of the influence on diabetes and overall health by utilizing his collected big data of glucose and metabolism index based on  medical conditions and lifestyle details, of two equal length sub-periods, about 4 years each.  The first sub-period is the medication period of 1,436 days, from 1/1/2012 to 12/7/2015, and the second sub-period is the non-medication period of 1,502 days, from 12/8/2015 to 1/18/2020.  After being on multiple diabetes medications for over 20 years, he ceased taking any medication on 12/8/2015.  The total period is approximately 8-years long from 1/1/2012 to 1/18/2020.

During the total period, he made 242 trips by air.  He separated his 242 trips for this period into 134 short travel trips (< 3 hours of flying time) and 108 long travel trips (>3 hours of flying time). He has purposely chosen glucose, metabolism, exercise, and daily life routines for this special investigation because he has had type-2 diabetes (T2D) for over 25 years and his main medical research work is based on endocrinology and metabolism.

Here are the findings from the observed results:

  • His performance during the non-medication sub-period is better than his medication sub-period with an improvement of 12% on glucose and 42%  on metabolism.
  • With the same observation from above, the non-medication is better than the medication sub-period, which has been repeated for both short trips and long trips.
  • The performance of the total period for glucose and metabolism index (MI) is better than short trips, while short trips are better than long trips.  As a result, traveling hurts his diabetes control and overall health status.
  • The MI deviations of 33% to 38% are wider than glucose deviations of 11%-14%.

To compare the results of these two sub-periods, medication and non-medication, we can see that the glucose control and metabolism maintenance for the non-medication sub-period are within a range of 11% to 38% better than the medication sub-period.  This observation indicates that medication successfully suppresses the diabetes symptoms; however, it cannot cure the disease or prevent it from getting worse.  On the other hand, by strengthening metabolism via lifestyle management can assist in diabetes control and overall health improvement from the core or at the fundamental level.  It is true that most patients are not capable or unwilling to go through a stringent lifestyle management program and always seek a “quick fix”. Unfortunately, there is no “quick fix” in the area of chronic diseases (but may exist in some other diseases or surgical cases), which are mainly caused by an unhealthy lifestyle except for some genetic dispositions.  The author understands all of these arguments and viewpoints from his 25 years of fighting against diabetes and its multiple serious complications.  Nevertheless, he wants to offer a quantitative proof with high precision to patients with chronic diseases and healthcare professionals by enhancing their knowledge and increasing experiences on both diabetes control and metabolic improvements.

Introduction
The author conducted a special investigation of the influence on diabetes and overall health by utilizing his collected big data of glucose and metabolism index based on medical conditions and lifestyle details, of two equal length sub-periods, about 4 years each.  The first sub-period is the medication period of 1,436 days, from 1/1/2012 to 12/7/2015, and the second sub-period is the non-medication period of 1,502 days, from 12/8/2015 to 1/18/2020.  After being on multiple diabetes medications for over 20 years, he ceased taking any medication on 12/8/2015.  The total period is approximately 8-years long from 1/1/2012 to 1/18/2020.

During the total period, he made 242 trips by air.  He separated his 242 trips for this period into 134 short travel trips (< 3 hours of flying time) and 108 long travel trips (>3 hours of flying time). He has purposely chosen glucose, metabolism, exercise, and daily life routines for this special investigation because he has had type-2 diabetes (T2D) for over 25 years and his main medical research work is based on endocrinology 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 developed GH-Method: 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.  His selected glucose target is 120 mg/dL which is equivalent to a score of 1.0 or 100% as the perfect score of this category of measured glucose.

3. Metabolism
Metabolism is an overly complex subject which warrants a section to explain this sophisticated mathematical model.

The author spent the past 10 years to self-study and research the subjects on endocrinology, especially diabetes, metabolism, and its related lifestyle details.  After the first 4 years (2010-2013) of self-studying endocrinology and food nutrition, he spent the entire year of 2014 to develop a sophisticated mathematical model of metabolism.  This model contains four easily measured biomarkers of medical conditions such as body weight, glucose, blood pressure, lipids and others, along with six lifestyle details including food portion quantity & nutritional quality balance, drinking water intake, appropriate exercise, sufficient sleep amount & sleep quality, stress reduction, and daily life routine regularity.  He applied the concept of topology from pure mathematics and the modeling technique of finite element method from structural and mechanical engineering to develop this mathematical model of metabolism which became the cornerstone of his medical research work during the past decade.

In 2014, he further defined a specific output parameter of his metabolism model as “metabolism index” or MI. The MI contains 10 categories with ~500 detailed elements by utilizing artificial intelligence techniques to manage its complex data contents.  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.  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 metabolism conditions including both medical conditions and lifestyle details.

4. Traveling
When he traveled between two cities with a total flying time less than three hours, these short trips only affect one meal and he did not suffer any jet lag from this kind of trip.  On the contrary, when his flying time is more than 3 hours, these long trips affect at least two meals and he will also suffer jet lag if it involves crossing time zones.  As we know, jet lag impacts many other categories of metabolism, including sleep, stress, daily routine of life, etc.

In his previous research reports, he has identified a variety of air-travel food and meals which are exceedingly unhealthy for glucose control.  Other eating options include home-cooked meals, chain restaurant food, individual restaurant food, and supermarket prepared meals which would have different levels of impact on glucoses.

As a result, based on the information above, he separated his air travel category into short trips versus long trips.  During this 2,938 days over an 8-year period, he had a total of 242 air trips, where he traveled every 12 days, which is about 8.2% of days in the total period.  In summary, he had 134 short trips (4.6% of total period) and 108 long trips (3.7% of total period).

Furthermore, he also separated these short trips and long trips into medication sub-period and non-medication sub-period.  The medication sub-period has 39 long trips and 69 short trips, whereas the non-medication sub-period has 69 long trips and 65 short trips.  The excessive 30 long trips during the non-medication sub-period are contributed by attending medical conferences in different international cities during 2018-2019.

Results
In Figure 1, it shows the background data table of this study.  Figures 2 and 3 reflected the daily average glucose and daily average MI score for the medication and non-medication sub-periods, respectively. Figures 4 and 5 depicted the impacts on glucose value and MI score along with their deviations from the perfect condition (100% of glucose and 73.5% of MI) due to medication and traveling for medication and non-medication sub-periods, respectively. It should be noted that the glucose values indicate his diabetes control level and MI scores signify his overall health status.  His defined  “baseline” or “break-even” levels are 120 mg/dL for glucose values and 73.5% for MI scores.

Following are his four conclusive findings from the observed results in Figures 1 through 5.

  1. His performance during the non-medication sub-period is better than his medication sub-period with 12% improvement on glucose and 42% on metabolism. The Medication period: glucose 131 mg/dL and MI 82.14%; Non-Medication period: glucose 117 mg/dL & MI 57.89% (Figures 1 and 2)
  2. For both short trips and long trips, repeat the same conclusion above, the non-medication is better than the medication sub-period (Figures 4 and 5)
  3. The performance of the total period for glucose and MI is better than short trips, while short trips are better than long trips.  As a result, traveling hurts his diabetes control and overall health status.  His total period contains 92% of “non-traveling” days.  A combination of short trips and long trips would occupy only 8% of his total period, but it has brought significant and noticeable damages to his health (Figures 4 and 5)
  4. The deviations from the perfect conditions of 100% for glucose at 120 mg/dL and MI at 73.5% are listed below:  Long glucose = 14%; Short glucose = 11%; Total glucose = 12%; Long MI = 38%; Short MI = 35%; Total MI = 33%, which indicate that the MI deviations of 33% to 38% are wider than glucose deviations of 11% to 14%.
Figure 1: Background data tables (1/1/2012 - 1/18/2020)
Figure 2: Daily averaged glucose and daily metabolism index (MI) score during With-Medication period of 1/1/2012 to 12/7/2015 (1,436 days)
Figure 3: Daily averaged glucose and daily metabolism index (MI) score during Without-Medication period of 12/8/2015 to 1/18/2020 (1,502 days)
Figure 4: Glucose comparison and glucose deviations from baseline of 120 mg/dL resulted from both medication and traveling
Figure 5: MI comparison and MI deviations from baseline of 73.5% resulted
from both medication and traveling

Conclusions
To compare the results of these two sub-periods, medication and non-medication, we can see that the glucose control and metabolism maintenance for the non-medication sub-period are within a range of 11% to 38% better than the medication sub-period.  This observation indicates that medication can successfully suppress the symptoms of diabetes; however, it cannot cure the disease or prevent it from getting worse.  On the other hand, by strengthening metabolism via lifestyle management can strongly assist in diabetes control and overall health improvement from the core or at the fundamental level.  It is true that most patients are not capable or unwilling to go through a stringent lifestyle management program and always seek a “quick fix” of their diseases. Unfortunately, there is no “quick fix” for chronic diseases (but may exist in some other diseases or surgical cases), which are mainly caused by an unhealthy lifestyles except for some genetic dispositions (usually less than 20% to 25%).  The author understands all of these arguments and viewpoints from his 25 years of fighting against diabetes and its various painful complications.  Nevertheless, he still wants to offer a solid quantitative proof with high precision to patients with chronic diseases and healthcare professionals by enhancing their knowledge and increasing experiences on both diabetes control and metabolic improvements.

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”
  10. Hsu, Gerald C., eclaireMD Foundation, USA;  “Traveling impact on glucose, metabolism, and cardiovascular risk percentage based on 9-years data using GH-Method: math-physical medicine (No. 332)”
  11. 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)”
  12. Hsu, Gerald C., eclaireMD Foundation, USA; “Traveling impact on glucose, metabolism, exercise, and daily life routines based on 8 years big data using GH-Method: math-physical medicine (No. 335)