Section 6: Glucose and Food and Exercise

Date: 10/25/2016 13:00

In order to study the relationship between glucose and food, I have developed an APP known as SmartPhoto for the iPhone.  Within the SmartPhoto, I constructed a relational database structure to attach with each picture stored in the iPhone album.

The data structure has 5 levels:
1. Group: (USA, Japan, France, etc.)
2. Category: (home cooking, chain restaurant, individual restaurant, airline, cruise, etc.)
3. File: (Denny’s, McDonald’s, Greek Restaurant, Asian Food, etc.)
4. Name: (restaurant name, dish name, menu item, etc.)
5. Content: (anything you want to keep a record, e.g. Nutrition ingredients, etc.).

Once the food photos with its data structure are stored in SmartPhoto, they can be sorted and searched any way a person chooses.

Please see Figure 6-1: SmartPhoto Samples of food & meal with glucose level attached with each photo. From May 1, 2015 through August 13, 2017, I have collected a total of 2,476 pictures of food and meal with an average glucose level (PPG) of 119.7 mg/dL. During the same period, my daily average glucose level (including both FPG and PPG) is 119.93 mg/dL.

Figure 6-1: SmartPhoto Sample Pictures of Food & Meal

From the years of 2012 through 2014, during my glucose analysis, I came to a tentative conclusion that my high glucose periods (close to 140 mg/dL) were contributed to traveling overseas. Please see Figure 6-2: Glucose results from 2012 to 2014. I found that the majority dishes from eastern Asia (excluding Northern China), Hawaii, and Tahiti contain high contents of sugar in their cooking process. Rice, flour, and/or taro are the main sources of carbohydrates. However, during my extended stay in various eastern Asian countries and Hawaii over 8 months brought another prominent fact. My average glucose level dropped below 120 mg/dL – a drop of 20 points from previous periods. Please see Figure 6-3: Glucose results from 2015 through 2016.

Figure 6-2: Glucose during period of 2012-2014
Figure 6-3: Glucose during period of 2015-2016

After careful analysis of this average glucose decline, I discovered the following four reasons:
(1) I followed my rule of choosing food material and picking menu items more cautiously when I use the glucose prediction capability of my tool;
(2) I spread my daily walking exercise to three post-meal time frames, averaging 4,000 steps after each meal, instead of concentrating on one walk in the evening (this will be discussed in future sections);
(3) I watched my food and meal intake and walking exercise more carefully on traveling days. For example, after eating a meal at the airport restaurant or airline lounge, I made an effort to walk 3,000 to 4,000 steps in the aisles of the connecting boarding gates;
(4) My SmartPhoto tool’s analysis capability also provided me many insights regarding dining locations, food menus, and cooking material selection.

From examining the big picture data in SmartPhoto, I tabulated the results in Figure 6-4: Summary Table of Average Glucose at Different Eating Locations. There are a total of 1,591 food and meal pictures with an average of PPG value of 121.8 mg/dL. During the same period from May 1, 2015 to October 20, 2016, my average daily glucose from my APP tool, including FPG, is 121.41 mg/dL – this is another supporting point of why I decided to use my average daily glucose value as the initial predicted FPG value – while my 90-day average glucose is 123.75 mg/dL as shown in Figure 6-5: Glucose during SmartPhoto Period from May 1, 2015 to October 20, 2016.

Figure 6-4: Summary Table of Average Glucose & Different Eating Places
Figure 6-5: Glucose during SmartPhoto Period ​(05/01/2015-10/20/2016)

My preliminary explanation and interpretation of causes for these summarized results are as follows:
(1) The average glucose values in all the studied nations are similar, measuring between 119.9 and 125.6 mg/dL. From 2015 to 2016, I followed strict rules for food and meal intake along with the similar ratio between eating at home and eating outside in every country.

(2) Home cooking equates to a 115.3 mg/dL of glucose value, eating in chain restaurants (where nutritional ingredients information is published) equates to 125.2 mg/dL, eating in individual restaurants (where nutrition information is unavailable) equates to 132.3 mg/dL, eating at an airport, in an airline lounge, and in-flight meals equates to 134.0 mg/dL, and eating ready-cooked food from supermarkets equates to 140.6 mg/dL.

(3) Airline-related food produces high glucose points due to the fact that there are limited options on food items and limited space for post-meal exercising.

(4) After studying the nutrition of major food items, I have tried not to eat processed foods. However, when I have limited options, I can still eat them provided that I read the nutrition facts on the labels carefully (especially the information regarding carbohydrates and sugar).

(5) Further detailed analysis regarding individual restaurants received the following average glucose values:

USA: 129.8 mg/dL
Japan: 139.6 mg/dL
Taiwan: 136.7 mg/dL
Other Nations: 130.8 mg/dL

In general, American and Western food do not include sugar in the cooking process (except in desserts). Japanese, Korean, southern Chinese, and southeast Asian cultures add both sugar and salt into dishes during the cooking process. Please see Figure 6-6: Measured Average Glucose for Different Eating Places.

Figure 6-6: Measured Average Glucose for Different Eating Places

(6) I discovered one interesting observation from analyzing a particular popular brand of chain restaurants. Usually, I avoid eating lunch or dinner at any chain restaurant. However, breakfast is an exception since the portions are usually smaller due to economic reasons. As a result, the portion of carbohydrates and sugar are also greatly reduced in certain chain restaurants’ breakfast foods. This same particular brand of American chain restaurant has an average glucose value of 122.9 mg/dL, while Japan has 117.4 mg/dL, Taiwan has 125.3 mg/dL, and China has 126.2 mg/dL. My observation is that this particular chain restaurant in Taiwan and China add some local flavors to the menu items; furthermore, I suspect that its standard operating procedures (SOP) of procurement and cooking may not completely comply with its headquarter’s requirements.

(7) From 2013 to 2014, while I was studying food and nutrition, I drew an incorrect conclusion that I could eat as many vegetables as I wanted. Later in 2015, after I compiled several million points of data on food nutrition, I discovered the differences among various vegetables.

One way I distinguish between how different vegetables affect my glucose is by color. Please see Figure 6-7: Summary Contents of Carbs and Sugars in Vegetables. I came to the conclusion that if I eat large quantities of vegetables, my PPG can increase to a higher value. I must pay attention to the color of vegetables when I eat them in order to get a more accurate glucose prediction.

(8) When I have a craving for snacks, desserts, and/or fruits, I can definitely consume them, however, I must limit the quantity in order to control both my glucose value and weight. The best practice for me is to eat limited amount of them between meals, for example at 10 am or 3 pm. I avoid giving in to my cravings before bedtime to assist with my weight control. Fruits are important for overall physical health; however, it is important to avoid eating high sugar content fruits (such as pineapples, bananas, etc.) and also limit the quantity consumed. With this control mechanism, I can maintain a healthy level of glucose.

It would be interesting to analyze the “extreme” cases in my records, e.g. studying glucose over 200 mg/dL. Figure 6-8 displays all of my 17 meals which contributed to glucose over 200 mg/dL from May 1, 2015 to October 20, 2016.

Figure 6-7: Summary Contents of Carbs & Sugars in Vegetables
Figure 6-8: 17 Meals contributed to PPG over 200 mg/dL ​(5/1/2015-10/20/2016)

It should be noted that the 3 major sources contributing to my extremely high PPGs are eating at individual restaurants offering east Asian food, American chain restaurants, and meals on airlines & cruises. I can still eat at these locations provided that I have knowledge of the food nutrition, use the right tool to predict post-meal glucose value, and have sufficient willpower to resist giving into cravings at the wrong time of day.

Furthermore, as indicated in the following Figure 6-9: Analysis of Causes for Glucose Values Greater Than 140 mg/dL, it is clear that high carbs & sugar food and Asian food have contributed about 58% of higher glucose values (>140mg/dL) causes. Another interesting fact is that about 10% of unknown reasons occurred, which means I could not explain the actual causes of those high glucose values.

Figure 6-9: Analysis of Causes for Glucose Values Greater Than 140 mg/dL

Research has shown that carbohydrates and sugars directly affect glucose levels. By using the following rules, I can estimate my glucose level before I consume my meal by controlling the food quality and quantity.

(1) I can find the ingredients on the Nutrition Facts label on the food packaging. I use the amount provided in terms of grams divided by 20 to get the portion estimate. For example, carbohydrate has 16 grams, then calculate it as 16/20=0.8. The value of 0.8 is entered into the carbs input box of the tool. I also calculate the sugar amount by using the same method.
(2) When I cook at home, I need to estimate the percentage based on using my open-hand area for estimation or my fist size for volume estimation as 100%. However, based on my observation for the past few years of portion estimation, I have noticed recently that I need to reduce my 100% estimation to 2/3 of my hand or fist size. My guess is that my body’s toleration of carbohydrates and sugar has been reduced due to the effects of diabetes. After collecting more data regarding this phenomenon, I may need to build another layer of AI to address this organic change.
(3) My tool can also search each item of my food components from the food bank, and then add them up to get the total consumption of both carbohydrates and sugar.
(4) Most fruits have both carbohydrates and sugar, but some fruits such as bananas, pineapples, and grapes have higher carbohydrates and sugar content.
(5) It is highly recommended not to eat any desserts, since they contain high carbohydrates, sugar, salt, and fat, which are not healthy for you. Try to eat plenty of green leafy vegetables; but avoid or reduce non-green vegetables such as beets, carrots, corn, onions, and tomatoes which have higher sugar content.
The most important principle for diabetic patients is to “even out” their glucose wave during the entire day, i.e. push the high tide downward (reduce hyperglycemia) and lift the low tide up (avoid shock from low glucose) like an ocean wave. Once you are able to maintain your target weight and have a balanced nutrition, your diabetes and other chronic disease conditions should be under control.

Correlation between PPG and Diet (3/8/2017)
PPG (post-meal glucose) values are greatly influenced by our lifestyle, including primarily diet (intake of carbohydrates and sugar) and exercise (within a 2-hour period after each meal). In Figures 6-10 and 6-11, the correlation results of r = 64.7% and r2 = 41.8% which show that a very strong positive correlation exists between daily average PPG and the average intake of carbs and sugar during daily meals.

Figure 6-10: Correlation between PPG & diet (carbs and sugar in gm)
Figure 6-11: Detailed daily average intake of carbs & sugar per meal ​(around 15 gm per meal)

More detailed analysis was done for investigating dinner’s glucose and intake of carbs + sugar by using both a Mean (Average) method and Least Square Mean (LSM) method. The results are displayed in both Figure 6-12 and 6-13 which show strong positive correlation values of r = 64.4% and r2 = 41.5%. These dinner’s correlation results are almost identical as the daily meal average values. Using the Mean method, I get an average post-dinner glucose of 115.64 mg/dL and an average intake of carbs + sugar of 13.90 gm; however, using the LSM method, I get average post-dinner glucose of 115.66 mg/dL while keeping an average intake of carbs + sugar of 13.90 gm. This shows that there is no significant difference between using Mean or LSM method for this case.

Figure 6-12: Least Square Mean Calculation for correlation ​between PPG & diet
Figure 6-13: Average Glucose and Carbs/Sugar using Mean Calculation

Glucose and Exercise:
Other than food and meals, exercise was another important factor that contributed to my glucose reduction. In my APP, I included many different types of exercises. In my many years of experience, I believe that walking at average speed is the best kind of exercise for many senior citizens. My average walking speed is 2.5 miles per hour, about 6,000 steps per hour, or 100 steps per minute.

In 2012, I walked an average of 8,000 steps, or 3.3 miles, per day. During that time, it was difficult for me to walk too long because I was overweight and had weak knees. By 2016, I gradually increased to 17,200 steps per day, or 7.2 miles per day without any difficulty. Please see Figures 6-14, 6-15, and 6-16.

In the beginning of 2015, I discovered my PPG would significantly decrease if I spread out my daily walking exercise into 3 segments, i.e. exercising within 2 hours after finishing each meal. By examining my glucose data for extended periods of time, I also learned that when my average glucose was around 140 mg/dL, with every 1,000 steps taken after a meal, I could reduce my glucose level 7 to 10 points. However, when my average glucose value dropped to around 120 mg/dL, I could reduce my glucose level 4 to 6 points with every 1,000 steps after a meal. This difference is due to the assumptions I made in my mathematical models.

Figure 6-14: Walking Exercise (2012 – 2016)
Figure 6-15: Waking Exercise Concentrating in the Evening ​(2012 – 2014)
Figure 6-16: Walking Exercise spread out after 3 meals ​(2015 – 2016)

Correlation between PPG and Exercise:
In Figure 6-17 the correlation results of r= 27.1% and r2 = 7.4% showed that a weaker but still significant negative correlation between PPG and average walking steps after each meal. Please note that, as indicated in the same figure, I have walked around 4,000 steps within a 2-hour period after each meal. In this way, I could take full advantage of walking exercise to improve my daily post-meal metabolism and reduce my PPG values.

Currently, I walk approximately 18,000 steps or 7.5 miles per day. About 2 months ago, I felt some pain on my heels and I worried about over-exercising my joints. Therefore, I decided to replace a part of my daily walking exercise with Tai Chi, a slower body motion and stretching movements. I plan to collect big data over a much longer period to study the effect of Tai Chi on my glucose control situation more precisely.

Figure 6-17: Correlation between PPG & exercise ​ (post-meal walking steps)

Summary regarding PPG and Food Quality, Exercise; FPG and Weight:
Figure 6-18: Summary of PPG and Food Quality (Carbs & sugar), Exercise, provides the following two important conclusions:
(1) Every gram of carbs or sugar adds approximate 2 mg/dL of my PPG value. Since I take about 14 grams of carbs/sugar per meal in average, my average PPG increase amount is 28 mg/dL.
(2) If I walk between 2,000 to 4,000 steps after each meal, I could reduce my PPG by 10 to 20 mg/dL. This post-meal exercise can bring down my PPG net gain from 28 mg/dL to 8 to 18 mg/dL. That is why my average PPG falls into the range of 108 mg/dL to 118 mg/dL.

Also shown in Figure 6-19: Correlation coefficients among key variables, we can see the following two conclusions very clearly.
(1) FPG and weight are directly connected. There are no direct correlation between FPG and carbs & sugar, or exercise.
(2) PPG and carbs & sugar (food quality) are directly connected (60%), while exercise is the secondary factor (-27%). PPG and weight have no direct connection even though weight is related to food quantity, i.e. meal’s portion.

Figure 6-18: Summary of PPG & Food Quality ​(Carbs & sugar), Exercise
Figure 6-19: Correlation coefficients among key variables