Segmentation and Pattern Analyses for Three Meals of Postprandial Plasma Glucose Values and Associated Carbs/Sugar Amounts Using GH-Method: Math-Physical Medicine (No. 326)

2020 ◽  
Vol 2 (2) ◽  
pp. 1-4
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Plasma Glucose ◽  
Research Work ◽  
Exercise Program ◽  
Management Program ◽  
Conversion Ratio ◽  
Lifestyle Management ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Glucose Levels ◽  
Segmentation Analysis

The author describes the results of segmentation and pattern analyses of postprandial plasma glucose levels (PPG) and carbs/sugar intake amount (carbs), which are associated with his three daily meals. In this paper, there are three consistent ranges of low, medium, and high for PPG values and carbs/sugar amounts that are used for each meal but with different units. One of the final objectives for this analysis is to calculate the most reasonable and effective conversion ratio between measured PPG in mg/dL and carbs/sugar intake amount in grams, by discovering how much PPG amount would be generated from 1 gram of carbs/sugar intake. This investigation utilized the PPG data and carbs/sugar amount collected during a period of 2+ years from 5/5/2018 to 9/6/2020 with a breakdown of 855 days, including 2,565 meals, 33,345 glucose data, and 33,345 carbs/sugar data. By using the segmentation analysis of his 33,345 PPG data and 2,565 carbs/sugar data, the author has conducted a pattern recognition and segmentation analysis from his PPG profiles with its associated carbs/sugar intake of his food and meals in the past 855 days. Since 12/8/2015, he ceased taking any diabetes medications. In other words, his diabetes control is 100% dependent on his lifestyle management program with no chemical intervention from any medications. Subsequently, he has maintained a stringent exercise program after each meal; therefore, the development of his simplified PPG prediction model, excluding the exercise factor, can be expressed solely with carbs/sugar intake amount. Predicted PPG = (baseline glucose) + (conversion ratio * carbs/sugar amount) In his research work, he found the reasonable and effective conversion ratio between PPG and carbs that ranges from 1.8 mg/dL per gram to 2.5 mg/dL per gram. This simple equation could assist many type 2 diabetes (T2D) patients in controlling their diabetes via carbs/sugar intake amount. During this particular time period, his PPG control via a stringent lifestyle management without medication is highly successful. His estimated mathematically derived HbA1C values should be between 5.56% to 6.05%, which is a satisfactory HbA1C level for a 73-year-old male with a 25-year history of severe diabetes. It should be mentioned that he had an average daily glucose of 280 mg/dL and HbA1C of 11% in 2010. This segmented pattern analyses based on his PPG data and carbs/sugar intake amount offer a useful tool for analyzing other types of biomarkers in a deeper investigation with a wider entry point of research.

scholarly journals Segmentation and pattern analyses for three meals of postprandial plasma glucose values and associated carbs/sugar amounts using GH-Method: math-physical medicine (No. 326)

2020 ◽  
Vol 4 (4) ◽  
Keyword(s):  
Plasma Glucose ◽  
Exercise Program ◽  
Management Program ◽  
Physical Medicine ◽  
Lifestyle Management ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Glucose Levels ◽  
The Past ◽  
Segmentation Analysis

The author describes the results of segmentation and pattern analyses of postprandial plasma glucose levels (PPG) and carbs/sugar intake amount (carbs), which are associated with his three daily meals. In this paper, there are three consistent ranges of low, medium, and high for PPG values and carbs/sugar amounts that are used for each meal but with different units. One of the final objectives for this analysis is to calculate the most reasonable and effective conversion ratio between measured PPG in mg/dL and carbs/sugar intake amount in grams, by discovering how much PPG amount would be generated from 1 gram of carbs/sugar intake. This investigation utilized the PPG data and carbs/sugar amount collected during a period of 2+ years from 5/5/2018 to 9/6/2020 with a breakdown of 855 days, including 2,565 meals, 33,345 glucose data, and 33,345 carbs/sugar data. By using the segmentation analysis of his 33,345 PPG data and 2,565 carbs/sugar data, the author has conducted a pattern recognition and segmentation analysis from his PPG profiles with its associated carbs/sugar intake of his food and meals in the past 855 days. Since 12/8/2015, he ceased taking any diabetes medications. In other words, his diabetes control is 100% dependent on his lifestyle management program with no chemical intervention from any medications. Subsequently, he has maintained a stringent exercise program after each meal; therefore, the development of his simplified PPG prediction model, excluding the exercise factor, can be expressed solely with carbs/sugar intake amount.

Postprandial plasma glucose segmentation analysis of influences from diet and exercise between the preCOVID-19 and COVID-19 periods (No. 321)

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
GERALD C. HSU
Keyword(s):  
Plasma Glucose ◽  
Input Data ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Segmentation Analysis ◽  
Diet And Exercise ◽  
High Range

In this paper, the author describes the special segmentation analysis of impact on his post-prandial plasma glucose (PPG) via diet and exercise during the pre-COVID-19, from 5/5/2018 - 1/18/2019, and COVID-19, from 1/19/2020 - 8/28/2020, periods. He segmented his carbs/sugar intake amount and post-meal walking step into two ranges, low range and high range, for two periods, respectively. Initially, he calculates their associated PPG values for each range of input data, then he compares these two segmented outcomes against the total results from the combined periods.

scholarly journals A simple formula based on postprandial plasma glucose prediction using 5,640 meals data via GH-Method: math-physical medicine (No. 301)

2020 ◽  
Vol 5 (5) ◽  
Keyword(s):  
Plasma Glucose ◽  
Research Work ◽  
The Other ◽  
Diabetes Research ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Glucose Testing ◽  
The Status ◽  
Food Nutrition

This article is based on the continuation of the author’s research work, a simple and practical, yet highly accurate postprandial plasma glucose (PPG) prediction formula for type 2 diabetes (T2D) patients. His methodology is the developed GH-Method: math-physical medicine (MPM) which has been utilized repeatedly in the past decade. The predicted PPG formula-based on the status of fasting plasma glucose (FPG), carbs/sugar intake amount, and postmeal walking steps are as follows: Predicted PPG = 0.97 * FPG + (carbs/sugar grams * 1.8) - (post-meal walking steps in thousand * 5) The conclusive results have the order of values m1 / m2 /m3 / prediction accuracy %. Case A: 1.8 / 5.0 / 0.97 / 99.8% Case B: 2.0 / 5.0 / 0.945 / 99.9% Case C: 2.2 / 5.0 / 0.92 / 99.9% Exercise is important, contributing ~3% higher than food, is easily achieved compared to the required knowledge of diet. As a result, the author spent four years to study food nutrition. Most T2D patients are seniors; therefore, he suggests that walking is the best form of exercise. However, the most difficult part of exercise is the behavior psychology related to the issue of “discipline and persistence”. T2D patients need to walk between 2,000 to 4,000 steps after each meal. The author walks an average of 4,300 steps after each meal. On the other hand, diet (carbs/sugar amount and nutritional balance) requires much more and deeper knowledge of food nutrition in order to control diabetes. Therefore, the author developed an AI-based tool to assist T2D patients. For non-tech patients, the following simple guidelines can assist with meal intake: Starchy food: Eat an amount half of your fist or hand at most Colorful vegetables: Eat an amount limited to one fist or hand. Green vegetables: Eat an amount limited to 2.5 fists or hands. Please note: you must combine two types of vegetable together in order to get the total intake limitation. The author highly recommends the patients to measure their FPG at least several times a quarter, in order to get a quarterly average FPG value. The other three PPG values can then utilize the formula-based predicted PPG to control their overall diabetes conditions. The described method mentioned above in regard to the predicted PPG formula along with the post-meal walking exercise and carbs/sugar intake amount can help patients control their diabetes without painful and troublesome fingerpiercing glucose measurements. The author has been measuring his glucoses for 8.5 years (3,126 days) with fingerpiercing glucose testing combined with his 10-years of diabetes research work. He hopes this article can provide useful guidelines to other diabetes patients to take back their lives from this dreadful chronic disease.

scholarly journals Postprandial Plasma Glucose Segmentation Analysis of Influences from Diet and Exercise between the Pre-COVID-19 and COVID-19 Periods (No. 321)

2020 ◽  
Vol 3 (4) ◽  
Keyword(s):  
Plasma Glucose ◽  
Input Data ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Segmentation Analysis ◽  
Diet And Exercise ◽  
High Range

In this paper, the author describes the special segmentation analysis of impact on his post-prandial plasma glucose (PPG) via diet and exercise during the pre-COVID-19, from 5/5/2018 - 1/18/2019, and COVID-19, from 1/19/2020 - 8/28/2020, periods. He segmented his carbs/sugar intake amount and post-meal walking step into two ranges, low range and high range, for two periods, respectively. Initially, he calculates their associated PPG values for each range of input data, then he compares these two segmented outcomes against the total results from the combined periods.

scholarly journals A simple formula based on postprandial plasma glucose prediction using 5,640 meals data via GH-Method: math-physical medicine (No. 301)

2020 ◽  
Vol 5 (5) ◽  
Keyword(s):  
Plasma Glucose ◽  
Research Work ◽  
Physical Medicine ◽  
Prediction Formula ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
The Past ◽  
The Status ◽  
Glucose Prediction

This article is based on the continuation of the author’s research work, a simple and practical, yet highly accurate postprandial plasma glucose (PPG) prediction formula for type 2 diabetes (T2D) patients. His methodology is the developed GH-Method: math-physical medicine (MPM) which has been utilized repeatedly in the past decade. The predicted PPG formula-based on the status of fasting plasma glucose (FPG), carbs/sugar intake amount, and postmeal walking steps are as follows:

Effect of High ß-Glucan Barley on Postprandial Plasma Glucose Levels in Subjects with Normal Glucose Tolerance and Patients with Type 2 Diabetes

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 772-P
Author(s):  
MARIKO HIGA ◽  
AYANA HASHIMOTO ◽  
MOE HAYASAKA ◽  
MAI HIJIKATA ◽  
AYAMI UEDA ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Glucose Tolerance ◽  
Plasma Glucose ◽  
Normal Glucose Tolerance ◽  
Postprandial Plasma Glucose ◽  
Glucose Levels ◽  
Normal Glucose

scholarly journals Randomized, Double-Blinded, Double-Dummy, Active-Controlled, and Multiple-Dose Clinical Study Comparing the Efficacy and Safety of Mulberry Twig (Ramulus Mori, Sangzhi) Alkaloid Tablet and Acarbose in Individuals with Type 2 Diabetes Mellitus

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Mengyi Li ◽  
Xuemin Huang ◽  
Hui Ye ◽  
Yao Chen ◽  
Jing Yu ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Clinical Trial ◽  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Efficacy And Safety ◽  
Postprandial Plasma Glucose ◽  
Glucose Levels ◽  
Fasting Plasma ◽  
Significant Difference

Aims. To evaluate the efficacy and safety of mulberry twig alkaloid (SZ-A) tablet compared with acarbose in patients with type 2 diabetes.Methods. This clinical trial enrolled 38 patients who were randomized into two groups (SZ-A: 23; acarbose: 15) and were treated for 24 weeks. Patients and clinical trial staffs were masked to treatment assignment throughout the study. The primary outcome measures were glycated hemoglobin (HbA1c) and 1-hour and 2-hour postprandial and fasting plasma glucose levels from baseline to the end of treatment. Analysis included all patients who completed this study.Results. By the end of this study, HbA1c level in SZ-A group was decreased from baseline significantly (P<0.001). No significant difference was found when compared with acarbose group (P=0.652). Similarly, 1-hour and 2-hour postprandial plasma glucose levels in SZ-A group were decreased from baseline statistically (P<0.05), without any significant differences compared with acarbose group (P=0.748and 0.558, resp.). The fasting plasma glucose levels were not significantly changed in both groups. One of 23 patients in SZ-A group (4.76%) and 5 of 15 patients in acarbose group (33.33%) suffered from gastrointestinal adverse events.Conclusions. Compared with acarbose, SZ-A tablet was effective and safe in glycemic control in patients with type 2 diabetes.

scholarly journals Acute Effects of Morning Light on Plasma Glucose and Triglycerides in Healthy Men and Men with Type 2 Diabetes

2017 ◽  
Vol 32 (2) ◽  
pp. 130-142 ◽  
Author(s):  
Ruth I. Versteeg ◽  
Dirk J. Stenvers ◽  
Dana Visintainer ◽  
Andre Linnenbank ◽  
Michael W. Tanck ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Light Intensity ◽  
Plasma Glucose ◽  
Bright Light ◽  
Metabolic Effects ◽  
Ambient Light ◽  
Acute Effects ◽  
Postprandial Plasma Glucose ◽  
Glucose Levels

Ambient light intensity is signaled directly to hypothalamic areas that regulate energy metabolism. Observational studies have shown associations between ambient light intensity and plasma glucose and lipid levels, but human data on the acute metabolic effects of light are scarce. Since light is the main signal indicating the onset of the diurnal phase of physical activity and food intake in humans, we hypothesized that bright light would affect glucose and lipid metabolism. Therefore, we determined the acute effects of bright light on plasma glucose and lipid concentrations in 2 randomized crossover trials: (1) in 8 healthy lean men and (2) in 8 obese men with type 2 diabetes. From 0730 h, subjects were exposed to either bright light (4000 lux) or dim light (10 lux) for 5 h. After 1 h of light exposure, subjects consumed a 600-kcal mixed meal. Primary endpoints were fasting and postprandial plasma glucose levels. In healthy men, bright light did not affect fasting or postprandial plasma glucose levels. However, bright light increased fasting and postprandial plasma triglycerides. In men with type 2 diabetes, bright light increased fasting and postprandial glucose levels. In men with type 2 diabetes, bright light did not affect fasting triglyceride levels but increased postprandial triglyceride levels. We show that ambient light intensity acutely affects human plasma glucose and triglyceride levels. Our findings warrant further research into the consequences of the metabolic effects of light for the diagnosis and prevention of hyperglycemia and dyslipidemia.

scholarly journals A Neuroscientific Study of the Brain Stimulator and Simulation Model to Predict Breakfast PPG Using GH-Method: Math-Physical Medicine

2020 ◽  
Vol 3 (3) ◽  
Keyword(s):  
Simulation Model ◽  
Plasma Glucose ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Segmentation Analysis ◽  
The Brain

To prove his hypothesis in this paper, the author interprets the brain stimulator and its associated simulation model of predicted breakfast postprandial plasma glucose (PPG) via a food or meal segmentation analysis and Sensor PPG waveform characteristics study.

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