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 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:

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 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)

2020 ◽  
Vol 5 (1) ◽  
Keyword(s):  
Plasma Glucose ◽  
Pattern Analysis ◽  
Communication Model ◽  
Physical Medicine ◽  
Internal Organs ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Liver And Pancreas ◽  
Segmentation Pattern ◽  
The Brain

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.

scholarly journals Investigation of Two Glucose Coefficients of GH.f-Modulus and GH.p-Modulus based on the Data of Three Clinical Cases During the COVID-19 Period using linear Elastic Glucose Theory of GHMethod: Math-Physical Medicine, Part 8 (No. 360)

2021 ◽  
pp. 1-6
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Health Conditions ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Sugar Intake ◽  
Linear Elastic ◽  
Behavior Study ◽  
Obesity And Diabetes ◽  
Near Future

This article is Part 8 of the author’s linear elastic glucose behavior study which focuses on the deeper understanding of these two newly defined glucose coefficients, GH.f-modulus and GH.p-modulus. Findings have shown the sensitive relationships with health conditions such as obesity and diabetes and certain lifestyle components, e.g. carbs/sugar intake amount and post-meal walking steps. Hopefully in the near future, he will be able to develop a reasonable and applicable numerical ranges of these two glucose coefficients. He used his measured glucose data and predicted glucose models for both fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) for three patients with chronic diseases over the COVID-19 quarantined period, from 3/1/2020 to 11/10/2020.

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.

scholarly journals Haemostatic functions and metabolic profile of subclinical hypothyroid and hypothyroid patients.

2018 ◽  
Vol 17 (4) ◽  
pp. 532-536
Author(s):  
Aarti Sood Mahajan ◽  
R Mahaur ◽  
T Singh ◽  
AK Jain ◽  
DK Dhanwal ◽  
...  
Keyword(s):  
Platelet Count ◽  
Total Cholesterol ◽  
Plasma Glucose ◽  
Metabolic Profile ◽  
Ldl Cholesterol ◽  
Medical Science ◽  
Normal Range ◽  
Postprandial Plasma Glucose ◽  
The Status ◽  
Hypothyroid Patients

Objectives: Both hypercoagulable and hypocoagulable states have been proposed for hypothyroidism, whether in overt or subclinical spectrum. The status of haemostatic functions, metabolic profile and their relationship in hypothyroid disorders need to be evaluated.Methods and Material: This prospective case control study was undertaken in 30- 50 years old female subclinical and hypothyroid patients. Haemostatic functions like bleeding time (BT), clotting time (CT), prothrombin time (PT), activated partial thromboplastin time (APTT), platelet count and metabolic parameters like plasma glucose and lipid levels and clinical variables like blood pressure and body mass index were noted and compared. In addition the strength of correlation of TSH, T4, T3, lipid profile with the haemostatic functions was evaluated.Results: Both groups of patients were obese, normotensive with normal haemostatic parameters. The platelet count correlated with TSH in subclinical hypothyroid patients and with T4 levels in hypothyroid patients. Although within normal range, total cholesterol and LDL cholesterol levels were higher and postprandial plasma glucose (PPPG) levels lower in hypothyroid patients compared to subclinical hypothyroid patients. A positive correlation was seen between TSH and LDL, PPPG levels, between fT3 and BMI, and also of antiTPO with total cholesterol, LDL, Fasting plasma glucose (FPG) in hypothyroid patients. The BMI was negatively associated with fT3 levels in subclinical hypothyroid patients.Conclusion: This study found normal haemostatic and metabolic functions in both subclinical and hypothyroid patients. Although within normal range, hypothyroid patients had higher total and LDL cholesterol. TSH and antiTPO levels correlated with LDL levels in these patients. Correlation of platelet count with TSH in subclinical hypothyroid and T4 levels in hypothyroid patients advocate a difference in mechanism involved. Therefore it can be connoted that thyroid status influences metabolic profile, and platelet count.Bangladesh Journal of Medical Science Vol.17(4) 2018 p.532-536

A biophysical investigation on the relationships between the inputs of meal quantity percentage, carbs/sugar amount, and exercise, along with the outputs of postprandial plasma glucose, and body weight using GH-Method: Math-physical medicine (No. 295)

2020 ◽  
Keyword(s):  
Body Weight ◽  
Plasma Glucose ◽  
Influential Factors ◽  
Research Note ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
The Relationship

This research note describes the author’s investigation on differences among his three meals, which include breakfast, lunch, and dinner, in terms of their influential factors and their respective PPG data and waveforms. He further described the relationship between his body weight and meal quantity percentage for his normal portion. During this period, from 5/5/2018 to 7/14/2020, he collected detailed information of his 2,403 meals and ~64,000 glucose data.

scholarly journals Coefficient of GH.F-Modulus in the Linear Elastic Fasting Plasma Glucose Behavior Study Based on Health Data of Three Diabetes Patients Using the GH-Method: Math-Physical Medicine, Part 4 (No. 356)

2020 ◽  
pp. 1-7
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Health Data ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Linear Elastic ◽  
Fasting Plasma ◽  
Behavior Study ◽  
Diabetes Patients

This article is Part 4 of the author’s linear elastic glucose behavior study, which focuses on fasting plasma glucose (FPG) component. It is the continuation of his previous three studies, Parts 1, 2, and 3, on linear elastic postprandial plasma glucose (PPG) behaviors

scholarly journals Accuracy of Predicted Glucose using both Natural Intelligence and Artificial Intelligence via GH-Method: Math-Physical Medicine (No. 320)

2021 ◽  
pp. 1-4
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Artificial Intelligence ◽  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Daily Average ◽  
Fasting Plasma ◽  
Average Glucose

This paper describes the accuracy of using natural intelligence (NI) and artificial intelligence (AI) methods to predict three glucoses, fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and daily average glucose, in comparison with the actual measured PPG by using the finger-piercing (Finger) method. The entire glucose database contains 7,652 glucoses (4 glucose data per day) over 1,913 days from 6/1/2015 through 8/27/2020

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