Math-physical Medicine Research Articles

Relationships of Chronic Kidney Disease Risks and Five Inputs of Bmi, Whr, Eag, Diet and Exercise Using Viscoplastic Energy Model of Gh-Method: Math Physical Medicine

Relationships of Chronic Kidney Disease Risks and Five Inputs of Bmi, Whr, Eag, Diet and Exercise Using Viscoplastic Energy Model of Gh-Method: Math Physical Medicine

Relationships of Visceral Fat Ratio and Four Metabolic Disorders Using Viscoplastic Energy Model of Gh- Method: Math-Physical Medicine

Relationships of Visceral Fat Ratio and Four Metabolic Disorders Using Viscoplastic Energy Model of Gh- Method: Math-Physical Medicine

The relationship between body weight versus food portion, sleep hours, walking exercise level using both statistical correlation and the viscoplastic energy model of GHMethod: math-physical medicine (No. 952, VMT #351, 11/13/2023)

The relationship between body weight versus food portion, sleep hours, walking exercise level using both statistical correlation and the viscoplastic energy model of GHMethod: math-physical medicine (No. 952, VMT #351, 11/13/2023)

Relationships of cardiovascular disease risks and three glycemic intensities of type 2 diabetes using viscoplastic energy model of GH- Method: math-physical medicine

Relationships of cardiovascular disease risks and three glycemic intensities of type 2 diabetes using viscoplastic energy model of GH- Method: math-physical medicine

Relationships of Type 2 Diabetes and Five Inputs of Bmi, Whr, Food Portion, Exercise and Sleep Using Viscoplastic Energy Model of Gh-Method: Math-Physical Medicine

Relationships of Type 2 Diabetes and Five Inputs of Bmi, Whr, Food Portion, Exercise and Sleep Using Viscoplastic Energy Model of Gh-Method: Math-Physical Medicine

Relationships of Diabetic Neuropathy Risks and Five Inputs of Bmi, Whr, Eag, Diet and Exercise Using Viscoplastic Energy Model of Gh-Method: Mathphysical Medicine

Relationships of Diabetic Neuropathy Risks and Five Inputs of Bmi, Whr, Eag, Diet and Exercise Using Viscoplastic Energy Model of Gh-Method: Mathphysical Medicine

The Relationship of Postprandial Plasma Glucose Versus Fasting Plasma Glucose in the Early Morning, Carbohydrates and Sugar Intake Amount, and Post-Meal Walking Steps using both Statistical Correlation and Viscoplastic Energy Model of GH-Method: Mathphysical Medicine (No. 954, VMT #353, 11/15/2023)

The Relationship of Postprandial Plasma Glucose Versus Fasting Plasma Glucose in the Early Morning, Carbohydrates and Sugar Intake Amount, and Post-Meal Walking Steps using both Statistical Correlation and Viscoplastic Energy Model of GH-Method: Mathphysical Medicine (No. 954, VMT #353, 11/15/2023)

Relationships of Chronic Kidney Disease Risks and Four Lifestyle Details, Food Portion, Food Quality, Walking Steps, Sleep Score Using Viscoplastic Energy Model of GH- Method: Math-Physical Medicine (No. 967, VMT #366, 11/24-25/2023)

Relationships of Chronic Kidney Disease Risks and Four Lifestyle Details, Food Portion, Food Quality, Walking Steps, Sleep Score Using Viscoplastic Energy Model of GH- Method: Math-Physical Medicine (No. 967, VMT #366, 11/24-25/2023)

Summary Report from Diet through Obesity, Diabetes to Six Mortality Diseases Using the Viscoplastic Energy Model of Gh-Method: Math-Physical Medicine (No. 957, VMT #356, 11/17/2023)

Summary Report from Diet through Obesity, Diabetes to Six Mortality Diseases Using the Viscoplastic Energy Model of Gh-Method: Math-Physical Medicine (No. 957, VMT #356, 11/17/2023)

Relationships of Chronic Kidney Disease Risks and Three Glycemic Intensities of Type 2 Diabetes Using Viscoplastic Energy Model of Gh- Method: Math-Physical Medicine

Relationships of Chronic Kidney Disease Risks and Three Glycemic Intensities of Type 2 Diabetes Using Viscoplastic Energy Model of Gh- Method: Math-Physical Medicine

Relationships of Visceral Fat Ratio and T2D with Four Lifestyle Details Using Viscoplastic Energy Model of GH-Method: Math-Physical Medicine (no. 995, vmt #393, 12/15/2023)

Relationships of Visceral Fat Ratio and T2D with Four Lifestyle Details Using Viscoplastic Energy Model of GH-Method: Math-Physical Medicine (no. 995, vmt #393, 12/15/2023)

Pathophysiological discussions and statistical results between diabetes caused by obesity versus cancers caused by obesity via SD-VMT areas comparison using data collected from 2010 to 2023 and based on space-domain viscoplastic energy model of GH-Method: Math-Physical Medicine (No. 893, VGT #293)

The objective of this methodology article is to assess and compare the risks of developing type 2 diabetes (T2D) and cancers caused by obesity as a single input factor. The study spans a timeframe of approximately 14 years, from 2010 to 2023. The author personally collected and analyzed their input data, although there were limitations in obtaining sufficient lab-tested data between 2010 and 2012. From 2013 to 2018, the data collection methods involved more lab testing and personal data obtained through finger-piercing glucose testing. A more complete and comprehensive dataset was obtained from 2018 to 2023, utilizing laboratory tests, automated glucose sensors, and wearable health devices. Despite these limitations, the annual dataset used in this study is considered adequate for drawing useful and reasonably accurate conclusions. The author has been diagnosed with T2D since 1996 but has not received any cancer diagnoses. The assessment of cancer risk in this study is conducted using a sophisticated metabolism index (MI) model developed by the author in 2014. This model takes into account factors such as lifetime bad habits, environmental factors, genetic concerns, four metabolic disorders, and six lifestyle details. To analyze and compare the contribution of T2D (measured through HbA1C levels) and cancer risks based on the single input cause of obesity (measured through body weight, m1), this study utilizes the space-domain viscoplastic energy model from GH-Method: Math-Physical Medicine. In summary, this research presents two key findings: Firstly, the author conducts two separate analyses to evaluate the impact of obesity on his existing T2D condition and his future risks of developing cancer. The SD-VMT analysis shows that the energy level for T2D and obesity is 110.1, while the energy level for cancers and obesity is only 1.18, representing just 1% of the diabetes energy level. This finding indicates that despite being a T2D veteran of 26 years, the author has not shown any signs of developing cancer. Furthermore, his SD time-zone analysis reveals that 98% of the total energy is concentrated in the Y10-Y13 for T2D and Y10-Y17 for cancers, while only 2% falls in the Y14-Y23 for T2D and Y18-Y23 for cancers. Secondly, as an exploratory medical research methodology, he considers obesity as the output symptom, with diabetes and cancers being two separate input causes. The SD-VMT energy ratios demonstrate that diabetes accounts for 69% of the total energy, while cancers account for 31%. Although diabetes has twice as much energy as cancers, the disparity is not as substantial as the 99-fold difference observed in the first case. This finding emphasizes that the author's existing diabetes condition is significantly more severe than his risks of developing cancer.

Viscoelastic and viscoplastic glucose theory (VGT #121): Prevention of breast cancer via weight control, daily exercise, and food quality using a dataset over 11.5 years with 12 annual data of a hypothetic female patient based on GHmethod: math-physical medicine (No. 711)

This paper starts with a brief excerpt from Mayo Clinic’s website “Breast cancer care at Mayo Clinic “Breast cancer is cancer that forms in the cells of the breasts.

Quantitative Evidence of Benefit on Longevity from the Traditional Medicine Practice of Lifestyle Management Using Collected Data from Y2014 to Y2022 While Applying the Space-Domain Energy Model of Viscoplastic Medicine Theory Based On Gh-Method: Math-Physical Medicine

Since 2010, the author has practiced the lifestyle of choosing high-quality food from the Mediterranean diet which includes fresh vegetables & fruits, nuts & seeds, fish, and olive oil, while avoiding red meat, processed foods, unhealthy snacks, and sugary food. He also controlled his food portion size around 50% to 75% of his previous food consumption amount prior to Y2010 (as the 100% yardstick). He walks about 7 miles (10 km or 16,000 steps) each day, including 4,000 steps after each meal. In addition, he practices Chinese Tai-chi movement and arms/leg stretching. He maintains 7-8 hours of sleep with minimal interruption, such as nighttime urination. He avoids stressful events or incidents as much as possible and practices meditation but does not take herbs or receive acupuncture, spiritual therapies, etc.

Viscoelastic or Viscoplastic Glucose Theory (VGT #64): Estimated Risk Probability Percentages of Prostate Cancer and its Moving Trend Over 12+ Years From 1/1/2010 to 4/18/2022 Using Metabolism Index, Obesity, Diet and Exercise as the 4 Influential Factors for Prostate Cancer Risk % based on GH-Method: Math-Physical Medicine (No. 653)

Recently, a friend of the author died from prostate cancer and leukemia. This news caused the author to read a few published medical articles regarding prostate cancer. He then outlines some key information in the Introduction section. The selected information sources have been referenced within this section and will not be listed in the Reference section.

Viscoelastic or Viscoplastic Glucose Theory (VGT #56): Using the VGT tool to Estimate the Relative Energy Associated with Different Calculated Annual Cancer Risk Percentages Under the Influence of Estimated Average Glucose (eAG) and Body Weight in The Early Morning (BW) Over Two 5-Year Periods, Y2012-Y2016 and Y2017-Y2021, based on GH-Method: Math-Physical Medicine (No. 644)

The author was a professional engineer working in the fields of the space shuttle, naval battleships, nuclear power plants, computer hardware and software, artificial intelligence, and semiconductor chips. After his retirement, he started to self-study and research internal medicine with an emphasis on biomarker relationships exploration and disease prevention. Since 2010, he has utilized the disciplines learned from 7 different universities along with various work experiences to formulate his current medical research work during the past 13 years.

Viscoelastic or Viscoplastic Glucose Theory (VGT #47): Using Viscoplasticity/Viscoplasticity to Explore the Inter-Relationships on Cancer Risk % Versus Sensor Measured Fasting Plasma Glucose and Body Weight Based on Eleven Collected Annual Data Over A 10+ Year Period From 1/1/2012 to 3/20/2022 using the GHMethod: Math-Physical Medicine (No. 632)

The purpose of this research article is to explore a possible link between cancer risk probability percentage (Cancer %) and body weight (BW) in the early morning. Body weight is very easy to measure by standing on a weight scale. Weight in the early morning is highly correlated with fasting plasma glucose (FPG) measured in the early morning or averaged glucose during sleeping hours. FPG also serves as the baseline of postprandial plasma glucose (PPG) and contributes around 25% to 29% of daily average glucose (eAG). Daily average glucose levels or quarterly HbA1C values (average eAG during the past 90-115 days) determine the severity of diabetes disease. Finally, diabetes is one of the major influential chronic diseases for developing many kinds of diseases and complications, including cancers. From the author’s mathematical view, in this biomedical field, an output biomarker sometimes turns into an input biomarker of a third biomarker. Therefore, many biomedical problems can be described as a scenario of the matrix with “m by n by p” dimensions.

Viscoelastic or Viscoplastic Glucose Theory (VGT #65): Estimated Risk Probability Percentages of Having Kidney Cancer and its Moving Trend Over a 7+-Year Period from 1/1/2015 to 4/20/2022 Using A Combined Score of Hyperglycemia, Insulin Resistance, Hypertension and Albumin-To-Creatinine Ratio, Obesity, Diet and Exercise as the 4 Key Influential Factors for Developing Kidney Cancer Based on GH-Method: Math-Physical Medicine (No. 654)

Viscoelastic or Viscoplastic Glucose Theory (VGT #65): Estimated Risk Probability Percentages of Having Kidney Cancer and its Moving Trend Over a 7+-Year Period from 1/1/2015 to 4/20/2022 Using A Combined Score of Hyperglycemia, Insulin Resistance, Hypertension and Albumin-To-Creatinine Ratio, Obesity, Diet and Exercise as the 4 Key Influential Factors for Developing Kidney Cancer Based on GH-Method: Math-Physical Medicine (No. 654)

Viscoelastic or Viscoplastic Glucose Theory (VGT #66): Relative Risk Probability of Developing Ovarian and/or Uterine Cancers in Female Patients Using a Type 2 Diabetes and Obesity Patient Collected Data Over 12+ Years From 1/1/2010 To 4/23/2022 With 5 Gender-Independent Generic Cancer risk factors, i.e., Obesity, Diabetes, Lipids, Diet, and Exercise to Validate the Suitability of VGT Tool for Gynecological Cancer

Viscoelastic or Viscoplastic Glucose Theory (VGT #66): Relative Risk Probability of Developing Ovarian and/or Uterine Cancers in Female Patients Using a Type 2 Diabetes and Obesity Patient Collected Data Over 12+ Years From 1/1/2010 To 4/23/2022 With 5 Gender-Independent Generic Cancer risk factors, i.e., Obesity, Diabetes, Lipids, Diet, and Exercise to Validate the Suitability of VGT Tool for Gynecological Cancer

Viscoelastic Or Viscoplastic Glucose Theory (VGT #39): Applying the VEGT or VPGT to Study the Effect of Role Switching for Cause and Symptom, CVD Risk Versus Cancer Risk and Cancer Risk Versus CVD Risk, And Using Viscoelastic Perturbation Model to Predict the Risks of Having CVD or Cancer Using Calculated Risk Data of a 12+ Years Period from Y2012 to

Since 2012, the author has been collecting his body weight and finger-piercing glucose values each day. In addition, he accumulates medical conditions data including blood pressure (BP), heart rate (HR), and blood lipids along with lifestyle details of diet, exercise, sleep, stress, water intake and daily routine details. Based on these collected big data, he further organized them into two main groups. The first group is medical conditions (MC) with 4 categories: weight, glucose, BP, and lipids. The second group is lifestyle details (LD) with 6 categories: food, exercise, water intake, sleep, stress, and daily routines. He collects his daily data and then calculates a unique combined score for each of these 2 groups of MC and LD and their 10 categories. His calculated risk probability percentage of developing complicated diseases, such as cardiovascular disease (CVD) or cancers, are based on this above-described metabolism model. This metabolism index model (MI) includes the root causes of 6 lifestyle details and 4 disease symptoms including rudimentary chronic diseases: obesity, diabetes, hypertension, and hyperlipidemia.