GH-METHODS

Math-Physical Medicine

NO. 263

Risk Probability of Having a Metabolic Disorder Induced Cancer
(GH-Method: MPM)

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

Abstract
Cancer is an exceedingly difficult and complicated disease that can affect any organ within the body, where abnormal cells divide and mutate rapidly, and destroying healthy normal cells in the process. The possible cause of cancer can result from a combination of many different reasons. The author has dedicated the past decade on researching endocrinology and metabolism. He considers that both endocrinology and cancer are quite similar from the viewpoint of “digging into the black box”. However, based on his rudimentary understanding of cancer, he also feels that the diseases caused by cancer are probably at least 10 times more complicated than endocrinology. Although he is not an oncology expert, only a veteran and research scientist on chronic diseases and metabolism, he still has a strong curiosity and motivation in wanting to know what his own risk probability of having cancer is. This reason inspires his cancer research work by using his strength of metabolism knowledge to conduct his assessment on the relationship between metabolism and cancer.

Introduction
This paper describes the author’s investigation regarding his risk probability of having cancer which is closely related to his overall metabolism status.

Method
The author has spent ten years collecting big data (~2 million data) of his health and lifestyle details in order to conduct his own research on chronic diseases and their various complications. Since 1995, he has suffered three chronic diseases, including diabetes, hyperlipidemia, and hypertension. He has also endured five cardiovascular episodes (CVD) from 1994 to 2006, chronic kidney disease (CKD) in 2010, bladder infection, foot ulcer, diabetic retinopathy (DR), and hyperthyroidism for the past decade. By 2017, most of his metabolic disorders induced chronic diseases and complications have been well controlled. During the same year, he started to self-study cancer diseases with a particular interest on its causes and prevention via the metabolism improvement.

Since 2014, by using topology concept and finite element engineering modeling, he developed a complex mathematical metabolism model to check his overall metabolism state on a daily basis. From 2015 to 2017, by using optical physics, wave theory, energy theory, quantum mechanics, big data analytics, artificial intelligence, segmented pattern analysis, and various statistics tools (time-series, spatial analysis, frequency domain analysis), he has developed four prediction models for four biomarkers, i.e. body weight, fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and HbA1C. All of these models have achieved greater than 95% prediction accuracy. From 2018 to 2019, he further developed two risk assessment models for having CVD/ Stroke or CKD. For the first four months of 2020, he started his research on both DR and hyperthyroidism complications.

Cancer is an exceedingly difficult and complicated disease that can affect any organ within the body, where abnormal cells divide and mutate rapidly, and destroying healthy normal cells in the process. The possible cause of cancer can result from a combination of many different reasons. The author has dedicated the past decade on researching endocrinology and metabolism. He considers that both endocrinology and cancer are quite similar from the viewpoint of “digging into the black box”. However, based on his rudimentary understanding of cancer, he also feels that the diseases caused by cancer are probably at least 10 times more complicated than endocrinology. Although he is not an oncology expert, only a veteran and research scientist on chronic diseases and metabolism, he still has a strong curiosity and motivation in wanting to know what his own risk probability of having cancer is.

This reason inspires his cancer research work by using his strength of metabolism knowledge to conduct his assessment on the relationship between metabolism and cancer.

Figures 1, 2, and 3 demonstrate three parts of a summarized table developed by the author which connect certain cancer causing or influencing factors and organ systems affected by cancer. He still has a lot to learn about cancer diseases, for example, which organs in one particular organ system are most likely to be affected by which influential factor. Therefore, this article only serves as the beginning of his long journey of cancer research using his developed GH-Method: math-physical medicine (MPM approach).

Figure 1: Sub-areas of genetic and personal bad habits
Figure 2: Sub-areas of environmental and viral infection
Figure 3: Sub-areas of chronic diseases and lifestyle details

The article indicates that there are 23 cancer factors to cause a total of 45.2% of entire cancer cases in China (around 2.3 million cases per year) [1]. Most of these 23 influential factors happen to be a part of components identified in his developed mathematical model of metabolism.

He started his investigation from identifying major causes and the possible organ systems affected by cancer. Of course, like many other branches of medical research, he started with the sub-area of genetics, including his age, race, gender, and family genetic background. He has assigned 5% of weight to this sub-area of genetic factors.

Secondly, he delved into the sub-area of personal bad habits including smoking or chewing tobacco, drinking alcohol, and/or taking illicit drugs that would lead into various kinds of cancer affecting different organ systems. In addition, he also looked into other components, such as having unhealthy diet, inactive.

RAS Oncology & Therapy
lifestyle, high stress life, poor sleep quality, and personal medical history along with types, amounts, and duration of medication intake that would also lead into different kinds of cancers. He assigned 25% of weight to this sub-area of personal bad habits factors.

Thirdly, the sub-area of environmental factors includes toxic chemicals, air pollution (e.g. PM 2.5), water pollution, food pollution, poison, hormone therapy, nuclear radiation (e.g. X-ray, CT), UV radiation, infection from parasites and bacteria, or other cancer-causing chemicals, and more. He assigned 15% of weight to this sub-area of environmental factors. As an example, relatively speaking, China could have a higher percentage of cancer cases in this sub-area due to its highly polluted environment, including land, water, and air.

Fourth, the sub-area of viral infection factors includes Helicobacter Pylori, Hepatitis B Virus, Hepatitis C Virus, HIV Virus, Human Papilloma Virus, Epstein-Barr virus (EBV), Paragonimus Sinensis, Human Herpes Virus Type 8, Kaposi’s Sarcoma, Hodgkin’s Lymphoma, and others. He assigned 10% of weight to this sub-area of viral infection factors.

Fifth, the sub-area of metabolic disorder induced chronic diseases and their various complications include obesity, diabetes, hypertension, hyperlipidemia, CVD, stroke, CKD, bladder infection, hyperthyroidism, bladder infection, foot ulcer, RD, and more. He assigned 15% of weight to this sub-area of chronic diseases factors.

Finally, the sub-area of lifestyle details should be the foundation of the causes mentioned in the above situations except for the genetic factor. This sub-area include six categories, i.e. food and diet, exercise, water drinking, sleep, stress, and daily routine life pattern. These categories in combined with the fifth sub-area of chronic diseases would have approximately 500 detailed elements (from finite “element” method of engineering).

The author spent 10 years to develop and continuously enhance a sophisticated and customized software program to collect all kinds of input data and process them dynamically in order to provide a daily guideline to himself for the purpose of improving his overall metabolism. Once his metabolism is in good condition, then his immune system will be strong enough to defend against three major disease groups that cause death [2]. These 3 major diseases are chronic diseases with various complications (50%), cancers (29%), infectious diseases (11%), along with non-diseases causing death (10%). The death percentages mentioned above can be observed in Figure 4.

Figure 4: Risk probability % of having cancer induced by metabolic disorders

Results
The author’s calculated risk probabilities, expressed in %, of having cancer for the past 10 years are listed (Figure 5):

  1. Year of 2010: 85% – obesity BMI 31, 3 chronic diseases, 5 cardiac episodes
  2. Year of 2012: 57% – weight reduction started, more careful about diet, started daily exercise, CKD
  3. Year of 2013: 53%
  4. Year of 2014: 50% – developed metabolism model and started his lifestyle management program
  5. Year of 2015: 46% – BMI 25, FPG under control
  6. Year of 2016: 43% – both PPG and HbA1C under control
  7. Year of 2017: 41% – overall metabolism reached to the best condition
  8. Year of 2018: 42% – heavy traveling for medical conferences
  9. Year of 2019: 42% – heavy traveling for medical conferences
  10. Year of 2020: 41% – first 5 months, returning to the fine status of 2017 due to COVID-19 quarantine
Figure 5: Annual Death cases and percentages by disease groups (US CDC, 2017).

It should be noted here that the risk probability percentages are expressed on a “relative” scale, not on an “absolute” scale. Nevertheless, the overall trend of risk % of having cancer is reducing which is an encouraging news to him.

Figure 6 displays his overall metabolism index and body weight during the period of 2012 through 2020.

It is obvious that his risk probability of having cancer is reduced year after year It should be noted here that the risk probability percentages are expressed on a “relative” scale, not on an “absolute” scale. Nevertheless, the overall trend of risk % of having cancer is reducing which is an encouraging news to him.

No drinking, no drugs, and continuously reducing his BMI from his peak at 32 down to a constant level of 25, controlling his glucose and HbA1C, blood pressure, and lipids, within normal ranges, and watching out for other important biomarkers, such as ACR, TSH, etc. via his metabolism model by following his own stringent lifestyle management program.

Figure 6: Risk probability % of having CVD/Stroke and CKD
As shown in Figure 7, it is not surprising to notice that, similar to his cancer risk %, both of his risk probabilities of having CVD/Stroke and CKD are reducing as well [3].
Figure 7: Metabolism Index and Body tabolism IndeWx and eight (2012-2020)

Conclusion
These calculated cancer risk probability results are also validated by his many health examination reports for the past 17 years. This big data based on a dynamic simulation model using GHMethod: math-physical medicine approach could provide an early warning to himself about which factors to watch out for and then improve his healty conditions comtinuously in combined with paying extra attention on needed areas.

The author decided to write this reaearch note to share with other people, who have similar interest to reduce their probability of getting cancer. As he said repeatedly, he is not an expert on oncology but a research scientist in metabolism. However, metabolism and cancer have an extremely strong relationship between each others. Therefore, hopefully, his reaserch method and findings would have some merit to help others.

Acknowledgement
First and foremost, the author wish to express his sincere appreciation to an especially important person in his life, Professor Norman Jones at MIT. Not only did he give him the opportunity to study at MIT, but he also trained him extensively on how to solve problems and conduct scientific research with big vision, decency, and honesty.

The author would also like to thank Professor James Andrews at the University of Iowa. He helped and supported him tremendously when he first came to the United States. He believed in him and prepared him to build his engineering foundation during his undergraduate and master’s degree work.

References

  1. Wanqing C, Xia C, Zheng R, et al. Disparities by province, age, and sex in site-specific cancer burden attributable to 23 potentially modifiable risk factors in China: a comparative risk assessment. Lancet Glob Health. 2019;7(2):E257-E269.
  2. Hsu CG. Relationship between metabolism and probability risks of having cardiovascular diseases or renal complications using GH-Method: Math-Physical Medicine. 2020.