Abstract

The author has been reading dementia articles for over the past two years.Recently, he read four articles about Alzheimer’s dementia (AD) diseases cited in References.^1–4 He was inspired by them and decided to utilize his developed GH-Method: math-physical medicine (MPM) and his collected big data of health conditions and lifestyle details to calculate his relative risk change in developing AD.

This article applies many concepts, theories, and techniques from mathematics, physics, engineering, computer science and artificial intelligence, which the author learned in his lifetime.  This big data analytics project contains 9 years of health conditions and lifestyle details.  The basic concept of this article is similar to his previous research work and published articles regarding various relative changes of risk on having a cardiovascular disease (CVD), stroke, chronic kidney disease (CKD), and cancer.  However, this specific article focuses on metabolism and certain lifestyle factors which influence AD.  

Over the past 11 years, the author has self-studied and conducted his own medical research with his developed MPM approach, where the original focus was diabetes and its related complications.  However, he soon discovered that the root cause of his diabetes and multiple complications are a result of his poor lifestyles since 1980.  In 2010, when he initiated his own study and research on endocrinology, he made a vow to himself that within 10 years, he would understand his entire health situation and related medical issues using his acquired academic tools, i.e. mathematics, physics, engineering, and computer science, without biology and chemistry.  He has researched all of his chronic diseases and their induced complications, including obesity, diabetes, hypertension, hyperlipidemia, CVD, CKD, foot ulcer, diabetic retinopathy, and hypothyroidism.  

Eleven years later, he finally realized that the biomedical system is the most sophisticated and also complicated system he has ever dealt with.  The main reason is that body cells and internal organs are living organic materials, different from his familiar inorganic engineering materials.  They go through many lifecycles and stages, such as growth, sickness, damage, healing, mutation, rebirth, and death which are different from the materials he has studied, such as steel, concrete, soil, ocean water, etc. in various systems of aerospace, defense, computer, electronics andsemiconductorsdevices, mechanical and structural engineering, financial and management systems.  

During this time frame of continuous self-study and medical research, he has identified a mainstream of his thoughts and his research ways, i.e. starting from lifestyle through metabolism and immunity, and before reaching various diseases.  This route has guided him to expand his interests and efforts from diabetes into many other related branches of medicine, such as dementia and cancer.  For example, the numerous articles mentioned in Reference 5, as shown in Figure 3, clearly indicates dementia prevention through lifestyle management.  Even cancers have approximately 45.2% cases related to various metabolic disorders and poor lifestyles.⁵

The above descriptions outline his philosophy, causes and efforts in producing this particular research note regarding AD.  

The author estimates 65% to 70% of deaths from various disease are either directly or indirectly related to metabolic disorders which are linked to poor lifestyles in most cases. In other words, those death-causing diseases have a common root cause, which is lifestyle.  Unfortunately, so many patients are “lazy” and seek for a “quick fix” through their medical doctors.  In fact, there is no true “fix” or permanent “repair” for many endocrinological diseases, particularly when they occur inside the human internal organs, including the brain. Lifestyle management combined with strong willpower and persistence is the only way to deal with the root cause of many of these diseases.

The author wants to share his acquired knowledge and learned experiences with other patients in order to avoid developing many diseases or to “reduce the risks of having those death-causing diseases”.  

In this article, he has identified six extremely high correlation coefficients (>90%) existing among AD risk, metabolism, and certain specific lifestyle factors.  This means that the AD risk probability is indeed closely related to both metabolic conditions and lifestyle details as mentioned in the four referenced articles.  

Furthermore, through his sensitivity analysis using two different sets of weighting factor split between metabolism index and lifestyle factors, i.e. 50% vs. 50% and 33% vs. 67%, the nominal difference of <4% risk is observed from these 2 resulting AD risk percentages.  

He was pleased to discover that his relative changes of risk on having AD is continuously being reduced year over year, even though these risk percentages are only “relative figures”, not “absolute numbers”. This encouraging phenomenon is directly resulting from his continuous efforts on improving his metabolism through lifestyle management.  

 

Keywords

mathematics, physics, engineering, computer science and artificial intelligence, genetic variations, regular exercise, not smoking, drinking sensibly, post-meal walking, including physical, emotional, financial, social, environmental, dementia study, coefficients calculations