Author's Sidebar #1: I created this web page (along with the Clinical Studies web page and several other web pages) for those of you who love science and like to do in-depth research, because you want to understand the science behind how diabetes really works in the human body.
You knew that by understanding the science behind diabetes that you would be able to better evaluate any diabetes program or book (including mine) and quickly assess whether this diabetes program had the potential to actually work.
And, because there are literally thousands of diabetes websites and books on the Internet, you knew that by understanding the science and/or the research, that you could more easily identify the charlatans and be able to recognize a diabetes scam. You would be able to tell whether the authors or website promoters were authentic or just using junk science along with certain marketing buzz words to get people to buy their book, ebook or DVD.
Author Sidebar #2: For people who weren't into the science and doing the in-depth research, I created a How to Recognize a Diabetes Scam web page so that you would know what to look for before buying any diabetes book, including mine.
I also created this web page (along with the Clinical Studies web page and several other web pages) to demonstrate that my program was science-based. Anyone can say that their book or program is science-based. Saying it and showing it are two entirely different things.
In addition, I created these web pages for those of you who may have some doubts about my program and wanted to know the resources I used for some of my specific recommendations about various foods, super foods, and nutritional supplements.
I also added medical resources and web links at the bottom of many of my web pages, so that you would realize that I wasn't making this stuff up or just giving you my opinion without any science to back up my claims and recommendations.
p.s. I also created these science-based web pages (along with the Science of Diabetes book and training program/kit) for diabetics and healthcare professionals who wanted to understand more about diabetes beyond what I wrote in my book, Death to Diabetes.
The Sciences Behind the Death to Diabetes Program
Because of my background in multiple areas of engineering and medical science, I decided to take a science-based and multi-prong approach to designing a nutritional protocol and a wellness program that would address the many problems that Type 2 diabetes presents to each of us.
Most people know that diet is one of the keys to defeating diabetes and other similar system ailments such as heart disease, obesity, chronic fatigue, high blood pressure, and high cholesterol.
However, most diets don't work because the nutritional science principles are based on out-of-date information. In addition, most diets don't work because everyone reacts different to the same foods. And, of course, most diets don't work because most of us don't really like to follow diets. :-)
When I was diabetic, I discovered a lot of confusing and conflicting information about diabetes, nutrition, diets, etc.
So, I decided to take an in-depth "bottoms-up" scientific approach to nutrition and diabetes (at the cellular and molecular levels) by utilizing various sciences, methodologies and technologies.
The key science areas included:
- Engineering Science
- Nutritional Science
- Medical Science
- Cell Biology
Note: These science areas are discussed in detail in my Science of Diabetes book.
By looking at all of these science areas, I was able to gain a very in-depth understanding of nutritional science and the other sciences and their impacts on diabetes and our cells.
And, because of this understanding, I was able to break down and explain these sciences into layman terms; transform complex concepts into simple and easy-to-understand charts and diagrams; and, design a self-paced, easy-to-follow online training program with colorful and animated PowerPoint Slides.
As a bonus, this insight into the sciences gave me a lot of confidence, especially when I had to talk to various medical doctors and research scientists.
FYI: In the beginning, I met with a lot of resistance. Some of the doctors and researchers were offended that "an engineer" could figure out what they couldn't. But, once I showed them how I used the Scientific Method as well as some of their own research, they were very impressed.
They liked my thoroughness and ability to document complex processes in a way that a lay person could understand. Although it was difficult for some of them to accept, most of them were impressed by the feedback and testimonials from diabetics in the audience who had read and used my book.
As a result, some of them bought multiple copies of my book; while others took the time to write testimonials about my book and how it helped their patients. Unfortunately, at least two of the doctors got into trouble with the American Medical Association (AMA) for going public with their support.
Here are a few links to feedback from doctors either about my book/program or other reverse diabetes programs:
I am truly blessed, thankful and humbled by their comments, feedback and support.
During my research, I identified a minimum of 17 major biochemical and biological factors that cause people to react differently to the same foods. That's a major reason why diets must be customizable because we all respond differently to the same foods.
Engineering sciences and methodologies that I used included:
- Failure Modes & Effects Analysis (FMEA)
- Failure Modes, Effects and Critical Analysis (FAMECA)
- Diagnostics Engineering Analysis (DEA)
- Multiple Failure Diagnostics Analysis (MFDA)
- Knowledge Base Engineering (KBE)
- Sequential Events Flow Charting/Analysis (SEFCA)
- Fault Tree Algorithms (FTA)
- Root Cause Analysis (RCA)
- Solution Selection Analysis & Optimization (SSA&O)
- Reverse Engineering (RE)
- Statistical Engineering Analysis (SEA)
- Force Field Analysis (FFA)
- Current State/Desired State Gap Analysis (GA)
- Mathematical Modeling & Computational Methods
- Predictive Modeling
- Biomedical Engineering
- Stress Test Analysis (STA)
- System State Diagrams (SSD)
- Functional Block Diagrams (FBDs)
- Process Engineering
Note: For those of you who are not engineers, definitions of these terms are listed below.
While in the hospital recovering from a near-death diabetic coma, it appeared hopeless because I was facing a potential stroke along with possible blindness, kidney failure, and a lower leg amputation. I had several major health issues, including pancreatitis, a non-ketotic hyperglycemic hyperosmolar coma, severe hyperglycemia (BG of 1337), hyperinsulinemia, hypertriglyceridemia, and 2 major blood clots due to DVT (deep vein thrombosis).
I was placed on a 1200-calorie diet and a drug protocol of 4 insulin shots a day (5-10 units Humalog before meals, 40-50 units Lantus in the evening), Coumadin (a blood-thinner), and 30 mg Lipitor (a cholesterol-lowering drug). I was told that I would be on insulin for the rest of my life.
Once I left the hospital (after 13 days), there were three major engineering technologies that helped my recovery and saved me from a life of drugs and poor health.
Those 3 major technologies were the following:
1. Blood glucose meter (1981)
2. Computer (1977)
3. Internet (1991)
The blood glucose meter allowed the me to measure and analyze my blood glucose readings and determine which were the better foods to eat and which foods to avoid. The blood glucose meter also helped with data analysis, although I relied mostly on MS tools such as Excel (spreadsheets).
To further help, a new meter had just come out that provided alternate site testing. This allowed the me to test a lot more frequently (8 to 10 times a day) and not have to worry about getting sore fingers from all the needle pricking. As a result, I was able to collect a lot more data a lot more quickly, so it didn't take me long to figure out what corrective actions to take -- to better control my blood glucose and insulin levels.
The blood glucose meter was a true Godsend! It helped me to better under the disease and how food actually affected me. Initially, the data helped me with controlling my blood glucose level. Then, I learned how to use the data to do more than just "control" my disease -- I could actually use the data to help reverse my diabetes!
Unfortunately, most diabetics (and healthcare professionals) are not taught how to properly use the glucose meter and the data to do more than determine the number of carbs or the amount of insulin to take. I was shocked to discover how little diabetes educators understood about data analysis!
The computer contained software tools such as MS Excel that allowed me to download my blood glucose data from my glucose meter to my desktop computer and, also, to my laptop. As a result, I was able to perform trend analysis, Pareto analysis, and other statistical analyses that helped me to understand my blood glucose data.
In addition, I was able to add other medical data such as my blood test results and other medical data from my physical exams and other lab tests. Data included blood pressure, hemoglobin A1C, cholesterol, weight, CRP, WBC, RBC, homocysteine, and other blood tests and urine tests.
The Internet allowed the me to research a lot about diabetes, nutrition, drugs, and clinical studies -- all in the comfort of my home (and, also at work). Without the Internet, I would have had to drive to one or more of the public libraries to gain access to all of this invaluable information.
Author's Perspective: I couldn't believe the amount of information on the Internet about diabetes! But, a lot of the information was conflicting, so I bought several books. But that didn't help. I was also shocked to discover that my endocrinologist was not aware of some of the diabetic clinical studies! I would have thought that they at least were aware of the studies on the Medline/PubMed websites (National Library of Medicine) and the reports on the NEJM and NIH websites.
Technologies Keep Giving Back!
Later on, after my recovery, the computer and the Internet technologies played another important role in helping me to write my first book ("Death to Diabetes").
Because I was very familiar with MS Word, Excel, and PowerPoint, it was pretty easy to put together a book manuscript. And, since I knew how to use MS Word to lay out my book manuscript, I didn't have to pay or rely on a publisher to do the work.
Several years later, these same tools again helped me to design my own website and blog. Originally, I had paid a web designer to develop my website, but I wasn't satisfied with the work -- so, I finally buckled down and did the work myself.
Then, because I was familiar with various Adobe tools, I began writing ebooks for my International clients who didn't want to pay the shipping charges for mailing the paperback book.
Recently, I used my knowledge of PowerPoint to develop a set of slides for a corporate training class; and, that led to the online training classes and programs for health coaches and other healthcare professional.
And, it continues to this day: these technologies continue to help me develop new products and services for my ever-growing customer base.
If you are not an engineer, here are the definitions of some of the engineering methodologies mentioned above. Once you read these definitions, you'll be able to see some of the connections between engineering science and medical science.
Failure Modes & Effects Analysis (FMEA)
A failure modes and effects analysis (FMEA), is a procedure in product development and operations management for analysis of potential failure modes within a system for classification by the severity and likelihood of the failures.
A successful FMEA activity helps a team to identify potential failure modes based on past experience with similar products or processes, enabling the team to design those failures out of the system with the minimum of effort and resource expenditure, thereby reducing development time and costs. It is widely used in manufacturing industries in various phases of the product life cycle and is now increasingly finding use in the service industry.
Failure modes are any errors or defects in a process, design, or item, especially those that affect the customer, and can be potential or actual. Effects analysis refers to studying the consequences of those failures.
1. Failure Mode and Effects Analysis (FMEA)—A procedure by which each potential failure mode in a system is analyzed to determine the results, or effects thereof, on the system and to classify each potential failure mode according to its severity.
2. Failure mode—The manner by which a failure is observed. Generally describes the way the failure occurs and its impact on equipment operation.
3. Failure effect—The consequence(s) a failure mode has on the operation, function, or status of an item. Failure effects are usually classified according to how the entire system is impacted.
4. Failure cause—The physical or chemical process, design defects, part misapplication, quality defects, or other processes that are the basic reason for failure or which initiate the physical process by which deterioration proceeds to failure.
Failure Modes, Effects and Critical Analysis (FAMECA)
Failure Mode, Effects, and Criticality Analysis (FMECA) is a powerful design analysis tool that is used to increase system reliability. It can be applied during the initial design phase or to existing equipment. To be more effective, the FMECA should relate to the nature of the design process itself. In either case, it considers overall design, operating, and service problems, while at the same time addressing process and safety problems.
If used as a design tool, the benefit of FMECA depends upon the timeliness in which information is communicated in the early design phase. Timeliness is probably the most important factor in differentiating between effective and ineffective implementation of the FMECA. The efforts and sophistication of the approach used depend greatly on the requirements of each individual program. In any case, the FMECA should contribute to the overall program decision.
Diagnostics Engineering Analysis (DEA)
Engineering analysis involves the application of scientific analytic principles and processes to reveal the properties and state of the system, device or mechanism under study. Engineering analysis is decompositional, it proceeds by separating the engineering design into the mechanisms of operation or failure, analyzing or estimating each component of the operation or failure mechanism in isolation, and re-combining the components according to basic physical principles and natural laws.
Diagnostics engineering refers to specialty branch of engineering that looks at how a machine should be diagnosed for repair and maintenance.
Multiple Failure Diagnostics Analysis (MFDA)
Failure analysis is the process of collecting and analyzing data to determine the cause of a failure. It is an important discipline in many branches of manufacturing industry, such as the electronics industry, where it is a vital tool used in the development of new products and for the improvement of existing products. It relies on collecting failed components for subsequent examination of the cause or causes of failure using a wide array of methods, especially microscopy and spectroscopy.
The NDT or nondestructive testing methods are valuable because the failed products are unaffected by analysis, so inspection always starts using these methods.
Multiple failure diagnostics looks at more than one failure at a time, and how those multiple failures need to be analyzed for diagnosis.
Knowledge-Based Engineering (KBE)
Knowledge-based engineering (KBE) is a discipline with roots in computer- aided design (CAD) and knowledge-based systems but has several definitions and roles depending upon the context. An early role was support tool for a design engineer generally within the context of product design. Success of early KBE prototypes was remarkable (see History); eventually this led to KBE being considered as the basis for generative design with many expectations for hands-off performance where there would be limited human involvement in the design process.
Fault Tree Analysis (FTA)
Fault tree analysis (FTA) is a failure analysis in which an undesired state of a system is analyzed using boolean logic to combine a series of lower-level events. This analysis method is mainly used in the field of safety engineering to quantitatively determine the probability of a safety hazard.
Root Cause Analysis (RCA)
Root cause analysis (RCA) is a class of problem solving methods aimed at identifying the root causes of problems or incidents. The practice of RCA is predicated on the belief that problems are best solved by attempting to correct or eliminate root causes, as opposed to merely addressing the immediately obvious symptoms. By directing corrective measures at root causes, it is hoped that the likelihood of problem recurrence will be minimized. However, it is recognized that complete prevention of recurrence by a single intervention is not always possible. Thus, RCA is often considered to be an iterative process, and is frequently viewed as a tool of continuous improvement.
RCA, initially is a reactive method of problem detection and solving. This means that the analysis is done after an incident has occurred. By gaining expertise in RCA it becomes a pro-active method. This means that RCA is able to forecast the possibility of an incident even before it could occur. While one follows the other, RCA is a completely separate process to Incident Management.
Root cause analysis is not a single, sharply defined methodology; there are many different tools, processes, and philosophies of RCA in existence. However, most of these can be classed into five, very-broadly defined "schools" that are named here by their basic fields of origin: safety-based, production-based, process-based, failure-based, and systems-based.
Safety-based RCA descends from the fields of accident analysis and occupational safety and health.
Production-based RCA has its origins in the field of quality control for industrial manufacturing.
Process-based RCA is basically a follow-on to production-based RCA, but with a scope that has been expanded to include business processes.
Failure-based RCA is rooted in the practice of failure analysis as employed in engineering and maintenance.
Systems-based RCA has emerged as an amalgamation of the preceding schools, along with ideas taken from fields such as change management, risk management, and systems analysis.
Despite the seeming disparity in purpose and definition among the various schools of root cause analysis, there are some general principles that could be considered as universal. Similarly, it is possible to define a general process for performing RCA.
Solution Selection Analysis & Optimization (SSA&O)
Selection Analysis & Optimization examines the development and applications of functional analysis and operator theoretic methods in numerical analysis, approximation theory, optimization, control and systems theory, harmonic analysis, and signal processing.
Emphasis is placed on interaction and unification of these fields, and the use of abstract methods to provide insight and fundamental contributions to problems and models in the natural, physical, engineering, and decision sciences to define the "best solution" for a problem.
Reverse Engineering (RE)
Reverse engineering (RE) is the process of discovering the technological principles of a device, object or system through analysis of its structure, function and operation. It often involves taking something (e.g., a mechanical device, electronic component, or software program) apart and analyzing its workings in detail to be used in maintenance, or to try to make a new device or program that does the same thing without utilizing any physical part of the original.
Reverse engineering has its origins in the analysis of hardware for commercial or military advantage. The purpose is to deduce design decisions from end products with little or no additional knowledge about the procedures involved in the original production. The same techniques are subsequently being researched for application to legacy software systems, not for industrial or defense ends, but rather to replace incorrect, incomplete, or otherwise unavailable documentation.
Statistical Engineering Analysis (SEA)
Statistics is the formal science of making effective use of numerical data relating to groups of individuals or experiments. It deals with all aspects of this, including not only the collection, analysis and interpretation of such data, but also the planning of the collection of data, in terms of the design of surveys and experiments.
Statistical analysis methods can be used to summarize or describe a collection of data; this is called descriptive statistics. This is useful in research, when communicating the results of experiments.
In addition, patterns in the data may be modeled in a way that accounts for randomness and uncertainty in the observations, and are then used to draw inferences about the process or population being studied; this is called inferential statistics. Inference is a vital element of scientific advance, since it provides a prediction (based in data) for where a theory logically leads.
To further prove the guiding theory, these predictions are tested as well, as part of the scientific method. If the inference holds true, then the descriptive statistics of the new data increase the soundness of that hypothesis. Descriptive statistics and inferential statistics (a.k.a., predictive statistics) together comprise applied statistics.
Force Field Analysis (FFA)
Force field analysis is an influential development in the field of social science. It provides a framework for looking at the factors (forces) that influence a situation, originally social situations. It looks at forces that are either driving movement toward a goal (helping forces) or blocking movement toward a goal (hindering forces). The principle, developed by Kurt Lewin, is a significant contribution to the fields of social science, psychology, social psychology, organizational development, process management, and change management.
Current State/Desired State Gap Analysis (GA)
In business and economics, gap analysis is a tool that helps a company to compare its actual performance with its potential performance. At its core are two questions: "Where are we?" and "Where do we want to be?". If a company or organization is not making the best use of its current resources or is forgoing investment in capital or technology, then it may be producing or performing at a level below its potential. This concept is similar to the base case of being below one's production possibilities frontier.
The goal of gap analysis is to identify the gap between the optimized allocation and integration of the inputs, and the current level of allocation. This helps provide the company with insight into areas which could be improved. The gap analysis process involves determining, documenting and approving the variance between business requirements and current capabilities.
Gap analysis naturally flows from benchmarking and other assessments. Once the general expectation of performance in the industry is understood, it is possible to compare that expectation with the company's current level of performance. This comparison becomes the gap analysis. Such analysis can be performed at the strategic or operational level of an organization.
Gap analysis provides a foundation for measuring investment of time, money and human resources required to achieve a particular outcome (e.g. to turn the salary payment process from paper-based to paperless with the use of a system).
Note: GAP analysis can also been used to determine how well a person should be doing versus how well he/she is doing after receiving treatment for that illness.
Mathematical Modeling & Computational Methods
A mathematical model uses mathematical language to describe a system. The process of developing a mathematical model is termed mathematical modeling. Mathematical models are used not only in the natural sciences (such as physics, biology, earth science, meteorology) and engineering disciplines, but also in the social sciences (such as economics, psychology, sociology and political science); physicists, engineers, computer scientists, and economists use mathematical models most extensively.
Mathematical models can take many forms, including but not limited to dynamical systems, statistical models, differential equations, or game theoretic models. These and other types of models can overlap, with a given model involving a variety of abstract structures.
Many mathematical models can be classified in some of the following ways: Linear vs. nonlinear, Deterministic vs. probabilistic (stochastic), Static vs. dynamic, and Lumped vs. distributed parameters.
Predictive modeling is the process by which a model is created or chosen to try to best predict the probability of an outcome. In many cases the model is chosen on the basis of detection theory to try to guess the probability of an outcome given a set amount of input data.
Another definition: Predictive modeling is a process used in predictive analytics to create a statistical model of future behavior. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends.
A predictive model is made up of a number of predictors, which are variable factors that are likely to influence future behavior or results.
In marketing, for example, a customer's gender, age, and purchase history might predict the likelihood of a future sale. Similarly, in the medical world, a patient's age, blood test results, weight, blood pressure, eating habits, and family history can help to predict one's probability of becoming diabetic.
In predictive modeling, data is collected for the relevant predictors, a statistical model is formulated, predictions are made and the model is validated (or revised) as additional data becomes available. The model may employ a simple linear equation or a complex neural network, mapped out by sophisticated software.
Biomedical engineering is the application of engineering principles and techniques to the medical field. This field seeks to close the gap between engineering and medicine. It combines the design and problem solving skills of engineering with medical and biological sciences to improve healthcare diagnosis and treatment.
Biomedical engineering has only recently emerged as its own discipline, compared to many other engineering fields; such an evolution is common as a new field transitions from being an interdisciplinary specialization among already-established fields, to being considered a field in itself.
Much of the work in biomedical engineering consists of research and development, spanning a broad array of subfields (see below). Prominent biomedical engineering applications include the development of biocompatible prostheses, various diagnostic and therapeutic medical devices ranging from clinical equipment to micro-implants, common imaging equipment such as MRIs and EEGs, biotechnologies such as regenerative tissue growth, and pharmaceutical drugs and biopharmaceuticals.
Biomedical engineering is a highly interdisciplinary field, influenced by (and overlapping with) various other engineering and medical fields. This often happens with newer disciplines, as they gradually emerge in their own right after evolving from special applications of extant disciplines. Due to this diversity, it is typical for a biomedical engineer to focus on a particular subfield or group of related subfields.
Sometimes, disciplines within BME are classified by their association(s) with other, more established engineering fields, which can include:
- Chemical engineering - often associated with biochemical, cellular, molecular and tissue engineering, biomaterials, and biotransport.
- Electrical engineering - often associated with bioelectrical and neural engineering, bioinstrumentation, biomedical imaging, and medical devices. This also tends to encompass Optics and Optical engineering - biomedical optics, imaging and related medical devices.
- Mechanical engineering - often associated with biomechanics, biotransport, medical devices, and modeling of biological systems.
Stress Test Analysis (STA)
Stress testing is a form of testing that is used to determine the stability of a given system or entity. It involves testing beyond normal operational capacity, often to a breaking point, in order to observe the results. Stress testing may have a more specific meaning in certain industries, such as fatigue testing for materials.
In software testing, "System stress test" refers to tests that put a greater emphasis on robustness, availability, and error handling under a heavy load, rather than on what would be considered correct behavior under normal circumstances. In particular, the goals of such tests may be to ensure the software does not crash in conditions of insufficient computational resources (such as memory or disk space), unusually high concurrency, or denial of service attacks.
Note: I used stress testing when I ate a bowl of ice cream to see how my body responded to this "stressful" event. If my body responded well (i.e. blood glucose returns to normal within 2 hours), then, I passed the stress test.
System State Diagrams (SSD)
A state diagram is a type of diagram used in computer science and related fields to describe the behavior of systems. State diagrams require that the system described is composed of a finite number of states; sometimes, this is indeed the case, while at other times this is a reasonable abstraction. There are many forms of state diagrams, which differ slightly and have different semantics.
State diagrams are used to give an abstract description of the behavior of a system. This behavior is analyzed and represented in series of events, that could occur in one or more possible states. Hereby "each diagram usually represents objects of a single class and track the different states of its objects through the system".
Functional Block Diagrams (FBDs)
A functional block diagram (or block schematic diagram (BSD)) is a diagram, that describes a function between input variables and output variables, usually based upon a functional specification. A function is described as a set of elementary blocks. Input and output variables are connected to blocks by connection lines.
A block diagram is a diagram of a system, in which the principal parts or functions are represented by blocks connected by lines, that show the relationships of the blocks. They are heavily used in the engineering world in hardware design, software design, and process flow diagrams.
A functional specification (also, functional spec, functional specifications document (FSD), or Program specification) in systems engineering and software development is the documentation that describes the requested behavior of an engineering system. The documentation typically describes what is needed by the system user as well as requested properties of inputs and outputs (e.g. of the software system).
Note: I used these types of diagrams to help create many of my PowerPoint presentation slides.
One of the keys to being able to perform specific engineering tasks consistently well, regardless of who is performing them, is to have a well-documented set of processes and metrics.
These documented processes are used to teach new engineers how to perform specific tasks as well as the engineering experts. And, out of these processes comes many of the engineering department's best practices.
Note: Process Engineering was the foundation for defining the Diabetes Management Process and its 7 key wellness factors so that diabetic clients would understand how to consistently manage their diabetes on a daily basis. As a result, the Diabetes Management Process became the foundation for defining the Death to Diabetes 10-Step Wellness Program.
Because of my training in the use of Lean Six Sigma, I used various quality tools to measure and analyze blood glucose test data. Those tools included Ishikawa diagrams, control charts, histograms, Pareto charts, and scatter diagrams.
I also used statistical methods such as sampling, statistical hypothesis testing, design of experiments, multivariate analysis, and other research analysis methods.
Note: Many of the sequence charts and flow diagrams that you may have seen in the YouTube videos were based upon many of these tools.
Nutritional science investigates the metabolic and physiological responses of the body to diet. With advances in the fields of molecular biology, biochemistry, and genetics, the study of nutrition is increasingly concerned with metabolism and metabolic pathways: the sequences of biochemical steps through which substances in living things change from one form to another.
The human body contains chemical compounds, such as water, carbohydrates (sugar, starch, and fiber), amino acids (in proteins), fatty acids (in lipids), and nucleic acids (DNA and RNA). These compounds in turn consist of elements such as carbon, hydrogen, oxygen, nitrogen, phosphorus, calcium, iron, zinc, magnesium, manganese, and so on. All of these chemical compounds and elements occur in various forms and combinations (e.g. hormones, vitamins, phospholipids, hydroxyapatite), both in the human body and in the plant and animal organisms that humans eat.
Interestingly, water, carbohydrates, proteins, and fats happen to be the 4 major macronutrients that I identified when I developed my Super Meal Plate nutritional model. I concluded that if the cells of your body are defective and not working properly, and, if cells are comprised of these 4 macronutrients, then, what would happen to those defective cells if we consumed "the best of the best" in terms of water, carbohydrates, proteins, and fats?
Note: I admit that this is an oversimplification of a very complex set of biochemical and hormonal functions at the cellular level, but my objective here is not to impress you with my knowledge about the various sciences. Instead, my objective is to communicate to you in a way that makes sense so that you can make "the connection" between nutrition and disease. For more information, refer to my Super Meal Plate Model, which includes "super" versions of water, carbohydrates, proteins, and fats.
The human body consists of elements and compounds ingested, digested, absorbed, and circulated through the bloodstream to feed the cells of the body. In a typical adult, about seven liters of digestive juices enter the lumen of the digestive tract. These digestive juices break chemical bonds in ingested molecules, and modulate their conformations and energy states. Though some molecules are absorbed into the bloodstream unchanged, digestive processes release them from the matrix of foods. Unabsorbed matter, along with some waste products of metabolism, is eliminated from the body in the feces.
Studies of nutritional status must take into account the state of the body before and after experiments, as well as the chemical composition of the whole diet and of all material excreted and eliminated from the body (in urine and feces). Comparing the food to the waste can help determine the specific compounds and elements absorbed and metabolized in the body.
The effects of nutrients may only be discernible over an extended period, during which all food and waste must be analyzed. The number of variables involved in such experiments is high, making nutritional studies time-consuming and expensive, which explains why the science of human nutrition is still slowly evolving.
In general, eating a wide variety of fresh, whole (unprocessed), foods has proven favorable for one's health compared to monotonous diets based on processed foods. In particular, the consumption of whole-plant foods slows digestion and allows better absorption, and a more favorable balance of essential nutrients per Calorie, resulting in better management of cell growth, maintenance, and mitosis (cell division), as well as better regulation of appetite and blood sugar. Regularly-scheduled meals (every few hours) have also proven more wholesome than infrequent or haphazard ones.
There are six major classes of nutrients: carbohydrates, proteins, fats, water, vitamins, and minerals.
These nutrient classes can be categorized as either macronutrients (needed in relatively large amounts) or micronutrients (needed in smaller quantities). The macronutrients include carbohydrates, fats, protein, water, and fiber. The micronutrients are minerals and vitamins.
When the 4 major macronutrients (carbohydrates, proteins, fats, water) are ingested in the form of food, they are used to generate energy internally, measured in Joules or kilocalories (often called "Calories" and written with a capital C to distinguish them from little 'c' calories). Carbohydrates and proteins provide 17 kJ approximately (4 kcal) of energy per gram, while fats provide 37 kJ (9 kcal) per gram.
However, the net energy from either depends on such factors as absorption and digestive effort, which vary substantially from instance to instance. Vitamins, minerals, and fiber do not provide energy, but are required for other reasons.
Molecules of carbohydrates and fats consist of carbon, hydrogen, and oxygen atoms. Carbohydrates range from simple monosaccharides (glucose, fructose, galactose) to complex polysaccharides (grain, starch). Fats are triglycerides, made of assorted fatty acid monomers bound to glycerol backbone. Some fatty acids, but not all, are essential in the diet: they cannot be synthesized in the body.
Protein molecules contain nitrogen atoms in addition to carbon, oxygen, and hydrogen. The fundamental components of protein are nitrogen-containing amino acids, some of which are essential in the sense that humans cannot make them internally.
Some of the amino acids are convertible (with the expenditure of energy) to glucose and can be used for energy production just as ordinary glucose in a process known as gluconeogenesis. By breaking down existing protein, some glucose can be produced internally; the remaining amino acids are discarded, primarily as urea in urine. This occurs normally only during prolonged starvation.
Other micronutrients include antioxidants and phytochemicals, which influence (or protect) some body systems. But, their necessity is not as well established as in the case of, for instance, vitamins. Refer to the Clinical References web page for more information.
Most foods contain a mix of some or all of the nutrient classes, together with other substances, such as toxins of various sorts. Some nutrients can be stored internally (e.g., the fat soluble vitamins), while others are required more or less continuously. Poor health can be caused by a lack of required nutrients or, in extreme cases, too much of a required nutrient. For example, both salt and water will cause illness in excessive amounts.
Malnutrition is the insufficient, excessive or imbalanced consumption of nutrients. A number of different diseases and nutrition disorders may arise, depending on which nutrients are under or overabundant in the diet.
Malnutrition is primarily associated with Third World countries, however, malnutrition is running rampant in the United States! This has led to a dramatic increase in diseases and ailments such as diabetes, heart disease, chronic fatigue, obesity, high blood pressure, and high cholesterol.
When you see an overweight, obese person, the last thing that you think is that this person is suffering from malnutrition. But, most of the diseases of the 20th and 21st Centuries are driven by malnutrition, or poor nutrition.
Once the public learns to make the connection between disease and malnutrition, they will realize that their diseased state can be easily corrected with proper nutrition. The public will also realize that the medications they're taking for diabetes, high blood pressure, etc. do not contain the "missing nutrients" that their body requires; and they will stop taking these medications prescribed by their doctors.
Unfortunately, it is going to take a major health and education movement to overcome the onslaught of drug commercials and the doctors to convince people to stop taking the drugs.
These engineering methodologies and sciences were mapped to areas of medical science that allowed me to "see into" various components of medical science, including the following:
- Pathological biochemistry
- Morbidity & Mortality
- Clinical Research
Note: There are more studies and reports coming out that Type 2 Diabetes is reversible, and more doctors are admitting as such.
In medicine, pathology is the study and diagnosis of disease. The related scientific study of disease processes is called "general pathology." Medical pathology is divided into two main branches, anatomical pathology and clinical pathology. Medical pathologists work through examination of organs, tissues, bodily fluids, and whole bodies (autopsies).
Pathophysiology is the study of the changes of normal mechanical, physical, and biochemical functions, either caused by a disease, or resulting from an abnormal syndrome. More formally, it is the branch of medicine which deals with any disturbances of body functions, caused by disease or prodromal symptoms.
An alternate definition is "the study of the biological and physical manifestations of disease as they correlate with the underlying abnormalities and physiological disturbances."
The study of pathology and the study of pathophysiology often involves substantial overlap in diseases and processes, but pathology emphasizes direct observations, while pathophysiology emphasizes quantifiable measurements.
The term pathogenesis means step by step development of a disease and the chain of events leading to that disease due to a series of changes in the structure and /or function of a cell/tissue/organ being caused by a microbial, chemical or physical agent. The pathogenesis of a disease is the mechanism by which a disease is caused. The term can also be used to describe the development of the disease, such as acute, chronic and recurrent. The word comes from the Greek pathos, "disease", and genesis, "creation".
Types of pathogenesis include microbial infection, inflammation, malignancy and tissue breakdown.
Most diseases are caused by multiple pathogenetical processes together. For example, certain cancers arise from dysfunction of the immune system (skin tumors and lymphoma after a renal transplant, which requires immuno-suppression).
Often, a potential etiology is identified by epidemiological observations before a pathological link can be drawn between the cause and the disease.
Biochemistry is the study of the chemical processes in living organisms. It deals with the structures and functions of cellular components such as proteins, carbohydrates, lipids, nucleic acids and other biomolecules. Over the last 40 years biochemistry has become so successful at explaining living processes that now almost all areas of the life sciences from botany to medicine are engaged in biochemical research.
Today the main focus of pure biochemistry is in understanding how biological molecules give rise to the processes that occur within living cells which in turn relates greatly to the study and understanding of whole organisms.
Among the vast number of different biomolecules, many are complex and large molecules (called polymers), which are composed of similar repeating subunits (called monomers). Each class of polymeric biomolecule has a different set of subunit types.
For example, a protein is a polymer whose subunits are selected from a set of 20 or more amino acids. Biochemistry studies the chemical properties of important biological molecules, like proteins, and in particular the chemistry of enzyme-catalyzed reactions.
The biochemistry of cell metabolism and the endocrine system has been extensively described. Other areas of biochemistry include the genetic code (DNA, RNA), protein synthesis, cell membrane transport, and signal transduction.
Etiology (alternatively aetiology, aitiology) is the study of causation, or origination. The word is most commonly used in medical and philosophical theories, where it is used to refer to the study of why things occur, or even the reasons behind the way that things act.
Etiology is also used in philosophy, physics, psychology, government, medicine, theology and biology in reference to the causes of various phenomena. An etiological myth is a myth intended to explain a name or create a mythic history for a place or family.
Note: Etiology correlates to Root Cause Analysis (RCA) in the engineering world.
Epidemiology is the study of factors affecting the health and illness of populations, and serves as the foundation and logic of interventions made in the interest of public health and preventive medicine. It is considered a cornerstone methodology of public health research, and is highly regarded in evidence-based medicine for identifying risk factors for disease and determining optimal treatment approaches to clinical practice.
In the study of communicable and non-communicable diseases, the work of epidemiologists ranges from outbreak investigation to study design, data collection and analysis including the development of statistical models to test hypotheses and the documentation of results for submission to peer-reviewed journals.
Epidemiologists also study the interaction of diseases in a population, a condition known as a syndemic. Epidemiologists rely on a number of other scientific disciplines such as biology (to better understand disease processes), biostatistics (the current raw information available), Geographic Information Science (to store data and map disease patterns) and social science disciplines (to better understand proximate and distal risk factors).
Morbidity & Mortality
Morbidity is the percentage of people in a population that gets sick of a particular disease.
Mortality is the percentage of those who die in that population from that disease.
Note: Epidemiology, Morbidity, and Mortality correlate to mathematics and Statistical Analysis in the engineering world.
Clinical research is comprised of clinical trials, which are conducted to allow safety and efficacy data to be collected for health interventions (e.g., drugs, diagnostics, devices, therapy protocols). These trials can take place only after satisfactory information has been gathered on the quality of the non-clinical safety, and Health Authority/Ethics Committee approval is granted in the country where the trial is taking place.
Depending on the type of product and the stage of its development, investigators enroll healthy volunteers and/or patients into small pilot studies initially, followed by larger scale studies in patients that often compare the new product with the currently prescribed treatment. As positive safety and efficacy data are gathered, the number of patients is typically increased. Clinical trials can vary in size from a single center in one country to multi-center trials in multiple countries.
Due to the sizable cost a full series of clinical trials may incur, the burden of paying for all the necessary people and services is usually borne by the sponsor who may be a governmental organization, a pharmaceutical, or biotechnology company. Since the diversity of roles may exceed resources of the sponsor, often a clinical trial is managed by an outsourced partner such as a contract research organization or a clinical trials unit in the academic sector.
Note: For more detail about these various sciences, refer to the 420-page Science of Diabetes ebook, which is now available as an 81/2 x 11 spiral-bound printed hardcopy.
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