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(No. 265) How an engineer investigates his expected lifespan based on his
metabolic conditions and lifestyle management program
No. 265
By: Gerald C. Hsu
eclaireMD Foundation, USA
5/28-31/2020
Introduction
In this paper, the author described how to apply his engineering background,
including mathematics, physics, and computer science to conduct his medical
research on the subject of effective health age” (i.e. expected lifespan). He
reviewed his past 8-years of data from 2012 through 2019, focusing on both of his
metabolic conditions and health lifestyle details. He then created a simple model
of Effective Health Age” in comparison with the Real Biological Age” using the
GH-Method: math-physical medicine approach.
As a part of his research, he applied his acquired mechanical and structural
engineering knowledge to develop several biomedical models to control his severe
diabetes and estimate his risk probability % of having chronic diseases induced
complications, including but not limited to heart disease, stroke, kidney
complications, retinopathy, and more. He also applied the concept of elastic and
plastic structural behaviors to investigate his diabetes due to pancreatic beta cells
insulin regeneration capability.
The engineering analogy of expected lifespan can be explained simply by using an
example of new machine or new bridge. If we develop a monitoring system to
continuously measure, record, and analyze the strength of material, as well as the
relationship between stress (lifestyle details) and strain (medical conditions), we
can then have a clear idea how long this machine or bridge is going to last which is
their usage life or expected lifespan.
Method
As shown in Figure 1, approximately 2.1 million people died in 2017 from
multiple causes of death in the United States. Among them, the first and largest
group, ~1.1 million deaths or 50% of the total were directly related to metabolic
disorders and their various complications. The second group, ~600,000 deaths or
29% were caused by a variety of cancer diseases. Furthermore, within the cancer
cases, about 45% of them were related to metabolism conditions. The third group,
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~215,000 deaths or 11% of the total were caused by various infectious
diseases. This last group requires excellent medical treatments and a strong
immunity to fight against infection from these different virus or bacteria. The
medical community has already proven that immunity and metabolism are closely
related to each other, like two sides of the same coin (Reference 1). In summary,
90% of the total death cases are related, either directly or indirectly, to
metabolism. The final remaining 10% of death cases are not disease related.
Topology is a newer branch of mathematics which was created around 1900. It
studies key properties of spaces”, such as metabolism of the human body space,
that are invariant under any continuous deformation happened during the
lifespan. Those few key properties or characteristics are not going to change as
long as the space itself is not encountering a break” situation, such as a
discontinuity by death. Topology optimization is a mathematical method that
optimizes material layout within a given design space, for a given set of loads,
boundary conditions and constraints with the goal of maximizing the performance
of the system. As a matter of fact, topology optimization has been applied by some
engineers on obtaining the best layout design of some automotive components
(Reference 2). When we look into the human organs and try to figure out how to
achieve some predetermined health goals, we can recognize that it is also a form of
topology optimization problem. This problem can then be solved by using some
available mathematical programming method in combination with finite element
modeling method from both structural and mechanical engineering disciplines to
conduct the targeted analysis to obtain an optimized organ performance or
response.
Based on the above learned academic knowledge and acquired professional
experience, the author spent the entire year of 2014 to develop a mathematical
metabolic model. This human metabolism model consists of a total of 10
categories, including 4-categories of disease (body outputs, like strain) and 6-
categories of lifestyle details (body inputs, like stress). Similar to a finite element
model, these 10 categories further consist of about 500 detailed elements. Finally,
utilizing complicated mathematical programming techniques, he was able to
proceed his topological response analysis and obtained 14-pages of long output
which was used in his programming tasks for a rather sophisticated metabolism
software.
After developing software for his iPhone, he began collecting his own data of
weight and glucose beginning on 1/1/2012. He then started category by category
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to enter his detailed lifestyle data for the period of 2013 to 2014. Thus far, he has
already collected nearly 2 million data regarding his body health and lifestyle
details. Finally, by the end of 2014, he compiled all the big data together and
expressed them in terms of two newly defined biomedical terms: the metabolism
index (MI), which is a combined daily score to show the body health situation, and
general health status unit (GHSU), which is the 90-days moving average number to
show the health trend. He has also identified a
break-even line at 0.735 or
73.5% to separate his metabolic conditions between the healthy state (below 0.735)
and unhealthy state (above 0.735).
Figures 2 and 3 demonstrate the details mentioned above for his metabolism index
(MI) and medical conditions with lifestyle details for the past 8 years (2012-
2019).
With those 2 million data, initially, he focused on weight and glucose to conduct
further analysis in order to put his severe diabetes under control which was his top
priority. Like engineers looking at a projects design data or cardiologists
reviewing a patients EKG chart, he adopted the traditional time-series analysis
approach. He then quickly realized that he could easily obtain a different
conclusion dependent upon a specific time window he chose. On the other hand, if
he analyzed all data using the entire long period of time with big data, he could
easily see a bigger picture, such as the datas relationship and trend from spatial
analysis. Sometimes, the conclusion derived from a global view via spatial
analysis might not be consistent with certain local views via time series analysis
from a shorter time period. One day, as he studied the history of medicine, he
found a story about how Dr. John Snow from the UK discovered the cholera
outbreak, which spread in the Broad Street area of London in 1854 (Figure 4). He
decided to adopt this similar concept, i.e. spatial analysis, from statistics as an
additional tool to analyze his big and complicated medical data. An example of the
tight relationship between body weight and fasting glucose in the morning via
spatial analysis is shown in Figure 5. Spatial analysis is powerful to provide a
rather clear view of the relationship and trend provided that data size is large
enough.
He also applied Fourier transform to convert a time domain data into frequency
domain in order to calculate and compare associated energy between high
frequency with lower amplitude glucose components versus low frequency with
higher amplitude glucose components. Here, energy theory from mechanical
engineering is frequently applied to calculate different degrees of damage on the
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internal organs by different glucose components which carry different amounts of
energy.
Sometimes, he utilized signal processing techniques from wave theory (electronic
engineering, radio-wave communication, and geophysics) to decompose a glucose
waveform into many component-based sub-waveforms to study impact on glucose
by food, exercise, etc. He even applied a simplified formula from perturbation
theory (one variable and first-order only) of quantum mechanics to build an
approximate postprandial glucose waveform before the patients eat their
meals. Remarkably, it achieved a greater than 95% of accuracy (Reference 3).
The author has suffered many complications resulting from his obesity, diabetes,
hypertension, and hyperlipidemia, including five cardiac episodes, critical kidney
condition, bladder infection, foot ulcer, diabetes retinopathy, and more. By using
metabolism as the foundation, he built up three mathematical simulation models to
calculate his risk probability percentages of having heart attack, stroke, kidney
failure, and even cancer. In those extended study of disease complication risks,
genetic factors were included.
Among these three chronic disorder diseases such as diabetes, hypertension, and
hyperlipidemia, diabetes causes the most fundamental damage to our blood
system. The blood cells carry both nutrition via glucose and oxygen from the
lungs then circulate through the blood vessels. When elevated glucose flows
through the arteries, it would alert the immune cells within the artery wall;
therefore, these cells will treat them as an invader” and start to fight against
them. This fight will result in a situation similar to the inflammation on the artery
wall, causing the blood vessel wall to thicken with a non-smooth surface. This
rough surface allows the build up of lipids in the blood with the formation of
plaque. As a result, the combination of high glucose and high lipids will create an
artery blockage (~70% cases). When high blood pressure is added into the picture,
an artery rupture becomes a possibility (~30% cases). These two situations can
lead to a heart attack or stroke. For micro blood vessels, elevated glucose causes
many microscopic leakages instead. The kidneys normal functions are to
discharge body waste and recycle protein back into the body. These microscopic
leaking holes will reverse these two functions, which means the leaking of protein
out of the body via urination and recycling body waste back into the body can be
toxic. This is why dialysis is utilized to mechanically perform the kidneys normal
expected functions. Other complications, such as erectile dysfunction, bladder
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