obesity-summit-diabetes-conference-laser-2018-tracks

23 / 25

Page 50
Note:
N o v e m b e r 0 5 - 0 6 , 2 0 1 8 | P h i l a d e l p h i a , U S A
3rd INTERNATIONAL OBESITY SUMMIT AND EXPO
&
&
DIABETES, NUTRITION, METABOLISM & MEDICARE
2nd International Conference on
Joint Event on
OF EXCELLENCE
IN INTERNATIONAL
MEETINGS
YEARS
LASER, OPTICS AND PHOTONICS
World Conference on
Obesity Summit 2018 & Diabetes Conference 2018 & Laser Photonics Conference 2018 Biomedical Research | ISSN: 0976-1683| Volume 29
Gerald C Hsu, Biomed Res 2018, Volume 29 | DOI: 10.4066/biomedicalresearch-C7-019
FROM ENERGY AND FOOD NUTRITION VIA
METABOLISM TO DIABETES CONTROL AND
RISK REDUCTION OF COMPLICATIONS
Gerald C Hsu
EclaireMD Foundation, USA
Introduction: The author uses “math-physics medicine” instead of the tra-
ditional biochemical medicine to study the situation of energy imbalance
transmitting into metabolic disorders, resulting in chronic diseases and their
complications.
Methods: He applied energy theory to study the disequilibrium between en-
ergy infusion, as in food nutrition intake, and energy consumption, such as
exercise, work, and activities. These energy imbalances are caused by poor
lifestyle management and shown as metabolic disorders, involving weight,
glucose, blood pressure, and lipids. In 2014, he developed a metabolism
equation using structural engineering modeling and various mathematics
techniques. During 2015 to 2017, he developed a postprandial glucose (PPG)
prediction model by applying optical physics and signal processing tech-
niques. During 2015 to 2016, he developed a fasting plasma glucose (FPG)
prediction model by applying energy theory and spatial analysis techniques.
Finally, he used big data analytics, machine learning, and artificial intelligence
to process and analyze ~1.5 million data associated with four chronic diseas-
es, especially type 2 diabetes and its complications.
Results: The energy theory and spatial analysis identified >80% correlation
between FPG and weight (physical representation of human body’s internal
energy exchange). Both FPG and PPG prediction models have achieved
99.9% linear accuracy. He also identified weight contributing 85% of FPG for-
mation and the combination of carbs/sugar intake and post-meal exercise
contributing 80% of PPG formation. Furthermore, by applying hemodynamics
with solid mechanics and fluid dynamic, he calculated his risk probability of
having a heart attack or stroke reducing from 74% to 26%.
Conclusion: The author has quantitatively proven that, as one of the major
energy infusion factors, excessive “left-over” food nutrition combined with
inactive lifestyle can cause metabolic disorders which further induce chronic
diseases and their complications.
Gerald C Hsu received an honorary PhD in mathematics
and majored in engineering at MIT. He attended different
universities over 17 years and studied seven academic
disciplines. He has spent 20,000 hours in T2D research,
initially studying six metabolic diseases and food nutri-
tion during 2010-2013, then conducting his own diabetes
research during 2014-2018. His approach is a “quantita-
tive medicine” based on mathematics, physics, optical
and electronics physics, engineering modeling, signal
processing, computer science, big data analytics, sta-
tistics, machine learning, and artificial intelligence. He
named it “math-physical medicine”. His main focus is on
preventive medicine using prediction tools. He believes
that the better the prediction, the more control you have.
BIOGRAPHY