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Diabetes Conference 2019 Journal of Diabetology | Volume 3
Page 13
July 25-26, 2019 | Amsterdam, Netherlands
OF EXCELLENCE
IN INTERNATIONAL
MEETINGS
YEARS 3rd International Conference on
DIABETES, NUTRITION,
METABOLISM & MEDICARE
FROM ENERGY AND FOOD NUTRITION
VIA METABOLISM TO DIABETES
CONTROL AND RISK REDUCTION OF
COMPLICATIONS
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
energy 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 modelling and various mathemat-
ics techniques. During 2015 to 2017, he developed a postprandial glucose
(PPG) prediction model by applying optical physics and signal processing
techniques. During 2015 to 2016, he developed 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 analyzes ~1.5million 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 inter-
nal 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 hemodynam-
ics 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, J Diabetol 2019, Volume 3
Gerald C Hsu has completed his PhD in Mathe-
matics and has been majored in Engineering at
MIT. He has attended different universities over
17 years and studied seven academic disciplines.
He has spent 20,000 hours in T2D research. First,
he studied six metabolic diseases and food nutri-
tion during 2010-2013, then conducted research
during 2014-2018. His approach is math-physics
and quantitative medicine based on mathe-
matics, physics, engineering modelling; signal
processing, computer science, big data analyt-
ics, statistics, machine learning and AI. His main
focus is on preventive medicine using prediction
tools. He believes that the better the prediction,
the more control you have.
Gerald C Hsu
EclaireMD Foundation, USA
BIOGRAPHY