Atherosclerosis: Open Access
Open Access

Relationship Between Metabolism and Risk of Cardiovascular Disease and Stroke, Risk of Chronic Kidney Disease, and Probability of Pancreatic Beta Cells Self-Recovery Using GH-Method: Math-Physical Medicine (No 258 & 259)

Gerald C Hsu^*

EclaireMD Foundation, California, USA

Received Date: Jul 29, 2020 / Accepted Date: Aug 17, 2020 / Published Date: Aug 24, 2020

Citation: Hsu GC (2020) Relationship Between Metabolism and Risk of Cardiovascular Disease and Stroke, Risk of Chronic Kidney Disease, and Probability of Pancreatic Beta Cells Self-Recovery Using GH-Method: Math-Physical Medicine. Atheroscler Open Access 5: 133.

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Copyright: © 2020 Hsu GC. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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The author uses GH-Method: math-physical medicine (MPM) approach to investigate his risk probability on metabolic disorder induced cardiovascular disease (CVD), stroke, or chronic kidney disease (CKD), as well as probability of pancreatic beta cells self-recovery. He addresses the damages caused by metabolic disorders affecting arteries and micro-vessels in terms of blockage, rupture, or leakage along with the probability assessment of pancreatic beta cells self-recovery. Furthermore, he uses mathematical correlations to distinguish the weighted impact by metabolism on heart, brain, kidney, and pancreas. These annualized big data analytics using four different sophisticated mathematical models for MI, CVD, CKD, and Pancreatic beta cells have demonstrated the close relationships between metabolism and two major chronic diseases induced complications, CVD/Stroke (heart/brain) and CKD (kidney), as well as the beta cells self-recovery rate. By using the GH-Method: MPM math-physical medicine approach (mathematics, physics, engineering modeling, and computer science), it can certainly attain similar conclusions without lengthy and expensive biochemical experiments performed in a laboratory.

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