## GH-METHODS

Math-Physical Medicine

### NO. 034

Risk Probability of Kidney Complications Resulting from Chronic Diseases (Math-Physical Medicine)

Corresponding Author: Gerald C. Hsu, eclaireMD Foundation, USA.

Introduction
Since 1997, the author has been diagnosed with three chronic diseases such as type 2 diabetes (T2D), hypertension, and hyperlipidemia.  His health reached a critical condition by 2010; therefore, he launched his type 2 diabetes (T2D) research in order to save his own life.  He is a research scientist on chronic diseases.  He has endured many complications from T2D for 25 years involving kidney, bladder, foot ulcer, and five separate cardiac episodes.  In this paper, he focuses on investigating the risk probability of having kidney complications.

Methods
Instead of using traditional biology and chemistry, he utilized mathematics, physics, engineering modeling, computer science to conduct his research.  He has spent 20,000 hours and collected and processed ~1.5M data during 2010-2018.

He built up a baseline model, including genetic (unchangeable conditions) and semi-permanent factors such as weight, waistline, bad habits (hard to change conditions).  He then applied his collected ~80,000 data of chronic disease conditions for the past eight years to calculate their contributions to kidney complications, including glucose, blood pressure, kidneys, glomeruli, bladder, and urinary tract.

Finally, to make his last but most important part of the calculation, he used lab-tested data of albumin, creatinine, and ACR for the past seven years.

After combining these three parts, he obtained an annual percentage of having kidney complications resulting from chronic diseases.

Results

• Key data in 2010:
Glucose – 280 mg/dL
A1C – 10%
ACR – 116.4
• Kidney Risk Probability 57%
Key data in 2018:
Glucose – 115 mg/dL
A1C – 6.5%
ACR -14.6
• Kidney Risk Probability 34%
As shown in Table 1, Figures 1 and 2, detailed data and graphics illustrate the reduction of his kidney complications.

Conclusion
This investigation does not focus on kidney data alone.  His main purpose is to study the relationship between chronic diseases, especially T2D, and kidney complications from a larger pool of associated data.