GH-METHODS

Math-Physical Medicine

NO. 039

Using GH-Method: Math-Physical Medicine to Investigate the Risk Probability of Renal Complications from Chronic Diseases with Input from Sensor-Based Glucose Data

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

Introduction
The author, who has type 2 diabetes (T2D) for 25 years, is a research scientist on chronic diseases.  He has endured many complications from T2D involving kidney, bladder, foot ulcer, and five separate cardiac episodes.  In this paper, he focuses on investigating the risk probability of having renal complications due to chronic diseases, which involves additional sensor-based glucose data.

Methods
Instead of using traditional biology and chemistry knowledges, he utilized mathematics, physics, engineering modeling, computer science as research tools to conduct his research.  He has spent ~23,000 hours during the past 8.5 years along with collecting and processing ~1.5M data of his medical conditions and lifestyle details.

He built up a baseline model including genetic and family history (unchangeable conditions) together with the semi-permanent factors such as weight, waistline, and bad habits (hard to change conditions).  He then applied his collected ~100,000 data of chronic disease conditions during the past ~8.5 years to calculate their contributions to kidney complications, including glucose, blood pressure, kidneys, glomeruli, bladder, urinary tract, etc.

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

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

In addition, he has collected ~25,000 more glucose data from a continuous glucose monitoring device (CGM or Sensor).  He utilized this additional set of glucose data to modify his predicted risk probability of renal complications due to 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, the detailed data and graphics illustrate the reduction for his risk of having kidney complications.

Table 1: Kidney Conditions (2012-2018)
Figure 1: ACR Data (2012-2018)
Figure 2: Probability of 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 renal complications from a larger pool of associated data.

The additional glucose data set from the Sensor has provided +2% on his predicted risk probability of having renal complications.  For his 2018 Risk Probability, the result of 34% has changed to 36% with the Sensor glucose input.