Corresponding Author: Gerald C. Hsu, eclaireMD Foundation, USA.
The author uses math-physical medicine approach to investigate three sets of correlation between:
- Weight vs. Glucose/HbA1C – daily average glucose, including both fasting plasma glucose (FPG) and postprandial plasma glucose (PPG).
- Weight vs. blood pressure (BP).
- Glucose/HbA1C vs. BP
The selected time frame is from 4/1/2014 to 12/31/2018 (1,735 days) with big data of ~18,000 metabolic conditions such as Weight, Glucose/A1C, and BP. This paper utilized both time-series and spatial analysis to investigate those triangular relationships.
The time-series analysis results show strong correlations among these three pairs of curves.
- Weight vs. Glucose / A1C: 78% / 78%
- Weight vs. BP: 62%
- Glucose / A1C vs. BP: 84% / 85% (1% difference is due to numerical tolerance).
In spatial analysis, all of the three sets in the diagram demonstrate various degree of strong correlation between any two variables. In addition, the three diagrams have 94% to 99% of data covered by +/- 20% variance band. The reason for the lower slopes between weight vs. glucose is due to the fact that FPG has higher correlation with weight, whereas PPG does not. The most interesting observation is the exceedingly high correlation or higher slope existing between glucose vs. BP.
The major difference between influences from Glucose and A1C is shown by the gap between two adjacent data clouds. This is due to the author’s glucose and A1C data that suddenly dropped significantly during the period, the second half of 2014 through the first half of 2015, after following his developed mathematical metabolism model and some practical ways to control his diabetes. When he switched the X-axis and Y-axis in the spatial analysis graphic diagrams, the gap still exists in each diagram although the general orientations of data clouds are somewhat different.
Figure 1: Time-series comparison between Glucose vs. Weight/BP and A1C vs. Weight/BP
Figure 2: Spatial analysis comparison between Glucose vs. Weight/BP and A1C vs. Weight/BP
Figure 3: Observation of A1C variance via switching X-axis and Y-axis
By using the GH-Method: math-physical medicine approach, strong triangular relationships among weight, glucose/A1C and blood pressure are obvious. This confirms the many qualitative descriptions that existed in the medical community.
- Hsu, Gerald C. (2018). Using Math-Physical Medicine to Control T2D via Metabolism Monitoring and Glucose Predictions. Journal of Endocrinology and Diabetes, 1(1), 1-6.
- Hsu, Gerald C. (2018). Using Signal Processing Techniques to Predict PPG for T2D. International Journal of Diabetes & Metabolic Disorders, 3(2),1-3.
- Hsu, Gerald C. (2018). Using Math-Physical Medicine and Artificial Intelligence Technology to Manage Lifestyle and Control Metabolic Conditions of T2D. International Journal of Diabetes & Its Complications, 2(3),1-7.
- Hsu, Gerald C. (2018, June). Using Math-Physical Medicine to Analyze Metabolism and Improve Health Conditions. Video presented at the meeting of the 3rd International Conference on Endocrinology and Metabolic Syndrome 2018, Amsterdam, Netherlands.
- Hsu, Gerald C. (2018). Using Math-Physical Medicine to Study the Risk Probability of having a Heart Attack or Stroke Based on Three Approaches, Medical Conditions, Lifestyle Management Details, and Metabolic Index. EC Cardiology, 5(12), 1-9.