Corresponding Author: Gerald C. Hsu, eclaireMD Foundation, USA.
In 2017, public health data revealed that the United States had 2 million deaths which included diabetes, heart diseases, stroke, and nephrosis that occupied 45% (~907,000) of this number. Furthermore, >85% of type 2 diabetes (T2D) patients are overweight and >50% are obese.
The author spent 23,000 hours during the past 8.5 years using math-physical medicine to conduct his research. He has collected and processed ~1.5 million data, including ~300,000 medical conditions, and ~1.2 million lifestyle details. He then utilized the GH-Method: math-physical medicine (MPM) which involves advanced mathematics, optical physics, signal processing, energy and wave theories, statistics, big data analytics, machine learning, artificial intelligence to develop five prediction models, including weight, FPG, PPG, adjusted glucose, and HbA1C.
His clinical case studies have offered the following results:
- BMI reduction from 32 (obese) to 24.7 (normal).
- FPG reduction from ~200 mg/dL to ~105 mg/dL; PPG from 279 mg/dL to 119 mg/dL; Daily average glucose from >250 mg/dL to ~116 mg/dL; HbA1C from 10% to <6.5%.
- Risk reduction of having cardiovascular diseases and stroke from 74% prior to 2010 (suffered 5 cardiac episodes) to 26.4% in 2017.
- Average carbs/sugar intake amounts (38% contribution on PPG): 14.5 gram/meal and ~60 grams/day (low carb diet). Exercise amount (41% contribution on PPG): 4,300 steps/meal and 18,000 steps/day.
Figure 1: Health Exam Results Comparison
Figure 2: Metabolism Index and HbA1C
Figure 3: FPG
Figure 4: PPG
Figure 5: Risk Probability of CVD & Stroke
Figure 6: Correlation between Glucose and CVD Risk
Figure 7: Nursing Guide of T2D Control
Figure 8: T2D Control Flow Diagram
The author’s MPM methodology and prediction models (>99% accuracy) are proven to be effective tools on controlling T2D. His flow diagram can also provide an effective guidance to patients to control and improve their conditions on obesity, diabetes, and heart problems. These technology-based prediction and prevention models can be used as educational tools to help diabetes patients through public-health platforms, channels and programs.