Math-Physical Medicine

NO. 157

Effective age resulting from metabolic changes

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

In this research note, the author reviewed his past 8-years data from 2012 through 2019 by focusing on the relationship between his metabolism and overall health conditions.  He decided to write this paper regarding his “Effective Age” using the GH-Method: math-physical medicine approach.

He defined the “Effective Age” based on the appearance and evaluation of his medical examination reports and his ~2 million data of his lifestyle, metabolism, and diseases.  This is different from the “Real Age” or chronological age which is the actual amount of time a person has been alive.

As shown in Figure 1, approximately 2.1 million people died in 2017 from the leading causes of death in the United States.  Among them , almost 79% (~1.7 million deaths) were related to metabolic conditions, directly or indirectly.  It should be noted that in 2018, the total death figure has reached to more than 2.8 million people with ~2.2 million deaths related to metabolic conditions.

Figure 1: US leading death causes

In 2014, the author developed a mathematical model of metabolism measurements, including 4-categories of diseases (body outputs) and 6-categories of lifestyle details (body inputs).  He started to collect his detailed data on 1/1/2012.  Thus far, he has collected nearly 2 million data regarding his body health and lifestyle details.  He further assembled those 10-categories (with ~500 detailed elements) and combined them into two new terms:  the metabolism index (MI), which is a combined daily score to show the body health situation, and general health status unit (GHSU), which is the 90-days moving averaged number to show the trend.

Figures 2 and 3 demonstrate the above-mentioned details of his metabolism conditions during the past 8-years (2012 – 2019).

Figure 2: Metabolism model of inputs and outputs
Figure 3: MI & GHSU (2012-2019)

He has also identified a “break-even line” at 0.735 (73.5%) to separate his metabolism conditions between healthy (below 0.735) and unhealthy (above 0.735).

He further developed an equation to calculate his effective age as follows:

  • Effective Age = Real Age * (1+((MI-0.735)/0.735)/2)

He then utilized his annualized MI data to calculate his effective age in order to compare against his real age.

As shown in Figure 4, both of his MI and GHSU were >73.5% during 2012-2014 (unhealthy) and <73.5% during 2014-2019 (healthy).  In 2014, his health improved.  His MI and GHSU during the years 2018 and 2019 were increased slightly due to his heavy travel schedule of attending more than 60 medical conferences.

Figure 4: Annualized MI & GHSU

Figure 5 depicts the comparison between his real age and effective age.  Of course, the real age increases annually, while the effective age was higher than his real age during 2012-2014 and lower than his real age during 2015-2019.  These changes are results from his bad metabolic conditions that were significantly improved over the period of 2015 through 2019.  Figure 5 also shows the age difference between effective age and real age.  The age difference has changed from +8 years in 2012 to -7 years in 2019.

Figure 5: Real & Effective Ages

The life expectancy of an American male is 78.69 years (2016 data).  If the author continues his metabolic maintenance and improvement program, he may have the opportunity to extend his life for an additional 14+ years (real age at 87).

This simple calculation based on big data analytics and sophisticated mathematical metabolism model has depicted a possible way to extend our life expectancy via an effective metabolic improvement and maintenance program.  This practical method has been utilized and proven in the application of his diabetes control very effectively.  The author hopes that this method can also be applied in the field of geriatrics for other people as well.


  1. 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.
  2. 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.
  3. Hsu, Gerald C. (2018). Using Signal Processing Techniques to Predict PPG for T2D. International Journal of Diabetes & Metabolic Disorders, 3(2),1-3.
  4. Hsu, Gerald C. (2018). A Clinic Case of Using Math-Physical Medicine to Study the Probability of Having a Heart Attack or Stroke Based on Combination of Metabolic Conditions, Lifestyle, and Metabolism Index. Journal of Clinical Review & Case Reports, 3(5), 1-2.
  5. Hsu, Gerald C. (2019). Using Wave and Energy Theories on Quantitative Control of Postprandial Plasma Glucose via Optimized Combination of Food and Exercise (Math-Physical Medicine). International Journal of Research Studies in Medical and Health Sciences, 5(4), 1-7.