GH-METHODS

Math-Physical Medicine

NO. 400

Analyzing postprandial plasma glucose wave fluctuations using GH-Method: math-physical medicine

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

Abstract
In this article, the author provides his personal perceptions and opinions regarding the concept and approach of “Glycemic Variability” (GV).  Instead of using certain newly defined biomarkers, such as the mean amplitude glycemic excursions (MAGE), he chose to study the postprandial plasma glucose (PPG) wave fluctuations via a simple yet straightforward expression of the magnitude of glucose wave variances.  He uses his simple definition of  a glucose wave fluctuation equals to the maximum glucose value minus the minimum glucose value.

He compares four different sets of wave fluctuation related variables during five half-year periods between 5/5/2018 to 12/31/2020: Periods A, B, C, D, and E.  These four variables consist of K-line daily PPG, Synthesized daily PPG, PPG rising speed, and PPG declining speed.

The key observation from this study is that these four variables are gradually reducing from Period A, B, C, and D, reaching to the lowest value in Period E.  Defining Period A’s value as the 100% baseline, the four variables with the lowest values at Period E are:

  • K-line PPG: 42%
  • Synthesized PPG: 75%
  • PPG rising speed: 63%
  • PPG declining speed: 60%

The interpretation of this data declination means that the PPG wave is steadily becoming “calmer” from Period A to Period E.  In combining with the same trend and pattern of mean (average) value of PPG from Period A to Period E, we can safely draw the conclusion that the PPG is under better control from Period A moving toward to Period E.  All of the recent biomarker examination records confirmed his overall health condition results, including diabetes, insulin resistance, and associated complications reached to the best levels during year 2020.

This article utilized a simple method that is easier to comprehend and apply by physicians and patients regarding the application of the GV concept.

Introduction
In this article, the author provides his personal perceptions and opinions regarding the concept and approach of “Glycemic Variability” (GV).  Instead of using certain newly defined biomarkers, such as the mean amplitude glycemic excursions (MAGE), he chose to study the postprandial plasma glucose (PPG) wave fluctuations via a simple yet straightforward expression of the magnitude of glucose wave variances.  He uses his simple definition of  a glucose wave fluctuation equals to the maximum glucose value minus the minimum glucose value.

Methods
1. MPM Background:
To learn more about his developed GH-Method: math-physical medicine (MPM) methodology, readers can read the following three papers selected from the published 400+ medical papers.

The first paper, No. 386 (Reference 1) describes his MPM methodology in a general conceptual format.  The second paper, No. 387 (Reference 2) outlines the history of his personalized diabetes research, various application tools, and the differences between biochemical medicine (BCM) approach versus the MPM approach.  The third paper, No. 397 (Reference 3) depicts a general flow diagram containing ~10 key MPM research methods and different tools.

2. The authors case of Diabetes:
The author was a severe type 2 diabetes patient since 1996.  He weighed 220 lb. (100 kg) at that time.  By 2010, he still weighed 198 lb. with an average daily glucose of 250 mg/dL (HbA1C of 10%).  During that year, his triglycerides reached to 1161 and albumin-creatinine ratio (ACR) at 116.  He also suffered from five cardiac episodes within a decade.  In 2010, three independent physicians warned him regarding his needs of kidney dialysis treatment and his future high risk of dying from his severe diabetic complications.

In 2010, he decided to self-study endocrinology, diabetes and food nutrition.  During 2015 and 2016, he developed four prediction models related to diabetes conditions, i.e., weight, postprandial plasma glucose (PPG), fasting plasma glucose (FPG), and HbA1C (A1C).  As a result, from using his developed mathematical metabolism index (MI) model and those four prediction tools, by end of 2016, his weight was reduced from 220 lbs. (100 kg) to 176 lbs. (89 kg), waistline from 44 inches (112 cm) to 33 inches (84 cm), averaged finger glucose from 250 mg/dL to 120 mg/dL, and HbA1C from 10% to ~6.5%.  One of his major accomplishments is that he no longer takes any diabetes medications since 12/8/2015.

In 2017, he had achieved excellent results on all fronts, especially glucose control.  However, during the pre-COVID period of 2018 and 2019, he traveled to approximately 50+ international cities to attend 65+ medical conferences and made ~120 oral presentations.  This hectic schedule inflicted damage to his diabetes control, through dinning out frequently, post-meal exercise disruption, jet lag, and along with the overall metabolism impact due to his irregular life patterns through a busy travel schedule; therefore, his glucose control was affected during this two-year period.

By 2020, his weight was further reduced to 165 lbs. (BMI 24.4) and his HbA1C was at 6.2% without any medications intervention or insulin injection.  Actually, during 2020 with the special COVID-19 quarantined lifestyle, not only has he published approximately 400 medical papers in journals, but he has also achieved his best health conditions for the past 26 years.  These good results are due to his non-traveling, low-stress, and regular daily life routines.  Of course, his rich chronic diseases knowledge, practical lifestyle management experiences, and his developed various high-tech tools also contribute to his excellent health status since 1/19/2020.

On 5/5/2018, he applied a continuous glucose monitoring (CGM) sensor device on his upper arm and checks his glucose measurements every 15 minutes for a total of ~96 times each day.  He has maintained the same measurement pattern to present day.  Therefore, during the past 11 years, he could study and analyze his collected ~2 million data regarding his health status, medical conditions, and lifestyle details.  He applies his knowledge, models, and tools from mathematics, physics, engineering, and computer science to conduct his medical research work.  His medical research work is based on the aims of achieving both “high precision” with “quantitative proof” in the medical findings, not just through linguistic expressions of qualitative words, vague statements, or complex terminologies.

3. Other GV research work:
There are many available articles regarding GV; however, the author decided to combine five published articles into one excerpt (References 4, 5, 6, 7 and 8).  These five references cite 200+ published papers where the readers do not need to search for key information from a long list of the cited reference articles.

Reference 4 concentrates on the comparison of many published GV articles.  Reference 5 focuses on an algorithm, method, and firmware design of a web-based APP software in calculating the GV values.  Reference 6 evaluates the relationship between GV and pancreatic beta cell dysfunction.  Reference 7 from the American Diabetes Association (ADA) describes the overall picture of GV.

Reference 8 defines the mathematical equation of MAGE.

Here is the combined excerpt:

Several pathophysiological mechanisms were reported, unifying the two primary mechanisms: excessive protein glycation end products and activation of oxidative stress, which causes vascular complications.  Intermittent high blood glucose exposure, rather than constant exposure to high blood glucose, has been shown to have deleterious effects in experimental studies.  In in-vitro experimental settings and in animal studies, glycemic fluctuations display a more deleterious effect on the parameters of CV risk, such as endothelial dysfunction.  There is a significant association between GV and the increased incidence of hypoglycemia. Hypoglycemic events may trigger inflammation by inducing the release of inflammatory cytokines. Hypoglycemia also induces increased platelet and neutrophil activation. The sympathoadrenal response during hypoglycemia increases adrenaline secretion and may induce arrhythmias and increase the cardiac workload. Underlying endothelial dysfunction leading to decreased vasodilation may contribute to CV risk.  Published studies have demonstrated that GV, particularly when associated with severe hypoglycemia, could be harmful not only to people with diabetes but also to non-diabetic patients in critical care settings. Overall, the pathophysiological evidence appears to be highly suggestive of GV being an important key determinant of vascular damage. In addition to HbA1c, GV may have a predictive value for the development of T1DM complications. In insulin-treated T2DM, the relevance of GV varies according to the heterogeneity of the disease, the presence of residual insulin secretion and insulin resistance.  HbA1c is a poor predictor of hypoglycemic episodes because it only considers 8% of the likelihood of severe hypoglycemia; on the contrary, GV can account for an estimated 40% to 50% of future hypoglycemic episodes.  HbA1c is a poor predictor of hypoglycemic risk, whereas GV is a strong predictor of hypoglycemic episodes. GV was an independent predictor of chronic diabetic complications, in addition to HbA1c. We should note that PPG and GV are not identical, even if they are closely related.  The attention dedicated to GV is derived from the above evidence concerning its effects on oxidative stress and, from the latter, on chronic diabetes complications.  Control of GV has been the focus of a number of interventional studies aimed at reducing this fluctuation. Diet and weight reduction are the first therapeutic instrument that can be used for reducing GV.  

Despite the various formulas offered, simple and standard clinical tools for defining GV have yet to evolve and different indexes of GV should be used, depending on the metabolic profile of the studied population.  Moreover, the absence of a uniformly accepted standard of how to estimate postprandial hyperglycemia and GV adds another challenge to this debate.

The majority of these studies have used time-averaged glucose values measured as glycosylated hemoglobin (HbA1c), an indicator of the degree of glycemic control, which is why HbA1c has become the reference parameter for therapies aimed at reducing the risk of complications from diabetes. Chronic hyperglycemia is almost universally assessed by HbA1c which has been shown to correlate closely with mean glucose levels over time, as determined by continuous glucose monitoring (CGM).  However, the relative contribution of postprandial glycemic excursions and fasting to overall hyperglycemia has been the subject of considerable debate. Monnier et al. suggested that the relative contributions of fasting and postprandial glucose differ according to the level of overall glycemic control.  Fasting glucose concentrations present the most important contribution to hemoglobin glycosylation, whereas at lower levels of HbA1c, the relative contribution of postprandial hyperglycemia becomes predominant. Collectively, GV is likely to be incompletely expressed by HbA1c, particularly in patients with good metabolic control.  

GV is a physiological phenomenon that assumes an even more important dimension in the presence of diabetes because it not only contributes to increasing the mean blood glucose values but it also favors the development of chronic diabetes complications. It appears that GV is poised to become a future target parameter for optimum glycemic control over and above standard glycemic parameters, such as blood glucose and HbA1c.  Avoiding both hyperglycemia and hypoglycemia by careful use of SMBG and the availability of new agents to correct hyperglycemia without inducing hypoglycemia is expected to reduce the burden of premature mortality and disabling CV events associated with diabetes mellitus.  However, defining GV remains a challenge primarily due to the difficulty of measuring it and the lack of consensus regarding the most optimal approach for patient management.

The risk of developing diabetes-related complications is related not only to long-term glycemic variability, but may also be related to short-term glucose variability from peaks to nadirs.  Oscillating glucose concentration may exert more deleterious effects than sustained chronic hyperglycemia on endothelial function and oxidative stress, two key players in the development and progression of cardiovascular diseases in diabetes.  Percentages of hyperglycemia (levels between 126 and 180 mg/dl) and hypoglycemia (levels below 70.2 mg/dl) episodes should be used in the GV related research.  

Mean amplitude of glycemic excursions (MAGE), together with mean and SD, is the most popular parameter for assessing glycemic variability and is calculated based on the arithmetic mean of differences between consecutive peaks and nadirs of differences greater than one SD of mean glycemia.  It is designed to assess major glucose swings and exclude minor ones.  

The features discouraging use of glycemic variability as a parameter in clinical practice and trials are the difficulty of interpreting numerous parameters describing this phenomenon and a limited number of computational opportunities allowing rapid calculation of glycemic variability parameters in CGM data.

The UK Prospective Diabetes Study (UKPDS) showed that after an initial improvement, glycemic control continues to deteriorate despite the use of oral agents to enhance insulin secretion and to reduce insulin resistance.  This deterioration can be attributed to the progressive decline of β-cell function.  Even in subjects with well-controlled type 2 diabetes, 70% of the variability of A1C can be explained by abnormalities in postprandial glucose.  Chronic sustained hyperglycemia has been shown to exert deleterious effects on the β-cells and the vascular endothelium.  In vivo studies have convincingly demonstrated that hyperglycemic spikes induce increased production of free radicals and various mediators of inflammation, leading to dysfunction of both the vascular endothelium and the pancreatic β-cell.

4. Mean amplitude of glycemic excursions (MAGE):
Furthermore, such a measure should be simple in concept and faithful to the physiological basis for the glucose swings.  Because interest lay in the amplitude of glycemic swings and not in the dispersion of all the glucose data, SD was considered to be unsuitable.  The criterion, which did recognize all of the meal-related glucose excursions for all of the normal subjects, was the SD of the mean BG for each 24-h period of study (288 values taken q5min from the continuous record) for each individual. In contrast, 0.5 SD and 1.5 SD were less inclusive/exclusive. Although the numerical value of 1 SD will perforce differ in absolute value from person to person, it nevertheless acts as an individualized standard. By convention, a glycemic excursion (both trough-to-peak and peak-to-trough) must exceed 1 SD of the respective 24-h BG profile. For continuous recordings exceeding 24 h, the use of 1 SD calculated for the whole period of study may result in the inclusion of the same excursions as use of the separate 24-h SDs, since SDs from successive days do not differ by much (even in type 1 diabetic patients as long as therapy has not changed during the period of monitoring).  Only one limb of the excursion, ascending or descending, determined by the initial excursion (which is not always an inflection especially in type 1 diabetic patients) is used for calculation of subsequent excursions.  Should the subsumed excursion be of a magnitude observed for normal subjects its exclusion may be inconsequential relevant to the risk for the development of microvascular complications of diabetes.  The arithmetic mean of the glycemic excursions for the period of study (24 h, 48 h, or longer) is the value of mean amplitude of glycemic excursions (MAGE).  An automated algorithm has been created for the calculation of MAGE.  Although created for determination from continuous BG analysis, MAGE has been applied to intermittent (7- and 22-point sampling/24 h) measurements as well as continuous interstitial glucose monitoring.  

5. The authors view on glucose wave fluctuations (glycemic excursions):
There is a concluding remark from one of the reference articles which was expressed above and now is copied again at below.

The features discouraging use of glycemic variability as a parameter in clinical practice and trials are the difficulty of interpreting numerous parameters describing this phenomenon and a limited number of computational opportunities allowing rapid calculation of glycemic variability parameters in CGM data.”

The author also believes that any newly created biomarker should accurately describe the biomedical phenomena of a disease, but at the same time, it should be easily enough for physicians and/or patients to comprehend and apply it to their day-to-day diabetes control.

The concept and discussion of GV have existed more than a decade based on the clinical usage and results of the continuous glucose monitoring (CGM) device to monitor severe diabetes patients and insulin treatments in hospitals.  The self-monitored glucose devices (SMGD) became available to outpatients for general diabetes use starting from 2016-2017.  As a result, most of the published research reports are based on glucose data collected during a relatively short period of 2 to 3 days from hospitalized diabetes patients.  Although many GV medical papers have been published based on a larger patient numbers, who were probably hospitalized, it lacks the needed longer time period.  Furthermore, there are only few of those research papers that provide connections to the intuitive comprehension and easy application on daily diabetes control.  That is why the subject of GV is only a research topic in the medical research community, instead of being truly utilized as a clinical tool for practical usage by both diabetes patients and their physicians.  For example, the author has had type 2 diabetes (T2D) for 26 years and has been under the care of multiple physicians associated with renown medical institutes.  For the past decade, he has attended 65 medical conferences and met more than 1,000 medical doctors, professors, and clinical physicians, but he has never heard of GV mentioned once or its related discussions by the physicians he has met.

Starting on 5/5/2018, he placed a SMGD on his arm to collect two sets of glucose data.  The first set of data are measured every 15 minutes with a total of 96 data per day.  In addition, since 2/19/2020, he has applied Bluetooth technique to collect his second set of data which are measured every 5 minutes with a total of 288 data per day.  In this article, he decided to use the 15-minute dataset with 96 data per day for his analysis due to its longer time period of available glucose data.  With a longer time span, he can observe more changes on both glucose and insulin resistance situations.  Statistics analysis based on 1000+ patients with only a few days can certainly provide some useful information.  However, the author offers a similar analysis from a perpendicular angle, i.e., one patient with big data associated with a much longer period of time.  His data include a span of ~2.8 years (~32 months, 971 days) and 93,216 daily glucose or 37,869 PPG values.  The reason he chose PPG wave as his research target in this article because its fluctuations are usually the most “violent” kind in comparison with glucoses in other segments in a day.

Defining GV remains a challenge primarily due to the difficulty of data collection with its associated follow-on necessary tasks, such as data transfer, data cleansing, data processing, and data analysis that can lead into the ambiguity of GV’s existing interpretations, different versions of expressions, along with the lack of consensus regarding the optimal approach for its clinical management.  For example, one of the major GV derivations, mean amplitude of glycemic excursion (MAGE), involves the usage of standard deviation (SD) from statistics.  Although SD is widely used in statistics, it has some limitations since its inherited assumption of measured data are normally distributed, which is typically not the case for most glucose data and waves.

All of the above-mentioned tasks are still challenging for most diabetes patients, physicians, and even some medical research scientists.  Due to the lack of professional training and academic knowledge in this domain, most patients and clinical physicians have encountered difficulties in understanding and applying the GV related materials.

The author is a professionally trained mathematician, physicist, and engineer.  He has further used various analytical approaches to analyze his own health data and glucose waveforms from many different research angles. Therefore, he thoroughly understands the behaviors and characteristics of many glucose data and waves collected from his own body.

Based on the theoretical and technical viewpoints, the author decided to conduct this study on applying the basic concept of GV (i.e., glucose fluctuation between peak and trough) in combination with the primary characteristics of wave theory (mainly frequencies, amplitudes, phases, and associated energies of glucoses).  This would assist in his investigation of the self-recovery of his pancreatic beta cells and various internal organ impacts from the energy associated with glucose waves, including GV (glucose fluctuations).  It also benefits on his research on the subject of risk probability of having complications, e.g. cardiovascular diseases, from chronic diseases.  In this article, the author applies the following simple formula of glucose fluctuation rate to use for his GV study where the glucose fluctuation is defined as the glucose peak as the maximum glucose minus the glucose trough as the minimum glucose.

Glucose fluctuation rate = [Summation (i = 1 to n) of : ((maximum glucose – minimum glucose) / average glucose)] / n

6. Data and analysis in this study:
The author has collected 96 glucose data per day (every 15 minutes) using a CGM device since 5/5/2018 and 288 glucose data collected per day (every 5 minutes) since 2/17/2020.  During the past 971 days (5/5/2018 – 12/31/2020), he has collected 93,216 glucose data, using the 15-minute model.  He decided to use this model for his analysis due to its sufficient long time period of data collection.

In this article, he chose not to use the defined MAGE term and equation because there is no need to apply such a complex mathematical equation.  This MAGE involves mathematical operations such as mean, logarithm, and standard deviation which is difficult for both patients and physicians to understand.  More importantly, during the complex definition and multiple mathematical operations, it has lost its direct linkage with an easy-to-understand biomedical intuition to users.  That is why the MAGE remains as a terminology in medical research arena only.  In the author’s opinion, once physicians or patients can understand, feel, and then control their “glucose fluctuation” situations, either within a few days or a few years (of course, the longer period of time the better), their objective of diabetes control can then be met and not have to deal with a complicated mathematical equation for biomarker such as MAGE.

It should also be cautioned that mathematically, the normal distribution’s characteristic function is defined by just two moments: mean (average) and variance (standard deviation, the “SD”).  The mean tells you where the center of your distribution is, while the standard deviation tells you how close to the center your data is. Therefore, for normal distribution, the standard deviation is especially important, because it contributes half of these two moments.  However, for other data distribution types, the standard deviation is in some ways less important because of the non-normal distribution types contain other moments.  We cannot assume that most of our data (business, social, economic, scientific, or medical origin) are approximately “Normal”, i.e., they are generated by a Gaussian process or by a sum of multiple such processes.  Therefore, when we define a new biomarker with its mathematical operations heavily dependent on SD, we must be aware of the limitation of this biomarker’s accuracy and applicability due to its non-Gaussian distribution type in biomedical applications in real life.

Instead, this research work was conducted by using four different but inter-related key components which are easier for both patients and physician to understand.  The first component is the glucose fluctuation of Candlestick K-line of PPG wave which will be described in the following section and in Figure 1. The second component is the glucose fluctuation of synthesized PPG waveforms.  The third component is the PPG rising speed.  The fourth component is the PPG declining speed.  Putting these four information components together, we will obtain a fairly clear picture regarding glycemic variability or glycemic fluctuation of PPG waves.

7. Candlesticks (K-Line) Model:
A Japanese merchant, who traded in the rice market in Osaka, Japan, started the candlestick charting around 1850.  An American fellow, Steve Nison, brought the candlestick model concept and method to the Western world in 1991.  These techniques are largely used in today’s stock market to predict the stock price trend.

The author was the CEO of a public-traded company.  Therefore, he is quite familiar with the Candlestick model or the K-line model.  On 4/17/2018, he had an idea to study glucose behavior by using the candlestick chart and subsequently developed a customized software to analyze his big data of glucose.  The analogies between fluctuations of stock price and glucose value are described as follows:

  • Stock prices are closely related to the psychology of the buyers and sellers, which is similar to the glucoses related to a patient body’s biochemical interactions and lifestyle behaviors.
  • Stock price wave of a public traded company is dependent upon its product line, internal management, marketing efforts, and public events and customer perception.  This is remarkably similar to the PPG wave of a diabetes patient being dependent on his/her complex food & diet (buying stock), exercise pattern and amount (selling stock), weather temperature (buying and selling stock), and pancreatic beta cell insulin function (similar to SEC regulations).  From a trained mathematician’s eyes, these biomedical wave and financial wave are just two different but similar mathematical representations.  Wave theory can be applied on both of their behaviors.
  • When there are more buyers than sellers, the stick price goes up, which is similar to the glucose value rising when carbs/sugar intake increases which infuses the energy generation (more buyers) or lack of exercise which reduces the energy consumption (less sellers).
  • When there are more sellers than buyers, price goes down, which is similar to the glucose value decreasing when carbs/sugar intake decreases (less buyers) or exercise increases (more sellers).

During his period of using the CGM sensor to collect his glucoses data, his standard PPG wave covers a period of 180 minutes, or 3 hours from the first bite of his meal.  Each PPG waveform contains the following five key characteristic data:

  1. “Open” value as his PPG at first-bite, 0 minute
  2. “Close” value as PPG at 180 minutes
  3. “Minimum” value as the lowest PPG
  4. “Maximum” value as the highest PPG
  5. “Average” glucose (HbA1C) as the average value of 13 recorded PPG data per meal over 3 hours

Results

Figure 1 shows the sample diagram of PPG waveforms in terms of both synthesized PPG and Candlestick K-Line PPG.  Since each PPG wave covers a 3-hour timeframe with 13 glucose data points each, once all of the PPG data and wave are assembled during a long time period, we will obtain one synthesized PPG data table and waveform which is the “synthesized daily PPG”.  On the other hand, based on the candlestick K-line description above, each daily PPG candlestick would pick up its “absolute” maximum and “absolute” minimum PPG values and shown on the candlestick’s two ends.  Therefore, generally speaking, the K-line values are more “extreme values” than the “average values” reflected in the synthesized data table and synthesized waveform.  Figure 1 also displays the definition of rising speed and declining speed of a PPG wave or a PPG curve as follows:

  • Rising speed = (PPG rising amount:  R) / (corresponding time interval:  t1)
  • Rising speed = (PPG rising amount:  D) / (corresponding time interval:  T2D)
Figure 1: Sample of Synthesized PPG waveform, Candlestick K-line glucose fluctuation, & rising and declining speeds of a PPG wave

Figure 2 represents the data table of all input data and calculated data.  The idiom of “the devil is in the details” refers to a catch or mysterious element hidden in the details.  Therefore, usually, a detailed data table contains more hidden truths or useful discoveries if you know how to find and analyze them.  For example, the author did not use the SD and mean (average) in this particular analysis, nevertheless, for comparison and reference purposes, he included the glucose fluctuation amount versus SD value in this calculated data table in order to provide a different physical feeling of his data.  

Figure 2: Data table of input data and calculated data for 5 time periods

Figure 3 is a representative bar diagram of this analysis results.   It demonstrates the differences among these four variables (Candlestick K-line PPG fluctuation, Synthesized PPG fluctuation, PPG rising speed, and PPG declining speed) over five different “half-year” time periods.  

Figure 3: Comparison of Synthesized PPG wave fluctuation, Candlestick K-line glucose wave fluctuation,
& rising and declining speed variances of PPG wave during 5 time periods

A key comparison between the beginning Period A of 5/5/2018 – 12/31/2018 and the ending Period E of 7/1/2020 – 12/31/2020 can depict the significant end-difference between these two extreme time periods.  A table lists the results in the format of Period A / Period E / % of difference (also see Figure 4):

  • K-line fluctuation: 66 / 28 / 42%
  • Synthesized fluctuation: 48 / 36 / 75%
  • PPG Rising speed: 32 / 20 / 63%
  • PPG Declining speed: 20 / 12/ 60%

From the above data table and bar-diagram in Figure 3, it is clear that all of these four values related to glucose fluctuations are steadily declining from period to period with Period E’s results being the lowest one.  

Figures 2 and 3 revealed his GV situations, by using a more simplified PPG glucose fluctuations instead of using the complex MAGE equation, more easily and clearly.  These four sets of data have been improving (means “declining”) along with the time scale.  It can also safely indicate that his insulin resistance situation and cardiovascular risks are reducing as well.  

Figure 4 indicates the declining percentages of K-line PPG, synthesized PPG, PPG rising speed, and PPG declining speed of five different periods, using a line chart.

Figure 4: Declining % of Synthesized PPG wave fluctuation, Candlestick K-line glucose wave fluctuation,
& rising and declining speed variances of PPG wave during 5 time periods

Conclusions
The author compares four different sets of wave fluctuation related variables during five half-year periods between 5/5/2018 to 12/31/2020: Periods A, B, C, D, and E.  These four variables consist of K-line daily PPG, Synthesized daily PPG, PPG rising speed, and PPG declining speed.

The key observation from this study is that these four variables are gradually reducing from Period A, B, C, and D, reaching to the lowest value in Period E.  Defining Period A’s value as the 100% baseline, the four variables with the lowest values at Period E are:

  • K-line PPG: 42%
  • Synthesized PPG: 75%
  • PPG rising speed: 63%
  • PPG declining speed: 60%

The interpretation of this data declination means that the PPG wave is steadily becoming “calmer” from Period A to Period E.  In combining with the same trend and pattern of mean (average) value of PPG from Period A to Period E, we can safely draw the conclusion that the PPG is under better control from Period A moving toward to Period E.  All of the recent biomarker examination records confirmed his overall health condition results, including diabetes, insulin resistance, and associated complications reached to the best levels during year 2020.

This article utilized a simple method that is easier to comprehend and apply by physicians and patients regarding the application of the GV concept.

References

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  16. Hsu, Gerald C., eclaireMD Foundation, USA, “Applying the concept of glycemic variability (glucose fluctuation) for an extended study on the self-recovery of pancreatic beta cells and risk probability of having a cardiovascular disease or stroke using GH-Method: math-physical medicine (No. 390)
  17. Hsu, Gerald C., eclaireMD Foundation, USA, “Applying the concept of glycemic variability (glucose fluctuation) for an extended study on the self-recovery of pancreatic beta cells and risk probability of having a cardiovascular disease or stroke using GH-Method: math-physical medicine (No. 390)”