Literature DB >> 27821611

Correlation of Electrophotonic Imaging Parameters With Fasting Blood Sugar in Normal, Prediabetic, and Diabetic Study Participants.

Romesh Kumar Bhat1, Guru Deo1, Ramesh Mavathur1, Thaiyar Madabusi Srinivasan1.   

Abstract

INTRODUCTION: Electrophotonic imaging (EPI), also known as gas discharge visualization, is a technique of capturing images of phenomena not quantifiable by the naked eye. Different sectors at the tip of fingers represent various organs and systems as per the Chinese system of acupuncture. The images from these fingertips can be used to determine the state of health. This is done with the help of a CCD camera fitted in the EPI equipment and the specific software relevant for analysis. AIM: To observe the correlation between EPI parameters and fasting blood sugar (FBS) levels in normal, prediabetic, and diabetic study participants.
MATERIALS AND METHODS: A total of 102 participants were selected for this study from various yoga camps and Arogyadham at Swami Vivekananda Yoga Anusandhana Samsthana Yoga University, Bengaluru, India. The selected participants belonged to 3groups-normal, prediabetic, and diabetic-depending on the FBS levels. The distribution of participants was 29 normal, 13 prediabetic, and 60 diabetic.
RESULTS: Regression analysis in the case of prediabetics showed a significant relationship of FBS with pancreas and right kidney. In the case of normal participants, a significant relationship of FBS was found with area and form coefficient of the EPI gram. For diabetics, regression analysis showed significant relationship of FBS with immune organs, left kidney, area, intensity, and entropy of EPI grams.
CONCLUSION: FBS correlates differently in the normal, prediabetic, and diabetic groups. In the prediabetic group, correlation of FBS with EPI parameters pancreas and right kidney is noteworthy and in line with latest findings in medical research.

Entities:  

Keywords:  correlation; diabetes; electrophotonic imaging; fasting blood sugar; parameters

Mesh:

Substances:

Year:  2016        PMID: 27821611      PMCID: PMC5871158          DOI: 10.1177/2156587216674314

Source DB:  PubMed          Journal:  J Evid Based Complementary Altern Med        ISSN: 2156-5899


Diabetes is a condition in which either the body produces insufficient insulin or it produces sufficient insulin but body cells are not able to use it properly.[1,2] There are 2 types of diabetes: type 1, which is called insulin-dependent diabetes mellitus, and type 2, which is called non-insulin-dependent diabetes mellitus.[3] Type 2 is more prevalent and is rapidly increasing in number across the globe. Undiagnosed and untreated, this disease leads to a host of serious conditions in which multiple organs are affected. Worldwide, diabetes has raised concern and is receiving increased attention.[4] With the limitations of modern medical system in containing diabetes, there is great momentum in the direction of holistic approach of treatment.[5,6] This is because diabetes is greatly linked to stress. Alternative/supplementary medicine is a proven therapy against stress. Yoga has a prominent place in the area of complementary medicine.[7] It works both at the gross and subtle levels.[8] Most of the physical problems in the human body have its origin in the mind, which is subtle in nature. As per yoga and ayurveda, disturbances in mind result in the disease and further progression leads to chronic illness.[9] There are innumerable studies on the effects of yoga on various ailments and stress levels.[10] Modern research has demonstrated interrelatedness of various disciplines from the level of basic biological processes to a dynamic system or “biofield” level.[11] Molecular, cellular, organic function, and regulation are thus interwoven with and can be influenced by emotion, cognition, and psychosocial factors, suggesting the existence of a “subtle,” that is, low-energy system of biofield interactions connecting these activities. The term biofield is defined as “an organizing principle for the dynamic information flow that regulates biological function and homeostasis.”[12] Another development in the field of subtle energies is electrophotonic imaging (EPI), which is a biofield device. This equipment is used widely in Russia for measuring subtle phenomena in the body.[13] Subtle changes in the body result in Chi energy disturbances and these are captured through the images taken from tips of 10 fingers. The mechanism of working of EPI is simple. The fingertips represent various organs/systems within the human body. This is as per the Chinese medical system of acupuncture.[14] The finger is placed on a glass surface and a high-voltage pulse of amplitude 10 kV, with a frequency of 1024 Hz, is applied. A very small and safe level of current, in milliamperes, is applied for less than a millisecond at the fingertips of the human body.[15] The electrons extracted thus form an image that is captured by a camera in EPI. The images are analyzed and processed with the help of software, and data are generated in the form of energy diagrams, numerical data, and some other parameters. Values of different parameters are indicative of the health profile.[16,17] The prominent parameters are area, entropy, form coefficient, fractility, and intensity.[18] There are parameters specific to organs and systems. So every organ/system is evaluated through integral area and entropy specific to it. Based on the literature on type 2 diabetes mellitus in the modern medical system, we have considered integral area parameter of liver, pancreas, and immune organs[19]; coronary vessels[20]; cerebral vessels[21]; left kidney and right kidney[22] for our study. Literature survey has revealed that EPI can be used for the study of effects of conventional/alternative medicine on asthma, cancer, autism, among other diseases.[23-25] There are studies on the effects of music and meditation on stress levels as measured by EPI. The parameter used in these studies is activation coefficient.[26,27] The parameters of EPI are standardized for the European population. However, there is a recent study on the standardization for the Indian population as well.[28] It is easier for the scientific community to accept new concepts if there is a degree of correlation with established norms or parameters. The present study is focused on this aspect.

Materials and Methods

This study was carried out on participants who attended various yoga camps connected with the “Stop Diabetes Movement” campaign of Swami Vivekananda Yoga University, Bengaluru, India. Besides, there were participants from Arogyadham, a residential health center of Swami Vivekananda Yoga University. The study was to observe correlation between fasting blood sugar (FBS) and EPI parameters. Participants were divided into 3 groups depending on the FBS level: normal, prediabetic, and diabetic. Normal and prediabetic participants had reported no health problems. In fact, the prediabetic participants came to know of their FBS level for the first time. In the diabetic group, there were some whose FBS level was within normal limits and some whose FBS level showed uncontrolled diabetes. Some diabetics were on medication and yet had persistently FBS levels greater than 126 mg/dL. For our study, both the groups (with medication and with or without control of diabetes) were considered as one and designated as type 2 diabetes mellitus. The FBS level for normal participants was 70 to 100 mg/dL, prediabetes 100 to 125 mg/dL, and diabetes 126 mg/dL and more.[29] The data were taken from June 2015 to September 2015.

Participants

Two hundred participants from various yoga camps in the rural areas of Bengaluru and Arogyadham at Swami Vivekananda Yoga University were scrutinized. Out of these, 102 participants were selected for the study. Those for whom there were defects in the electrophotonic image and/or biochemical (FBS) reports were not available did not qualify for the study. Of those selected, 42 participants were in the category of no health problem. The sample comprised 29 normal and 13 prediabetic participants; 60 participants were in the category of diabetics irrespective whether the diabetes was under control or uncontrolled. The 3 groups were designated as normal, prediabetic, and diabetic. Out of 42 (mean age 46.5 ± 11.7) in the nondiabetic group, 17 were males (mean age 48.47 ± 13.26) and 25 were females (mean age 45.2 ± 10.6). The normal group consisted of 29 (mean age 44 ± 11), and the prediabetic group consisted of 13 (mean age 51.2 ± 12.3). In the diabetic group of 60 (mean age 54 ± 9.6), there were 35 males (mean age 56.83 ± 8.72) and 25 females (mean age 50 ± 9.4). Participants in the age range of 18 to 75 years, male or female, and willing to participate were included in the study.

Exclusion Criteria

The following subjects were excluded from the study: participants with comorbidity[30] and taking any medicine in the case of normal and prediabetic participants; diabetic participants taking medicines apart from diabetes medications; participants suffering from any infectious or contagious diseases; physically handicapped persons with missing fingers; and females having menstruation or pregnancy on the day of measurement.

Sampling Time

The data were taken in the morning hours with a gap of at least 3 hours after the last meal. The data in the camps were taken on the inaugural day of the camp. Data at Arogyadham was taken in the morning as well in the evening but ensuring a gap of 3 hours after the last meal. EPI was calibrated each time the place of taking measurement changed or as required. Informed consent was taken from all the participants before conducting the study. The study was approved by the institutional ethics committee of the university.

Instrument

Kirlionics Technologies International (St Petersburg, Russia; GDV camera Pro with analog video camera, model number: FTDI.13.6001.110310) was used for the assessment purpose. Along with the EPI software, it provided various features such as EPI screening, EPI scientific laboratory, EPI diagram.

Parameters Analyzed

From the EPI scientific laboratory the following parameters were analyzed: Total area is an absolute value and is measured as the number of pixels in the image having brightness above the threshold; intensity is the evaluation of light intensity averaged on the area of the image; form coefficient and fractility are measures of irregularity in the image external contour; entropy reflects the level of nonuniformity of image, in other words, the level of stability of the energy field. EPI diagram/EPI screening grams give the integral area parameter, which is an index of the particular sector of image related to the organ in accordance with the principles of traditional Chinese medicine. This parameter, corresponding to organs liver, pancreas, immune organs, coronary vessels, cerebral vessels, left kidney, and right kidney, were analyzed. Integral area is a relative value and shows the extent to which the EPI gram deviates from an ideal model. It is an indicator of general health.

Data Analysis

Data analysis was done with the help of Microsoft Office Excel 2007 and R studio along with R cmdr. Statistical tests were conducted for correlation and linear regression.

Results

The participants were divided into 3 groups: normal, prediabetic, and diabetic. Independent samples t test showed change in average values of the selected parameters in these groups (Tables 1 and 2). First, correlation analysis was done between FBS and each of the selected EPI parameters in the 3 groups (Table 3). In normal subjects, a high correlation (but not significant) was observed between FBS and form coefficient (P = .06, r = .35). Significant correlation was found between FBS and right kidney (P = .03, r = −.60) in the prediabetic group. In the case of diabetics, a high correlation (but not significant) was observed of FBS with immune organs (P = .06, r = −.25), with coronary vessels (P = .09, r = .22), and with entropy (P = .08, r = .23). Noteworthy results of correlation analysis are given in Table 4 and summarized in Table 5. Negative sign indicates that the predictor variable and the responding variable increase in opposite directions. In the diabetic group, further analysis was done separately for males and females. There is no significant correlation of FBS with any EPI parameter but some correlation with immune organs (P = .11, r = −.27) in the male subgroup and entropy (P = .19, r = −.26) and fractility (P = .17, r = .27) in the female subgroup. Significant results of regression analysis for the 3 different groups done on the basis of the aforementioned results are depicted in Table 6. The detailed gender-based correlation and regression analysis results are reflected in Tables 7 and 8, respectively. Another analysis carried out on the EPI parameters in the 3 groups yielded a significant change from normal to prediabetic to diabetic (Table 9).
Table 1.

Electrophotonic Imaging Diagram/Screening Analysis (Independent Samples t Test).

VariablesNormal, Mean ± SDPrediabetic, Mean ± SDDiabetic, Mean ± SD
Average liver−0.05 ± 0.470.10 ± 0.330.27 ± 0.53
Average immune organs−0.20 ± 0.31−0.17 ± 0.290.08 ± 0.38
Average pancreas−0.26 ± 0.590.10 ± 0.390.22 ± 0.53
Average coronary vessels−0.13 ± 0.37−0.05 ± 0.240.21 ± 0.32
Average cerebral vessels−0.02 ± 0.290.01 ± 0.310.26 ± 0.35
Average left kidney−0.05 ± 0.440.05 ± 0.390.26 ± 0.43
Average right kidney−0.09 ± 0.36−0.06 ± 0.380.24 ± 0.46
Table 2.

Electrophotonic Imaging Scientific Laboratory Analysis (Independent Samples t Test).

VariablesNormal, Mean ± SDPrediabetic, Mean ± SDDiabetic, Mean ± SD
Average area11487.62 ± 1416.9811597.54 ± 1425.69512003.11 ± 1451.19
Average intensity78.0867 ± 5.86377.3890 ± 8.34584.06 ± 7.62
Average form coefficient14.9347 ± 4.79215.5009 ± 6.06511.34 ± 3.24
Average entropy1.8603 ± 0.1611.7691 ± 0.1801.96 ± 0.16
Average fractility1.9229 ± 0.1741.9860 ± 0.1201.85 ± 0.05
Table 3.

Correlation Analysis (All Parameters).

FBS/With t df P r
NormalAverage liver0.96627.342.182
Average immune organs1.39227.175.258
Average pancreas0.04727.962.009
Average coronary vessels0.99527.328.188
Average cerebral vessels−0.15727.875−.030
Average left kidney0.83227.412.158
Average right kidney0.55327.584.105
Average total area1.45427.157.269
Average intensity−0.71827.478−.136
Average form coefficient1.94327.062.350
Average entropy0.06627.947.012
Average fractility−0.17927.858−.034
PrediabeticAverage liver−1.20311.254−.341
Average immune organs−0.55511.589−.165
Average pancreas0.80011.440.234
Average coronary vessels0.92111.376.267
Average cerebral vessels0.00611.994.002
Average left kidney0.29911.770.089
Average right kidney−2.45911.031−.595
Average total area0.18211.858.055
Average Intensity1.03511.322.298
Average form coefficient−0.25111.805−.075
Average entropy1.19311.257.338
Average fractility−0.77311.453−.227
DiabeticAverage liver−0.74258.461−.096
Average immune organs−1.95658.055−.248
Average pancreas−0.57858.565−.075
Average coronary vessels−1.75258.085−.224
Average cerebral vessels−0.65658.514−.085
Average left kidney−0.74958.456−.097
Average right kidney−0.97958.331−.127
Average total area0.93558.353.121
Average Intensity−1.64758.104−.211
Average form coefficient0.96058.340.125
Average entropy−1.77158.081−.208
Average fractility1.62158.110−.226

Abbreviations: FBS, fasting blood sugar; df, degrees of freedom.

Table 4.

Correlation Analysis (Noteworthy).

GroupFBS/With t df P a r b
NormalAverage form coefficient1.94327.062.350
PrediabeticAverage right kidney−2.45911.031−.596
DiabeticAverage immunity−1.95658.055−.248
Average coronary vessels−1.75258.085−.224
Average entropy−1.77258.0810.208

Abbreviations: FBS, fasting blood sugar; df, degrees of freedom.

aP, level of significance, <.05 considered significant.

br, correlation coefficient varies between −1 and +1.

Table 5.

FBS and EPI Correlation in the 3 Categories.

FBS/WithNormalPrediabeticDiabetic
Average areaYesNoYes
Average intensityNoNoYes
Average entropyNoNoYes
Average form coefficientYesYesNo
Average PancreasNoYesNo
Average immunityNoNoNo
Average left kidneyNoNoNo
Average right kidneyNoYesNo
Table 6.

Regression Analysis.

Normal GroupEstimateStandard Errort ValuePr(>|t|)
Intercept49.47510.9644.512.000122***
Average area0.0020.0013.085.004780**
Average form coefficient0.7790.2293.398.002196**
Residual standard error = 5.167***P < .001, **P < .01
FBS = α + β1X1 + β2X2 + ∊
Where α = 49.475, β1 = 0.0024, β2 = 0.7796, ∊ = 5.167; X1 = total area; X2 = Form coefficient
PrediabeticEstimateStandard Errort ValuePr(>|t|)
Intercept116.8567.24616.1261.74e-08***
Average pancreas63.73420.1873.157.01021*
Average right kidney−90.25720.572−4.387.00136**
Residual standard error = 24.16***P < .001, **P < .01, *P < .05
FBS = α + β1X1 + β2X2 + ∊
Where α = 116.856, β1 = 63.734, β2 = −90.257, ∊ = 24.16; X1 = integral area of pancreas; X2 = integral area of right kidney
DiabeticEstimateStandard Errort ValuePr(>|t|)
Intercept494.6139.43.548.000812***
Average immune organs−96.1131.78−3.024.003809**
Average intensity−3.0821.176−2.621.011356*
Average left kidney51.3328.121.825.073502+
Average entropy−149.352.15−2.863.005965**
Average area0.017.0062.711.008983**
Residual standard error = 61.42***P < .001, **P < .01, *P < .05, +P < .1
FBS = α + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + ∊
Where β = 494.6, β1 = −96.11, β2 = −3.082, β3 = 51.33, β4 = −149.3, β5 = 0.01698, ∊ = 61.42; X1 = integral area of immune organs; X2 = intensity; X3 = integral area of left kidney; X4 = entropy; X5 = area

Abbreviations: FBS, fasting blood sugar; α, constant; β1, coefficient of variable X1; β2, coefficient of variable X2;…; β, coefficient of variable X. t, t test value; ∊, residual standard error; P < .001 very highly significant; P < .01 highly significant; P < .05 significant; P < .1 not significant.

Table 7.

Correlation Analysis Diabetic Males and Females (Separately).

FBS/With t df P r
MalesAverage liver−0.04633.963−.008
Average immune organs−1.62733.113−.272
Average pancreas0.26033.795.045
Average coronary vessels−1.0233.315−.174
Average cerebral vessels−0.65633.514−.085
Average left kidney−0.00533.995−.000
Average right kidney−0.20333.840−.035
Average area1.04933.301.179
Average intensity−0.74033.464−.127
Average form coefficient0.35533.724.061
Average entropy−0.94433.351−.162
Average fractility0.88833.380.152
FemalesAverage liver−0.55523.584−.114
Average immunity−0.46523.646−.096
Average pancreas−1.13623.267−0.230
Average coronary−0.77423.446−.159
Average cerebral−0.01323.989−.002
Average left kidney−0.47723.635−.099
Average right kidney−1.02123.317−.208
Average area0.50723.617−.105
Average intensity−1.23923.227−.250
Average form coefficient0.71823.480.148
Average entropy−1.32523.198.279
Average fractility1.39423.176.152

Abbreviations: FBS, fasting blood sugar; df, degrees of freedom.

Table 8.

Regression Analysis, Diabetics Males and Females (Separately).

MalesEstimateStandard Errort ValuePr(>|t|)
Intercept707.686309.5722.286.03*
Average immune organs−90.09540.229−2.240.03*
Average intensity−4.1562.022−2.055.04*
Average left kidney64.57639.6641.628.11
Average right kidney−18.65233.699−0.553.58
Average entropy−252.386122.559−2.059.04*
Average area0.0230.0112.067.04*
Residual standard error = 64.05*P < .05
FBS = α + β1X1 + β2X2 + β3X3 + β4X4 + β5X5 + β6X6 + ∊
Where α = 707.6; β1 = −90.09, β2 = −4.15, β3 = 64.57, β4 = −18.65, β5 = −252.38, β6 = 0.02; ∊ = 64.05; X1 = integral area of immune organs; X2 = intensity; X3 = integral area of left kidney; X4 = integral area of right kidney; X5 = entropy; X6 =area
FemalesEstimateStandard Errort ValuePr(>|t|)
Intercept−1123.12605.61−1.855.07+
Average immune organs−117.4667.09−1.751.09+
Average left kidney80.0058.341.371.185
Average entropy−168.0368.86−2.440.024*
Average fractility864.54349.142.476.022*
Residual standard error = 61.04*P < .05, +P < .1
FBS = α + β1X1 + β2X2 + β3X3 + β4X4 + ∊
Where α = −1123.12; β1 = −117.46, β2 = 80.00, β3 = −168.03, β4 = 864.54; ∊ = 61.04; X1 = integral area of immune organs; X2 = integral area of left kidney; X3 = entropy; X4 = fractility

Abbreviations: FBS, fasting blood sugar; α, constant; β1, coefficient of variable X1; β1, coefficient of variable X2;…; β, coefficient of variable X. t, t test value; ∊, residual standard error; P < .001 very highly significant; P < .01 highly significant; P < .05 significant; P < .1 not significant.

Table 9.

Summary of Independent Samples t Test Between the 3 Groups.

ParameterGroupsLevel of Significance of Differencea
Average intensityDiabetes–NormalSignificant
Diabetes–PrediabetesSignificant
Normal–PrediabetesNot significant
Average form coefficientDiabetes–NormalHighly significant
Diabetes–PrediabetesSignificant
Normal-PrediabetesNot significant
Average entropyDiabetes–NormalHighly significant
Diabetes–PrediabetesHighly significant
Normal–PrediabetesNot significant
Average fractilityDiabetes–NormalSignificant
Diabetes–PrediabetesHighly significant
Normal-Pre diabetesNot significant
Average liverDiabetes–NormalHighly significant
Diabetes–PrediabetesNot significant
Normal–PrediabetesNot significant
Average immune organsDiabetes–NormalVery highly significant
Diabetes–PrediabetesVery highly significant
Normal–PrediabetesVery highly significant
Average pancreasDiabetes–NormalHighly significant
Diabetes-PrediabetesNot significant
Normal–PrediabetesSignificant
Average coronaryDiabetes–NormalVery highly significant
Diabetes–PrediabetesHighly significant
Normal–PrediabetesNot significant
Average cerebralDiabetes–NormalVery highly significant
Diabetes–PrediabetesSignificant
Normal–PrediabetesNot significant
Average left kidneyDiabetes–NormalHighly significant
Diabetes–PrediabetesNot significant
Normal–PrediabetesNot significant
Average right kidneyDiabetes–NormalHighly significant
Diabetes–PrediabetesSignificant
Normal–PrediabetesNot significant

aP < .001 very highly significant; P < .01 highly significant; P < .05 significant.

Electrophotonic Imaging Diagram/Screening Analysis (Independent Samples t Test). Electrophotonic Imaging Scientific Laboratory Analysis (Independent Samples t Test). Correlation Analysis (All Parameters). Abbreviations: FBS, fasting blood sugar; df, degrees of freedom. Correlation Analysis (Noteworthy). Abbreviations: FBS, fasting blood sugar; df, degrees of freedom. aP, level of significance, <.05 considered significant. br, correlation coefficient varies between −1 and +1. FBS and EPI Correlation in the 3 Categories. Regression Analysis. Abbreviations: FBS, fasting blood sugar; α, constant; β1, coefficient of variable X1; β2, coefficient of variable X2;…; β, coefficient of variable X. t, t test value; ∊, residual standard error; P < .001 very highly significant; P < .01 highly significant; P < .05 significant; P < .1 not significant. Correlation Analysis Diabetic Males and Females (Separately). Abbreviations: FBS, fasting blood sugar; df, degrees of freedom. Regression Analysis, Diabetics Males and Females (Separately). Abbreviations: FBS, fasting blood sugar; α, constant; β1, coefficient of variable X1; β1, coefficient of variable X2;…; β, coefficient of variable X. t, t test value; ∊, residual standard error; P < .001 very highly significant; P < .01 highly significant; P < .05 significant; P < .1 not significant. Summary of Independent Samples t Test Between the 3 Groups. aP < .001 very highly significant; P < .01 highly significant; P < .05 significant.

Discussion

Fasting blood sugar is an established biochemical test to check the glucose levels in the blood. Research advances for the development of new technologies to provide more accurate diagnosis. EPI is such an approach. This study was carried out to find correlation of various EPI parameters with FBS in different stages of diabetes mellitus type 2. The 3 stages were normal, prediabetic, and diabetic. Summary of results is presented in Table 6. For normal subjects, the FBS value is in the range of 70 to 100 mg/dL. In this condition, as anticipated, no organ is affected due to diabetes. However, the FBS value depends on general health conditions. Total area is representative of general health, and form coefficient is a measure of irregularity in the image. Thus, normal FBS is correlated to these 2 parameters. There could be small variations in FBS within the normal limits in this group. Since no 2 human bodies function alike, there would be variation in area and form coefficient between persons as well within the same person under different conditions, which is natural. The more interesting observation is prediabetics where FBS showed a relationship with pancreas and right kidney. While both organs are prone in diabetes, the relationship in prediabetic stage was not known. It has very recently been reported that the changes in organs, particularly the kidney and pancreas, set in much earlier to diabetes being manifested.[31] Kidney damage from diabetes may thus begin much sooner than previously thought, according to the aforementioned study. A recent study has shown that above-normal sugar levels, which is found in prediabetes, could also result in kidney abnormalities that could finally cause kidney failure.[32,33] Normal blood tests in prediabetic stage may not show any damage but the subtle effects are already seen to be happening in some connected organs. Our study on EPI clearly demonstrates this and significantly substantiates and is in alignment with the latest medical research on the subject. Among diabetics, there were subjects whose diabetes was under control and also subjects whose diabetes seemed uncontrolled. This observation was based on FBS readings. For our study, we considered these 1 groups as one. In this group, we observe that FBS is related both to general EPI parameters as well the organ values. Among organs are immune organs and left kidney. Since immune functions and kidneys are showing lesser energy, they can have overall impact on the general health parameters. Thus, we find correlation with area (measure of general health), entropy (measure of disturbances in the body, it increases with diabetes), and intensity. The results are in consonance with current medical literature and hence EPI may be a research tool to understand the energy status of various organs/systems before the full-fledged manifestation of disease. Earlier studies on EPI were mostly focused on comparison of parameters in the 2 states, viz., pre- and postintervention, where the reference for comparison was the EPI parameter itself.[34] In this study, we have compared the results with known and established biochemical parameters. A workable relationship has been established between the EPI and biochemical parameters and this can help a healer in diagnosis and to assess the effectiveness of treatment.[17] Separate analysis of males and females yields interesting outcomes. Noteworthy among them is the negative correlation with immunity, which is significant in males than in females. This perhaps is due to the fact that immunity in the case of women is more than that of men.[35-37] Very high significant to significant difference in the selected parameters were observed when average values of EPI parameters in the 3 groups were compared by independent samples t test. The difference was more pronounced between normal and diabetics; prediabetics and diabetics. The difference between normal and prediabetics was not significant in most of the EPI parameters except pancreas and immune organs. This is an important observation showing that these 2 organs get affected the most at the prediabetic stage.

Strength of the Study

EPI can be used by practitioners of conventional, alternative, and holistic medicine to have a preliminary idea of glucose levels in blood. This study has indicated changes at the organ level in the prediabetic stage itself. Statistical tools were of great help in arriving at the results. Correlation between FBS and right kidney at the prediabetic stage is the greatest strength of this study. It conforms to the latest research in the modern medical system.[31] It establishes that the EPI technique can be a reliable tool for observing changes at the premanifestation stage of the disease.

Limitations of the Study

Work needs to be taken up on much larger sample sizes especially for people with prediabetes. The distinction between controlled diabetes and uncontrolled diabetes needs to be studied in detail along with the mechanism. The linear regression equation needs to be more refined and residual error reduced.

Future of EPI

Future studies need to focus on integration of EPI with genetics and molecular biology,[38,39] and of course, further developments on EPI itself and software for interpretation may be required for systematic evaluation of many disorders.

Conclusion

EPI can measure subtle energies that would be highly helpful to modern medicine in initiating preemptive action against diseases. The protocol of medicine might change and lead to formulating an energy-based paradigm. More research in the field of EPI will make this change happen sooner than later. Similar research can be undertaken on other serious diseases as well. EPI can be used by both modern medicine practitioners as well alternative medicine therapists and healers to assess the effectiveness of their treatment.
  26 in total

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Journal:  Diabetes Care       Date:  2010-01       Impact factor: 19.112

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Authors:  Katsuiku Hirokawa; Masanori Utsuyama; Yoshio Hayashi; Masanobu Kitagawa; Takashi Makinodan; Tamas Fulop
Journal:  Immun Ageing       Date:  2013-05-15       Impact factor: 6.400

9.  Clinical characteristics, complications, comorbidities and treatment patterns among patients with type 2 diabetes mellitus in a large integrated health system.

Authors:  Kevin M Pantalone; Todd M Hobbs; Brian J Wells; Sheldon X Kong; Michael W Kattan; Jonathan Bouchard; Changhong Yu; Brian Sakurada; Alex Milinovich; Wayne Weng; Janine M Bauman; Robert S Zimmerman
Journal:  BMJ Open Diabetes Res Care       Date:  2015-07-22

10.  Sex, gender and immunosenescence: a key to understand the different lifespan between men and women?

Authors:  Calogero Caruso; Giulia Accardi; Claudia Virruso; Giuseppina Candore
Journal:  Immun Ageing       Date:  2013-05-16       Impact factor: 6.400

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