interpreting health status of mixed population using bca.

Wyno Academic Journal of Medical Sciences Vol. 3, (1), PP. 1- 18 March, 2014 Available online at http://www.wynoacademicjournals.org/med_sciences.html ISSN :2320-1282 Copy Right ©2014 Wyno Academic Journals

INTERPRETING HEALTH STATUS OF MIXED POPULATION USING BCA. Munna Khan1, Shabana Mehfuz1 and Ghazala Perveen Khan1* Electrical Engineering Department Jamia Millia Islamia, New Delhi-110025. Email:[email protected] Accepted Date: 12Th March 2014.

ABSTRACT This paper aimed at developing a prediction equation for Indian and Pakistani Population using BCA Maltron-II BCA, and comparing the clinical validity of the data obtained with the one obtained by using developing Prediction Equation. Till now, scientists have studied the body composition analysis of different ethnicity, race, in different countries, but the BCA of multiple ethnic subjects have not been studied. This paper is aimed at developing the combined prediction equation for Indian and Pakistani subjects. Keywords: R(2.9.2) Software, Bio Electrical Impedance Analysis, Prediction Equation, MALTRON-II, Multiple Regression Analysis, Total Body Water, Fat Free Mass, Impedance Index, Body Mass Index (BMI) Methods: We have utilized the data of 6 Pakistani and 10 Indian individual in developing and building the prediction equation. Different parameters of Human body composition were analyzed to achieve this such as The impedance index (Ht.2/Z), BMI, Total Body Protein (TBPr.), Sex, Wt.(weight), Ht.(height), BCM(Body Composition Mass), ECW(Extracellular weight), age. To achieve this, The Multi Regression Technique was used. The main aim of this technique is to predict dependent variables from a set of independent variables. The no. of equation determines the no. of set of dependent variable. To develop this prediction equation we have used R software (version2.9.2) for TBW and FFM. Multi regression Analysis is an algebraic equation: Y=m1x1+m2x2+m3x3+………..+mnxn+c We have used TBW and FFM as dependent variables and they are used to develop linear model for human body as shown below: TBWf1,f2,f3,f4=(a0Zi f1,f2,f3,f4+a1BMI+a2TBPr.+a3Sex+a4Wt.+a5Ht.+a6BCM+a7ECW+a8age+c0)) FFMf1,f2,f3,f4=( b0Zi f1,f2,f3,f4+ b1BMI+ b2TBPr.+ b3Sex+ b4Wt.+ b5Ht.+ b6BCM+ b7ECW+ b8age+ d0)) The flowchart of the developed linear model is as shown below:

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START

N=16,Wt.,Ht., Zi are weight , height and impedance index of the subjects at frequency f1, f2, f3, f4 where f1=5KHz, f2=50KHz, f3=100KHz, f4=200KHz,

(Zi f1, f2, f3, f4=(Ht.2/Z))k=1…..N No

Yes

Is K=N

K=K+1

TBWf1,f2,f3,f4~(lm(Zi f1,f2,f3,f4+BMI+TBPr.+Sex+Wt.+Ht.+BCM+ECW+age)) FFMf1,f2,f3,f4~(lm(Zi f1,f2,f3,f4+BMI+TBPr.+Sex+Wt.+Ht.+BCM+ECW+age))

TBWf1,f2,f3,f4=(a0Zi f1,f2,f3,f4+a1BMI+a2TBPr.+a3Sex+a4Wt.+a5Ht.+a6BCM+a7ECW+a8age+c0)) FFMf1,f2,f3,f4=( b0Zi f1,f2,f3,f4+ b1BMI+ b2TBPr.+ b3Sex+ b4Wt.+ b5Ht.+ b6BCM+ b7ECW+ b8age+ d0))

STOP

Fig1: Flowchart showing the general process to develop linear model of TBW and FFM for Pakistani subjects at the frequencies of 5 KHz, 50 KHz, 100 KHz and 200KHz

INTRODUCTION Human Body Composition Analysis is necessary to yield data about normal growth, maturity and Life Span. It is a necessary area of research especially for diseased patients. It has been found that the unhealthy eating habits are responsible for the imbalance in body metabolism and body health. Normal people who eat improper food suffer from dehydration, cardiovascular metabolic diseases and certain types of cancer, while malnourished individuals suffer because they are not being provided with proper nutrition. As a result, there is fluid electrolyte imbalance in the body, and are prone to renal and reproductive varying degrees of disorder. To properly understand the problems that these malnourished and underweight individuals are faced with, there is the need to develop the prediction

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equation. This equation can even be used to solve the problem of improper nutrition. As discussed earlier, improper eating habits leads to unhealthy body composition, Assessing the energy intake and expenditure is the aim of the research. Nutritional care plan is important for proper energy intake and its expenditure. Proper energy intakes provide us with the healthy body development and body fat; and the general scenario of eating habits, disorders and body nutritionals. It also gives pharmaceutical/ Food Drug Companies a chance to develop medicine for healthy individual and patients suffering from diseases. In India and Pakistan when it comes to eating habits there is large disparity between people living in different region. These disparities in eating habits have led to different types of cohorts of individual. It has been found that a cohort of individuals who are overweight or obese suffer from Coronary heart disease, Hypertension, TypeII diabetes, Metabolic diseases, certain types of Cancer, and Menstrual irregularities; Whereas the cohort of individuals who are underweight or malnourished suffer from Fluid electrolyte imbalance, and Renal reproductive disorders. Body Composition Analysis of Indians and Pakistanis has been done to analyze the imbalance in the health status of individuals. It has been found that Obesity is the serious problem that the world is facing due to improper food intake. When it comes to determining about the health, both Indians and Pakistanis are facing the unhealthy body composition, because of unhealthy food intake. It is a well known fact that the Human body is composed of 70-75% of water. Many scientists in the past have contributed to the development of Prediction Equation of different ethnicities such as Asia, Africa, and America. As discussed earlier unhealthy food intake is the main cause for this unhealthy Human Body Development. The reason why there’s a lot of unhealthy food intake is that people these days have busy, and hectic days and don’t get the chance to eat proper and healthy food. This unhealthy lifestyle has exposed to a lot of diseases. The thrust of this research is to develop a Prediction Equation for this mixed population. To achieve this, Human Body has been injected with an excitation current of 800µA at different frequencies of 5 KHz, 50 KHz, 100 KHz, and 200 KHz. The black clips are for injecting the current and voltage is measured across the clip marked red. The choice of this current frequency range is because this is the much current the Human Brain Cells can bear. In the present research paper we have used the data that were taken in DRDO and utilized the data of reference [1] BRIEF HISTORY AND LITERATURE REVIEW: A lot of scientists in the past have contributed to the development of Body Composition Prediction Equation for cohorts of individuals belonging to different ages, sex, ethnicities based on their level of physical alertness. Kim et al. in 1994 developed the prediction equation for 84 Japanese boys in the age span of 9-14 years. Nayeli Macius et al. in 2007 developed and cross validated the BIA prediction equation for 155 Mexican subjects. Besides these several other scientist such as Duerenberg in 1991, Kushner in 1992 have also contributed to the development of the Prediction Equation. Till now, combined prediction equations for Indian and Pakistani population have not been developed. This equation was developed giving the hazy idea about the nutritional status of Indian and Pakistani subjects. And one of the main reasons why BCA is done is that though scientists have already developed the equation for %BF, they have not studied the comparative studies of combined Indian and Pakistani subjects. Our comparative work involves just putting the body component in the equations developed by scientists and see how close these values are with the values that are obtained from this instrument.

Subjects and Procedure: Combined clinical data of subjects (Indian and Pakistani) giving the vital information of Human Body parameters such as TBW(Total Body Water), TBF(Total Body Fat),FFM (Fat Free Mass), BCM(Body Composition Mass), TBPr.(Total Body Protein), Wt.(Weight) , Ht.(Height) and other such information were studied through Maltron-II Body Composition Impedance Analyzer. These human body composition data were then utilized to calculate the impedance index. Table [1] below shows the calculated Impedance Index.

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Table1: Clinical data of combined Indian and Pakistani Subjects (n=16) showing the calculated Impedance Index (Ht.2/Z) S.No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Sex Male Male Male Male Male Male Male Male Male Male Female Female Female Female Female Female

age 25 35 46 48 60 31 36 29 30 29 42 34 50 36 23 27

Z5k 700 701 690 840 690 698 705 818 917 847 744 900 846 899 818 835

Z50k 598 610 600 740 600 603 613 683 803 747 744 827 744 706 740 744

Z100k 582 570 580 682 560 562 574 631 757 693 690 771 695 657 697 762

Z200k 500 540 540 680 520 528 546 596 729 682 682 750 646 619 697 677

Zi5k 41.2857 40.2625 39.4565 35.6298 38.9797 40.4355 37.6865 33.282 32.6379 34.1204 28.402 28.801 28.398 28.916 28.993 28.402

Zi50k 48.327 46.268 45.375 40.446 44.8266 46.8059 43.3425 39.8609 37.271 38.688 39.526 31.343 32.29167 32.725 33.88 31.123

Zi100k 49.656 49.515 46.939 43.884 48.028 50.2206 46.2804 43.1458 39.536 41.702 34.371 33.619 37.296 36.567 34.0258 31.12335

Zi200k 57.8 52.266 50.41667 44.032 48.028 53.1545 48.66117 45.67953 45.679 42.375 34.774 34.561 34.469 37.324 34.025 35.03

Statistical Analysis: The body composition parameters of The Human Body was studied through Maltron –II BIA Analyzer , where excitation current of 800µA at frequencies of 5KHz, 50KHz, 100KHz, 200KHz were applied to the source or drivel distal electrode on the hand and foot; and the voltage drop due to impedance is detected on a sensor electrode at the wrist. The figure that is the measurement of the clinical data of the subjects is shown below:

Fig.2: Figure to obtain the clinical data of combined Indian and Pakistani Subjects (n=16) Table 1 shows the Impedance Index calculated for each subject. Finally multiple regression analysis of these data was carried out to develop and design a linear model with the help of R software taking BMI, TBPr., Sex, Wt., Ht., BCM, ECW, age as independent variable. R is an integrated suite of software for data manipulation, calculation and graphical display. It has an effective data handling and storage facilities. So, we have used R software as a tool for carrying linear multi-regression analysis at different frequencies.

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Prediction Equation developed: The obtained equations are of the form: TBWf1,f2,f3,f4=(a0Zi f1,f2,f3,f4+a1BMI+a2TBPr.+a3Sex+a4Wt.+a5Ht.+a6BCM+a7ECW+a8age+c0))…….(A) FFMf1,f2,f3,f4=( b0Zi f1,f2,f3,f4+ b1BMI+ b2TBPr.+ b3Sex+ b4Wt.+ b5Ht.+ b6BCM+ b7ECW+ b8age+ d0))…..(B) Where TBWf1,f2,f3,f4 and FFMf1,f2,f3,f4 is Total Body Water and Fat Free Mass at frequencies where f1=5KHz,f2=50KHz,f3=100KHz,f4=200KHz and Zi(f1,f2,f3,f4) is the calculated (Impedance Index at frequencies f1=5KHz,f2=50KHz,f3=100KHz,f4=200KHz) Table2: Descriptive statistics of combined Indian and Pakistani Subjects (n=16) Variables Mean±S.D. 32.98063±7.19688 TBW 40.08938±19.62077 FFM 23.45±2.877 BMI 10.11625±1.8313 TBPr. 10 males and 6 females Sex 59.75±7.365239 Weight(Wt.) 163.06±7.252298 Height(Ht.) 25.5068±5.264 BCM 14.83±1.9674 ECW 36.125±10.2808 Age 34.10545±5.024913 Zi at 5KHz 39.50629±5.930612 Zi at 50KHz 41.61992±6.4935 Zi at 100KHz 43.6425±7.8208 Zi at 200KHz RESULT AND DISCUSSION: The study that we carried out was able to develop 8 pairs of Prediction Equation. The study formulated the TBW and FFM content of the subjects at different frequencies of 5 KHz, 50 KHz, 100 KHz, and 200 KHz. The developed Prediction Equations for Indian and Pakistani subjects are shown below: 1. TBW 5k= -0.1297Zi5k-0.1334BMI-3.9504TBPr.+0.7891Sex -0.139Wt.+0.5762Ht.+1.4613BCM+1.1719ECW-0.3566age-55.8044…………………(1) 2. TBW 50k= -0.23726Zi50k-0.01005BMI-3.60504TBPr.+1.20438Sex -0.114Wt.+0.48212Ht.+1.49235BCM+1.80386ECW-0.36744age-44.9726……………(2) 3. TBW 100k= -0.26406Zi100k-0.26316BMI-3.98124TBPr.+1.8229Sex -0.01609Wt.+0.46187Ht.+1.51208BCM+1.93353ECW-0.3521age-39.5348………………(3) 4. TBW 200k= -0.3424Zi200k-0.1783BMI-4.2233TBPr.+2.58184Sex -0.1105Wt.+0.4862Ht.+1.8797BCM+1.6429ECW-0.4088age-36.8975………………(4) 5. FFM 5k= 0.014892Zi5k-0.06558BMI+1.003196TBPr.-1.101779Sex -0.010967Wt.-0.019654Ht.+1.278713BCM+0.425966ECW-0.004372age+1.45525………(5) 6. FFM 50k= 0.0228628Zi50k+0.04898BMI+0.9989087TBPr.+1.227228Sex - 0.0092512Wt.+0.0174922Ht.+1.2949738BCM+0.383506ECW+0.0001066age+1.1803797….(6) 7. FFM 100k= 0.01193Zi100k-0.06625BMI+1.018536TBPr.-1.143646Sex -0.02053Wt.+1.278953Ht.+0.427663BCM-0.004425ECW-0.004425age+1.4986565……(7) 8. FFM 200k= -0.006346Zi200k+0.06244BMI+0.9999TBPr.-0.983786Sex -0.009941Wt.-0.028557Ht.+1.305188BCM+0.415156ECW-0.005014age-3.171409………(8)

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TBW5k, TBW50k, TBW100k and TBW200k is Total Body Water of body of subjects at 5 KHz, 50 KHz, 100 KHz and 200 KHz frequencies respectively. And FFM 5k, FFM 50k, FFM 100k, and FFM 200k is Fat Free Mass of body of subjects at 5 KHz, 50 KHz, 100 KHz and 200 KHz frequencies respectively. Zi 5k, Zi 50k, Zi 100k, Zi 200k is Impedance Index (Ht.2/Z) of body of subjects at 5 KHz, 50 KHz, 100 KHz and 200 KHz frequencies respectively. BMI is Body Mass Index, TBPr. Is theTotal body Protein content of the body of the subjects. Value of sex is taken as 1 for male and 0 for female. Wt., Ht. BCM, ECW is body weight, height, Body Cellular Mass, Extra Cellular Mass of the subjects respectively. The figure, showing the statistical analysis of the subject is shown in table below: Table3: Descriptive statistics of combined Indian and Pakistani Subjects (n=16) Freque ncy 5KHz

Residual error 3.046 on 6df

Multiple R2 0.9283

Adjusted R2

TBW50k= -0.23726, Zi50k -0.01005, BMI 3.60504, TBPr +1.20438, Sex -0.114, Wt +0.48212, Ht +1.49235, BCM +1.80386, ECW - 0.36744, Age -44.9726

50KHz

2.989 on 6df

0.931

0.8275

3.

TBW100k= -0.26406, Zi100k -0.26316, BMI -3.98124, TBPr.+1.8229, Sex – 0.01609, Wt +0.46187, Ht + 1.51208, BCM + 1.93353, ECW -0.3521, Age -39.5348

100KHz

2.983 on 6df

0.9313

0.8283

4.

TBW200k= -0.3424, Zi200k -0.1783, BMI -4.2233, TBPr.+2.58184, Sex -0.1105, Wt +0.4862, Ht +1.8797, BCM +1.6429, ECW-0.4088, Age -36.8975

200KHz

2.846 on 6df

0.9374

0.8436

S.No . 1.

Prediction Equation developed

2.

TBW5k= -0.1297, Zi5K -0.1334, BMI -3.9504, TBPr.+0.789 Sex -0.0139, Wt.+0.5762, Ht.+1.4613, BCM+1.1719, ECW -0.3566, Age -55.8044

0.8209

5.

FFM5k= 0.014892, Zi5k -0.06558, BMI +1.003196, TBPr. -1.101779, Sex -0.010967, Wt. -0.019654, Ht.+1.278713, BCM + 0.425966, ECW - 0.004372, Age +1.45525

5KHz

0.2492 6df

on

0.9997

0.9992

6.

FFM50k = 0.0228628, Zi50k +0.04898, BMI +0.9989087, TBPr.+1.227228, Sex – 0.0092512, Wt. + 0.0174922, Ht.+1.2949738, BCM +0.383506, ECW+0.0001066, Age +1.18037

50KHz

0.2442 6df

on

0.9997

0.9992

7.

FFM100k= 0.01193, Zi100k - 0.06625, BMI +1.018536, TBPr. -1.143646, Sex -0.02053, Wt + 1.278953, Ht.+0.427663, BCM -0.004425, ECW -0.004425, Age +1.4986565

100KHz

0.2494 6df

on

0.9997

0.9992

8.

FFM200k= -0.006346, Zi200k +0.06244, BMI +0.9999, TBPr.-0.983786, Sex - 0.009941, Wt. – 0.028557, Ht.+1.305188, BCM +0.415156, ECW -0.005014, Age 3.171409

200KHz

0.2508 6df

on

0.9997

0.9991

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GRAPHICAL ANALYSIS: The graphical interpretation of data is made up of a Scatter matrix plot, Random scatter distribution, Normal distribution, Scale location plot, Residual verses, Leverage plot and Standardized verses, Cook’s distance plot at different frequencies.

11

45

65

20 30

30

35

60

11

45

65

20 30

30

60

25

TBW

Zi50k

20 28

35

Zi5k

BMI 11

11

BMI

TBPr.

Sex

Wt.

45

45

Wt.

Ht.

20

BCM

12 16

20

BCM

ECW

30

age 25 40

20 28

0.0 0.8

155

12 16

age

30

60

60

ECW

12 16

35

35

155

Ht.

155

65

65

Sex

0.0 0.8

0.0 0.8

7

7

TBPr.

20 28

28 36

25

TBW

7

40

7

40

28 36

25 40

20 28

0.0 0.8

155

12 16

Fig.3: Scatter Plot Matrix distribution of body composition of Indian and Pakistani subjects; showing the relationship between Total Body Water(TBW) formed by linear model TBWf1,f2,f3,f4=(a0Zif1,f2,f3,f4+a1BMI+a2TBPr.+a3Sex+a4Wt.+a5Ht.+a6BCM+a7ECW+a8age+c0)where f1=5KHz, f2=50KHz, f3=100KHz and f4=200KHz.

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Fig.4: Residual vs Fitted plot of body composition of Indian and Pakistani subjects; showing the relationship between Total Body Water(TBW) formed by linear model TBWf1,f2,f3,f4=(a0Zi f1,f2,f3,f4+a1BMI+a2TBPr.+a3Sex+a4Wt.+a5Ht.+a6BCM+a7ECW+a8age+c0) where f1=5KHz, f2=50KHz, f3=100KHz and f4=200KHz.

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Fig.5: Normal plot of body composition of Indian and Pakistani subjects; showing the relationship between Total Body Water(TBW) formed by linear model TBWf1,f2,f3,f4=(a0Zi f1,f2,f3,f4+a1BMI+a2TBPr.+a3Sex+a4Wt.+a5Ht.+a6BCM+a7ECW+a8age+c0) where f1=5KHz, f2=50KHz, f3=100KHz and f4=200KHz.

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Fig.6: Scale Location plot of body composition of Indian and Pakistani subjects; showing the relationship between Total Body Water(TBW) formed by linear model TBWf1,f2,f3,f4=(a0Zi f1,f2,f3,f4+a1BMI+a2TBPr.+a3Sex+a4Wt.+a5Ht.+a6BCM+a7ECW+a8age+c0) where f1=5KHz, f2=50KHz, f3=100KHz and f4=200KHz

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2

Residuals vs Leverage

0

1 0.5 0.5 1

-1

Standardized residuals

1

2

-2

1

5

Cook's distance 0.0

0.2

0.4

0.6

0.8

Leverage lm(TBW ~ Zi5k + BMI + TBPr. + Sex + Wt. + Ht. + BCM + ECW + age)

Fig.7: Residual vs Leverage plot of body composition of Indian and Pakistani subjects; showing the relationship between Total Body Water(TBW) formed by linear model TBWf1,f2,f3,f4=(a0Zi f1,f2,f3,f4+a1BMI+a2TBPr.+a3Sex+a4Wt.+a5Ht.+a6BCM+a7ECW+a8age+c0) where f1=5KHz, f2=50KHz, f3=100KHz and f4=200KHz

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45

65

20 30

30

28 36

60 20 50

28 36

FFM

20 28

Zi5k

7 10

BMI

0.8

TBPr.

65

0.0

Sex

45

Wt.

20 30

155

Ht.

16

BCM

60

12

ECW

30

age 20 50

20

28

0.0

0.8

155

12

16

Fig.8: Scatter Plot Matrix distribution of body composition of Indian and Pakistani subjects; showing the relationship between Fat Free Mass(FFM) formed by linear model FFMf1,f2,f3,f4=( b0Zi f1,f2,f3,f4+ b1 BMI+ b2TBPr.+ b3Sex+ b4Wt.+ b5Ht.+ b6BCM+ b7ECW+ b8age+ d0)) where f1=5KHz, f2=50KHz, f3=100KHz and f4=200KHz.

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Fig.9: Residual vs Fitted plot of body composition of Indian and Pakistani subjects; showing the relationship between Fat Free Mass(FFM) formed by linear model FFMf1,f2,f3,f4=( b0Zi f1,f2,f3,f4+ b1BMI+ b2TBPr.+ b3Sex+ b4Wt.+ b5Ht.+ b6BCM+ b7ECW+ b8age+ d0)) where f1=5KHz, f2=50KHz, f3=100KHz and f4=200KHz

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Fig.10: Normal plot of body composition of Indian and Pakistani subjects; showing the relationship between Fat Free Mass(FFM) formed by linear model FFMf1,f2,f3,f4=( b0Zi f1,f2,f3,f4+ b1BMI+ b2TBPr.+ b3Sex+ b4Wt.+ b5Ht.+ b6BCM+ b7ECW+ b8age+ d0)) where f1=5KHz, f2=50KHz, f3=100KHz and f4=200KHz.

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Fig.11: Scale-Location plot of body composition of Indian and Pakistani subjects; showing the relationship between Fat Free Mass(FFM) formed by linear model FFMf1,f2,f3,f4=( b0Zi f1,f2,f3,f4+ b1BMI+ b2TBPr.+ b3Sex+ b4Wt.+ b5Ht.+ b6BCM+ b7ECW+ b8age+ d0)) where f1=5KHz, f2=50KHz, f3=100KHz and f4=200KHz.

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Fig.12: Residual vs Leverage plot of body composition of Indian and Pakistani subjects; showing the relationship between Fat Free Mass(FFM) formed by linear model FFMf1,f2,f3,f4=( b0Zi f1,f2,f3,f4+ b1BMI+ b2TBPr.+ b3Sex+ b4Wt.+ b5Ht.+ b6BCM+ b7ECW+ b8age+ d0)) where f1=5KHz, f2=50KHz, f3=100KHz and f4=200KHz. Table 4: Comparative study of measured and predicted value of TBW at different frequencies of participants (N= 16) S.No. Sex age TBW TBW5k TBW50k TBW100k TBW200k Male 49.09401 47.55384 1 25 45.4 41.2857 47.9404 Male 38.6944 37.86934 2 35 39.7 40.2654 38.945 Male 40.60126 39.37722 3 46 43.2 39.4565 39.5415 Male 44.27001 43.75121 4 48 43.2 35.629 42.92177 Male 29.77447 28.64927 5 60 25.8 38.9797 29.80401 Male 38.03258 36.35454 6 31 36.9 40.4355 36.926 Male 36.4542 34.96652 7 36 35.81 37.6855 34.9899 Male 35.13663 34.54494 8 29 33.16 33.282 34.0744 Male 34.17812 32.08057 9 30 33.33 32.6379 32.99497 Male 35.33404 34.89186 10 29 34.46 34.1204 34.26304 Female 42 24.65308 23.58385 11 24.9 28.402 23.4462 Female 34 28.23215 27.79132 12 26.9 28.801 28.0213 Female 50 24.88895 24.84491 13 26.95 28.398 24.7797 Female 36 29.56065 29.07768 14 27.92 28.916 29.59203 Female 23 24.55858 24.93375 15 25.43 28.9926 28.38476 Female 57 26.15504 25.67349 16 24.54 28.402 25.67716

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Table 5: Comparative study of measured and predicted value of FFM at different frequencies of participants (N= 16) S.No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Sex Male Male Male Male Male Male Male Male Male Male Female Female Female Female Female Female

age

FFM

FFM5k

FFM50k

FFM100k

FFM200k

25 35 46 48 60 31 36 29 30 29 42 34 50 36 23 57

62.6 55.7 57.7 57.7 53.3 51.97 49.47 47.34 50 50.52 41.4 39.26 36.57 37.52 38.73 36.41

64.18962 56.979 59.2342 58.99302 54.7997 53.32278 50.99615 48.543 51.733 52.0955 42.53651 40.71196 37.96865 39.08344 40.0745 37.92734

72.69007 63.78379 67.1253 65.784 62.591 61.529 58.488 55.342 57.907 58.548 49.3601 45.749 43.3206 44.346 45.702 42.978

62.72044 55.51203 57.79616 57.48997 53.37422 51.86493 49.56269 47.13 50.23172 50.61842 41.20165 39.30186 36.63834 37.7642 38.7516 36.56515

62.70602 55.5183 57.807 57.529 53.344 51.766 49.573 47.1509 50.233 50.6506 41.2137 39.6193 36.619 37.779 38.742 36.586

CONCLUSION: The Human body composition parameters of the samples (16 subjects) measured through Maltron-II Body Composition Impedance Analyzer at frequencies of 5KHz, 50KHz, 100KHz and 200KHz were utilized to obtain BIA equation. To do the same, Multiple Regression Analysis was carried out on clinical data through R software (version 13.1.2) to develop linear models shown by equation (A) and (B). The Prediction Equations developed at different frequency were shown by equations (1) to (8) and a comparative study was done. Table (4) and Table (5) shows the comparisons done between clinical data and the results of TBW (Total Body Water) and FFM (Fat Free Mass) developed at different frequencies. From the Table it is seen that the data obtained through the Developed Prediction Equation is very close to the clinical data obtain through BIA analyzer. The major advantage of this instrument is that it is safe, rapid, non-invasive, and portable and requires minimal operator training. Through the results obtained it is observed that the results obtained at higher frequencies were much accurate to clinical results rather than the result obtained at lower frequencies. This is due to the fact that at low frequency the current cannot bridge the cell membrane and will pass predominantly through Extra Cellular space, whereas at high frequency penetration of cell membrane occurs and the current is conducted by both Extra Cellular Water (ECW) and Intra Cellular Water (ICW). Based on the BIA equations, general ideas about the dietary habits of Indian and Pakistani subjects were carried out. And the analysis done obtained the health status of the subjects. The study done is helpful because as we know the Human body needs certain amount of Proteins, Calories and Energy. So this study has helped in analyzing health status of The Human Body and give pharmaceutical Companies useful data to develop Food Supplements for healthy individuals and patients suffering from diseases as the case may be. ACKNOWLEDGEMENT: The authors express their sincere gratitude to all those who participated in this study. Without the useful guidance of Supervisor Professor Munna Khan of The Department of Electrical Engineering, Jamia Millia Islamia; CoSupervisor Dr. Shabana Mehfuz of The Department of Electrical Engineering, Jamia Millia Islamia, it might not have been possible to collect the useful clinical data. This work is an extension part of the theses of The Author Ghazala Perveen Khan. CORRESPONDING AUTHOR: GHAZALA PERVEEN KHAN3* 4/154K Hamdard Nagar-A, Near Masjid Fatima, Aligarh-202002 Email: [email protected]

18. Med. Sci.

REFERENCES Khan GP. Thesis, under the supervision of Professor Munna Khan and Dr. Shabana Mehfuz; Prediction of Body Composition of Indian Population using Bio Electrical Impedance Analysis, Jamia Millia Islamia; 2013 Heyward VH, Wagner DR. Book Applied Body Composition, 2nd Edition.Champain, IL: Human Kinetics, 2004 Haas VK, Allen JR, Kohn MR, Clarke SD, Zhang S, Briody JN, Gruca M, Madden S, Müller MJ, Gaskin KJ. Total body protein in healthy adolescent girls: validation of estimates derived from simpler measures with neutron activation analysis. The American Journal of Clinical Nutrition (2007). Vol-85(1): pp-66-72 Kehoe SH, Krishnaveni GV, Lubree HG, et al. Prediction of percentage body fat from skin skin fold and Bio Impedance measurements in Indian school children. European Journal of Clinical Nutrition 2011 Dec; 65(12):126370. Khan M, Mehfuz S, Khan GP. Bio Electrical Impedance Analysis(BIA) for Assessing TBW and FFM of Indian Females accepted for Publication Feb-2014; International Journal of Computational Engineering Research(IJCER) Khan G, Khan M, Mehfuz S. Multifrequency Bioelectrical Impedance Analysis for assessing TBW and FFM of Indian Males. International transactions in Mathematical Sciences and Computer. Vol 4(1) Jan-June 2011 Khan G, Khan M, Mehfuz S. Development of Bioelectrical Impedance Analysis Equations (BIA) Equations to Predict Body Composition of Indian Males. World Applied Programming, Vol (3), Issue (1), January 2013. 14-33 ISSN: pp 2222-2510. Khan G, Khan M, Mehfuz S. Developing linear multiple regression model of Indian males and validating the results with bia analyser. Journal of Medical Sciences Vol. 1(1), pp. 12-24 Dec. 2012. Khan G, Khan M, Mehfuz S. Comparitive study of 2C molecular level, 3C water molecular level and 3C mineral molecular level of Indian subjects. International Journal of Advancements in Research & Technology, Volume 2, Issue2, February-2013 1 ISSN 2278-7763 Khan M, Reggie OH, Pohlman RL, Goldstein DB, Guha S.K. Multi-Dimension Applications of Bioelectrical Impedance Analysis. International Journal of Exercise Physiology (Online) 2005; February Vol.8 (1): pp 56-71. Patil BR, Patkar DP, Mandlik SA, Evans et.al. Validation of Bio Impedance Analysis Equation to Skin Fold and BIA prediction equation, Journal of Medical Engineering and Technology 2011 Feb ;Vol.35, pp 109-114. Ritchie CB and Davidson RT Regional body composition in college-aged Caucasians from anthropometric measures, Nutrition & Metabolism 2007, doi:10.1186/1743-7075,pp:4-29 Shah AH and Bilal R; Body composition, its Significance and model for assessment:Pakistan Journal of Nutrition 2009.Vol-2,pp:198-202.