Vol.2, No.7, 741-747 (2010) Natural Science
http://dx.doi.org/10.4236/ns.2010.27093
Comparison of factor loadings for anthropometric and
physiometric measures among type 2 diabetic males,
pre- and post-menopausal females in North Indian
Punjabi population
Badaruddoza*, Basanti Barna, Amarjit Singh Bhanwer
Department of Human Genetics, Guru Nanak Dev University, Amritsar, India; *Corresponding Author:
Received 10 April 2010; revised 18 May 2010; accepted 23 May 2010.
ABSTRACT
Background: The objective of the present study
was to compare the relationship of anthropom-
etric and physiometric characteristics using
principal component factor analysis among
three groups of type 2 diabetic subjects such as
males, pre- and post-menopausal females in
North Indian Punjabi population. Method: A total
of 349 type 2 diabetic subjects (males 157; fe-
males 192; 88 pre- and 104 post-menopausal)
were ascertained for the present study. Different
anthropometric and physiometric measure-
ments were taken. Principal component factor
analysis (PCFA) was applied to identify the
components which are more close to type 2
diabetes among the three groups. Results:
PCFA revealed five uncorrelated components
which explained 79% of the total variance
among diabetic males and six unrelated com-
ponents which explained 78% of the total vari-
ance among pre- and post-menopausal females.
The important two factors could be identified as
central obesity (factor 1) and blood pressure
(factor 2) among these three groups. Conclu-
sion: Higher clustering of obesity and blood
pressures were found in diabetic males as com-
pared to pre- and post-menopausal diabetic
females in North Indian Punjabi population
whereas, waist to hip ratio (WHR) has maximum
loading in post-menopausal females as com-
pared to others.
Keywords: Factor Analysis; Blood Pressure; Type
2 Diabetes; Anthropometry; Punjabi Population
1. INTRODUCTION
The relationships between type 2 diabetes mellitus
(T2DM), anthropometric variables and blood pressures
are statistically complex [1,2]. Strong inter-correlation
between anthropometric and physiometric variables cre-
ates complexities in the analysis and interpretation of
independent associations of these variables with the de-
velopment of type 2 diabetes. Principal Component
Factor Analysis (PCFA) is the technique to reduce a
large number of variables to a smaller number of factors
which are more closely associated with antecedent [2-4].
The objective of the present study using principal com-
ponent factor analysis is to compare the relationship
between anthropometric and physiometric components
with diabetic males, pre- and post-meno-pausal females
in North Indian Punjabi population. The attention has
also been given to find out which factors can be used as
significant predictors of T2DM.
2. MATERIALS AND METHODS
Present study was conducted at the different clinical
centres such as Heart Station and Diabetic Clinic, A.P.
Hospital and Heart Care Centre, Diabetic Clinic and
Research Institute in Amritsar district in the state of
Punjab among Punjabi population. Punjabi population
may be defined as similar genotype groupings and ag-
gregate of similar cultural practices, life style pattern,
social influence and similar ethnic characteristics with
Punjabi language speaking and at least reside in Punjab
for the last 20 years. A total of 349 type 2 diabetic indi-
viduals participated in the baseline examination for the
present study which occurred from October 2008 to
September 2009. Among total individuals 157 and 192
are males and females respectively whereas, among fe-
males 88 and 104 are pre- and post-menopausal. All par-
ticipants provided written informed consent.
2.1. Anthropometric Measurements
Actual age and age on the onset of the disease were re-
corded from the subject’s health card provided by the

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clinical centres. Height, weight, circumferences of waist
(WC), hip (HC), arm (AC) and Calf (CC) and four skin
fold thickness (biceps, triceps, sub-scapular and su-
pra-iliac) were taken by female author on each individ-
ual using standard anthropometric techniques and tools
[5,6]. Height and weight were measured to the nearest
0.5 cm and 0.1 kg respectively. Body mass index (BMI)
was calculated for an estimate of overall adiposity using
the formula: BMI = weight (kg)/height (m2). Waist and
Hip circumference (WC and HC) for an estimate of cen-
tral obesity [7] were measured to the nearest 0.5 cm with
a steel tape. Waist to hip ratio (WHR) was calculated
using the standard formula: WHR = WC (cm)/HC (cm).
A Lange skinfold calliper was used to measure the skin-
folds to the nearest 0.2 mm. Two subsequent measure-
ments were taken and averages were used in the analysis.
2.2. Physiometric Measurements
Left arm blood pressures (first phase systolic and fifth
phase diastolic) were taken from each participant with
standard mercury sphygmomanometer after a 5 min rest.
The average of the two subsequent measurements was
used for analysis. All efforts were made to minimize the
factors which affect the blood pressure like anxiety, fear,
stress, laughing and recent activity [8]. Mean arterial
blood pressure (MBP) was calculated for each of the two
readings taken for SBP and DBP by using the formula:
MBP = SBP ＋ (SBP-DBP)/3 [9]. The radial artery at
the wrist was used to count the pulse. It was counted
over one minute. The difference of SBP and DBP was
used as pulse pressure.
3. STATISTICAL ANALYSIS
Descriptive statistics such as means, standard deviations
and coefficient of skewness were calculated for all vari-
ables. All statistical analysis including factor analyses
were conducted by SPSS (Statistical Package for Social
Sciences, version 17.0, SPSS Inc. USA). Each of the
anthropometric and physiometric variables is highly
inter-correlated with each others and creates a methodo-
logical problem for analysis the data. PCFA is used when
variables are highly correlated and this multivariate sta-
tistical tool able to reduce a large number of inter-corre-
lated variables to a smaller number of principal compo-
nents which account for most of the variance in the data
[10,11]. Factor analysis has done on the basis of correla-
tion matrix which helps to understand the amount of
association between the variables, factor extraction and
orthogonal rotation to make factors easily interpretable.
Hence, PCFA was used to extract uncorrelated factors
and varimax rotation, which is an orthogonal rotation in
which the factors are assumed to act independently, was
used in the present study. Factor loadings were equiva-
lent to the correlation coefficients between the variables
(rows) and factors (columns). The final factors pattern
was interpreted using factor loadings of ≥ 0.4. Extracted
factors or number of factors to be retained was based on
eigenvalue criteria ≥ 1.0. Eigenvalues indicate the
amount of variance explained by each factor. A factor
with low eigenvalue has a little contribution to explain
the variances in the variables and may be ignored. The
first and second principal components were identified
through largest and second largest amount of variance in
the data and so on. Communality is the squared multiple
correlation for the variable (as dependent) using as pre-
dictors. Hence, the communality estimates is the meas-
ure the percent of variance in a given variable explained
by all factors. A communality of 0.75 and 0.25 consid-
ered large and low respectively. Low communality indi-
cates variables are negligibly related to each other. The
probability values less than or equal to 0.05 (two-tailed)
were considered to be significant.
4. RESULTS
Table 1 presents the mean, standard deviation (SD) and
skewness of anthropometric and physiometric variables.
All right skewed distributions have converted to a nor-
mal distribution by square root transformation whereas;
reciprocal transformation is used for left skewed distri-
bution among type 2 diabetic males, pre- and post-
menopausal females. The highest mean age for onset of
T2DM was found among post-menopausal females
(52.46 ± 6.22years) and the lowest mean age for the on-
set of disease was found among pre-menopausal females
(36.97 ± 5.96 years) as compared to males (45.19 ± 7.79
years). The other highest mean values of important an-
thropometric indicator such as BMI, hip circumference,
biceps skinfold, triceps skinfold and arm circumference
were found among diabetic post-menopausal females as
compared to males and pre-menopausal females. The
diabetic males have higher mean values for WHR and
waist circumference. The physiometric variables such as
SBP, DBP, pulse rate and pulse pressure have not shown
any specific trend among three groups of diabetic sub-
jects. Bivariate correlations of the traits were examined
among type 2 diabetic males, pre- and post-menopausal
females and are presented in Tables 2 to 4. Waist cir-
cumference, hip circumference, biceps skinfold, triceps
skinfold, arm circumference and calf circumference with
Weight and BMI; hip circumference, biceps skinfold,
triceps skinfold, arm circumference and calf circumfer-
ence with waist circumference have been found signifi-

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743
Table 1. Descriptive Statistics of Anthropometric and Physiometric variables among type 2 diabetic male, pre- and post-menopausal
females in the present study population (n = 349).
FEMALE (N = 192)
MALE (N = 157) PRE-MENOPAUSAL (N = 88) POST-MENOPAUSAL (N = 104)
VARIABLES
MEAN SD SKEW-
NESS MEAN SD SKEWNESSMEAN SD
SKEW-
NESS
Age (yrs) 53.994 8.093 0.466 46.89 7.85 −0.009 58.39 7.49 0.45
Onset age (yrs) 45.197 7.792 0.836 36.97 5.96 −0.28 52.46 6.22 1.02
Height (cm) 169.131 8.582 −5.437 154.95 5.599 0.19 155.80 6.95 1.57
Weight (Kg) 76.697 6.976 0.533 63.52 7.55 0.36 67.70 8.20 0.43
BMI(kg/m2) 26.555 3.870 0.370 26.25 3.34 0.21 28.00 5.60 0.62
WHR 0.941 0.0732 0.586 0.88 0.07 0.75 0.87 0.07 −0.17
Waist circumference(cm) 96.014 8.512 0.267 87.23 9.90 0.597 90.26 9.32 0.49
Hip circumference (cm) 101.94 9.056 0.242 99.18 9.59 0.68 103.07 10.97 0.52
Biceps skinfold (mm) 10.806 5.968 1.745 13.11 5.05 0.395 13.26 5.58 1.03
Triceps Skinfold (mm) 14.005 5.053 3.201 17.12 5.19 0.299 17.54 6.76 0.58
Subscapular skinfold (mm) ------- ------- -------- 24.94 4.88 −0.39 26.66 5.399 0.04
Supra-iliac skinfold (mm) ------- -------- -------- 21.37 3.82 −0.14 24.37 4.43 0.10
Arm Circumference (cm) 27.838 3.192 0.282 27.27 3.39 0.18 28.20 3.90 0.87
Calf circumference (cm) 33.548 3.868 0.502 31.97 3.62 0.24 32.34 3.67 0.04
Systolic blood pressure
(mmHg) 124.656 9.266 1.802 121.72 8.82 0.86 124.95 9.49 0.798
Diastolic blood pressure
(mmHg) 80.083 10.475 1.090 79.55 9.93 −0.12 79.70 9.81 1.04
Mean Blood Pressure
(mmHg) 95.924 9.384 2.081 93.56 9.67 0.57 96.14 8.27 2.695
Pulse Rate 83.229 8.471 0.251 85.10 8.85 0.12 83.98 8.24 0.15
Pulse Pressure 44.790 7.232 1.660 42.27 7.58 0.77 46.41 7.08 1.296
Significant at least at p ≤ 0.05
SD = Standard Deviation
cantly associated at least 5% level (p < 0.05) among all
diabetic males, pre- and post-menopausal females. SBP
and DBP were found to be significantly associated with
weight, BMI, waist circumference, hip circumference,
biceps skinfold and triceps skinfold at least 5% level (p
< 0.05) among diabetic pre- and post-menopausal fe-
males. Whereas, WHR was found significantly associ-
ated (p < 0.05) with other anthropometric variables
among only type 2 diabetic males.
The comparison of factor loading pattern of six factors
(components) is presented in Table 5 among diabetic
males, pre- and post-menopausal females. Only variables
with factor loading greater than or equal to 0.4 were
considered for present interpretation among three groups.
After Varimax rotation, weight, BMI, waist circumfer-
ence, hip circumference, arm and calf circumferences
are relatively large and positively loaded (> 0.7) on fac-
tor 1 among males, pre- and post-menopausal females.
However, on factor 1 highest loading was found in
weight (0.944) for males, hip circumference (0.883) for
pre-menopausal females and BMI (0.940) for post-
menopausal females. The physiometric variables such as
SBP, DBP and pulse pressure are grouped together and
loaded positively on factor 2 among three groups.
Maximum loading has found for SBP (> 0.90) on factor
2 for all three groups, whereas loading of DBP for this
factor is just above the cut-off value (0.4) for post-
menopausal females. Both mean age (actual age and
onset age of type 2 diabetes) have found maximum
loading (0.952 and 0.940) on factor 3 among males,
whereas, skinfold thickness (biceps, triceps, sub-scapular
and supra-iliac) grouped together and loaded signifi-
cantly among pre- and post-menopausal females. Only
triceps skinfold and biceps skinfold among males have
positive loading on factor 4, whereas, actual mean age
and the mean age of onset of the disease have grouped
for higher positive loading among pre- and post-meno-
pausal females. Only WHR has positive loading on fac-
tor 5 among males, whereas, WHR and waist circum-
ference for pre-menopausal females and WHR, height
and waist circumference for post-menopausal females
have positive loading on this factor. However WHR has
maximum positive loading (≥ 0.80) among pre- and
post-menopausal females but for males it is just above
cut-off value (0.55). Only Height and pulse rate have
positive loading on factor 6 among pre- and post-meno-
pausal female whereas, all variables are extracted on this
factor among males. The five factors explained 79% of
the total variance among males in which the first two
factors cumulatively explained 54% of the total variance.
Whereas, the six factors explained 78% of the total
variance among pre- and post-menopausal females in
which first two factors cumulatively explained 48% and
49% of the total variance respectively. The eigenvalue of
the first two factors have also been seen maximum
among males, pre- and post-menopausal females. The

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Table 2. Inter-correlation matrix of selected anthropometric variables among males of Type 2 Diabetes Mellitus (T2DM).
VARI-
ABLES
Age
(yrs)
OA
(yrs)
Ht
(cm)
Wt
(Kg)
BMI
(kg/m2) WHR WC (cm)
HC
(cm)
BSkn
(mm)
T Skn
(mm)
AC
(cm)
CC
(cm)
SBP
(mmHg)
DBP
(mmHg)
MBP
(mmHg) PR PP
Age (yrs) .81* −.05 −.06 −.007 .03 .05.03−.003 −.08 −.04 −.09 −.05 −.05 −.10 −.12 .02
Onset age
(yrs) −.03 .09 .16 .09 .17.16.09.01.10.04−.08 −.005 −0.12 −.13 −.06
Height (cm) .09 −.15 −.12 −.06 −.007 −.17 −.27 −.02 −.03 −.007 −.07 −0.04 −.04 .03
Weight (Kg) .91* .48* .87*.84*.58*.40*.73*.73*.08 .18 0.11 −.11 .05
BMI(kg/m2) .50* .85*.80*.57*.46*.73*.73*.07 .18 0.11 −.12 .03
WHR .68*.20*.33*.40*.42*.34*.03 .12 0.04 −.19 −.001
Waist cir-
cumference
(cm)
.82*.62*.45*.75*.70*.01 .13 0.05 −.20* −.004
Hip cir-
cumference
(cm)
.60*.32*.70*.70*−.006.09 0.05 −.09 −.009
Biceps skin-
fold (mm) .72*.64*.50*.003 .07 −0.02 −.13 .011
Triceps
Skinfold
(mm)
.46*.32*−.04 .04 −0.04 −.13−.03
Arm Cir-
cumference
(cm)
.68*.03 .13 0.06 −.13 −.005
Calf cir-
cumference
(cm)
.001 .04 0.01 −.10 −.001
Systolic
blood pres-
sure
(mmHg)
.73 0.86* .08.90*
Diastolic
blood pres-
sure
(mmHg)
0.80* −.004 .40*
Mean Blood
Pressure
(mmHg)
.053.67*
Pulse Rate .09*
Pulse Pres-
sure
Significant at least at p ≤ 0.05; OA = Onset age, yrs = years, Ht = Height, Wt = Weight, BMI = Body Mass Index, WHR = Waist Hip Ratio, WC =
Waist Circumference, HC = Hip Circumference, BS = Biceps skinfold, TS = Triceps skinfold, SS = Sub-scapular skinfold, SiS = Supra-iliac skinfold,
AC = Arm Circumference, CC = Calf Circumference, SBP = Systolic Blood Pressure, DBP = Diastollic Blood Pressure, MBP = Mean Blood Pressure,
PR = Pulse Rate, PP = Pulse Pressure.
common greater communality estimates (> 0.70) have
found on age, onset age of disease, weight, BMI, waist
circumference, hip circumference, triceps skinfold, SBP
among three groups. WHR has maximum communality
estimates among pre- and post-menopausal females.
5. DISCUSSION
The present quantitative analysis have shown that which
of the anthropometric and physiometric traits (BMI,
WHR, weight, waist circumference, hip circumference,
skinfolds, SBP, DBP, and pulse pressure) are more
closely associated and act as a good predictors for fur-
ther risk among three groups of T2DM individuals such
as males, pre- and post-menopausal females in North
Indian Punjabi population. The present study also pro-
vides through PCFA among three groups that which of
the traits would require more attention to clinicians for
raised risk of T2DM.
The many previous studies suggested that obesity, over-
weight, glucose intolerance, hypertension and elevated
blood pressures are closely associated with T2DM [12-
18].The present analysis showed a common association of
BMI, WHR, waist circumference, hip circumference and
subcutaneous fat with T2DM incidence among males,
pre-and post-menopausal females.

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745
Table 3. Inter-correlation matrix of selected variables among pre-menopausal females of Type 2 Diabetes Mellitus (T2DM).
VARIABLES Age
(yrs)
OA
(yrs)
Ht
(cm)
Wt
(Kg)
BMI
(kg/m2) WHR WC
(cm)
HC
(cm)
BSkn
(mm)
T Skn
(mm)
SS
(mm)
SiS
(mm)
AC
(cm)
CC
(cm)
SBP
(mmHg)
DBP
(mmHg)
MBP
(mmHg) PR PP
Age (yrs) .54* −.08 −.05 −.14 .10 .08.03.05.07.06.04−.09 −.14.13 −.04 .02 −.06.22
OA (yrs) −.03 −.03 −.06 −.05 −.03.02 −.02.08 −.006 .19−.04.06.17 .10 .10 .05 .19
Ht (cm) .30−.08 .10 .06.0097.013.04.16.06.15.09.02 .03 .03 .22 .006
Wt (Kg) .83* .15 .80*.82*.53*.42.36*.50*.70*.51*.17 .11 .19 −.06.13
BMI(kg/m2) .08 .68*.73*.58*.45.34*.43*.68*.61*.16 .09 .18 −.13.15
WHR .51*−.16 .009−.12.10.14 −.03 −.02−.05 −.02 .007 .04 −.08
WC(cm) .75*.48*.30*.22*.42*.58*.46*.18 .20* .24* −.08.08
HC (cm) .56*.45*.21*.40*.69*.53*.24*.24* .28* −.14.14
BS (mm) .80*.45*.46*.66*.49*.15 .03 .099 −.30*.19
T S (mm) .38*.36*.54*.38*.13 −.07 .04 −.25*.25
SS (mm) .66*.24*.16−.05 −.05 −.06 −.19 −.03
SiS (mm) .35*.31*.12 .09 .10 −.05.10
AC (cm) .66*.008−.03 .03 −.13.02
CC (cm) .008.04 .05 −.14 −.008
SBP (mmHg) .71* .91* −.09.87*
DBP
(mmHg) .85* .09 .29*
MBP
(mmHg) −.009.65*
P R −.17
P P
Significant at least at p ≤ 0.05; OA = Onset age, yrs = years, Ht = Height, Wt = Weight, BMI = Body Mass Index, WHR = Waist Hip Ratio, WC =
Waist Circumference, HC = Hip Circumference, BS = Biceps skinfold, TS = Triceps skinfold, SS = Sub-scapular skinfold, SiS = Supra-iliac skinfold,
AC = Arm Circumference, CC = Calf Circumference, SBP = Systolic Blood Pressure, DBP = Diastolic Blood Pressure, MBP = Mean Blood Pressure,
PR = Pulse Rate, PP = Pulse Pressure.
Table 4. Inter-correlation matrix of variables among post-menopausal females of Type 2 Diabetes Mellitus (T2DM).
VARIABLES Age
(yrs)
OA
(yrs)
Ht
(cm)
Wt
(Kg)
BMI
(kg/m2) WHR WC
(cm)
HC
(cm)
BSkn
(mm)
T Skn
(mm)
SS
(mm)
SiS
(mm)
AC
(cm)
CC
(cm)
SBP
(mmHg)
DBP
(mmHg)
MBP
(mmHg) PR PP
Age (yrs) .73* −.12 .007 .06 −.01 .17.15−.08 −.02 −.20* −.07 −.04.03.06 −.07 .14 .15 .09
OA (yrs) −.08 −.03 .006 .04 .07.03−.04 .04 −.09 −.05 −.08 −.02.06 .099 .16 .15 .005
Ht (cm) .19 −.23* .17 .06−.05.04 .10 .03−.08 −.09 −.02 −.15 −.07 −.11 −.03 −.19
Wt (Kg) .90* .18 .80*.83*.53*.40*.54*.52*.76*.74*.13 .18 .10 .05 −.005
BMI(kg/m2) .07 .77*.85*.50*.35*.52*.55*.80*.74*.20 .20* .16 .08 .09
WHR .47*−.08.10 .10 .10.03.01−.02.002−.09 .10 −.03.05
WC(cm) .796*.53*.42*.495*.46*.65*.62*.19 .083 .20 .10 .15
HC (cm) .50*1.0.55*.53*.77*.75*.16 .12 .14 .05 .09
BS (mm) .78*.50*.47*.44*.44*.06 .05 .009 .013.06
T S (mm) .44*.37*.33*.33*.03 −.05 .01 .013.10
SS (mm) .60*.49*.43*−.09 −.02 −.15 −.002 −.12
SiS (mm) .48*.49*.18 .12 .03 −.03.12
AC (cm) .69*.12 .21* .12 .05 .007
CC (cm) .20*.16 .12 .06 .14
SBP (mmHg) .59* .72* .13 .86*
DBP (mmHg) .56* .13 .16
MBP (mmHg) .003.53*
PR .06
PP
Significant at least at p ≤0.05; OA = Onset age, yrs = years, Ht = Height, Wt = Weight, BMI = Body Mass Index, WHR = Waist Hip Ratio, WC =
Waist Circumference, HC = Hip Circumference, BS = Biceps skinfold, TS = Triceps skinfold, SS = Sub-scapular skinfold, SiS = Supra-iliac skinfold,
AC = Arm Circumference, CC = Calf Circumference, SBP = Systolic Blood Pressure, DBP = Diastolic Blood Pressure, MBP = Mean Blood Pressure,
PR = Pulse Rate, PP = Pulse Pressure.

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Table 5. Comparison of factor loadings by principal component analysis with Varimax rotation and communalities of the risk factors
among type 2 diabetic male, pre-menopausal and post-menopausal females (n = 349).
FACTOR 1 FACTOR 2 FACTOR 3 FACTOR 4 FACTOR 5 FACTOR 6
COMMU-
NALITY
ESTIMATES
VARIABLES
M PRF POF M PRF POFMPRF POFMPRF POFMPRFPOF M PRF POF MPR
F
PO
F
Age (yrs) −.046 −.083.042 −.025 .038 .041.952.057 −.072.008 .860 .930 .053 .136−.021 ---- −.134 .010 .912.787.874
Onset age (yrs) .114 .013−.035 −.054 .105 .048.940.021.021.015.868.873.054 −.097.006 ---- .111 .168 .902.787.794
Height (cm) .056 .041−.099 −.035 .030 −.217 −.080 .303.104−.827 −.084 −.196.118.007.652 ---- .726 .404 .709 .629 .695
Weight (Kg) .944 .833.903 .091 .117 .017−.147 .260.160−.043 −.042 −.029.102.255.241 ---- .173 .129 .912.872.917
BMI(kg/m2) .905 .841 .940 .088 .101 .112 .054.171 .111.154−.108.054 .083 .154−.057 ---- −.118 −.026 .861.796 .916
WHR .432 −.029.072 .061 −.060 .092.007.107.034.278 .025 .080 .550 .913.831 ---- .006 −.296 .569 .849.799
Waist circumference(cm) .900 .735 .796 .033 .133 .139.078 .087.215.117 .039 .182 .253.620.412 ---- −.030 −.072 .894 .953.908
Hip circumference (cm) .911 .883 .903 .002 .202 .048 .088.060 .184−.031 .039.103 −.088.019 −.064 ---- −.037 .027 .846.827.867
Biceps skinfold (mm) .663 .633.419 −.020 .067 .025 −.002 .527.794.473.043−.046 .106−.125 .064 ---- −.271 .045 .675.775.814
Triceps Skinfold (mm) .429 .511 .245 −.051 .033 .021 −.110.540 .880.680.114.045 .245−.267 .103 ---- −.257 .036 .722.704.848
Subscapular skinfold (mm) -- .137.571 -- −.071 −.197-- .871.469---−.030 −.179-- .103.005 ---- .032 −.037 ---- .795.619
Supra-iliac skinfold (mm) -- .346.577 -- .079 .091 -- .684.413 ---.125−.104-- .202−.143 ---- .105 −.107 ----- .661 .554
Arm Circumference (cm) .838 .863.871 .030 −.071 .037−.006 .175.077.115−.041 −.078 .113−.096 −.053 ---- .014 .070 .729.792.780
Calf circumference (cm) .837 .763.811 −.025 −.046 .103−.062 .048.141−.011−.021 −.006 .002−.135 −.053 ---- .031 .057 .705.606.694
Systolic blood pressure
(mmHg) .006 .056.102 .970 .975 .957−.028 .050.031−.013 .103 −.002.036−.034 −.057 ---- −.066 .101 .944.972.941
Diastolic blood pressure
(mmHg) .097 .076.190 .823 .817 .579−.009 −.117 −.163.061 −.063 −.135.086.082 −.084 ---- .178 .539 .698.729.714
Mean Blood Pressure
(mmHg) .035 .104.102 .934 .965 .833−.082 −.043 −.115 −.007 −.014.103 −.003.046.112 ---- .046 .108 .880.948.752
Pulse Rate −.049 −.084.028 .060 −.032 .035 −.097 −.254 .062.088.065.235−.862 −.005 −.059 ---- .752 .642 .769.642.477
Pulse Pressure .007 .015−.032 .814 .767 .846.020.165.178−.029 .207.066−.084 −040 −.060 ---- −.209 −.215 .671 .721.802
Eigenvalue 6.016 5.917 6.408 3.190 3.159 2.876 1.8371.762 1.8041.318 1.574 1.444 1.034 1.2281.201 --- 1.204 1.034 ---------
Variance Explained(%) 35.389 31.140 33.726 18.767 16.626 15.135 10.8069.2749.4967.7568.2867.601 6.0846.4646.322 --- 6.336 5.440 -----------
Cumulative Variance(%) 35.389 31.140 33.726 54.157 47.767 48.860 64.96257.04
1 58.35672.71865.32765.95778.80171.79172.28 --- 78.127 77.719 ---------
Factor Loadings ≥ 0.4; M = Males, PMF = Pre-menopausal females, POF = Post-menopausal females.
PCFA is applied to identify the significant association
with T2DM among three groups. As far as concern in the
North Indian Punjabi population, very little information
[15,19-21] to identify the underlying factors/components
of the T2DM are available. In this consideration the
present work has been undertaken among the males and
females (pre- and post-menopausal). PCFA have identi-
fied five factors with 79% explained variance among
male diabetic subjects and six factors with 78% ex-
plained variance among pre- and post-menopausal dia-
betic female subjects.
It is important to note that neither of the anthropomet-
ric and physiometric variables equally loaded on all five
or six components. Factor 1 is the most diverse among
three groups. It could be identified as weight, BMI, waist
circumference for males; hip circumference, BMI,
weight for pre-menopausal females and BMI, weight,
hip circumference for post-menopausal females. How-
ever weight for males, hip circumference for pre-meno-
pausal and BMI for post- menopausal females are heav-
ily loaded. The second factor could be identified as SBP
and DBP for males and pre-menopausal females and
DBP for post-menopausal females. This component is
most clearly and heavily loaded. Therefore, among dia-
betic individuals, males and pre-menopausal females
were very closely associated with SBP and DBP whereas,
post-menopausal diabetic females were more concerned
with DBP only. Among male diabetic subjects factor
three was grouped with actual age and age of onset of
the disease, whereas, subcutaneous fat was identified as
factor three. Factor four could be identified as subcuta-
neous fat for males, whereas actual age and age of onset
of the disease were grouped together for factor four.
Factor five could be identified as WHR for the three
groups. However, WHR is heavily loaded for pre- and
post-menopausal females. Pulse rate could be identified
as factor six for both pre- and post-menopausal females,
whereas, no sixth factor is identified for males.
The present factor analysis confirmed that cluster of at
least three variables such as, weight, BMI, waist cir-
cumference which have identified as factor one ex-
plained 35%, 31% and 34% of the total variance among
diabetic males, pre- and post-menopausal females re-
spectively. Therefore, the cluster of weight, BMI and
waist circumference could be classified as central obe-
sity and this cluster is equally associated with diabetic
males and post-menopausal females. Furthermore, in the
present study, the second factor, that is blood pressures
explained 19%, 17% and 15% of the total variance
among diabetic males, pre- and post-menopausal fe-
males. The blood pressures (SBP and DBP) were posi-
tively and significantly associated with diabetic males.
Therefore the above two types factors such as, central
obesity and blood pressures are more predispose among
diabetic males as compared to females. PCFA also con-
firmed that WHR and pulse pressure are significantly

Badaruddoza et al. / Natural Science 2 (2010) 741-747
747
747
associated with diabetic pre- and post-menopausal fe-
males as compared to diabetic males. Therefore, it is
very difficult to single out of the particular variable
which is more associated with male or pre- and post-
menopausal females due to the fact that many overlap-
ping variables have found as more than one factor
among all the three groups. Further, research with PCFA
is required on other Indian ethnic groups to compare the
present trend of the study.
6. ACKNOWLEDGEMENTS
The authors are greatful to Dr Rohit Kapoor; Dr. A. P. Singh and Dr.
Puneet Arora for their co-operation during the data collection. This
work is financially supported by University Grants Commission, New
Delhi [DRS I (UGC-SAP)].
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