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Research Notes



RESEARCH ARTICLE Open Access



Assessing clustering of metabolic syndrome
components available at primary care for Bantu
Africans using factor analysis in the general
population

John Nasila Sungwacha 1 , Joanne Tyler 2 , Benjamin Longo-Mbenza 3 *, Jean Bosco Kasiam Lasi On'Kin 4 ,
Thierry Gombet 5,6 and Rajiv T Erasmus 7



Abstract

Background: To provide a step-by-step description of the application of factor analysis and interpretation of the
results based on anthropometric parameters(body mass index or BMI and waist circumferenceor WC), blood
pressure(BP), lipid-lipoprotein(triglycerides and HDL-C) and glucose among Bantu Africans with different numbers
and cutoffs of components of metabolic syndrome(MS).

Methods: This study was a cross-sectional, comparative, and correlational survey conducted between January and
April 2005, in Kinshasa Hinterland, DRC. The clustering of cardiovascular risk factors was defined in all, MS group
according to IDF(WC, BP, triglycerides, HDL-C, glucose), absence and presence of cardiometabolic risk(CDM) group
(BMI,WC, BP, fasting glucose, and post-load glucose).

Results: Out of 977 participants, 1 7.4%( n = 1 70), 1 1%( n = 107), and 7.7%(n = 75) had type 2 diabetes mellitus(T2DM),
MS, and CDM, respectively. Gender did not influence on all variables. Except BMI, levels of the rest variables were
significantly higher in presence of T2DM than non-diabetics. There was a negative correlation between glucose
types and BP in absence of CDM. In factor analysis for all, BP(factor 1) and triglycerides-HDL(factor 2) explained 55.4%
of the total variance. In factor analysis for MS group, triglycerides-HDL-C(factor 1), BP(factor 2), and abdominal
obesity-dysglycemia(factor 3) explained 75.1% of the total variance. In absence of CDM, glucose (factor 1) and
obesity(factor 2) explained 48.1% of the total variance. In presence of CDM, 3 factors (factor 1 = glucose, factor 2 = BP,
and factor 3 = obesity) explained 73.4% of the total variance.

Conclusion: The MS pathogenesis may be more glucose-centered than abdominal obesity-centered in not
considering lipid-lipoprotein , while BP and triglycerides-HDL-C could be the most strong predictors of MS in the
general population. It should be specifically defined by ethnic cut-offs of waist circumference among Bantu Africans.

Keywords: Factor analysis, Metabolic syndrome, Black Africans, Type 2 diabetes



* Correspondence: longombenza@gmail.com

3 Faculty of Health Sciences, Walter Sisulu University, Private Bag XI, Mthatha,
Eastern Cape 5117, South Africa

Full list of author information is available at the end of the article

O© 2013 Nasila Sungwacha et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the
BlOlVlGCl CentrBl Creative Commons Attribution License (http://creativecommons.Org/licenses/by/2.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.



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Background

Metabolic syndrome (MS) is defined by a cluster of
cardiovascular risk factors such as obesity (abdominal
obesity in particular), diabetes mellitus (DM), high blood
pressure (BP)/hypertension, dyslipidemia, insulin resis-
tance, and hypercoagulability [1-5]. Furthermore, MS is
nowadays a major public health problem worldwide [6].
Before the advent of the consensus of the definition of
the MS using International Diabetes Federation(IDF) [6],
Experts from WHO and EGIR required the measurement
of serum insulin levels, diabetes mellitus(DM) or glucose
intolerance, hypertension, triglycerides, HDL-Cholesterol
(C), body mass index(BMI), and waist circumference(WC)
with different cutoff points [7,8]. However, MS charac-
terizes by international cutoff points [6-9], are limited to
detect efficiently bantu Africans at higher cardiometabolic
(CDM) risk(hypertension, DM, atherosclerosis) in com-
parisons with ethnic specific definition of MS [10].
Africans have low or normal lipid profile [11,12] and a
high level of HDL-C [13]. Longo-Mbenza et al. include
low birth weight, coronary heart disease, malnutrition,
elevated fibrinogen, total cholesterol, and urea nitrogen
[11-13].

In sub-Saharan Africa, MS, obesity, dyslipidemia, DM,
hypertension and DM are emerging with cardiovascular
complications [14-21] because of urbanization, migra-
tion, epidemiologic transition, demographic transition,
and nutrition transition [22-24]. Identifying patterns of
Bantu Africans at the primary care level can explain, at
least in part, the differences observed in the prevalence
or incidence of MS and cardiovascular diseases between
different populations [25-30].

Several statistical methods can be used to identify
patterns of clustering in cardiovascular diseases such as
DM and hypertension. One such important and useful
technique is factor analysis - a multivariate technique
[31-36]. Indeed, Factor analysis is a statistical method
used to describe variability among observed variables in
terms of a potentially lower number of unobserved
variables called factors. Factor analysis searches for
such joint variations in response to unobserved latent
variables. The observed variables are modelled as linear
combinations of the potential factors, plus "error"
terms. The information gained about the interdepen-
dences between observed variables can be used later to
reduce the set of variables in a dataset. Furthermore, at
our knowledge, there is no information on the phy-
siogenic process including the mechanisms with which
the major components of the MS relate to each other
which could be one of the features to make preventive
startegies and control of emerging cardiovascular dis-
eases in Africa [17-20]. For that reason, the objective of
this study was to provide a step-by-step description of
the application of factor analysis and interpretations of



the results based on the clustering of anthropometric
parameters, blood pressure, triglycerides, HDL-C, and
plasma glucose in all, presence of MS defined by IDF,
absence and presence of CDM(exclusion of triglycerides
and HDL-C).

Methods

This study was a cross-sectional survey conducted bet-
ween January, and April 2005, in Kinshasa Hinterland
with details previously published [13]. This study was
carried out in compliance with the Helsinki Declaration
(59 th WMA General Assembly, Seoul, South Korea,
October 2008. http://www.wma.net/en/30publications/
10policies/b3/index.html). This research was approved
by the Ethics Committee of Lomo Medical Clinic
(Ref-00038-03-07) at Kinshasa Limete. Fully informed
and written consent was obtained from all adult
participants.

The survey was specifically and extensively designed
using a statistical multistage and stratified random model
at each level to recruit a study sample with similar and
representative characteristics of Kinshasa Hinterland de-
mographic and socioeconomic structure and results com-
parable with global data on DM.

Each region contributed with a number of cluster (EDs)
calculated by population number: 185, 112 inhabitants for
the upper urban area of Gombe, 161,410 inhabitants of
the semi-rural Kisero area, 153,265 inhabitants for the
urban Lukemi area and 146,034 inhabitants for the
deepest rural Feshi area. The sample size was calculated as
Z 2 xPxQx the expected prevalence of DM in each area,
Q = 1-P, d is the in the absolute accuracy of 2% ad f = 8.5
to correct the design effect.

The details of collection of weight, height, waist circum-
ference (WC), systolic blood pressure (SBP), diastolic
blood pressure (DBP), plasma fasting glucose and plasma
post load glucose have been described elsewhere [30-32].

Definitions

Body mass index (BMI) was obtained in dividing weigh
(kg) by height (m) 2 . In our setting with limited resources
and lack of routinely measured insulin resistance (gold
standard), we applied the criteria of MS diagnosis pro-
posed by the International Diabetes Federation (IDF) as
follows: raised systolic blood pressure (SBP > 130 mmHg)
and diastolic blood pressure (DBP > 85 mmHg), ele-
vated triglycerides (TG > 1.7 mmol/L), low high- density
lipoprotein cholesterol (HDL < 1.04 mmol/L in men
and <1.29 mmol/L in women) levels, abdominal obesity
defined by increased waist circumference (WC > 94 cm in
men and >80 cm in women), and fasting plasma glucose
(FPG > 5.6 mmol/L)(6).

CDM was defined by the constellation of 3 components
of WHO - defined MS such as diabetes, hypertension,



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and BMI > =30 kg/m2. However, absence of CDM was
defined in participants without pre-hypertension, abdo-
minal obesity, BMI > =25 kg/m2, and CDM. The defi-
nition of diabetes was based on clinical arguments and the
latest WHO/IDF criteria among persons with the fasting
venous plasma glucose level > =126 mg/dL or Post-load
venous blood plasma level > =200 mg/dL [7]. This was an
undiagnosed T2DM so that information about HbAlc,
duration of diabetes, and medications was not available
and compulsory.

Statistical analysis

Data were presented as mean ± SD. Factor analysis origi-
nated in psychometrics, and is used in behavioral sciences,
social sciences, marketing, product management, opera-
tions research, and other applied sciences that deal with
large quantities of data.

Factor analysis is based on the following statistical model
and definitions

Suppose we have a set of p observable random variables,
Xi,...,x^, with means. ftj,...,fi p .

Suppose for some unknown constants L and k unob-
served random variables Ft, where i e 1,.,., p and j e 1,...,
where k < p, we have

Xi — m = k\F\ + h ljkF k + £j

Here, the e/s are independently distributed error terms
with zero mean and finite variance, which may not be
the same for all i. Let Var(f,)i^ i , so that we have

Cov{e) = Diag(f 1 , f) = W and E(e) = 0

In matrix terms, we have

x-fi = LF + s

If we have n observations, then we will have the di-
mensions ~x. pxn , Lp^h and F^ xn . Each column of x and F
denote values for one particular observation, and matrix
L does not vary across observations.

Also we will impose the following assumptions on F.

1. F and e are independent.

2. E(F)=0

3. Cov(f)=/

Any solution of the above set of equations following
the constraints for F is defined as the factors, and L as
the loading matrix.

Suppose Cov(# - u) = E. Then note that from the con-
ditions just imposed on F, we have



Cov(x-fi) = Cov(LF + s),

or

L = LCov(F)L T + Cov(e),

or

Z = LL T + T.



Note that for any Orthogonal Matrix Q if we set L=LQ
and F=Q T F, the criteria for being factors and factor load-
ings still hold. Hence a set of factors and factor loadings
is identical only up to orthogonal transformations.

Common factor analysis, also called principal factor
analysis (PFA) or principal axis factoring (PAF), seeks
the least number of factors which can account for the
common variance (correlation) of a set of variables.

Analogous to Pearson's r, the squared factor loading is
the percent of variance in that indicator variable
explained by the factor. To get the percent of variance
in all the variables accounted for by each factor, the sum
of the squared factor loadings for that factor (column)
was added and divided by the number of variables. This
is the same as dividing the factor's Eigenvalue by the
number of variables.

The Eigenvalue for a given factor measured the vari-
ance in all the variables which is accounted for by that
factor. Eigenvalues measure the amount of variation in
the total sample accounted for by each factor.

Extraction sums of squared loadings were performed.
Factor scores were the scores of each case (row) on each
factor (column). To compute the factor score for a given
case for a given factor, the case's standardized score was
taken on each variable, multiplied by the corresponding
factor loading of the variable for the given factor; and
these products were summed.

For determining the number of factors, the Kaiser
criterion was used. The Kaiser rule is to drop all compo-
nents with Eigenvalues under 1.0.

The Cattell scree test plotted the components as the X
axis and the corresponding Eigenvalues as the Y-axis. As
one moves to the right, toward later components, the
Eigenvalues drop. When the drop ceases and the curve
makes an elbow toward less steep decline, Cattell's scree
test says to drop all further components after the one
starting the elbow.

Varimax Rotation served to make the output more
understandable and facilitated the interpretation of
factors. This is an orthogonal rotation of the factor axes
to maximize the variance of the squared loadings of a
factor (column) on all the variables (rows) in a factor
matrix, which has the effect of differentiating the ori-
ginal variables by extracted factor. This procedure yields



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results which make it as easy as possible to identify each
variable with a single factor. To avoid theoretical sup-
posed grounds, we used oblique Promax rotation as
additional alternative to varimax rotation for suited clus-
tering characteristics.

A P-value < 0.05 was considered as statistically signifi-
cant. All analyses were performed using the Statistical
Package for Social Sciences (SPSS) for windows version
18.0 (SPSS Inc) Chicago, II, USA.

Results

Out of the original population (n = 977 with 458 males and
519 females), 170(17.4%), 107(11%), and 75(7.7%) were
diagnosed for new T2DM, MS, and CDM, respectively.

Table 1 describes the mean levels of general characteris-
tics according to T2DM status. Except similar(P > 0.05)
values of BMI in presence and absence of T2DM, levels of
age, WC, SBP, DBP, and triglycerides were significantly
(P < 0.05) higher in T2DM participants than no diabetic
participants. However, HDL-C values were significantly
(P < 0.05) lower in T2DM presence than diabetes absence.
The mean levels of age, BMI, WC, SBP, DBP, triglycerides,
HDL-C, FPG,and post-load glucose in men were similar
(P > 0.05) with those from women(results not shown).

In the general population, factor analysis generated 2 fac-
tors which were explaining 55.4% of total variance: factor 1
(Blood pressure with variance = 29.6%; DBP = 0.881 and
SBP = 0.872), and factor 2(Dylipidemia with variance =
25.8%; HDL-C = -0.886 and triglycerides = 0.872).

In MS participants, factor analysis generated 3 fac-
tors with total variance of 75.1%: factor l(Dyslipi-
demia with variance = 29.5%; triglycerides = 0.911 and
HDL-C = -0.874), factor 2(Blood Pressure with vari-
ance = 27.9%; DBP = 0.869 and SBP = 0.837), and factor 3
(Abdominal obesity + Dysglycemia with variance = 18.1%;
WC = 0.836 and fasting plasma glucose = 0.609).



Table 1 Association of age and metabolic syndrome
components with incident type 2 diabetes mellitus in the
study population



Variables of
interest


Participants with
incident types 2 DM


Absence of diabetic
participants


P Values


Age (Years)


53 ±13


36+12


< 0.0001


BMI (Kg/m 2 )


24.4 ±5.6


23.3 ±5.4


0.881


WC (cm)


86 ±14.7


78.7 ± 14.3


< 0.0001


SBP (mm Hg)


131.1 ±31.1


117±17


< 0.0001


DBP (mm Hg)


80 ±16.7


70 ± 11.7


< 0.0001


FPG (mmd/L)


7.5 ±2


5.1 ±1


< 0.0001


Triglycerides
(mmd/L)


3.8 ±0.8


2.7 ± 0.9


< 0.0001


HDL-C
(mmd/L)


1 ±0.3


2 ±0.6


< 0.0001


Retrieved from "http://en.wikipedia.org/wiki/Factor_analusis."



Table 2 Characteristics in absence of cardiometabolic risk



Variables Mean±SD



BMI (Kg/m 2 )


24.6 ± 8.80


Waist Circumference (CM)


79.8 ± 1 3.6


SBP (mmHg)


11 3.4 ±12.4


DBP (mmHg)


66.6 ± 07.6


FPG (mg/dL)


82.0 ± 14.0


Post-Load PG (mg/dL)


123.21 ±18.0



Absence of CDM(n = 572)

Table 2 describes the mean values of variables analyzed
in participants without CDM. The correlation matrix in
absence of CDM is presented in Tables 3 and 4. Post-
load plasma glucose was significantly and positively
correlated to BMI and WC, but significantly and nega-
tively correlated to both SBP and DBP. SBP was signifi-
cantly and positively correlated to BMI but significantly
but negatively correlated to FPG. DBP was significantly
and negatively correlated to FPG.

Factor analysis revealed two uncorrelated factors that
cumulatively explained 48.1% of the observed variance
of the absence of CDM. The number of those two
factors was determined by the scree plot according to
Eigen-value (Figure 1). These two factors could be iden-
tified as Blood Glucose Metabolism Disordering (Factor
1; 26.3% of variance) and obesity (Factor 2; 22% of
variance) (Table 5 and Figure 2).

Presence of CDM

The mean values of variables analyzed in participants
with CDM are presented in Table 6. Factor analysis re-
vealed three uncorrelated factors that cumulatively
explained 73.6% of the observed variance in the presence
of cardiometabolic risk. The number of these three fac-
tors (Components) was determined by the scree plot
according to Eigen-values (Figure 3).

These three factors could be identified as: Blood Glucose
Metabolism Disordering (Factor 1; 35.6% of variance),

Table 3 Correlation matrix in absence of cardiometabolic
risk

WC FPG Post-load PG

BMI 0.132

P = 0.016

SBP -0.114

P = 0.031

DBP -0.125

P = 0.020

WC 0.035 0.146

P = 0.286 P = 0.008



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Table 4 Correlation matrix in absence of cardiometabolic
risk





BMI


SBP


DBP


BMI


-


0.104
P = 0.046


0.052
P = 0.197


SBP






0.128
P = 0.019


DBP








WC


0.200


0.063


-0.021




P < 0.0001


P = 0.286


P = 0.364


FPG


-0.086


-0.155


-0.118




P = 0.080


P = 0.006


P = 0.027



Blood Pressure (Factor 2; 20.3% of variance), and obesity
(Factor 3; 17.7% of variance) (Table 7).



general population, absence of CDM, presence of CDM,
and presence of MS among Bantu Africans living in DR
Congo(Central region).

The extent of T2DM, CDM (concurrent presence of 3
non-lipid components of MS), and MS defined by IDF 5
criteria such as 3 non-lipid components and 2 lipid-
lipoprotein components [6] was examined. The present
study also determined the interrelation of the main
CDM factors: BMI, WC, SBP, DBP, FPG, and post-load
glucose.

Emerging burden of MS

Contrary to the previous myths, non communicable
diseases (Diabetes, hypertension, MS, atherosclerosis) are
no longer rare in Africa [10-14]. The extent is increasing
and it is thought to be due to the shifting from traditional
African customs to the Western lifestyle [15-18].



Discussion

The present study identified MS combination for which
factor analysis would be appropriate among Bantu Africans.
For that reason, the steps involved in performing factor
analysis procedure were described. Thus, factor analysis
findings using SPSS software have been interpreted.

However, MS is a complex issue in health care. It does
not have a simple cause, but multiple risk factors. Its
natural course is influenced by genetic factors, personal
(Host) attributes, environmental characteristics, or some
interactions of both.

At our knowledge, this was the first study to
characterize factor analysis of possible risks for cluster-
ing of some traditional cardiovascular risk factors in the



MS pattern

The present study sought at identifying the physiogenic
factors responsible for the clustering of cardiometabolic
components. Factor analysis showed marked differences
in the MS pattern between the groups of 3 components
(CDM) and 5 components (MS).

Number of generated factors

In the general adult population, factor analysis identified
3 components for MS. This finding about MS was con-
sistent with a study conducted in Asian Indians from the
general population [1]. In India, however, the total vari-
ance of 65.3% [1] was lower than the total variance of
75.1% explained in the present study. However, in the



Scree Plot



Component Number
Figure 1 Eigen values among participants without cardio-metabolic risk.



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Table 5 Rotated component matrix in absence of
cardiometabolic risk





Factor 1


Factor 2


BMI


-0.131


0.738


SBP


-0.502


0.340


DBP


-0.455


0.114


WC


0.077


0.696


FPG


0.765


0.051


Post-Load PG


0.707


0.400



South African general population, 5 factors could be
identified in factor 1 (Obesity), factor 2(Hypertension),
factor 3(Hyperuricemia-hypertriglyceridemia), factor 4
(Hyperglycemia), and factor 5(Hyperinsulinemia) [1]. In
our findings, the first 2 factors cumulatively explained
58% of the total variance for MS. Only considering 3
non-lipid components, affordable in limited resources
areas, factor analysis had identified also 3 factors with
total variance almost 74.5% for CDM and similar with
that for MS. The first 2 factors(Dysglycemia and Hyper-
tension) cumulatively explained 56% of the total variance
of CDM.

In considering the entire population and the sub-
population without CDM, factor analysis generated only
2 factors. In all participants, the factors revealed such as
hypertension(factor 1) and dyslipidemia(factor 2) cumu-
latively explainedb55.4%bof the total variance of the
clustering pattern of atherogenic factors from MS. How-
ever, in the absence of CDM, BP was not loaded, while
only dysglycemia(factor 1) and obesity/BMI and WC(fac-
tor 2) were revealed the first factors which cumulatively



explained 48.1% of the total variance of the charac-
terization of this group by the clustering of non-lipid
components for MS.

The present study showed that no overlapping of vari-
ables on more than 1 factor indicated that more than 1
variable was responsible for the ultimate phenotype of
the MS. Our findings demonstrated that factor analysis
confirmed the general results from other factor analyses
of the MS on different ethnic groups that had 3-5 factors
revealed [1-4].

Our findings with the clustering of the variables in MS
as a result of multiple factors known modifiable in na-
ture raised the following question: would it be more effi-
cient to include all participants in one major factor
analysis model? Indeed, factor analysis is practically lim-
ited to develop a single-parameter screening tool for MS
in this study as mentioned in the literature [4]. IDF
recommended WC as the most frequently used an-
thropometric index to define abdominal obesity [6],
Paradoxically, WC, BP, lipid, and glucose levels were
similar among men and women in this study as reported
in the same general population [10]. However, WC cut-
off points differ by ethnic groups and gender worldwide
[1,4]. Older age was associated with T2DM in this study,
while age not considered as a component of MS, is a
confounding factor for anthropometric variables of MS
amon Taiwanese individuals [4].

Factor analysis was applied to see whether there was a
less complex space with fewer than the "n" dimensions
of the variables that had been analyzed. It was found
that a three dimensional space or a mixture of three fac-
tors could be used to explain a major part of the data. In



Component Plot in Rotated Space



i.o-



0.5



CM
*-•

c



0.0



-0.5 ■



-1 .0-



DBP

O



•1.0



— I —
-0.5



WC

o



qSObis
O



OFASTINO
O



— I —

0.5



0.0

Component 1

Figure 2 Two-component plot in rotated space among participants without cardiometabolic risk.



— i —

1.0



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Table 6 In participants with cardiometabolic risk 2



Variables



Mean±SD



BMI (Kg/m 2 )
Post-Load PG (mg/dL)
SBP (mmHg)
DBP (mmHg)
FPG (mg/dL)

Waist Circumference (CM)



43.40 ± 20.20
80.00±15.80
1 27.60 ± 26.70
80.00 ± 15.80
93.40 ± 1 9.80
1 82.84 ± 101.10



more precise mathematical terms the global and exam-
ined variables without dyslipidemia(with paradoxes of
triglycerides and HDL-C) could be reduced to three fac-
tors with eigenvalues greater than one, which explained
73.4% of the variance in MS Africans. The loadings on
these factors sorted out into three metabolic groupings.

Neither of the variables was loaded on all the three
components. These three factors could be identified as
Glucose Metabolism (Factor 1), Blood Pressure (Factor
2) and Obesity (Factor 3). This suggests that those non-
lipid components clustered naturally rather than as a re-
sult of chance.

No overlapping of variables on more than one factor
indicated that more than 1 variable is responsible for the
ultimate phenotype of the fats. The present factor ana-
lysis confirmed global results from other factor analyses
of fats among different populations that had 3 to 4 fac-
tors identified as non-modifiable/genetic risk factors and
modifiable/ environmental risk factors. The study
attempted to observe among BMI, WC, SBP, DBP, FPG,
and post-load PG group - which ones go together and
which ones do not [30]. Variables with a factor loading



of at least 0.3 have generally been considered for inter-
pretation although it is suggested that only loadings > 0.4
be used, which therefore shares at least 15% of the vari-
ance with a factor, should be used in the study [24].

In many studies, fats play a pivotal role in the occur-
rence of the onset of CVD, andT2DM. However, lipid
profile and fasting insulin are not available in the major-
ity of health centers in developing countries.

Therefore, identification of non-lipid components of
the metabolic syndrome would be helpful in understand-
ing the etiology among Bantu Africans. Virtually no
study has been performed on combination of the evalu-
ated variables in Sub- Saharan Africa.

Perspectives for Africa

This study highlighted the absence of obesity as a factor
of MS in type 2 diabetic Bantu Africans. Moreover, obes-
ity was the third factor of MS with lower variance in
comparisons with variances of factor 1 (Glucose) and fac-
tor 2(Blood pressure) among type 2 diabetic Africans
with MS. As reported on the factor analysis of risk vari-
ables associated with MS in adult Asian Indians [1], fur-
ther studies among larger sizes from Bantu Africans, are
needed to demonstrate the responsibility of more than
one underlying physiogenetic polymorphisms in the
present specific glucose-centered pattern for MS with
lower BMI and smaller WC.

Limitations and strengths

The advantages and disadvantages of factor analysis have
been reported in medical, physical, marketing economic
and environmental researches [31]. There are different



Scree Plot



2.5-



2.0



1.5



m 1.0



0.5



0.0



12 3 4

Component Number

Figure 3 Eigen values among participants with cardio-metabolic risk.



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Table 7 Rotated component matrix in presence of
cardiometabolic risk





Factor 1


Factor 2


Factor 3


BMI


0.077


-0.025


0.760


SBP


-0.064


0.881


-0.137


DBP


-0.224


0.833


0.116


WC


0.025


0.001


0.769


FPG


0.906


-0.106


0.096


Post-Load PG


0.894


-0.179


0.031



reasons of the limitations of this study, that is, ethnic
and cultural heterogeinity, genetic studies, gender, age
composition, number of risk variables included, sample
size, and cutoff points of MS and CMD[ ]. In Asian
Indians, angiotensin converting enzyme gene polymor-
phism(insertion/deletion) with BP was identified factor 3
along lipids and lipoproteins(factor 1) and centripetal fat
and BP(factor 3) associated with MS phenotype [1], In
these Asian Indians, DBP in factor 2 overlapped on
another variable in factor 3 [1],

Advantages of factor analysis

The rotation methods are useful in making the output
more understandable and for ease of interpretation of
the factors. The optimal variance of the squared loadings
of a factor (Column) on all the variables (rows) in a factor
matrix is due to varimax rotation (an orthogonal rotation
of the factor axes). Factor matrix differentiates the original
variables from extracted factors.

Groups of inter-related variables are identified and seen
in their manner to be related to each other.

In multi- factorial diseases, it is easy and inexpensive to
perform factor analysis which can be used to identify hid-
den dimensions which may not be apparent from analysis.

Disadvantages of factor analysis

It is not possible to pick the proper rotation using factor
analysis alone as all rotations represent different under-
lying processes and equally valid outcomes of standard
factor analysis optimization.

Though not a strictly mathematical criterion, there is
much to be said for limiting the number of factors to
those whose dimension of meaning is readily compre-
hensible. The same limitation is reported about variance
explained criteria.

The research is requested to choose the solution which
generates the most comprehensive evaluation of data.

The Kraiser criterion is the default in SPSS and most
computer programs but is not recommended when used
as the sole cut-off criterion for estimating the number of
factors.

Certain researchers prefer to keep enough factors to
account for 80%-90% of the variation. However, other



researchers explain variance with a few factors, but
lower than 50% (Parsimony).

Factor analysis cannot identify causality as interpreting
factor analysis is based on using a " heuristic" convenient
solution even if not absolutely "true". If important attri-
butes (such as lipid components of fats) at primary
health care in developing countries like DRC, the value
of the procedure was reduced for BMI in absence of MS.

It requires strong background knowledge of biology
and Pathophysiology or theory as multiple attributes
may be highly correlated for no apparent reason.
Varimax was an orthogonal rotation of the components to
maximize the variance of the squared loadings (unrotated
output accounted for by the first and subsequent factors)
of a dimension (Column) on all the variables(Rows) in a
factor matrix. Varimax rotation is the easiest and the most
simple and common rotation option used in MS [1-5].
However, oblique rotations might be more suited and
more preferred with methods inclusive [31]. In search of
underlying dimensions, the use (sometimes an abuse) of
factor analysis in Personnality and Social Psychology
literature [32]. There are also different rotation methods
such as quartimax rotation(an orthogonal alternative),
equimax rotation( a compromise between varimax and
quartimax criteria), direct oblimin rotation(standard me-
thod with a non-orthogonal/oblique rotation with higher
eigenvalues but lower interpretability of the factors), and
Promax rotation. In this study, we evaluated Promax
rotation in addition to varimax rotation. Indeed, Promax
rotation was computationally faster alternative non-
orthogonal/oblique rotation method than other oblique
methods such as direct oblimin rotation. The potential
limitations such as the inability of the investigators in
collecting sufficient set of product activities, unknown on
reasons of associated dissimilar attributes, and obscured
factors were excluded or minimized.

Implementation of factor analysis

The implementation of Factor analysis is well established
within robust statistical software such as SAS, BMDP
and SPSS and R programming language with the factanal
function (GPA rotations), and Open Opt [33]. This is
evidenced by both analysis and scree plots and the three
dimensional charts.

Conclusion

The factor analysis performed for this study suggests
that the clustering of the non-lipid variables is sufficient
to define CDM in black Africans at including glucose
metabolism, Blood pressure and Obesity. Since 3 factors
in sequencing dyslipidemia, hypertension, and abdominal
obesity-dysglycemia were identified for the Bantu Central
African MS phenotype, more one major factor could be
accounted for this specific MS. Early prevention and



Nasila Sungwacha et al. BMC Research Notes 2013, 6:228
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Page 9 of 10



management (diagnosis and proper intervention) strategies
for those modifiable loaded risk variables could reduce the
burden of type 2 DM, MS, and emerging cardiovascular
disease in Central Africa.

Competing interests

All authors declare that they have no competing interests.
Authors' contributions

All the co-authors have seen and approved the final version of the
manuscript and it is not currently under active consideration for publication
elsewhere, has not been accepted for publication, nor has it been personally
and actively involved in substantive work leading to the report, and will hold
themselves jointly and individually responsible for its content. JBKL0 was
responsible for the field work. JNS performed review literature related to
factor analysis. BLM conceived of the study, and participated in the study
design. JNS and BLM performed statistical analyses. JTL, JNS, BLM, JBKLO, and
GT participated in the coordination of writing of the study. All authors read
and approved the final manuscript.

Acknowledgements

We thank the medical officers, interns, social workers, and nursing staffs from
the University of Bandundu, the participants and the professionals of the
laboratory of Lomo Medical for the ultra-structural technical assistance.

Author details

'Department of Statistics, Walter Sisulu University, Mthatha, South Africa,
department of Statistics, University of Forte Hare, Eastern cape, South Africa.
3 Faculty of Health Sciences, Walter Sisulu University, Private Bag XI, Mthatha,
Eastern Cape 51 17, South Africa. 4 Department of Internal Medicine, University
of Kinshasa, Kinshasa, DR Congo. 5 Division of Cardiology and Intensive Care,
University of Maiden Ngouabi, Kinshasa, Congo. Emergency Department,
University Hospital Center, Brazzaville, Congo. 'Division of Chemical
Pathology, Stellenbosch University, Cape Town, Stellenbosch, South Africa.

Received: 23 January 2013 Accepted: 21 May 2013
Published: 12 June 2013

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