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Prevalence of Cardiometabolic
Syndrome and its Association With
Body Shape Index and A Body
Roundness Index Among Type 2
Diabetes Mellitus Patients: A
Hospital-Based Cross-Sectional
Study in a Ghanaian Population
Enoch Odame Anto
1,2
*, Joseph Frimpong
1
, Wina Ivy Ofori Boadu
1
,
Valentine Christian Kodzo Tsatsu Tamakloe
1
, Charity Hughes
1
, Benjamin Acquah
1
,
Emmanuel Acheampong
2,3
, Evans Adu Asamoah
3
, Stephen Opoku
1
, Michael Appiah
4
,
Augustine Tawiah
5
, Max Efui Annani-Akollor
3
, Yaw Amo Wiafe
1
, Otchere Addai-Mensah
1
and Christian Obirikorang
3
1
Department of Medical Diagnostics, Faculty of Allied Health Sciences, College of Health Sciences, Kwame Nkrumah
University of Science and Technology, Kumasi, Ghana,
2
School of Medical and Health Sciences, Edith Cowan University,
Perth, WA, Australia,
3
Department of Molecular Medicine, School of Medicine and Dentistry, College of Health Science,
Kwame Nkrumah University of Science and Technology, Kumasi, Ghana,
4
Department of Medical Laboratory Technology,
Accra Technical University, Accra, Ghana,
5
Department of Obstetrics and Gynaecology, Komfo Anokye Teaching Hospital,
Kumasi, Ghana
Cardiometabolic syndrome (MetS) is closely linked to type 2 diabetes mellitus (T2DM) and
is the leading cause of diabetes complications. Anthropometric indices could be used as a
cheap approach to identify MetS among T2DM patients. We determined the prevalence of
MetS and its association with sociodemographic and anthropometric indices among
T2DM patients in a tertiary hospital in the Ashanti region of Ghana. A comparative cross-
sectional study was conducted among 241 T2DM outpatients attending the Komfo
Anokye Teaching Hospital (KATH) and the Kumasi South Hospital for routine check-up.
Sociodemographic characteristics, clinicobiochemical markers, namely, systolic blood
pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), and glycated
hemoglobin (HbA1C) were measured. Anthropometric indices, namely, body mass index
(BMI), Conicity index (CI), body adiposity index (BAI), A body shape index (ABSI), body
roundness index (BRI), Waist-to-hip ratio (WHR), and Waist-to-height ratio (WHtR) were
computed based on either the Height, Weight, Waist circumference (WC) or Hip
circumference (HC) of the patients. Metabolic syndrome (MetS) was classified using the
National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) criteria.
Data entry and analysis were done using Excel 2016 and SPSS version 25.0 respectively.
Of the 241 T2DM patients, 99 (41.1%) were males whereas 144 (58.9%) were females.
Frontiers in Clinical Diabetes and Healthcare | www.frontiersin.org February 2022 | Volume 2 | Article 8072011
Edited by:
Mustafa Cesur,
Güven Hospital, Turkey
Reviewed by:
Martha Guevara-Cruz,
Instituto Nacional de Ciencias Me
´dicas
y Nutricio
´n Salvador Zubira
´n
(INCMNSZ), Mexico
Anthonia Ogbera,
Lagos State University, Nigeria
*Correspondence:
Enoch Odame Anto
odameenoch@yahoo.com
Specialty section:
This article was submitted to
Diabetes Cardiovascular
Complications,
a section of the journal
Frontiers in Clinical
Diabetes and Healthcare
Received: 01 November 2021
Accepted: 22 December 2021
Published: 09 February 2022
Citation:
Anto EO, Frimpong J, Boadu WIO,
Tamakloe VCKT, Hughes C,
Acquah B, Acheampong E,
Asamoah EA, Opoku S, Appiah M,
Tawiah A, Annani-Akollor ME,
Wiafe YA, Addai-Mensah O and
Obirikorang C (2022) Prevalence of
Cardiometabolic Syndrome and its
Association With Body Shape Index
and A Body Roundness Index Among
Type 2 Diabetes Mellitus Patients: A
Hospital-Based Cross-Sectional
Study in a Ghanaian Population.
Front. Clin. Diabetes Healthc. 2:807201.
doi: 10.3389/fcdhc.2021.807201
ORIGINAL RESEARCH
published: 09 February 2022
doi: 10.3389/fcdhc.2021.807201
The prevalence of cardiometabolic syndrome (MetS) was 42.7% with dyslipidemia and
hypertension recording a prevalence of 6.6 and 36.1%, respectively. Being a female
T2DM patient [aOR = 3.02, 95%CI (1.59–5.76), p= 0.001] and divorced [aOR = 4.05,
95%CI (1.22–13.43), p= 0.022] were the independent sociodemographic predictors of
MetS among T2DM patients. The 4th quartile for ABSI and 2nd to 4th quartiles for BSI
were associated with MetS on univariate logistic regression (p<0.05). Multivariate logistic
regression identified the 3rd quartile (aOR = 25.15 (2.02–313.81), p= 0.012) and 4th
quartile (aOR = 39.00, 95%CI (2.68–568.49), p= 0.007) for BRI as the independent
predictors of MetS among T2DM. The prevalence of cardiometabolic syndrome is high
among T2DM patients and this was influenced by female gender, being divorced, and
increased BRI. Integration of BRI as part of routine assessment could be used as early
indicator of cardiometabolic syndrome among T2DM patients.
Keywords: cardiometabolic syndrome, type 2 diabetes mellitus, body roundness index, body shape index,
anthropometric indices, risk factors, prevalence
INTRODUCTION
Diabetes mellitus (DM) has become a public health concern and its
morbidity and mortality rates have continued to rise gradually (1). It
was estimated that the global prevalence of type 2 Diabetes mellitus
(T2DM) in 2019 was 7.5% (374 million) and is expected to reach
8.0% (454 million) by 2030 and 8.6% (548 million) by 2045 (2).
Furthermore, reports are that in the 21st century, developing
countries will face the risk of this epidemic, with 80% of all new
DMcasesduetooccurinSub-Saharan African countries like Ghana
by 2025 (3). In Africa alone, an estimated 15.5 million adults aged
20 to 79 had diabetes, which represents a regional prevalence of
3.3% (4). Moreover, T2DM accounted for over 298,160 deaths (6%
of all deaths) in Africa region that same year (4). An estimated 19.4
million adults (20–79 years) lived with diabetes in the International
Diabetes Federation (IDF) Africa Region and this signifies a 3.9%
regional prevalence (5). The Region with the highest fraction of
undiagnosed diabetes is Africa, with 60% of adults still living with
diabetes and not aware of it (5). In urban Ghana, at least 6% were
diagnosed with T2DM and were related to obesity, age, and low
socioeconomic status, and often leading to cardiometabolic risk
factorssuchashypertensionanddyslipidemia(6). T2DM causes
more havoc by its strong association with cardiometabolic risk
factors such as dyslipidemia, metabolic syndrome and hypertension
and its primary driving factor is overweight and obesity (7). In a
Ghanaian population, cardiometabolic risk factors were found to
have increased among urban settlers as a result of increased physical
inactiveness and unhealthy eating habits among the urban settlers
and the association between obesity and T2DM has been well
documented (8–10). Overweight and Obesity are linked to increased
cardiometabolic risk but can differ significantly depending on
gender, age, eating habits, and even among subjects with morbid
obesity (9).
Anthropometric indices such as BMI used to access obesity
have been accepted in clinical practices due to its simplicity and
usefulness for the prediction of body fat distribution in Diabetes
Mellitus (10,11). Body mass index (BMI) has been the
traditional anthropometric index for general obesity diagnosis
and reflects the total body fat distribution (12). BMI is however,
limited by its inability to differentiate fat and muscle mass, and
also overall distribution of body fat (13). A previous report has
shown that the conventional anthropometric indices such as
BMI could not differentiate muscle mass and body fat (14). Other
anthropometric indices such as Waist Circumference (WC),
Weight to Height Ratio (WHtR), Waist to Hip Ratio (WHR),
Body Adiposity Index (BAI), and Conicity Index (CI) have been
used to predict the various cardiometabolic risk factors in T2DM
patients (15–17). Due to endpoint dissimilarity between men and
women and also different racial groups, the validity of WC has
also been questioned for clinical use in cardiometabolic risk
assessment (13,18). Likewise, WHR as a measure of fat
distribution necessitates endpoints for ethnic group and sex
(19). The use of WHtR as a standardized tool for ascertaining
central obesity between varied racial groups has also been
questioned (20). BAI is also limited by severe obesity and its
validity has been questioned (21,22) and optimal cut-off points
of CI is also limited by sex and age (23).
The development of other anthropometric indices to improve
the limitation of other anthropometric has been explored (24).
Two new body indices have been formulated lately (25,26). A new
body index referred to as A Body Shape Index (ABSI) was
introduced by Krakauer and Krakauer (25). This index takes
into account one’s waist circumference, height, and weight.
Krakauer and Krakauer discovered that ABSI values and
abdominal body fat were positively correlated and recent studies
have used ABSI to predict premature mortality (27,28). Thomas
et al. (26) introduced another new index called the Body
Roundness Index (BRI) in 2013. This index takes WC and
height into account. However, the controversy over the
association of the two new body indices (ABSI and BRI) and
cardiometabolic risk factors among diabetes patients is yet to be
explored in a Ghanaian setting (16).
Previous studies conducted showed that the two new indices
—ABSI and BRI were more related to cardiometabolic risk
Anto et al. Cardiometabolic Syndrome in Type 2 Diabetes Mellitus
Frontiers in Clinical Diabetes and Healthcare | www.frontiersin.org February 2022 | Volume 2 | Article 8072012
factors than WC and BMI (29–31). However, studies on the
association of BRI and ABSI with cardiometabolic risk factors
among Ghanaian Diabetes patients are non-existent. It is therefore
imperative that this study is done to evaluate the relationship
between the two new indices and cardiometabolic risk factors
among Type 2 Diabetes patients in the Ghanaian population.
MATERIALS AND METHODS
Study Design
A hospital-based comparative cross-sectional study was
conducted between March 2021 and June 2021 at the Komfo
Anokye Teaching Hospital (KATH) and Kumasi South Hospital,
Agogo after obtaining permission from the Institutional
Ethics Committee.
Study Setting
KATH is a 1,200-bed facility situated in Kumasi in the Ashanti
region, Ghana. The Ashanti region is situated centrally in
Ghana’s middle belt and lies between longitudes 0.15W and
2.25W, and latitudes 5.50N and 7.46N. Kumasi is second only to
Accra in population density. Its strategic geographic position
has granted it the status of the main transport depot and
guaranteed its central role in an immense and lucrative
distribution of goods not only in the country but beyond. This
has made KATH one of the nation’s most assessable tertiary
medical centers. In Kumasi, there are nine sub-metros,
including the Bantama sub-metro, where KATH is situated.
KATH is Ghana’s second-largest hospital and the only tertiary
health institution of the Ashanti Region. It is the primary referral
hospital for Ashanti, Northern, Brong Ahafo, and Western
Regions in Ghana. It also receives referrals from other
neighboring countries such as Burkina Faso and Ivory Coast.
For easy management and specialization, the hospital has been
divided into fifteen (15) Directorates. Out of the 15, two are non-
clinical and thirteen are clinical. Several clinical and non-clinical
supporting units are also there. Kumasi South Hospital is
situated in Agogo and is the second largest in the Southern
part of Ghana. The hospital was built in 1976, as an urban health
center and was later changed to be the Kumasi South Hospital.
It was upgraded to the status of Ashanti Regional Hospital in
2002. The Kumasi metropolis has a total population of 3,490,030
(2021 population census).
Study Population and Sample
Size Estimation
A total of 241 T2DM patients were recruited based on the
inclusion criteria of the study until the required sample size
was attained. The diagnosis of type 2 diabetes mellitus was made
based on the American Diabetes Association (ADA) criteria (32).
The sample size was estimated using the formula n = Z
2
×p(1−
p)/d
2
(Charan & Biswas, 2013), where n = sample size, Z = 1.96,
p = prevalence, and d = marginal error (0.05). Using a prevalence
of 90.6% obtained from a similar study conducted by Agyemang-
Yeboah et al. (33) in the Bantama sub-metro, the estimated
sample size (n) was 124. To increase statistical power and
account for non-response distribution, 241 T2DM patients
were sampled for the study.
Inclusion and Exclusion Criteria
Outpatients T2DM patients who gave consent to the study were
the only ones recruited. Outpatients 30 years to 78 years who had
type 2 diabetes mellitus and consented to participate were
recruited and included into the study. Pregnant women and
outpatients diagnosed with gestational diabetes or type 1 diabetes
mellitus were excluded from the study. Individuals below 30
years were also excluded from the study and those who suffered
from chronic conditions (hypertension, stroke, HIV, tuberculosis
and cancer) were as well excluded from this study.
Ethical Consideration
Approval was sought from the Committee on Human Research,
Publication and Ethics (CHRPE) at the School of Medical
Sciences of the Kwame Nkrumah University of Science and
Technology (KNUST) and the Komfo Anokye Teaching
Hospital (KATH), Kumasi and the Kumasi South Hospital.
Written informed consent was sought from each participant
before the commencement of the study.
Data Collection
Participants were first educated on the purpose of the study and
only those who gave consent to participate in the study were
recruited. A self-reported questionnaire was used to obtain
information about the name, age, gender, and sociodemographic
factors such as marital status, level of education, occupation,
family history of diabetes, level of physical activity, smoking
status, and alcohol status of the participants.
Blood Pressure Measurement
Blood pressure was measured by qualified nurses using a mercury
sphygmomanometer and stethoscope. Recommendation of the
American Heart Association (AHA) was used to take
measurements from the upper left arm after participants had sat
for more than 5 min (Kirkendall, Burton, Epstein, & Freis, 1967).
The average value for the two measurements (with a 5-minute
break interval between measurements) was recorded to the nearest
2.0 mmHg.
Anthropometric Measurements
Anthropometric measurements included height, weight, WC,
HC,BMI,WHR,WHtR,BAI,CI,andthetwonewindices—
ABSI and BRI. The height of subjects was measured to the
nearest 0.1 cm without shoes and weight was also measured to
the nearest 0.1 kg with participants in light clothing. A
bathroom scale (Zhongshan Camry Electronic Co. Ltd.,
Guangdong, China) was used to weigh the participants and
their height was measured with a stadiometer (Seca 213
mobile stadiometer, Germany). During height measurement,
participants stood upright with back straight, heels together,
and their feet slightly apart at a 60° angle. Waist circumference
(to the nearest 0.1 cm) was measured with a Gulick II spring-
loaded measuring tape (Gay Mills, WI) halfway between the
Anto et al. Cardiometabolic Syndrome in Type 2 Diabetes Mellitus
Frontiers in Clinical Diabetes and Healthcare | www.frontiersin.org February 2022 | Volume 2 | Article 8072013
inferioranglesoftheribsandthesuprailiaccrests.Thehip
circumference was measured at the widest diameter around
the gluteal protuberance to the nearest 0.1 cm. The other
anthropometric indices were calculated as follows:
WHR = waist(cm)=hip(cm)WHtR = waist(cm)=height(cm)
BMI was calculated according to Quetelet’s formula (34):
BMI = Weight(kg)
Height(m)2
CI was calculated from the formula (23):
CI = Waistcircumference(m)
0:109 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
Weight(kg)
Height(m)
q
The BAI was calculated from the formula (35):
BAI = ½hipcircumferenceðcmÞ÷ heightðmÞ1:5−18
ABSI was calculated from the formula (25):
ABSI = Waistcircumference(m)
BMI2
3Height(m)1
2
BRI was calculated by the formula (26):
BRI = 364:2–365:5(1 −½WC=2p2=½0:5height2Þ1
2:
Blood Sampling and Biochemical Analysis
A volume of five (5) milliliters (mls) venous blood samples were
collected after an overnight fast; 4 ml was dispensed into a serum
separator tube and 1 ml into fluoride-oxalate tubes. After
centrifugation at 500gfor 15 min, the serum and plasma were
stored at −80°C until assayed. Parameters included Fasting
plasma glucose (FPG), HbA1C, total cholesterol (TC), low-
density lipoprotein (LDL), triglycerides (TG), high-density
lipoprotein (HDL) cholesterol, and were assayed using the
COBAS INTEGRA
(R)
400 plus Automated Chemistry
Analyzer. The protocol for the determination of the
parameters was as indicated in the manufacturer’s instructions
(Fortress Diagnostics Limited, Unit 2C Antrim Technology Park,
Antrim BT41 1QS, United Kingdom).
Definition of Clinicobiochemical Terms
Dyslipidemia was defined as follows: high TC (>5.0 mmol/L),
high LDL-C (>3.0 mmol/L), high TG (>1.7 mmol/L), low
HDL-C (<1.0 mml/L for men and <1.2 mmol/L for women)
(36). Atherogenic dyslipidemia was defined as high TG
levels, low HDL cholesterol levels and an increase in LDL
(37). BMI was categorized into four groups according to the
conventional WHO classification (38): underweight (<18.5
kg/m
2
), normal weight (18.5–24.9 kg/m
2
), overweight (25–
29.9 kg/m
2
), and obese (≥30 kg/m
2
). Metabolic syndrome
(MetS) was defined according to the National Cholesterol
Education Program (NCEP) Adult Treatment Panel III (ATP
III), without waist circumference (39); BP over 130/85 mmHg,
TG >1.7 mmol/L, HDL-C levels less than 1.03 mmol/L (men)
or 1.29 mmol/L (women) and fasting blood glucose over
5.5 mmol/L. Blood pressure was defined as mean SBP
≥140 mmHg and/or a mean DBP ≥90mmHg or previously
diagnosed hypertension or patient on blood pressure
lowering drugs.
Statistical Analysis
Entry and analysis of data were done using Microsoft Excel 2016
and SPSS version 25.0. Categorical variables were presented as
frequencies with percentages while continuous variables were
presented as means with standard deviations or medians
with interquartile ranges after checking for normality. Chi-
square analysis was used to determine the association of
sociodemographic characteristics such as age categories,
gender, marital status, occupation and level of physical activity
with MetS among the type 2 diabetes mellitus patients.
Parametric data were analyzed using independent t-test
whereas nonparametric data were analyzed using Mann–
Whitney U-test. To assess the strength of the association
between continuous variables, partial Spearman’s correlation
coefficient was used. Multivariate logistic regression analysis
was conducted to compare the predictive capacities of
the anthropometric indices for cardiometabolic risk among the
participants. P-values less than 0.05 were considered statistically
significant for all analyses.
RESULTS
Socio-Demographic Characteristics of the
Study Population
Table 1 shows the socio-demographic characteristics of Type 2
diabetes patients with and without metabolic syndrome. A total
of 241 type 2 diabetes mellitus patients were recruited into the
study of which 138 (57.3%) had no metabolic syndrome (MetS)
and 103 (42.7%) had MetS.
Themedianageofthetotalparticipantswas58yearsand
statistically, there was no significant difference between the
median ages of participants without MetS and those with
MetS [57.50 versus 58.00, p= 0.625]. Majority of the
participants were in the age categories 50–59 (32.0%). Age
categories was however not significantly associated with MetS
status of participants (p= 0.068). The male:female ratio of the
overall participants was 1:1.43. Of the 103 participants with
MetS, 72.8% were females and 28.2% were males. Gender was
significantly associated with MetS status (p<0.0001). The
highest proportion of participants was married (68.0%).
Marital status of participants was significantly associated
with their MetS status (p= 0.018). Furthermore, higher
proportion of the participants had completed junior high
school (32.4%), were employed (63.1%), practice sedentary
lifestyle with frequent exercise (40.2%), had family history of
T2DM (76.7%), were non-smokers (85.2%) and non-alcoholic
beverage drinkers (57.3%). On the contrary, participants’
educational level, occupation, physical activity status, family
history of T2DM, smoking status and alcohol intake status
Anto et al. Cardiometabolic Syndrome in Type 2 Diabetes Mellitus
Frontiers in Clinical Diabetes and Healthcare | www.frontiersin.org February 2022 | Volume 2 | Article 8072014
were not proportionally significantly different in terms of
participants with and without MetS status (p>0.05).
Clinical, Anthropometric, and Lipid Profile
Variables of the Study Population
Table 2 shows the clinical, anthropometric and lipid profile
variables of the study population. Participants with MetS had
significantly higher median levels of SBP (148.00 mmHg versus
132.00 mmHg, p<0.0001) and DBP (88.00 mmHg versus 78.00
mmHg, p<0.0001) compared to the participants without MetS.
Levels of FBG (p= 0.067) and HbA1C (p= 0.158) were not
significantly different between the two groups. Also, except for
height which was significantly taller among participants without
MetS than that observed for participants with MetS [1.66 m
versus 1.64 m, p= 0.025], all the other anthropometric indices,
namely, Weight, BMI, CI, BAI, ABSI, BRI, WC, HC, and WHtR
were significantly higher among participants with MetS
compared to those without MetS (p<0.05). Participants with
MetS had significantly higher median concentrations of TG [1.37
mmol/L versus 1.05 mmol/L, p<0.0001], Coronary risk [5.40
versus 4.68, p<0.0001], and VLDL [0.62 mmol/L versus 0.48
mmol/L, p<0.0001] than that observed among participants
TABLE 1 | Socio-demographic characteristics of the study population.
Variables Total(n = 241) T2DM
Without MetS (n = 138) With MetS (n = 103) p-value
Age (years) 0.625
Median (IQR) 58.00 (50.00–65.00) 57.50 (50.00–66.00) 58.00 (49.00–63.00)
Age Categories n (%) 0.068
30–49 59 (24.5) 33 (23.9) 26 (25.2)
50–59 77 (32.0) 47 (34.1) 30 (29.1)
60–69 74 (30.7) 35 (25.4) 39 (37.9)
70–79 31 (12.9) 23 (16.7) 8 (7.8)
Sex n (%) <0.0001
Male 99 (41.1) 71 (51.4) 28 (27.2)
Female 142 (58.9) 67 (48.6) 75 (72.8)
Marital status n (%) 0.018
Single 4 (1.7) 3 (2.2) 1 (1.0)
Married 164 (68.0) 102 (73.9) 62 (60.2)
Divorced 18 (7.5) 4 (2.9) 14 (13.6)
Separated 7 (2.9) 3 (2.2) 4 (3.9)
Widowed 48 (19.9) 26 (18.8) 22 (21.4)
Educational level n (%) 0.765
Tertiary 36 (14.9) 24 (17.4) 12 (11.7)
Senior High School 57 (23.7) 33 (23.9) 24 (23.3)
Junior High School 78(32.4) 42 (30.4) 36 (35.0)
Lower Primary School 28(11.6) 15 (10.9) 13 (12.6)
No former education 42 (17.4) 24 (17.4) 18 (17.5)
Occupation n (%) 0.970
Student 1 (0.4) 0 (0.0) 1 (1.0)
Retired 32 (13.3) 19 (13.8) 13 (12.6)
Keeping House 23 (9.5) 14 (10.1) 9 (8.7)
Employed 152 (63.1) 87 (63.0) 65 (63.1)
Unemployed 31 (12.9) 17 (12.3) 14 (13.6)
Other 2 (0.8) 1 (0.7) 1 (1.0)
Physical activity n (%) 0.915
Primary sedentary 59 (24.5) 32 (23.2) 27 (26.2)
Sedentary with frequent activity 97 (40.2) 56 (40.6) 41 (39.8)
Primary physical 79 (32.8) 47 (34.1) 32 (31.1)
Physical with high intensity activity 6 (2.5) 3 (2.2) 3 (2.9)
Family history of T2DM n (%) 0.006
Yes 184 (76.7) 99 (72.3) 85 (82.5)
No 56 (23.3) 38 (27.7) 18 (17.5)
Smoking n (%) 0.223
Yes 33 (13.8) 22 (16.2) 11 (10.7)
No 206 (85.2) 114 (83.8) 92 (89.3)
Alcohol intake n (%) 0.991
Yes 102 (42.7) 58 (42.6) 44 (42.7)
No 137 (57.3) 78 (57.4) 59 (57.3)
Data is presented as median (IQR); Mann–Whitney test or n (%); Chi-square or Fisher’s test. p <0.05 was considered significant for Type 2 Diabetes patients with and without metabolic
syndrome.
n, number; IQR, Interquartile range.
Bold value indicates the statistically significant p-values.
Anto et al. Cardiometabolic Syndrome in Type 2 Diabetes Mellitus
Frontiers in Clinical Diabetes and Healthcare | www.frontiersin.org February 2022 | Volume 2 | Article 8072015
without MetS. Conversely, HDL-C concentration was
significantly lower among participants with MetS compared to
participants without MetS [1.20 mmol/L versus 1.32 mmol/L,
p<0.0001]. The median concentrations of TC and LDL-C
measures were however not statistically significantly different
between T2DM with and without MetS status of participants (p=
0.347 and p= 0.252 respectively).
Prevalence of Cardiometabolic Risk
Factors Among the Study Population
Stratified by Male and Female
Figure 1 shows the prevalence of cardiometabolic risk factors
among the study population stratified by male and female. Of the
241 subjects, 16 had dyslipidemia, 103 had metabolic syndrome
and 87 were hypertensive representing a prevalence of 6.6, 42.7,
and 36.1%, respectively. Stratifying by gender, a proportion of
6.3% (9/142) of female participants had dyslipidemia, 52.8% (75/
142) had MetS, and 35.2% (50/142) had hypertension. Of the 99
males, 7.1% had dyslipidemia, 28.3% had metabolic syndrome,
and 37.4% were hypertensive. There was a statistically significant
difference in the proportions between male and females in terms
of their MetS status (p= 0.0002). On the contrary, there was no
significant difference in the proportions between males and
females in relation to dyslipidemia (p= 0.8085) and
hypertension (p= 0.7309) status of the participants.
Anthropometric Indices,
Sociodemographic, Clinical indices and
Lipid Measures Associated With MetS
Among T2DM Patients
Table 3 shows the odds ratios of anthropometric indices,
sociodemographic, clinical indices, and lipid profile measures
associated with MetS. After adjusting for possible confounders in
multivariate logistic regression, BRI quartiles—Q3[a OR = 25.15,
95%CI (2.02–313.81), p= 0.012], Q4 [aOR = 39.00, 95%CI (2.68–
568.49), p= 0.007], being a female [aOR = 3.02, 95%CI (1.59–
5.76), p= 0.001] and divorced [aOR = 4.05, 95%CI (1.22–13.43),
p= 0.022], DBP [aOR = 1.07, 95%CI (1.03–1.10), p<0.0001] and
HDL-C [aOR = 0.10, 95%CI (0.03–0.35), p<0.0001] were the
independent predictors of MetS among T2DM.
Partial Spearman Correlation Coefficients
of Anthropometric Indices With
Hemodynamic and Lipid Markers Among
T2DM Patients
Table 4 illustrates the partial coefficients of Spearman correlation of
the anthropometric indices (ABSI, BRI, BMI, WC, BAI, CI, WHtR,
and WHR) with hemodynamic indices and the lipid markers
among the T2DM patients. After controlling for age and gender,
the new indices—ABSI and BRI were correlated moderately
TABLE 2 | Clinical, anthropometric and lipid profile variables of the study population.
Variable Total (n = 241) T2DM
Without MetS (n = 138) With MetS (n = 103) p-value
Clinical
SBP (mmHg) 136.00 (121.00–153.00) 132.00 (119.00–144.50) 148.00 (130.00–162.00) <0.0001
DBP (mmHg) 81.00 (72.00–90.00) 78.00 (70.00–84.00) 88.00 (77.00–97.00) <0.0001
FBS (mmol/L) 7.90 (6.30–11.40) 7.60 (5.60–11.70) 8.20 (6.98–11.00) 0.067
HbA1C (%) 8.00 (6.60–9.60) 7.80 (6.40–9.45) 8.15 (7.00–10.03) 0.158
Anthropometrics
Height (m) 1.65 (1.60–1.70) 1.66 (1.62–1.71) 1.64 (1.58–1.69) 0.025
Weight (kg) 68.65 (60.95–80.55) 66.78 (58.98–76.36) 75.45 (65.35–84.000 <0.0001
BMI (kg/m
2
) 25.51 (22.57–29.30) 24.07 (21.62–27.90) 27.72 (23.74–30.86) <0.0001
CI (m
3/2
/kg
1/2
) 1.31 (1.25–1.37) 1.29 (1.21–1.35) 1.34 (1.29–1.40) <0.0001
BAI (%) 29.14 (24.22–33.60) 26.11 (22.61–31.33) 31.66 (27.83–35.86) <0.0001
ABSI (m
11/6
kg
−2/3
) 0.083 (0.079–0.088) 0.082 (0.078–0.086) 0.084 (0.080–0.089) 0.001
BRI 4.70 (3.58–5.83) 3.87 (3.14–4.92) 5.18 (4.65–6.46) <0.0001
WC (cm) 92.77 ± 13.05 88.10 ± 13.56 99.02 ± 9.20 <0.0001
HC (cm) 99.41 ± 13.60 94.83 ± 13.97 105.55 ± 10.30 <0.0001
WHR 0.94 ± 0.06 0.93 ± 0.07 0.94 ± 0.06 0.237
WHtR 0.56 ± 0.08 0.53 ± 0.09 0.60 ± 0.07 <0.0001
Lipid profile
TG (mmol/L) 1.13 (0.89–1.51) 1.05 (0.84–1.35) 1.37 (0.95–1.79) <0.0001
TC (mmol/L) 4.80 (3.77–5.50) 4.70 (3.80–5.40) 4.90 (93.70–5.65) 0.347
HDL-C (mmol/L) 1.30 (1.10–1.50) 1.32 (1.20–1.60) 1.20 (1.10–1.40) <0.0001
LDL-C (mmol/L) 2.81 (1.93–3.61) 2.68 (1.94–3.43) 2.97 (1.93–3.78) 0.252
Coronary Risk 4.88 (3.83–5.96) 4.68 (3.58–5.53) 5.40 (4.21–6.49) <0.0001
VLDL-C (mmol/L) 0.52 (0.41–0.68) 0.48 (0.38–0.62) 0.62 (0.44–0.82) <0.0001
Non-parametric data is presented as median (IQR); compared using Mann–Whitney test. p<0.05 was considered statistically significant for Type 2 diabetes patients without metabolic
syndrome versus those with metabolic syndrome. Parametric data is presented as mean ± SD; compared using independent sample t-test. p<0.05 was considered statistically significant
for Type 2 diabetes patients without metabolic syndrome versus those with metabolic syndrome. n, number; IQR, Interquartile range; SD, Standard deviation; SBP, Systolic Blood
Pressure; DBP, Diastolic Blood Pressure; FBS, Fasting Blood Sugar; HbA1C, Glycated hemoglobin; BMI, Body mass index; CI, Conicity index; BAI, Body adiposity index; ABSI, A body
shape index; BRI, Body roundness index; WC, Waist circumference; HC, Hip circumference; WHR, Waist-to-hip ratio; WHtR, Waist-to-height ratio; TG, Triglycerides; TC. Total Cholesterol;
HDL-C, High Density Lipoprotein Cholesterol; LDL-C, Low Density Lipoprotein Cholesterol; Coronary risk, TC/HDL-C; VLDL, Very Low-Density Lipoprotein Cholesterol.
Bold value indicates the statistically significant p-values.
Anto et al. Cardiometabolic Syndrome in Type 2 Diabetes Mellitus
Frontiers in Clinical Diabetes and Healthcare | www.frontiersin.org February 2022 | Volume 2 | Article 8072016
(r=0.406,p<0.0001). The BRI had a strong positive correlation
with WHtR (r=0.992,p<0.0001), WC (r= 0.940, p<0.0001), BAI
(r= 0.895, p<0.0001), BMI (r= 0.709, p<0.0001), a moderate and
weak correlation with CI (r= 0.646, p<0.0001), and WHR
(r = 0.218, p <0.0001) respectively but showed a negative
correlation with height (r=−0.273, p<0.0001). However, ABSI
only showed a strong positive relationship with CI (r= 0.957,
p<0.0001) but moderate correlation with WC (r= 0.499, p<0.0001),
WHR (r= 0.461, p<0.0001), and BAI (r=0.355,p<0.0001). For
BMI (r=−0.313, p<0.0001), ABSI showed a moderate negative
correlation. ABSI was not associated with WHR and Height of
participants. Moreover, BRI showed a slight but significant positive
correlation with blood pressure (DBP) and two lipid markers—TG
and VLDL-C. This was however not true for ABSI as it was neither
associated with blood pressure nor any of the lipid markers.
DISCUSSION
Association between type 2 diabetes mellitus (T2DM) and
cardiometabolic syndrome have extensively been explored.
However, for the first time, this study evaluated the prevalence
of cardiometabolic syndrome and its association with two new
anthropometric indices among T2DM patients in two selected
hospitals in the Ashanti Region of Ghana.
The present study found 42.7% of the T2DM patients with
MetS and the prevalence was higher in females than in male
participants (Figure 1). Similarly, a cross-sectional study by
Yadav et al. (40) among Indian type 2 diabetes patients
reported MetS prevalence of 57.7% with females having a
higher prevalence than males. The slightly higher prevalence of
the previous study could be partly due to low sample size in the
present study and genetic differences between Caucasians and
Blacks. This study registered more females (58.9%) as compared
to males (41.1%). In this present study, there was a significant
association between sex of participants and their MetS status
(p<0.0001). Furthermore, being a female was significantly
associated with increased odds of having MetS as compared to
being a male. This finding is consistent with previous cross-
sectional studies which also reported that female type 2 diabetes
patients are at higher risk of having MetS when compared to
males with type 2 diabetes mellitus (T2DM) (41,42). Less
exercise, increased body weight, and an increased risk of
dyslipidemia in women could be the possible reason for the
higher odds of females having MetS than males (41). In this
study, marital status of participants was significantly associated
with MetS status. Being divorced was associated with significant
5-times increased odds of having MetS compared to being single
even after possible covariates were controlled. Chung and
colleagues reported a similar finding in a cross-sectional study
conducted among Korean adults (43). The driving factor for this
finding is however not well understood. Probable explanations
could be lack of social support and living alone after being
divorced which could compound the risk of having MetS (44).
Controversies still exist as to which anthropometric index
best predict cardiometabolic risk among T2DM. BMI is the most
widely used obesity marker and has been associated with
cardiometabolic risk and type 2 diabetes (45). In this present
study, the median Body Mass Index (BMI), Waist-to-Hip Ratio
(WHR), Waist-to-Height Ratio (WHtR), Body Adiposity Index
(BAI), and Conicity Index (CI) were significantly higher among
T2DM patients who had MetS as compared to those without
MetS. However, none of these anthropometric were independent
predictors of MetS after multivariate logistic regression. BMI is
reported to be a poor indicator of cardiometabolic syndrome
compared to the other obesity indices (46–48). BMI is unable to
distinguish between fat and muscle mass and also between fat
compartments such as visceral adipose tissue and subcutaneous
adipose tissue, which are closely linked to cardiometabolic
syndrome (49). Additionally, the possible explanation for
FIGURE 1 | Prevalence of cardiometabolic risk factors among the study population stratified by Male and Female.
Anto et al. Cardiometabolic Syndrome in Type 2 Diabetes Mellitus
Frontiers in Clinical Diabetes and Healthcare | www.frontiersin.org February 2022 | Volume 2 | Article 8072017
failure of BMI, BAI, CI, WC, WHR, and WHtR independently
predict MetS could be due to its weaker correlation with
cardiometabolic risk factors as compared to the other obesity
indices as observed in this study.
In this current study, the two new indices [A Body Shape
Index (ABSI) and Body Roundness Index (BRI)] were included
to the traditional anthropometric indices in quest to compare
their predictive capabilities for cardiometabolic risk among type
2 diabetes patients in a Ghanaian population. This current study
found that ABSI could not independently predict MetS among
the T2DM when it was compared to the other anthropometric
indices in an adjusted multivariate logistic model. This finding is
in consonant with previous studies conducted among the
Caucasians (50–52). Maessen et al. (52) found that the ABSI
TABLE 3 | Anthropometric indices, sociodemographic, blood pressure and lipid profile variables associated with MetS among T2DM patients.
Variables cOR (95%CI) p-value aOR (95%CI)* p-value
ABSI quartiles
Q1 Ref (1) Ref (1)
Q2 1.98 (0.93–4.20) 0.078 1.2 (0.41–3.60) 0.734
Q3 1.12 (0.54–2.47) 0.707 0.67 (0.22–2.03) 0.482
Q4 3.56 (1.61–7.90) 0.002 1.80 (0.59-5.51) 0.304
BRI quartiles
Q1 Ref (1) Ref (1)
Q2 5.15 (1.9–13.96) 0.001 8.04 (0.71–90.90) 0.092
Q3 14.18 (5.23–38.46) <0.0001 25.15 (2.02–313.81) 0.012
Q4 18.78 (6.82–51.71) <0.0001 39.00 (2.68–568.49) 0.007
BAI quartiles
Q1 Ref (1) Ref (1)
Q2 2.85 (1.21–6.76) 0.017 0.50 (0.13–1.91) 0.31
Q3 7.30 (3.09–17.28) <0.0001 0.65 (0.14–2.95) 0.574
Q4 7.86 (3.31–18.63) <0.0001 0.33 (0.05–2.16) 0.25
BMI categories
Underweight Ref (1)––
Normal weight 1.37 (0.26–7.17) 0.708 ––
Overweight 3.32 (0.63–17.50) 0.156 ––
Obese 5.25 (0.95–29.147) 0.058 ––
CI status
Normal Ref (1) Ref (1)
High risk 8.33 (2.45–28.40) 0.001 3.48 (0.59–20.60) 0.17
WHtR status
Normal Ref (1) Ref (1)
High risk 10.38 (3.94–27.33) <0.0001 0.60 (0.04–9.40) 0.717
WHR status
Normal Ref (1) Ref (1)
Overweight 2.41 (0.98–5.93) 0.055 1.02 (0.33–3.20) 0.971
Obese 3.80 (1.89–7.62) <0.0001 0.54 (0.10–2.83) 0.462
Sex
Male Ref (1) Ref (1)
Female 2.84 (1.64–4.91) <0.0001 3.02 (1.59–5.76) 0.001
Marital status
Single Ref (1) Ref (1)
Married 0.55 (0.06–5.39) 0.606 0.41 (0.04–4.28) 0.457
Divorced 5.76 (1.81–18.28) 0.003 4.05 (1.22–13.43) 0.022
Separated 2.19 (0.48–10.13) 0.314 1.42 (0.28–7.23) 0.676
Widowed 1.39 (0.73–2.67) 0.318 0.68 (0.31–1.53) 0.353
SBP (mmHg) 1.03 (1.01–1.04) <0.0001 1.01 (0.99–1.03) 0.306
DBP (mmHg) 1.07 (1.04–1.09) <0.0001 1.07 (1.03–1.10) <0.0001
TG (mmol/L) 2.89 (1.69–4.93) <0.0001 0.00 (0.00–Inf) 0.353
TC (mmol/L) 1.13 (0.92–1.38) 0.243 ––
HDL-C (mmol/L) 0.18 (0.07–0.45) <0.0001 0.10 (0.03–0.35) <0.0001
LDL-C (mmol/L) 1.17 (0.94–1.47) 0.159 ––
Coronary Risk 1.40 (1.17–1.69) <0.0001 1.10 (0.86–1.40) 0.455
VLDL (mmol/L) 10.86 (3.32–35.53) <0.0001 >100 (0.00–Inf) 0.324
Compared using univariate and multivariate logistic regression.
p <0.05 was considered significant.
*Adjusted for age and gender and marital status of participants.
cOR, Crude odds ratio; aOR: Adjusted odds ratio. Inf: infinity; Ref, reference; Q1, first quartile; Q2, Second quartile; Q3, Third quartile; Q4, Fourth quartile; ABSI, A body shape index; BRI,
Body roundness index; BAI, Body adiposity index; BMI, Body mass index; CI, Conicity index; WHtR, Waist-to-height ratio; WHR, Waist-to-hip ratio; SBP, Systolic Blood Pressure; DBP,
Diastolic Blood Pressure; TG, Triglycerides; TC, Total Cholesterol; HDL-C, High Density Lipoprotein Cholesterol; LDL-C, Low Density Lipoprotein Cholesterol; Coronary risk, TC/HDL-C;
VLDL, Very Low-Density Lipoprotein Cholesterol.
Bold value indicates the statistically significant p-values.
Anto et al. Cardiometabolic Syndrome in Type 2 Diabetes Mellitus
Frontiers in Clinical Diabetes and Healthcare | www.frontiersin.org February 2022 | Volume 2 | Article 8072018
was ineffective in identifying cardiometabolic risk factors among
Netherland population. Similarly, a study conducted by Li et al.
(51) showed that ABSI failed to significantly predict MetS and
T2DM among overweight and obese adults The poor correlation
between ABSI and MetS, on the other hand, is debatable in that
previous studies have linked ABSI to some cardiometabolic risk
factors (26,53). Disparities in anthropometric measures and
races may have a significant impact on the predictive value of
ABSI. Although the ABSI formula was adjusted for BMI, obesity
status differs for Africans, Europeans and Asians since there is
differences in WC across these races (25). Furthermore, Asians
are much shorter than Africans, Americans and Europeans,
which may confound the predictive value of ABSI. Ethnicity
has been a significant moderator in the relationship between
these obesity indices and cardiometabolic risk, and it holds true
for both genders. In a meta-analysis conducted by Rico-Martı
́
n
et al. (54), the AUCs for all anthropometric indices in the non-
Chinese population were better predictors of MetS than they
were for the Chinese population. A probable reason for the
failure of ABSI to superiorly predict MetS is that, it was originally
formulated as a risk assessment tool to predict mortality risk in a
follow-up study (25). However, we employed it in a cross-
sectional study to predict MetS among T2DM patients. There
is a possibility that this resulted in the failure of the ABSI to show
significant predictive power compared with the other indices.
Also, ABSI was moderately correlated with WC in the current
study but showed negative correlation with BMI from the partial
Spearman’s correlation coefficients. A negative correlation with
BMI suggests an inverse relationship between the two (ABSI
increases with decreasing BMI). Despite limitations of BMI and
WC, increasing measures of these indices are widely standard
markers to predict MetS and other cardiometabolic risk (55–58).
The moderate and negative correlation of ABSI with WC and
BMI respectively may have accounted for its failure to predict
MetS among the T2DM. Furthermore, ABSI showed no
significant correlation with the hemodynamic indices and the
lipid markers from the partial Spearman’s correlation test. These
markers are known predictors of MetS (59,60) and this could be
among the reasons why ABSI was ineffective to predict MetS
among the subjects.
The strength of this study is that BRI but not ABSI and other
anthropometric indices (BMI, WHR, WHtR, BAI, and CI) was
an independent predictor of cardiometabolic risk among the type
2 diabetes mellitus patients after controlling for age, gender, and
marital status of participants. BRI estimates the human as an
elliptical figure and improved body fat% and Visceral Adipose
Tissue (VAT)% as compared to the traditional anthropometric
indices. VAT% and MetS have a well-established association
(61). In the present study, BRI was the only independent
predictor of MetS. This implies that only BRI was significantly
associated with the higher odds of having MetS and is superior to
the traditional indices in predicting MetS among the subjects. In
keeping with our results, several studies have similarly reported
the superior power of BRI over the traditional anthropometric
indices in predicting MetS (51,62,63). In a meta-analysis of data
pooled from more than one fifty thousand people, increased BRI
odds was significantly associated with increased risk of having
MetS (54). Additionally, a study conducted among obese and
overweight Chinese adults found that BRI was a better predictor
of MetS and T2DM (51).
TABLE 4 | Partial Spearman correlation coefficients of anthropometric measures with hemodynamic and lipid markers among T2DM patients.
ABSI BRI BMI WC BAI CI WHtR WHR
ABSI 1 0.406** −0.313** 0.499** 0.355** 0.957** 0.461** 0.123
BRI 0.406** 1 0.709** 0.940** 0.895** 0.646** 0.992** 0.218**
BMI −0.313** 0.709** 1 0.646** 0.640** −0.030 0.677** 0.122
WC 0.499** 0.940** 0.646** 1 0.824** 0.725** 0.958** 0.197**
BAI 0.355** 0.895** 0.640** 0.824** 1 0.573** 0.908** −0.207**
CI 0.957** 0.646** −0.030 0.725** 0.573** 1 0.694** 0.162*
WHtR 0.461** 0.992** 0.677** 0.958** 0.908** 0.694** 1 0.188**
WHR 0.123 0.218** 0.122 0.197** −0.207** 0.162* 0.188** 1
Height 0.073 −0.273** −0.175* 0.035 −0.374** 0.022 −0.251** −0.004
SBP −0.031 0.101 0.128 0.105 0.063 0.013 0.104 0.105
DBP 0.012 0.190** 0.216** 0.216** 0.135* 0.081 0.204** 0.134
FBS −0.041 −0.268** −0.265** −0.277** −0.222** −0.123 −0.265** −0.083
HbA1C −0.107 −0.259** −0.181** −0.245** −0.223** −0.169* −0.253** −0.079
TG 0.144 0.199** 0.070 0.151* 0.130 0.171* 0.190** 0.189**
TC 0.055 −0.054 −0.092 −0.054 −0.064 0.023 −0.054 0.041
HDL-C −0.024 −0.270** −0.282** −0.275** −0.225** −0.110 −0.266** −0.103
LDL-C 0.039 −0.034 −0.047 −0.021 −0.040 0.020 −0.032 0.027
CR 0.060 0.092 0.060 0.089 0.057 0.076 0.087 0.094
VLDL-C 0.145 0.199** 0.069 0.151* 0.130 0.172* 0.190** 0.189**
All correlation coefficients were adjusted for age and gender.
**Correlation is significant at the 0.01 level (2-tailed).
*Correlation is significant at the 0.05 level (2-tailed). The index associated with the highest correlative strength to the variable in the same row has been highlighted.
ABSI, A body shape index; BRI, Body roundness index; BMI, Body mass index; WC, Waist circumference; BAI, Body adiposity index; CI, Conicity index; WHtR, Waist-to-height ratio; WHR,
Waist-to-hip ratio; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure; FBS, Fasting Blood Sugar; HbA1C, Glycated hemoglobin; TG, Triglycerides; TC, Total Cholesterol; HDL-
C, High Density Lipoprotein Cholesterol; LDL-C, Low Density Lipoprotein Cholesterol; CR, Coronary Risk; VLDL, Very Low-Density Lipoprotein Cholesterol.
Bold value indicates the statistically significant p-values.
Anto et al. Cardiometabolic Syndrome in Type 2 Diabetes Mellitus
Frontiers in Clinical Diabetes and Healthcare | www.frontiersin.org February 2022 | Volume 2 | Article 8072019
The current study found that BRI had a strong positive
correlation with WC and also with BMI regardless of the
exclusion of BMI in the BRI formulation but a negative
correlation with height after age and gender were controlled.
This finding is consistent with a study by Li et al. (51). In other
words, for a constant WC, height decreases whereas BMI increases
and the body assumes an elliptical shape. Elliptical shape and
increased odds of cardiometabolic risks has been well established
(64). Also, BMI and WC have been globally accepted as a tool for
predicting MetS despite some shortcomings (55–58). This may
have accounted for superiority of BRI in predicting MetS owing to
its strong correlation with these two markers after possible
cofounders were controlled. Furthermore, partial Spearman’s
correlation coefficients showed that BRI was marginal but
significantly associated with blood pressure (increasing DBP)
and the lipid markers (TG and VLDL-C). Several studies have
linked increasing blood pressure and lipid markers to a significant
increased likelihood of having MetS among T2DM (59,60). This
could possibly be another reason why BRI showed a superior
predictive capacity for MetS among the subjects over the
other indices.
Despite the novel findings, this study had some limitations that
are worth mentioning for consideration by future studies. First of
all, the female participants outnumbered the males which could
have introduced a bias in the prevalence of cardiometabolic
syndrome. Also, all the participants were aged (30 and above),
and we could not validate that the optimal anthropometric index
(BRI) would be superior to the other indices in other age groups.
Sample size was also small (241) which could have introduced a
bias in our analysis.
Conclusions
The prevalence of cardiometabolic syndrome was 42.7% among
the T2DM patients. Cardiometabolic syndrome was influenced
by female gender, being divorced, and increased body roundness
index (BRI). Integration of BRI as part of routine assessment
could be used as early indicator of cardiometabolic syndrome
among T2DM patients.
Further studies can be done with a larger population to establish
the relationship between these two new but simple anthropometric
indices and MetS among type 2 diabetes patients.
DATA AVAILABILITY STATEMENT
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
ETHICS STATEMENT
The studies involving human participants were reviewed and
approved by The Committee on Human Research, Publication
and Ethics, Kwame Nkrumah University of Science and
Technology. The patients/participants provided their written
informed consent to participate in this study.
AUTHOR CONTRIBUTIONS
Conceptualization EOA; methodology, JF, and EOA; formal
analysis, JF, EOA, and SO; investigation, JF, VCKTT, CH, and
BA; original draft preparation, EOA, and JF supervision, EOA.
All authors listed reviewed, edited have made a substantial,
direct, and intellectual contribution to the work and approved
it for publication.
ACKNOWLEDGMENTS
The authors are grateful to staff of the Komfo Anokye Teaching
Hospital, Kumasi South Hospital, research assistants and
volunteers who contributed in diverse ways for successful
implementation of the study.
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