Classical Cardiovascular Risk Factors and HIV are Associated With
Carotid Intima-Media Thickness in Adults From Sub-Saharan Africa:
Findings From H3Africa AWI-Gen Study
Engelbert A. Nonterah, MBChB, MSc; Palwende R. Boua, MSc; Kerstin Klipstein-Grobusch, PhD; Gershim Asiki, MBChB, PhD;
Lisa K. Micklesfield, PhD; Godfred Agongo, MPhil; Stuart A. Ali, PhD; Felistas Mashinya, PhD; Herman Sorgho, PhD;
Seydou Nakanabo-Diallo, MBChB, MSc; Cornelius Debpuur, PhD; Catherine Kyobutungi, MBChB, PhD; Marianne Alberts, PhD;
Shane Norris, PhD; Stephen Tollman, MBChB, PhD; Halidou Tinto, PhD; Cassandra C. Soo, MSc; Freedom Mukomana, MSc;
Scott Hazelhurst, PhD; Alisha N. Wade, MBBS, DPhil; Kathleen Kahn, MBChB, PhD; Abraham R. Oduro, MBChB, PhD;
Diederick E. Grobbee, MD, PhD; Osman Sankoh, PhD; Mich�ele Ramsay, PhD; Michiel L. Bots, MD, PhD; Nigel J. Crowther, PhD;
as members and collaborators of AWI-Gen and the H3Africa Consortium*

Background-—Studies on the determinants of carotid intima-media thickness (CIMT), a marker of sub-clinical atherosclerosis,
mostly come from white, Asian, and diasporan black populations. We present CIMT data from sub-Saharan Africa, which is
experiencing a rising burden of cardiovascular diseases and infectious diseases.

Methods and Results-—The H3 (Human Hereditary and Health) in Africa’s AWI-Gen (African-Wits-INDEPTH partnership for
Genomic) study is a cross-sectional study conducted in adults aged 40 to 60 years from Burkina Faso, Kenya, Ghana, and South
Africa. Cardiovascular disease risk and ultrasonography of the CIMT of right and left common carotids were measured.
Multivariable linear and mixed-effect multilevel regression modeling was applied to determine factors related to CIMT. Data
included 8872 adults (50.8% men), mean age of 50�6 years with age- and sex-adjusted mean (�SE) CIMT of 640�123lm.
Participants from Ghana and Burkina Faso had higher CIMT compared with other sites. Age (b = 6.77, 95%CI [6.34–7.19]), body
mass index (17.6[12.5–22.8]), systolic blood pressure (7.52[6.21–8.83]), low-density lipoprotein cholesterol (5.08[2.10–8.06]) and
men (10.3[4.75– 15.9]) were associated with higher CIMT. Smoking was associated with higher CIMT in men. High-density
lipoprotein cholesterol (�12.2 [�17.9– �6.41]), alcohol consumption (–13.5 [�19.1–�7.91]) and HIV (�8.86 [�15.7–�2.03])
were inversely associated with CIMT.

Conclusions-—Given the rising prevalence of cardiovascular diseases risk factors in sub-Saharan Africa, atherosclerotic diseases
may become a major pan-African epidemic unless preventive measures are taken particularly for prevention of hypertension,
obesity, and smoking. HIV-specific studies are needed to fully understand the association between HIV and CIMT in sub-Saharan
Africa. ( J Am Heart Assoc. 2019;8:e011506. DOI: 10.1161/JAHA.118.011506.)

Key Words: cardiovascular disease • carotid intima-media thickness • epidemiological transition • prevention • sub-Saharan Africa

From the Navrongo Health Research Centre, Ghana Health Service, Navrongo, Ghana (E.A.N., G. Agongo, C.D., A.R.O.); Julius Global Health, Julius Center for Health
Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands (E.A.N., K.K.-G., D.E.G., M.L.B.); Clinical Research Unit of
Nanoro, Institut de Recherche en Sciences de la Sant�e, Nanoro, Burkina Faso (P.R.B., H.S., S.N.-D., H.T.); Sydney Brenner Institute of Molecular Bioscience (P.R.B.,
S.A.A., C.C.S., F. Mukomana, S.H., M.R.), Division of Human Genetics (P.R.B., C.C.S., M.R.), Division of Epidemiology and Biostatistics, School of Public Health (K.K.-G.,
S.T., K.K., O.S.), MRC/Wits Developmental Pathways for Health Research Unit (L.K.M., S.N.), MRC/Wits Rural Public Health and Health Transitions Research Unit
(Agincourt), School of Public Health (S.T., A.N.W., K.K.), and Department of Chemical Pathology, National Health Laboratory Services (NHLS) (N.J.C.), Faculty of Health
Sciences, University of the Witwatersrand, Johannesburg, South Africa; African Population and Health Research Centre (APHRC), Nairobi, Kenya (G. Asiki, C.K.); Dikgale
Health Demographic Surveillance Site, Department of Pathology and Medical Sciences, School of Health Care Sciences, Faculty of Health Sciences, University of
Limpopo, Polokwane, South Africa (F. Mashinya, M.A.); INDEPTH-Network, Accra, Ghana (S.T., K.K., O.S.).

Accompanying Tables S1 and S2 are available at https://www.ahajournals.org/doi/suppl/10.1161/JAHA.118.011506

*A complete list of the H3Africa AWI-Gen consortium members can be found in the Appendix at the end of the manuscript.

Correspondence to: Engelbert A. Nonterah, MBChB, MSc, Navrongo Health Research Centre, Ghana Health Service, P. O. Box 114, Navrongo, Ghana. E-mail:
engelbert.nonterah@navrongo-hrc.org

Received November 13, 2018; accepted June 7, 2019.

ª 2019 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley. This is an open access article under the terms of the Creative Commons
Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for
commercial purposes.

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G lobal morbidity and mortality attributable to non-
communicable diseases are increasing with cardiovas-

cular diseases (CVD) being a significant contributor.1 A
greater proportion of the annual CVD-related deaths occur
between 30 to 70 years of age and 85% of these deaths occur
in low- and middle-income countries, and are projected to
increase further.2 A major contributor to the burden of
cardiovascular morbidity and mortality is the development of
atherosclerosis, a continuous process which often starts early
in life and progresses with age.3 Exposure to unfavorable
levels of established cardiovascular risk factors such as high
systolic blood pressure, smoking, and dyslipidemia leads to
the accelerated development of atherosclerosis.

The measurement of sub-clinical atherosclerosis by the
assessment of carotid intima-media thickness (CIMT) may
provide information about the cardiovascular status of a
population.4–7 However, epidemiological data on the preva-
lence and determinants of high CIMT arise largely from
studies in whites, Asians, and Africans in the diaspora or
blacks, which may not reflect the situation among Africans
living in sub-Saharan Africa (SSA).4,8,9

Africa is currently engulfed in a wave of complex
epidemiological transition that is characterized by extensive
urbanization with concomitant lifestyle changes such as

consumption of calorie dense diets and a decrease in
physical activity.10,11 The surge in CVD risk factors, such as
obesity and hypertension, adds to the complex milieu of a high
burden of infectious diseases such as HIV, tuberculosis, and
malaria.12 Despite this double burden of non-communicable and
infectious diseases in Africa, large cohort studies with harmo-
nized data on CVDs are lacking.10 To formulate appropriate
interventions to decrease CIMT as a proxy for subclinical
atherosclerosis, it is important to identify the specific risk factors
associated with CIMT in SSA populations.

Therefore, the aim of the study was to measure CIMT and
identify risk factors associated with CIMT in a large SSA
population. This was achieved by measuring CIMT levels
within the AWI-Gen (African-Wits-INDEPTH [International Net-
work for the Demographic Evaluation of Populations and their
Health in low- and middle-income countries] Partnership for
Genomic studies). This is a large pan-African epidemiological
and genetic study that has collected sociodemographic,
behavioral, anthropometric, metabolic, and genetic data on
close to 12 000 participants from 4 countries within SSA.13,14

Materials and Methods
All data and materials for the AWI-Gen study that support the
findings in this paper will be made available in the European
Genome-phenome Archive under the set of projects related to
the Human Heredity and Health in Africa (H3Africa) Consor-
tium. Details about access to data can be found in the
document titled “H3Africa Data and Biospecimen Access
Committee Guidelines,” available in the consortium docu-
ments section of the H3Africa website (www.h3africa.org).

Study Design and Study Population
We conducted a population-based cross-sectional study as
part of the AWI-Gen partnership, a National Institutes of
Health-funded Collaborative Centre of the Human Heredity
and Health in Africa (H3Africa) Consortium. Six study sites in
4 sub-Saharan African (SSA) countries were involved in the
AWI-Gen study (Figure 1). Three of the sites were in South
Africa of which 2 are rural and 1 is urban. The rural sites were
the Dikgale health and demographic surveillance site
(HDSS)15 affiliated with the Department of Pathology and
Medical Science, University of Limpopo, and the Agincourt
Health and Demographic Surveillance System Site (HDSS)16

managed by the Medical Research Council/Wits Rural Public
Health and Health Transitions Research Unit, University of the
Witwatersrand. The urban South African site is the MRC/Wits
Developmental Pathways for Health Research Unit , University
of the Witwatersrand, in Soweto.17 There was 1 urban site in
Nairobi, Kenya: the African Population and Health Research

Clinical Perspective

What Is New?

• This the first study involving indigenous African populations
drawn from 4 countries at different stages of epidemiolog-
ical transition, to demonstrate that classical cardiovascular
risk factors such as age, male sex, systolic blood pressure,
serum cholesterol, and obesity are major drivers of
increased carotid intima-media thickness.

• HIV infection was not associated with higher carotid intima-
media thickness levels.

What Are the Clinical Implications?

• Interventions focused on classical, modifiable cardiovascu-
lar disease risk factors will attenuate atherosclerotic risk in
sub-Saharan African populations.

• HIV infection may not be a risk factor for atherosclerotic
diseases in sub-Saharan African.

• Results from recent studies in sub-Saharan African have
reported a lower prevalence of cardiovascular disease risk
factors in subjects living with HIV and this new paradigm
suggests that the HIV care cascade may be an effective
resource for the prevention and control of cardiovascular
diseases among people living with HIV.

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Center HDSS.18 Finally, there were 2 rural sites in West Africa:
the Navrongo HDSS hosted by the Navrongo Health Research
Center in Ghana,19 and the Nanoro HDSS hosted by the
Institut de Recherche en Sciences de la Sant�e Clinical
Research Unit of Nanoro in Burkina Faso.20 Included in the
study were adults aged 40 to 60 years resident in the various
sites. Exclusion criteria were current pregnancy and inability
to complete the prescribed study procedures. Similar num-
bers of women and men were randomly sampled from each of
the sites using existing sampling frames for the respective
HDSS sites.13,14 Only men from Soweto were included in
these analyses as women contributing to the study population
in Soweto, were the caregivers of the Birth to 20+ Cohort17

and did not have CIMT measurements. Our study population
covers 3 of the 5 subcontinental blocks of Africa and
therefore represents a large proportion of the geographical
(covering both rural and urban areas) and social variability of
the SSA region.

Ethical Considerations
The AWI-Gen study received overall ethical approval from the
Human Research Ethics Committee of the University of the
Witwatersrand, Johannesburg, South Africa (approval identifi-
cation numbers: M121029; M170880), as well as from the
appropriate ethics committees covering the Dikgale, Navrongo,
Nanoro, and Nairobi sites. Community engagement activities
were completed at each site to introduce the study to

community leaders before commencement of the field work.
Written and signed or thumb-printed informed consent was
obtained from each participant before performing prescribed
study procedures. In compliance with good clinical practice,
participants who were found to have clinically overt CVDs were
linked to health care by issuing referral letters in accordance
with the healthcare system in the particular study site.

Data Collection
Details of data collection methods and procedures have
been described elsewhere.13,14,21 Briefly, a paper-based
questionnaire was used to collect information from 5 of
the sites while a Computer-Assisted Personalized Interview
was used to collect information from the Agincourt site in
South Africa. Information collected included demography,
family ethnicity, education, household attributes, substance
use (tobacco, alcohol, and drugs), infectious disease history
(HIV, TB, and malaria), history of cardiovascular and
metabolic diseases (diabetes mellitus, stroke, hypertension,
angina, heart attack, congestive heart failure, obesity, and
high cholesterol), thyroid disease, kidney disease, and
physical activity. Data were then entered into the REDCap
(Research Electronic Data Capture) system hosted at the
University of the Witwatersrand, Johannesburg.22,23 Data
entry quality control to identify outliers, duplicate informa-
tion, and missing data were completed on 10% of the data
per site.

Figure 1. The sites constituting the H3Africa AWI-Gen study. Reprinted from Ramsay et al13 with
permission. Copyright ©2016, Cambridge University Press.

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Carotid Ultrasonography
Training

To maintain uniformity across the sites, carotid ultrasonog-
raphy procedures were standardized, and technicians trained
centrally by a certified sonographer at the MRC/Wits
Developmental Pathways for Health Research Unit at the
Chris Hani Baragwanath Hospital, Soweto, South Africa. The
identified technicians from the study sites were clinicians,
nurses, or biomedical scientists. To ensure reproducibility and
reduce CIMT measurement variability, masked repeated
measurements of 15 volunteers were conducted by each
trainee and the lead trainer. The coefficient of variation
between and within trainees was calculated and maintained at
<2%. Subsequently, the same settings and calibrations of the
ultrasound equipment (linear-array 12L-RS transducer with a
B-mode LOGIQ e ultrasound machine, GE Healthcare, CT,
USA) were used for data collection at all sites throughout the
entire recruitment period.

Image acquisition

To measure the right carotid, the participant was asked to lie
down in a supine position with a pillow underneath the neck
for slight extension, head turned towards the left at a 45⁰
angle and gel applied to the exposed neck area. Using the 2
sternocleidomastoid muscles as landmarks, the exposed area
was scanned along the longitudinal plane until the common
carotid artery (CCA) was found, and an image frozen. The
operator then identified a continuous 1-cm segment (10 mm)
of the CCA far wall. The operator then placed a cursor
between 2 points (10 mm apart) on this identified segment of
the far wall with the proximal starting point 1 cm from the
bulb of the CCA. The ultrasound machine software then
automatically detected the intima-lumen and the media-
adventitia interfaces and calculated the minimum, maximum,
and mean common CIMT in millimeters and to 2 decimal
places. To measure the left carotid, the participant’s head was
turned to the opposite side, and the process was repeated.
This approach was selected over measuring “multiple carotid
segments” because it was easier to measure and more
reproducible enabling its widespread use at all study sites. A
recent study demonstrates that measuring CCA IMT is a good
alternative compared with multiple segments in terms of
prediction of risk of CVD events.24 Additional quality control
(QC) before analyses included the exclusion of CCA IMT
>1.5 mm as this is indicative of plaque. Images with >50%
differences between minimum and maximum CCA IMT were
also excluded from the analytical data set.5 The far walls of
both the left and right common carotid artery were averaged
to determine mean CIMT thickness in millimeters as the main
outcome variable. This was then converted to micrometers for
the regression analyses.

Assessment of Sociodemographic, Behavioral,
Anthropometric, Blood Pressure, Biochemical-
and HIV-Related Variables
Age at the time of data collection and sex of participants were
self-reported. Highest level of education attained was self-
reported and categorized as no formal education, completion
of primary, secondary or tertiary education. Household
socioeconomic status (SES) was assessed using the INDEPTH
health equity tool which is an asset index generated by using
principal component analysis to combine data on household
possessions (http://indepth-network.org/resources/indepth-hea
lth-equity-tool-measuring-socio-economic-status). The asset score
generated was categorized into quintiles (Q1=poorest, Q2=poorer,
Q3=poor, Q4=less poor and Q5=least poor) and this was
computed separately for each of the sites.

Smoking status was assessed by asking subjects if they
had ever smoked any tobacco products such as cigarettes,
cigars or pipes, and if they were current or past users of such
products, Smoking was then categorized as “never,” “cur-
rent,” and “past.” For the purpose of these analyses current
and past were combined as “smokers.” Self-reported alcohol
use was assessed using the 4-item CAGE (cut-annoyed-guilty-
eye) questionnaire25 and subsequently categorized as current
or never/previous use. The Global Physical Activity Question-
naire was used to compute moderate-to-vigorous intensity
physical activity (MVPA) in minutes per week. Participants
were said to be active if their MVPA was ≥150 min/week or
inactive if their MVPA was <150 min/week.26

Standing height to the nearest 0.1 cm and weight to the
nearest 0.1 kg were measured without shoes and in light
clothes using a Harpenden stadiometer (Holtain, Crymych,
Wales) fixed to the wall and a digital calibrated weighing scale,
respectively. Resting systolic blood pressure (SBP) and
diastolic blood pressure were measured using a digital
sphygmomanometer (Omron M6, Omron, Kyoto, Japan) with
the participant seated with their arm at the level of the chest
and with an appropriate-sized cuff. Three readings were taken
at 2-minute intervals with the first reading discarded and the
average of the final 2 readings taken as the current blood
pressure reading.

Overnight fasting serum lipids and glucose were measured
using an automated chemistry analyzer (Randox RX Daytona+,
Crumlin, Northern Ireland). All serum samples were analyzed
at the University of the Witwatersrand Developmental Path-
ways for Health Research Unit laboratory, Chris Hani Barag-
wanath Academic Hospital, Soweto, South Africa. The low-
density lipoprotein cholesterol (LDL-C) was calculated using
the Friedewald equation.27

Self-reported HIV status was determined at all sites and in
addition HIV testing (using locally-available rapid-test kits)
was offered to all participants from Kenya and South Africa. In

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South Africa all known HIV-positive participants were asked
whether they were receiving antiretroviral therapy (ART). In
Agincourt, HIV status was determined by use of Vironostika
Uniform 11 [Biomeriuex, France] screening assay as part of
the HAALSI (Health and Ageing in Africa: a Longitudinal Study
of an INDEPTH Community in South Africa) study,28 several
months before performing measurements on a subset of
these participants for the AWI-Gen study.14 The HIV status
could therefore have changed at the time of recruitment into
the AWI-Gen study. The prevalence of HIV infection at the
Burkina Faso and Ghana sites is known to be between 1% to
2%.29,30

Statistical Analysis
Continuous data are presented as means�SD while categor-
ical data are presented as proportions. Age- and sex-adjusted
mean (with standard error of the mean) CIMT was computed
and presented for the various sites stratified by sex. One-way
analysis of variance was used to determine the differences in
mean CIMT levels between the sites whilst the Student t-test
was used to determine differences between women and men
within the sites. We used various multivariable linear regres-
sion models to determine the factors associated with CIMT. In
model 1 we adjusted for the classical CVD risk factors. These
factors are often used in risk prediction equations31 to
determine the risk of dying from atherosclerotic cardiovascu-
lar diseases and include age, sex, current smoking, SBP,
fasting glucose, HDL-C, and LDL-C. We replaced total
cholesterol with LDL-C because of its key role in the
pathogenesis of atherosclerosis and treatment monitoring.3,32

In model 2, we adjusted for body mass index (BMI), physical
activity and alcohol consumption in addition to factors from
model 1. In model 3, since household SES and education are a
proxy for urbanization and social determinants of health,33,34

we adjusted for these in addition to factors from model 2.
Finally, considering the high burden of HIV infection in SSA,
we adjusted for HIV status in model 4 in addition to the
variables included in model 3. We also adjusted for the effect
of site in all 4 of the multiple linear regression models. The
final model (model 4) therefore included age, sex, smoking,
SBP, fasting glucose, HDL-C, LDL-C, BMI, alcohol consump-
tion, physical activity, education status, household SES, HIV
status and site. We checked this model for multi-collinearity
using the variance inflation factor ensuring that the variance
inflation factor for each variable was <10. We added
multiplicative interaction terms to this model to evaluate
whether relations between risk factors and common CIMT
differed by sex and between sites. If interaction terms were
statistically significant (P<0.05), findings were presented for
separate strata (men and women; across sites). In a further
sub-analysis involving only the South African sites and Kenya

we adjusted for objectively measured HIV status and for the
South African sites only we adjusted for the effect of ART. This
was because only at these sites was HIV status measured
objectively and ART status was assessed only in the South
African sites.

To fully account for within site differences, we further
conducted mixed-effect multi-level (ML) regression using site
as a random effect in the final model (model 4) of the multiple
linear regression analysis. We also checked for the role of
interaction of HIV with age, sex, and household SES in the
mixed-effect ML model. A likelihood ratio test was then used
to compare the best fitting model between the single
multivariable linear regression model, the mixed-effect ML
regression model and the mixed-effect ML regression model
that included interaction terms. Results are reported as
unstandardised b-coefficients with corresponding 95% CIs.
Statistical significance was set at a 2-sided P<0.05. For
educational status and household SES, which had >2
categories, a post estimation test was used to obtain a
single P value for the estimate in the models. All data were
analyzed using STATA 14.2 (College Station, TX 77845, USA)
software.

Results
Data were available for 10 363 participants drawn from all 6
sites. However, 1491 of these were excluded from the data
analysis because they either had no CIMT data or did not
meet quality control criteria. The final analytical data set
therefore consisted of 8872 participants.

Sociodemographic Characteristics
The mean (�SD) age of the total study population was
49.9�5.83 years and men comprised 50.8% of study partic-
ipants (Table 1). Nairobi, Kenya had the youngest
(48.5�5.43 years) while Navrongo, Ghana had the oldest
(51.1�5.75 years) population. The 2 West African sites,
Nanoro in Burkina Faso (83.0%) and Navrongo in Ghana
(70.5%), had the highest number of participants with no
formal education, while all 3 South African sites (8.45%
Agincourt, 3.56% Dikgale and 14.4% among men in Soweto)
and Nairobi (3.69%) had the highest number of participants
with tertiary education. More than two-thirds of participants in
all countries were in the 2 highest quintiles (less and least
poor) of household socio-economic status (Table 1).

Behavioral Factors
The prevalence of smoking was higher among men in Soweto
(53%) and Dikgale (29.7%) in South Africa and Navrongo,

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Ghana (20.6%) compared with Agincourt, South Africa
(12.4%), Nairobi, Kenya (12.0%) and Nanoro, Burkina Faso
(6.83%). Men in Soweto (71.1%) and the 2 West African sites
(65.3% in Ghana and 63.8% in Burkina Faso) recorded higher
current alcohol consumption than Dikgale (27.7%), Agincourt
(23.5%), and Nairobi (18.6%). The total population was fairly
active with more than two-thirds of adults across all
countries meeting physical recommendations of ≥150 min-
utes of moderate-to-vigorous intensity physical activity per
week (see Table 1).

Anthropometric, Blood Pressure, Biochemical
Variables, and HIV Infection

The average BMI was 23.9�6.09 kg/m2, with the rural sites
of Burkina Faso (20.9�3.45 kg/m2) and Ghana (21.6�
3.6 kg/m2) in West Africa recording lower BMIs than the
urban sites in Kenya (25.4�5.76 kg/m2) and the urban
(25.0�5.67 kg/m2 in Soweto) and rural (26.8�7.69 kg/m2

in Agincourt and 27.9�8.19 kg/m2 in Dikgale) sites in South
Africa (Table 1). The average SBP and diastolic blood

Table 1. Basic Characteristics of Study Paticipants by Site

All Sites Nanoro, BF Navrongo, GH Agincourt, SA Dikgale, SA Soweto*, SA Nairobi, KE

n=8872 n=2081 n=1976 n=795 n=1152 n=936 n=1932

Age, y 49.9�5.82 49.8�5.81 51.1�5.75 50.3�5.79 50.4�5.97 49.4�5.99 48.5�5.43

Men 4506 (50.8) 1043 (50.1) 908 (46.0) 384 (48.3) 355 (30.8) 936 (100) 880 (45.6)

Education status

No formal 3578 (40.5) 1716 (83.0) 1389 (70.6) 226 (28.5) 93 (8.07) 8 (0.85) 146 (7.55)

Primary 2468 (27.9) 239 (11.5) 376 (19.1) 262 (33.0) 381 (33.1) 105 (11.2) 1105 (57.2)

Secondary 2481 (28.0) 95 (4.63) 168 (8.54) 238 (29.9) 637 (55.3) 688 (73.5) 655 (33.9)

Tertiary 324 (3.69) 18 (0.87) 36 (1.82) 68 (8.56) 41 (3.56) 135 (14.4) 26 (1.34)

Household SES

Poorest 1242 (14.0) 342 (16.4) 366 (18.5) 130 (16.3) 148 (12.8) 24 (2.57) 232 (12.0)

Very poor 1696 (19.1) 404 (19.4) 356 (18.0) 201 (25.3) 259 (22.5) 45 (4.83) 431 (22.3)

Poor 1676 (18.9) 404 (19.4) 383 (19.4) 101 (12.7) 152 (13.2) 189 (20.2) 447 (23.1)

Less poor 1923 (21.7) 386 (18.5) 462 (23.4) 181 (22.8) 251 (21.8) 248 (26.5) 395 (20.4)

Least poor 2335 (26.3) 548 (26.2) 409 (20.7) 182 (22.9) 342 (29.7) 430 (45.9) 427 (22.1)

Current smoking 1622 (18.3) 142 (6.83) 406 (20.6) 98 (12.4) 249 (29.7) 495 (53.0) 232 (12.0)

Current alcohol consumption 4139 (46.7) 1323 (63.8) 1287 (65.3) 187 (23.5) 318 (27.7) 665 (71.1) 359 (18.6)

Insufficient MVPA
(<150 mins/week)

1302 (14.7) 402 (19.3) 293 (14.8) 176 (22.1) 43 (3.73) 256 (27.4) 132 (6.82)

BMI, kg/m2 23.9�6.09 20.9�3.45 21.6�3.60 26.8�7.69 27.9�8.19 24.9�5.67 25.4�5.76

Systolic blood pressure,
mm Hg

123�21.3 116�18.2 124�21.5 131�21.6 126�20.9 132�21.1 120�21.2

Diastolic blood pressure, mm Hg 78.4�13.1 73.5�10.6 76.9�12.7 78.6�12.8 81.1�12.8 89.2�13.2 78.4�12.9

Fasting glucose, mmol/L 5.07�1.69 5.05�1.25 4.54�0.81 5.12�1.83 5.21�2.49 5.28�1.54 5.43�1.99

HDL-C, mmol/L 1.18�0.42 1.12�0.37 1.14�0.38 1.19�0.41 1.19�0.41 1.20�0.46 1.26�0.47

LDL-C, mmol/L 2.21�0.87 1.92�0.76 1.85�0.71 2.23�0.79 2.49�0.89 2.48�0.91 2.58�0.87

HIV+ 978 (11.0) 9 (0.43) 16 (0.81) 278 (34.9) 250 (21.0) 188 (20.1) 237 (12.3)

Self-reported ART use 695 (84.7) ��� ��� 268 (96.4) 138 (55.2) 84 (44.6) 193 (81.4)

Left mean CIMT, lm 649�0.136 669�132 703�145 611�112 632�0.126 637�142 601�121

Right mean CIMT, lm 627�130 659�123 667�132 581�107 640�130 601�129 576�119

Average CIMT lm 640�123 667�118 689�129 598�100 638�117 618�118 590�107

Data presented as absolute numbers and percentages (%) or as means�SD. ART indicates antiretroviral therapy; BF, Burkina Faso; BMI, body mass index; CIMT, carotid intima-media
thickness; GH, Ghana; HDL-C, high-density lipoprotein cholesterol; KE, Kenya; LDL-C, low-density lipoprotein cholesterol; MVPA, moderate-to-vigorous physical activity; SA, South Africa;
SES, socioeconomic status.
*Data presented are for men only since there was no CIMT measurements for women.

DOI: 10.1161/JAHA.118.011506 Journal of the American Heart Association 6

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pressure were highest in all 3 South African sites and Kenya
compared with the West African sites. Fasting blood glucose
was highest in Kenya (5.43�1.99 mmol/L), followed by
the South African sites (5.12�1.83 mmol/L in Agincourt,
5.21�2.49 mmol/L in Dikgale and 5.28�1.54 mmol/L in
Soweto), Burkina Faso (5.05�1.25 mmol/L) and Navrongo
(4.54�0.81 mmol/L). A similar pattern was observed for
HDL-C and LDL-C. The prevalence of HIV was highest in
South Africa (34.9% in Agincourt, 21% in Dikgale and 20% in
Soweto) followed by Kenya (12.3%) and <1% in Burkina Faso
and Ghana (Table 1). Self-reported use of ART among HIV
positive participants was lowest at Soweto (44.6%) followed
by Dikgale with 55.2% and as high as 81.4% in Nairobi and
96.4% in Agincourt.

CIMT Measurements
Mean CIMT of the left CCA was higher than the right CCA
(Table 1) in all 6 sites (P<0.001 in all sites). Age and sex
adjusted mean (�standard errors) levels of the average CIMT
for each sample at the different sites are presented in
Figure 1. The average CIMT for the entire population was
640�113 lm. Mean CIMT was significantly different between
all sites (P<0.001) with Ghana (689�129 lm) and Burkina
Faso (667�118 lm) recording higher common CIMT levels
than the South African sites (598�100 lm in Agincourt,
638�117 lm in Dikgale and 0.64�0.002 lm in Soweto) and
Kenya (590�107 lm). Women in Kenya had higher CIMT

compared with men (P=0.022) while the reverse was
observed in Burkina Faso (P<0.001) (Figure 2). In Ghana
(P=0.657) and South Africa (Agincourt, P=0.501 and Dikgale,
P=0.935), women and men had similar levels of common
CIMT.

Factors Associated With Common CIMT
The factors associated with CIMT in the combined population
within univariate analyses are shown in Table S1. Adjusted
regression models were then built sequentially and the factors
associated with CIMT in the various models are displayed in
Table 2. These analyses show that of the variables included in
model 1, age, SBP, HDL-C and LDL-C remained significant
through to model 4, whilst the effect of sex became
significant from model 2 onwards. Smoking status was not
significantly associated with CIMT in any of the models but its
effect did strengthen across the models, whilst the effect of
glucose weakened. Among the variables added in model 2,
BMI and alcohol use both were significant and remained so,
whilst MVPA was not significantly associated with CIMT in any
of the models. Educational status and household SES included
in model 3 were not significantly associated with CIMT, whilst
HIV status, which was added in model 4, was significant.
Model 1 explained 23.4% of the variance in CIMT whilst model
4 explained 24.4% of the variance.

To further understand the effect of HIV infection on
CIMT, we conducted a sensitivity analysis, where we included

p<0.001 p=0.657 p=0.501 p=0.935 p=0.022 p=0.646

0
.5

1
1.

5

M
ea

n 
co

m
m

on
 C

IM
T

Nanoro, BF Navrongo, GH Agincourt, SA Dikgale, SA Soweto, SA Nairobi, KE All sites

W M W M W M W M *W M W M W M

Sex and site

Figure 2. Age- and sex-adjusted distribution of mean levels of common CIMT (in mm) across 4 SSA
countries stratified by sex. Differences between women (W) and men (M) were generated using sample
t-test with equal variance; *Data not available for women in Soweto. CIMT indicates carotid intima-media
thickness; SSA, sub-Saharan Africa.

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objectively assessed HIV status from the South African sites
and Kenya only. In this model we observed an even stronger
inverse association with CIMT (�15.3 [�24.2–�6.31]). We
conducted a further sub-analysis on HIV participants who
used ART and observed that ART was associated with lower
CIMT levels (�70.1 [�165–�25.6] in Agincourt; �5.05
[�9.52–�1.29] in Dikgale and �48.6 [�82.9–�14.2] in
Soweto). This model included only participants from South
Africa, which was the only country in which ART use was
assessed.

To determine the within site variation, we conducted a
mixed-effect ML regression analyses. Presented in Table 3 is
a comparison of multivariable linear (model 4) and mixed-
effect ML regression analyses (model 5) and mixed-effect ML
regression analyses including interaction terms (model 6). The
likelihood ratio test (LR v2=498.02; P<0.001) indicated that
the mixed–effect ML model (model 5) had a better fit than the
multivariable linear regression model from Table 2 (model 4).
In model 5, the association between classical cardiovascular
risk factors and CIMT were similar to the multivariable linear
regression model. However, HIV, although showing a similar
direction of effect (�5.81 [�13.82–2.21]), the observed
effect was no longer significant (P=0.156). Further to this,
physical activity was associated with lower CIMT levels
(�8.17 [�14.81–�1.53]; P=0.016). When interaction terms
were introduced into this model (model 6), we noted that HIV
did not interact with age (P=0.306), sex (P=0.252) or
household SES (P=0.877). The likelihood ratio test (LR
v2=47.88; P=0.318), showed a better fit for model 5 than
for model 6.

In sex stratified analyses (Tables 4 and 5), the observed
association of age, SBP, HDL-C, current alcohol consumption
and HIV infection with CIMT was similar between men and
women. In the pooled analysis (Table 2), the association
between smoking and CIMT had a trend towards a positive
association (6.26 lm [�0.83, 13.35]; P=0.084). However, in
men only current smoking was positively associated with
higher CIMT (10.7 lm [2.93, 18.53]). Also in men but not
women, a unit elevation in LDL-C (mmol/L) was likely to
present with a 6.05 lm [1.97, 10.13] higher CIMT, whilst
higher household SES among men but not women was
associated with a lower CIMT (P=0.042). We added multi-
plicative interaction terms to the final model to evaluate
whether relations between risk factors and common CIMT
differed by sites (Table S2). We observed some significant
interactions between site and certain risk factors. We
therefore conducted sub-analyses for each site stratified by
sex. In these site stratified analyses (Tables 4 and 5),
differential effects of some of the independent variables on
CIMT were observed. Significant associations of plasma
glucose levels with CIMT were observed in women in Nanoro
(�7.90 [�13.8–�2.04]) and Dikgale (3.68 [0.75, 6.62]) but

the effect was negative in the former and positive in the latter
group. In women, BMI correlated significantly and negatively
with CIMT in Navrongo (�28.7 [�52.9–�4.56]) but positively
in Dikgale (17.8 [7.34–28.2]). Men with primary, secondary,
and tertiary education in Soweto were likely to have higher
CIMT than men with no formal education. Alcohol consump-
tion was inversely associated with CIMT in men from Nanoro,
Burkina Faso. Similarly, high HDL was associated with lower
CIMT in women from Dikgale, South Africa and Nairobi, Kenya.
The MVPA had similar inverse associations with CIMT in men
from Navrongo and Burkina Faso (see Tables 4 and 5).

Discussion
The AWI-Gen study is the first large African study with
harmonized data collection in 4 countries across 3 subcon-
tinental African regions to report on the measurement of CIMT
and its associated cardiometabolic risk factors. Our study
shows both sex and regional differences in CIMT levels and
risk factor associations. In pooled analyses, our findings show
that the major factors that were consistently associated with
higher CIMT were age, men, SBP, BMI, and LDL-C while a high
HDL-C, current alcohol consumption, and HIV infection were
associated with lower CIMT. In a sex-stratified analysis,
smoking was associated with a higher CIMT in men.

The mean CIMT of the left CCA was higher than the right
and this has been reported previously in an adult Pakistani
population.35 This could be because of the fact that the left
CCA arises directly from the aortic arch and is therefore
exposed to greater hemodynamic stress and intimal damage
from the systolic pressure from the left ventricles. It may
therefore be clinically relevant to use right CIMT for screening
purposes. The mean CIMT of 0.64�0.003 mm in our study
population was lower than 0.71�0.19 mm reported in adult
populations from North America36 and 0.71�0.12 mm in
Europe.37 The difference is likely explained by the relatively
older ages of the North American and European cohorts
(mean ages 60.2�8.7 and 58.8�7.6, respectively) compared
with our population that had a mean age of 49.9�5.8 years.
The observed CIMT in our population was, however, higher
than reported in studies from India,38 China,39 Pakistan,35 and
South America40,41 which had a similar age to our population.
These observed variations in mean CIMT between white,
Asian, and African populations support the notion that CIMT
varies by ethnicity4 and may therefore have a substantive
genetic contribution, but it must also be noted that
differences in sampling methods, sample size, ratio of men
to women and CIMT measurement techniques across these
studies may also play a part.

In the combined analysis, mean CIMT levels were similar
for men and women. In Nanoro, Burkina Faso where women

DOI: 10.1161/JAHA.118.011506 Journal of the American Heart Association 9

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and men had similar ages, men had higher CIMT than women,
whereas despite being younger, women had higher CIMT than
men in Nairobi, Kenya. In other studies where women were

observed to have higher CIMT compared with men, the
differences were observed in women over the age of
45 years.42,43 However, when we adjusted for other risk

Table 3. Comparison of Linear Regression Model and Mixed-Effect Multilevel Model Depicting the Association Between Risk
Factors of CIMT in SSA

Risk Factors

Model 4 Model 5 Model 6

ß-Coefficients (95% CI) P Value ß-Coefficients (95% CI) P Value ß-Coefficients (95% CI) P Value

Age in y 6.77 (6.34, 7.19) <0.001 6.71 (6.29, 7.13) <0.001 6.78 (6.34, 7.22) <0.001

Men vs women 10.32 (4.75, 15.90) <0.001 13.11 (7.54, 18.69) <0.001 13.95 (8.22, 19.68) <0.001

Current vs never/previous smoking 6.26 (�0.83, 13.35) 0.084 3.69 (�3.46, 10.85) 0.312 3.75 (�3.41, 10.91) 0.304

SBP per 10 mm Hg 7.52 (6.21, 8.83) <0.001 7.78 (6.60, 8.95) <0.001 7.77 (6.59, 8.94) <0.001

Glucose per 1 mmol/L 0.94 (�0.67, 2.56) 0.252 0.79 (�0.62, 2.21) 0.269 0.82 (�0.59, 2.23) 0.256

HDL per 1 mmol/L �12.15 (�17.88, �6.41) <0.001 �12.71 (�18.42, �6.99) <0.001 �12.77 (�18.49, �7.06) <0.001

LDL per 1 mmol/L 5.08 (2.10, 8.06) 0.001 4.35 (1.42, 7.27) 0.004 4.34 (1.42, 7.27) 0.004

BMI per 10 kg/m2 17.61 (12.46, 22.76) <0.001 16.86 (12.09, 21.62) <0.001 16.71 (11.94, 21.48) <0.001

MVPA in mins/week �5.58 (�12.37, 1.21) 0.107 �8.17 (�14.81, �1.53) 0.016 �8.06 (�14.70, �1.42) 0.017

Current vs Never/previous alcohol
use

�13.51 (�19.11, �7.91) <0.001 �13.95 (�19.49, �8.41) <0.001 �13.92 (�19.46, �8.38) <0.001

Education status

No formal education Ref (0) Ref (0) Ref (0) Ref (0) Ref (0) Ref (0)

Primary �4.03 (�11.29, 3.24) 0.625 �7.96 (�15.13, �0.79) 0.175 �8.17 (�15.33, �0.99) 0.156

Secondary �7.77 (�8.66, 7.11) �7.17 (�15.36, 1.02) �7.42 (�15.61, 0.78)

Tertiary �3.81 (�18.60, 10.98) �5.97 (�20.28, 8.33) �6.27 (�20.57, 8.04)

Household SES

Poorest Ref (0) Ref (0) Ref (0) Ref (0) Ref (0) Ref (0)

Poorer �0.11 (�8.27, 8.04) 0.096 �0.62 (�8.72, 7.48) 0.142 �0.81 (�9.42, 7.79) 0.090

Poor �2.77 (�10.98, 5.44) �2.93 (�11.11, 5.25) �3.78 (�12.39, 4.82)

Less poor �7.18 (�15.20, 0.85) �6.68 (�14.68, 1.32) �7.84 (�16.2, 0.60)

Least 1.78 (�6.29, 9.85) 1.83 (�6.25, 9.92) 1.74 (�6.71, 10.20)

HIV+ vs HIV� �8.86 (�15.70, �2.03) 0.011 �5.81 (�13.82, 2.21) 0.156 26.80 (�41.40, 95.01) 0.441

Interaction terms

HIV9sex �8.82 (�23.91, 6.27) 0.252

HIV9age �0.69 (�2.01, 0.630) 0.306

HIV9SES

Poorest vs poorer 2.25 (�23.27, 27.76) 0.877

Poorest vs poor 8.54 (�18.27, 35.34)

Poorest vs less poor 10.88 (�14.53, 36.29)

Poorest vs least poor 2.24 (�23.42, 27.89)

Constant 215.8 (188.6, 242.9) <0.001 174.4 (132.5, 216.3) <0.001 171.6 (129.3, 214.0) <0.001

Observations 8872 8872 8872

Number of groups ��� ��� 6 ��� 6 ���
Likelihood ratio (LR) test ��� ��� Model 2 vs Model 1 LR

v2=498.02
<0.001 Model 3 vs Model 2 LR

v2=47.88
0.318

Model 1=Multivariable linear regression model; Model 2=Mixed effect ML regression analyses; Model 3=Mixed effect ML regression analyses+interaction terms (interactions term, HIV and
sex=HIV9sex, HIV and age=HIV9age and HIV vs socioeconomic status=HIV9SES). ART indicates antiretroviral therapy; BMI, body mass index; CIMT, carotid intima-media thickness; HDL-
C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MVPA, moderate-to-vigorous physical activity; SBP, systolic blood pressure; SES, socioeconomic status.

DOI: 10.1161/JAHA.118.011506 Journal of the American Heart Association 10

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DOI: 10.1161/JAHA.118.011506 Journal of the American Heart Association 11

Cardiovascular Risk Factors, HIV, and CIMT in SSA Nonterah et al
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IN
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ow

nloaded from
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ber 17, 2019



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DOI: 10.1161/JAHA.118.011506 Journal of the American Heart Association 12

Cardiovascular Risk Factors, HIV, and CIMT in SSA Nonterah et al
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factors including age, men were more likely to present with
higher CIMT compared with women. Differences in CIMT
levels between men and women have been attributed to
differences in exposure to CVD risk factors.44 In addition,
physiological factors, such as lumen diameter, have been
suggested as explanations for sex differences, with women
generally presenting with smaller blood vessels.45

The association between classical CVD risk factors and
CIMT has been established in various populations, predomi-
nantly white and Asian46–49 and only in a few studies, with
small sample sizes, from African populations.50,51 Classical
CVD factors that have been observed to be associated with
CIMT in the literature were similarly associated in the present
study, and this provides some level of external validity to our
data. Addition of other potential risk factors into a regression
model that included these traditional CVD risk factors had a
minimal effect on the variance in CIMT explained by the model.
These risk factors included BMI, alcohol intake, and HIV, all of
which did contribute significantly to the final model but slightly
attenuated the association of the other risk factors with CIMT.
Our findings are in agreement with the INTERHEART study that
showed that, although the magnitude of the relations differed
somewhat, established risk factors were associated with an
increased risk of acute myocardial infarction across the
world.52 These findings therefore suggest that interventions
aimed at reducing the classical risk factors will help prevent
the development of atherosclerosis and reduce CVDs and
associated mortality in SSA populations.

The association of SBP with high CIMT levels is of
particular interest as this presents a worrying phenomenon
since a previous AWI-Gen study demonstrated a high preva-
lence of hypertension across all sites with an associated low
awareness and control of high blood pressure.21 While we
advocate for preventive measures that should be targeted to
the local situation, early detection and management of
hypertension may particularly benefit these SSA populations.
Increased awareness, screening, early detection and subse-
quent management of CVD risk factors should be integrated
into current public health systems in SSA.

We observed that current alcohol consumption was
associated with lower CIMT and this is contrary to findings
from the USE Intima-Media Thickness (USE-IMT) 8 cohorts
collaboration conducted among North Americans and Euro-
peans, which reported lower CIMT among participants who
consumed little or no alcohol compared with those who
consumed >10 g of alcohol daily.53 A longitudinal study
further clarified this by reporting that sustained heavy drinking
in midlife was associated with higher CIMT.54 The observed
inverse association of alcohol consumption with CIMT has
also been documented in a Korean study.55 One possible
explanation for this relationship comes from a randomized
cross-over feeding trial among men which showed that

alcohol improves lipid profiles and reduces atherosclerosis-
related inflammatory markers in plasma. The phenolic content
of alcoholic drinks, which is high in sorghum-based beers,
which are consumed widely in the study settings, was found
to reduce leukocyte adhesion molecules and inflammatory
biomarkers.56,57 Furthermore, the ARIC (Atherosclerosis Risk
in Communities) study, using candidate gene analyses with a
Mendelian Randomization methodology, demonstrated that
low-to-moderate alcohol consumption improves serum lipid
levels.58 However, it should be noted that in the present
study, the inverse association between alcohol intake and
CIMT occurred independently of lipid levels. It is possible that
the crude assessment of alcohol intake used in the current
study may not be sensitive enough to allow more complex
investigations of these relationships, and longitudinal studies
using more objective measures of alcohol intake may be
required.

In our population and according to the mixed-effect ML
model, HIV infection showed a non-significant inverse asso-
ciation with lower CIMT after adjusting for age, education
status, household SES, and the classical cardiovascular risk
factors. This inverse association was shown to be significant
in multivariable regression analyses. Both findings are
contrary to studies from Brazil59 and Uganda60 reporting
higher CIMT among HIV-positive compared with HIV-negative
individuals. In line with these, a recent meta-analysis based on
17 cohort and case-control studies indicates a 2-fold higher
risk ratio for cardiovascular diseases in people living with HIV
in comparison with HIV-negative subjects. However, this
meta-analysis included only 1 case-control study from Africa
including 200 stroke cases and 398 controls.61 The non-
significant association observed in the current study is similar
to findings from rural South Africa,62 North America,63 and
Brazil.39,40 As HIV was not assessed objectively across all
sites, with the exception of the South African and Kenyan
sites, we conducted (multivariable linear regression) sub-
analyses on data from these sites to assess the effect of HIV
on CIMT. These showed a negative association between HIV
and CIMT. It is interesting to note that a recent study
conducted in rural South Africa among 5059 participants aged
≥40 years has shown that HIV-positive subjects had a lower
prevalence of classical CVD risk factors when compared with
HIV-negative subjects, in both men and women.64 This has
further been corroborated by more recent studies from South
Africa, which demonstrated lower prevalence levels of hyper-
tension and diabetes mellitus among HIV-infected sub-
jects.65,66 Considerably increased access to ART in SSA in
the past decade, particularly so in South Africa, which has the
largest ART roll-out program in the world,67 and application of
treatment guidelines to screen people living with HIV for CVD
risk,32,68 may have prompted diagnosis and consequent
treatment of identified CVD risk factors, or changes in

DOI: 10.1161/JAHA.118.011506 Journal of the American Heart Association 13

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lifestyle, in these subjects. These findings may well be
indicative of an emerging paradigm reflecting an improved HIV
care cascade.

Further to this, studies have reported that the effects of HIV
on CIMT are more pronounced in younger than older population
because of active viral replication and immune activation and a
greater level of high-CVD risk behaviours.69 Thus, potentially
long-term use of ART as noted in our older populationmay result
in chronic viral suppression leading to lower inflammation and
reduced CIMT among HIV participants. However, it is possible
that the negative association between HIV infection and CIMT
observed in our study may be explained by other factors and
thus, be attributed to residual confounding. Furthermore,
misclassification of HIV status, particularly at sites that did
not offer formal HIV testing could be possible, however, the low
prevalence of HIV infection observed at such sites is supported
by data from the literature.29,30 It should also be noted that the
level of ART coverage varied across the sites ranging from
44.6% in Soweto to 96.4% in Agincourt. Although data on ART
regimens were not collected, treatment guidelines in Kenya68

and South Africa32 at the time of this study were similar with
first line therapies consisting of tenofovir+lamivudine (3TC)+e-
favirenz/nevirapine and zidovudine (AZT)+lamivudine (3TC)+
nevirapine/efavirenz. Despite the heterogeneity across the
sites, the negative relationship between HIV infection and CIMT
was observed at all study sites in both men and women,
suggesting that this is a robust association. The paucity of data
on the role of HIV and atherosclerotic CVDs in Africa therefore
requires further studies with a wide range of HIV-defining
markers, such as viral load, and a larger sample size of subjects
with confirmed HIV and ART status.

Several variables demonstrated differential association with
CIMT across the sites. Possible reasons for this may include
differences across sites in: genetic variance, distribution of the
particular effector variables and level of influence of modifying
variables. These differential site-specific effects are important
to understand but are beyond the scope of the current study
and require in depth analysis at individual sites using larger
sample sizes and a broader array of input variables.

Limitations to our study include a lack of information on the
duration of HIV infection, the ART regimens in use and the
duration of therapy, and the absence of measurements of viral
load and CD4 counts, all of which may play a role in explaining
the association between HIV infection and CIMT. Furthermore
HIV status determined in the Agincourt site may have poten-
tially resulted in measurement bias as participants’ status may
have changed from the time of initial HIV diagnosis to the time
of actual recruitment for the AWI-Gen study. The main strength
of our study relates to the fact that this is the first large scale
study to determine CIMT levels and their association with
cardiovascular risk factors in 3 subcontinental blocks in Africa.
Measurements of most variables and CIMT were harmonised

across all sites minimising variability and making pooled
analysis and cross-site comparisons feasible. We were also
able to cover the geographical and social variability across
these regional blocks by recruiting in both rural and urban sites
thus providing representation of the different stages of the
epidemiological transition within Africa.

Conclusions
To our knowledge, this is the first large scale study from SSA
to report on the levels of, and factors associated with, CIMT.
We observed that the main drivers for higher CIMT were the
same CVD risk factors associated with CIMT in white and
Asian populations. Given the rising prevalence of these CVD
risk factors in SSA, atherosclerotic diseases may become a
major pan-African epidemic unless preventive measures are
taken particularly targeted at prevention of hypertension and
reduction in obesity. In addition, differential effects of certain
factors on CIMT were observed across the SSA sites. We
advocate for HIV-specific studies to fully understand the true
association between HIV and CIMT in SSA.

Appendix

H3Africa AWI-Gen Consortium Members
Conception and design of the original AWI-Gen study: Mich�ele
Ramsay (SBIMB, Wits), Osman Sankoh (INDEPTH), Alisha
Wade, Stephen Tollman and Kathleen Kahn (Agincourt),
Marianne Alberts (Dikgale), Catherine Kyobutungi (Nairobi),
Halidou Tinto (Nanoro), Abraham Oduro (Navrongo), Shane
Norris (Soweto), and Scott Hazelhurst, Nigel Crowther, Himla
Soodyall and Zane Lombard (Wits).

Site specific Investigators: Agincourt, South Africa (Frances
Xavier G�omez-Oliv�e Casas and Ryan Wagner), Dikgale, South
Africa (Felistas Mashinya, Ian Cook and Sam Ntuli), Nairobi,
Keny (Christopher Khayeka-Wandabwa, Tilahun Nigatu Har-
egu, Shukri F. Mohammed and Stella Muthuri), Nanoro,
Burkina Faso (Palwende R. Boua, Herman Sorgho, Seydou
Nakanabo-Diallo, Toussaint Rouamba), Navrongo, Ghana
(Godfred Agongo, Cornelius Debpuur, Engelbert A. Nonterah,
Eric Fato and Immaculate Anati and Lucas Amega-Etego),
Soweto, South Africa (Nomses Baloyi, Juliana Kagura, Richard
Munthali and Yusuf Guman) and Wits AWI-Gen Collaborative
Centre at SBIMB (Cassandra C. Soo, Freedom Mukomana,
Stuart A. Ali, Ananyo Choudhury).

Acknowledgments
The AWI-Gen study would not have been possible without the
generosity of the participants who spent many hours responding to
questionnaires, being measured, and having samples taken. We wish

DOI: 10.1161/JAHA.118.011506 Journal of the American Heart Association 14

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to acknowledge the sterling contributions of our field workers,
phlebotomists, laboratory scientists, administrators, data personnel
and other staff who contributed to the data and sample collections,
processing, storage, and shipping.

Author Contributions
Conception of paper (Nonterah, Klipstein-Grobusch, Bots and
Crowther), data analysis (Nonterah), writing of original
manuscript (Nonterah, PRB, Klipstein-Grobusch, Bots and
Crowther), critical review and approval of the manuscript (all
authors).

Sources of Funding
Nonterah is supported by a grant from theGlobalHealth Support
Program of the University Medical Center Utrecht (UMCU),
University of Utrecht, The Netherlands and the NavrongoHealth
Research Centre (NHRC), Ghana. The AWI-Gen Collaborative
Centre is funded by the National Human Genome Research
Institute (NHGRI), Office of the Director (OD), Eunice Kennedy
Shriver National Institute Of Child Health & Human Develop-
ment (NICHD), the National Institute of Environmental Health
Sciences (NIEHS), the Office of AIDS Research (OAR) and the
National Institute of Diabetes and Digestive and Kidney
Diseases (NIDDK), of the National Institutes of Health under
award number U54HG006938 and its supplements, as part of
the H3Africa Consortium. Additional funding was leveraged
from the Department of Science and Technology, South Africa,
award number DST/CON 0056/2014. This paper describes the
views of the authors and does not necessarily represent the
official views of the National Institutes of Health (USA). Boua is
funded by National Research Foundation/The World Academy
of Sciences (NRF/TWAS) through the program: “African
Renaissance Doctoral Fellowship.”

Disclosures
None.

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DOI: 10.1161/JAHA.118.011506 Journal of the American Heart Association 17

Cardiovascular Risk Factors, HIV, and CIMT in SSA Nonterah et al
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http://sanac.org.za/2016/06/22/global-aids-response-progress-report-garpr-2015/
http://sanac.org.za/2016/06/22/global-aids-response-progress-report-garpr-2015/
http://guidelines.health.go.ke:8000/media/Final_guidelines_re_print_11-09-2012.pdf
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SUPPLEMENTAL MATERIAL 

 

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Table S1. Univariable estimates for exposure variables and CIMT in the final combined 

model. 

Risk factors β-coefficient [95%CI] P-value 

Age (years) 7.91 [7.49,8.33] <0.001 

Men vs women 0.99 [-4.12,6.11] 0.704 

Current vs Never/previous 

smoking 

-11.93 [-18.45,-5.46] <0.001 

SBP per 10 mmHg 10.49 [9.22,11.75] <0.001 

Glucose per 1mmol/l 0.50 [-1.17,2.18] 0.556 

HDL-C per 1mmol/l -20.24 [-26.34,-14.15] <0.001 

LDL-C per 1mmol/l -4.04 [-7.05,-1.04] 0.008 

BMI per 10 kg/m2 -2.30 [-6.40,1.79] 0.271 

MVPA in mins/week -17.85 [-25.42,-10.270 <0.001 

Current vs Never/previous 

alcohol use 

15.03 [9.91,20.15] <0.001 

Educational status   

No formal education Ref  

Primary -49.29 [-55.52,-43.03] <0.001 

Secondary -58.97 [-64.99,-52.96]  

Tertiary -49.92 [-64.52,-35.33]  

Household SES   

Poorest Ref  

Poorer -8.37 [-17.37,0.63] 0.052 

Poor -8.93 [-17.94,0.08]  

Less poor -11.11 [-19.93,-2.29]  

Least poor -2.63 [-11.26,6.00]  

HIV+ vs HIV- -57.31 [-63.95,-50.68] <0.001 

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CI=confidence interval; SES= socio-economic status; MVPA=moderate-to-vigorous physical activity; SBP=systolic 

blood pressure; BMI=body mass index; LDL-C=low-density lipoprotein cholesterol; HDL-C=high-density 

lipoprotein cholesterol. 

 

 

 

 

 

 

 

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Table S2. Multiplicative interaction terms between sex and site with the risk factors. 

 

Site and risk factor interaction terms p-value 

Site with Age <0.001 

Site with Sex <0.001 

Site with Current smoking 0.844 

Site with Systolic blood pressure 0.043 

Site with Glucose 0.079 

Site with HDL-C 0.619 

Site with LDL-C 0.078 

Site with BMI <0.001 

Site with MVPA 0.299 

Site with Current alcohol 0.281 

Site with Educational status 0.003 

Site with Household SES 0.108 

Site with HIV status 0.113 

SES=socio-economic status; MVPA=moderate-to-vigorous physical activity; BMI=body mass index; LDL-C=low-

density lipoprotein cholesterol; HDL-C=high-density lipoprotein cholesterol 

 

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