CARDIOVASCULAR JOURNAL OF AFRICA • Vol 23, No 1, February 2012
AFRICA
5
Cardiovascular Topics
The 30-year cardiovascular risk profile of South Africans
with diagnosed diabetes, undiagnosed diabetes,
pre-diabetes or normoglycaemia: the Bellville, South
Africa pilot study
TANDI E MATSHA, MOGAMAT S HASSAN, MARTIN KIDD, RAJIV T ERASMUS
Abstract
The aim of this pilot study was to assess the 30-year risk for
cardiovascular disease (CVD) in the South Africa population
of mixed-ancestry in individuals with non-diabetic hyper-
glycaemia, and undiagnosed and self-reported diabetes.
Participants were drawn from an urban community of the
Bellville South suburb of Cape Town. In total, 583 subjects
without a history of CVD were eligible for lifetime CVD risk
estimation. Gender-specific prediction for CVD risk was
calculated using the 30-year CVD interactive risk calculator.
High CVD risk (
>
20%) was evident in normoglycaemic and
younger subjects (under 35 years). The significant predictors
of CVD were sibling history of diabetes, and triglyceride,
low-density lipoprotein cholesterol and glycated haemoglo-
bin levels (
p
<
0.001). The high lifetime risk in normoglycae-
mic and younger subjects may be considered a warning that
CVD might take on epidemic proportions in the near future
in this country. We recommend the inclusion of education on
CVD in school and university curricula.
Keywords:
cardiovascular diseases, diabetes mellitus, epidemi-
ology, obesity, South Africa
Submitted 7/4/10, accepted 26/10/10
Cardiovasc J Afr
2012;
23
: 5–11
DOI: 10.5830/CVJA-2010-087
Urbanisation, and demographic and epidemiological transitions
have rendered diabetes one of the major non-communicable
diseases in South Africa. Studies carried out in South Africa
have shown marked geographical and ethnic variations in the
prevalence of diabetes.
1-3
The mixed-ancestry population of
South Africa has the second highest prevalence of diabetes,
preceded by that of the Indians.
1,3
Diabetes mellitus (DM),
particularly type 2 DM is highly associated with cardiovascular
disease (CVD), and mortality from CVD is two- to four-fold
higher in those with the disease.
4
In South Africa, CVD is the
second leading cause of death after HIV/AIDS,
5
and the South
African National Department of Health has identified diabetes as
a major risk factor.
6
The relationship between dysglycaemia and CVD is linear
and sometimes starts below the diagnostic levels of diabetes.
7,8
Of the two pre-diabetic states, impaired fasting glucose (IFG)
and impaired glucose tolerance (IGT), the relationship between
IGT and CVD is well documented, however that of IFG and
CVD is controversial.
9,10
Fasting glucose levels compared to
postprandial glucose concentration were found not to be the
strongest determinants of intima–media thickness (IMT) or
death associated with hyperglycaemia,
7,9
however, the DECODE
study group
7
did find an increased risk of mortality for indi-
viduals with IFG compared to those with normoglycaemia. In
developing countries, diabetes is often undiagnosed and the risk
of CVD may therefore far exceed that of known cases due to the
unmanaged glycaemic state.
Various mathematical equations that incorporate the major
risk factors (age, gender, high blood pressure, smoking, dyslipi-
daemia and diabetes) have been developed for the assessment of
CVD risk over a 10-year period in general populations.
11-14
The
performance of the two frequently used models, the Framingham
Heart Study
13
and the UK Prospective Diabetes Study risk
engine (UKPDS), version 3,
14
have been evaluated in indi-
viduals with diabetes, hyperglycaemia and normoglycaemia.
15
The Framingham Heart Study performed better at classifying
subjects with a net gain in correct classification of –14 and
–12.4% for non-diabetic hyperglycaemia and normoglycaemia,
respectively.
15
However, the 10-year time frame of these models
has been criticised because an individual’s lifetime risk may be
high while the 10-year risk prediction may be low, therefore
delaying efforts to modify that risk.
Recently, an algorithm that allows for 30-year risk assessment
Department of Bio-Medical Sciences, Faculty of Health
and Wellness Science, Cape Peninsula University of
Technology, Cape Town, South Africa
TANDI E MATSHA, PhD,
Department of Nursing and Radiography, Faculty of Health
and Wellness Science, Cape Peninsula University of
Technology, Cape Town, South Africa
MOGAMAT S HASSAN, MSc
Centre for Statistical Consultation, University of
Stellenbosch, Cape Town, South Africa
MARTIN KIDD, PhD
Division of Chemical Pathology, Faculty of Health Sciences,
University of Stellenbosch, Cape Town, South Africa
RAJIV T ERASMUS, MBBS, FMC.Path (Nig) DABCC (Am Board
Cerified), FCPath (SA),
