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Prevalence of glucose intolerance and associated risk factors in rural and urban populations of different ethnic groups in Kenya
diabetes research and clinical practice 84 (2009) 303–310
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Diabetes Research and Clinical Practice journal homepage: www.elsevier.com/locate/diabres
Prevalence of glucose intolerance and associated risk factors in rural and urban populations of different ethnic groups in Kenya§ D.L. Christensen a,b,*, H. Friis c, D.L. Mwaniki d, B. Kilonzo d, I. Tetens e, M.K. Boit f, B. Omondi d,1, L. Kaduka d, K. Borch-Johnsen b a
Department of International Health, Immunology, and Microbiology, University of Copenhagen, Copenhagen, Denmark Steno Diabetes Center, Gentofte, Denmark c Department of Human Nutrition, University of Copenhagen, Frederiksberg, Denmark d Centre for Public Health Research, KEMRI, Nairobi, Kenya e National Food Institute, Department of Nutrition, Technical University of Denmark, Søborg, Denmark f Department of Exercise, Recreation and Sport Science, Kenyatta University, Nairobi, Kenya b
article info
abstract
Article history:
Objective: To assess the prevalence of glucose intolerance in rural and urban Kenyan
Received 23 April 2008
populations and in different ethnic groups. Further, to identify associations between life-
Received in revised form
style risk factors and glucose intolerance.
12 February 2009
Research design and methods: A cross-sectional study included an opportunity sample of Luo,
Accepted 10 March 2009
Kamba, Maasai, and an ethnically mixed group from rural and urban Kenya. Diabetes and
Published on line 9 April 2009
IGT were diagnosed using a standard OGTT. BMI, WC, AFA, AMA and abdominal subcutaneous and visceral fat thicknesses, physical activity and fitness were measured. Ques-
The study team is indebted to
tionnaires were used to determine previous diabetes diagnosis, family history of diabetes,
the late Benedict Omondi for
smoking habits, and alcohol consumption.
planning and coordinating
Results: Among 1459 participants, mean age 38.6 years (range 17–68 years), the overall age-
laboratory analysis.
standardized prevalence of diabetes and IGT was 4.2% and 12.0%. The Luo had the highest prevalence of glucose intolerance among the rural ethnic groups. High BMI, WC, AFA,
Keywords:
abdominal visceral and subcutaneous fat thickness, low fitness and physical activity,
Diabetes
frequent alcohol consumption, and urban residence were associated with glucose intoler-
Epidemiology
ance.
Luo
Conclusions: The prevalence of diabetes and IGT among different Kenyan population groups
Kamba
was moderate, and highest in the Luo. The role of lifestyle changes and ethnicity on the
Maasai
effect of diabetes in African populations needs further exploration. # 2009 Elsevier Ireland Ltd. All rights reserved.
§ Grant support: DANIDA; Cluster of International Health (University of Copenhagen); Steno Diabetes Center; Beckett Foundation; Dagmar Marshall Foundation; Dr. Thorvald Madsen’s Grant; Kong Christian den Tiende’s Foundation; Brdr. Hartmann Foundation. * Corresponding author at: University of Copenhagen, Øster Farimagsgade 5, P.O.B. 2099, DK-1014 Copenhagen K, Denmark. Tel.: +45 3532 6846; fax: +45 3532 7736. E-mail address: [email protected] (D.L. Christensen). 1 Deceased. 0168-8227/$ – see front matter # 2009 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.diabres.2009.03.007
304 1.
diabetes research and clinical practice 84 (2009) 303–310
Introduction
The first study attempting to estimate the diabetes prevalence in an African population was carried out in 1958 finding a very low prevalence of 0.4% among urban children and adults in Ghana [1]. For the following three decades, prevalence of <3.0% was found in rural and urban participants [2–4], except for studies carried out in South Africa, where a prevalence of 3.6% was found [5]. However, glycosuria was used for screening in most of these early studies, which led to underestimated prevalence rates of diabetes since glycosuria has a low sensitivity as a screening tool [6]. In 1993, an age-adjusted diabetes prevalence of 8.0% in urban South Africans [7] using oral glucose tolerance test (OGTT) was reported, and recently a prevalence of 6.8% was found among urban Nigerians [8]. The latter studies have indicated the beginning of a diabetes epidemic in sub-Saharan Africa (SSA), even though a prevalence of just 0.3% in rural and urban Gambians was found as late as 1997 [9], showing that considerable regional variation continue to exist within the African continent. A rural-urban difference in diabetes prevalence was shown in Tanzania [10], but could not be verified in rural vs. urban populations in Cameroon [11]. Thus, environmental factors may not affect different populations to the same extent when it comes to blood glucose dysregulation. Thus, the aim of the present study was to investigate the impact of urbanisation and ethnicity on the prevalence of diabetes and impaired glucose tolerance (IGT) in Kenyan populations and to identify the major risk factors for developing glucose intolerance.
rural participants, staff and students at Kenyatta University as well as members of two churches. All potential participants were presented with a standard statement by a local social mobiliser, describing the current study as a diabetes investigation. Among the rural populations, the Luo participants were examined at 13 different primary schools in Bondo district around Lake Victoria, The Kamba participants were examined at Mutomo Hospital in Kitui District in eastern Kenya. The Maasai participants were examined at Lolgorian Health Centre in Transmara district. In all, 1178 rural individuals participated. Among the urban population, the study populations included the Luo as well as a cluster of Kamba, Kikuyu, Embu and Meru and a cluster of Maasai and Kalenjin. Each cluster is known to be biologically and culturally closely intra-linked through intermarriage within the groups [12]. Hence, throughout the text the two urban clusters will be described as Kamba and Maasai, respectively. Further, Luhya, Kisii, Teso, and Mijikenda individuals were included in the study as part of the urban group. In all, 281 urban individuals participated. The age of all the participants was taken from their personal ID cards, or by their own account. If the participant did not know his/her age, an estimate according to personal events such as circumcision and age-set membership was made. All participants gave written or oral informed consent. The study was approved by the National Ethical Review Committee in Kenya and the Danish National Committee on Biomedical Research Ethics in Denmark.
2.3.
2.
Materials and methods
2.1.
Study area and population
A cross-sectional study in Kenya was conducted among three rural populations – the Luo, Kamba, and Maasai – and in an urban population of mixed ethnic origin. The Luo are subsisting on mainly cereal foods as staple foods supplemented with fish, the Kamba on cereal foods and tubers, and the Maasai on animal husbandry and maize [12].
2.2.
Selection procedure
Inclusion criteria for the study were age 17 years and Luo, Kamba or Maasai ethnicity in the rural areas and the same ethnicities or biologically and culturally related ethnic groups in the urban area. Exclusion criteria for participation were pregnancy, serious illnesses such as malaria, inability to walk unassisted and severe mental disease. To participate in the urban group residency 2 years in Nairobi was required. Among the Luo and Kamba rural populations, 955 individuals attending local village meetings were randomly selected after signing up for the study. Of these individuals, 11.3% did not show up for the study. Instead, they were replaced by volunteers. All Maasai (n = 365) of 12 different villages were invited and participated. In Nairobi, participants were recruited among biological family members of the
Oral glucose tolerance test
The participants arrived in the morning after an overnight fast. A standard 75-g OGTT was performed beginning with the collection of a venous fasting blood sample which was carried out between 7.30 AM and 11 AM. All blood samples were analysed immediately following collection of the blood. Participants, who had diabetes based on the fasting blood sample or known diabetes, were not asked to consume the glucose solution. Glucose tolerance was classified according to World Health Organization (WHO) criteria [13]. Fasting venous glucose 6.1 mmol/l or 2-h venous glucose 10.0 were taken to indicate diabetes, and IGT was defined as fasting venous glucose <6.1 mmol/l and 2-h venous glucose 6.7 and <10.0 mmol/l, while fasting venous glucose of 5.6 and <6.1 was defined as impaired fasting glycaemia (IFG). Individuals with previous diagnosis of diabetes were categorized as having diabetes, and consequently did not undergo an OGTT. The blood glucose was determined by the glucose dehydrogenase method using haemolysation and deproteinisation on ¨ ngelholm, a HemoCue B-Glucose 201+ device (HemoCue AB, A Sweden) with 5 mL of blood.
2.4.
Anthropometry
Weight and height were measured with the participants wearing undergarments. Body mass index (BMI) was calculated as weight/height2 (kg/m2). On the standing participant, waist circumference (WC) was measured with a body tape
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diabetes research and clinical practice 84 (2009) 303–310
midway between the iliac crest and the costal margin following a quiet expiration and hip circumference at the greater trochanters. Mid-upper arm circumference (MUAC) was measured on the left arm to the nearest 0.1 cm without compressing the tissue and triceps skinfold thickness (TSF) to the nearest 0.1 mm using a Harpenden caliper (Model HSB-BI, West Sussex, UK). Arm muscle area (AMA) and arm fat area (AFA) were calculated, without adjusting for bone area, according to Frisancho [14].
2.5.
Ultrasonography
Abdominal visceral and subcutaneous fat thickness (cm) was measured using ultrasonography (Aquila Basic Unit, Esaote, Pie Medical Equipment, Masstricht, the Netherlands) with a 3.5/5.0 MHz transducer (Probe Article no. 410638 Curved Array HiD probe R40 Pie Medical Equipment, Maastricht, the Netherlands).
2.6.
Physical activity and fitness measurements
The activity measurements were carried out using a combined heart rate (HR) and movement sensor (Actiheart, Cambridge Neurotechnology Ltd, Cambridge, UK) as described by Brage et al. [15]. A step test using a 22-cm high step board (Reebok, Lancaster, UK) was used to estimate VO2max in ml O2kg 1min 1, and to calibrate HR to physiological intensity at the individual level. Monitoring of habitual physical activity (combined HR and accelerometry) to estimate activity energy expenditure (AEE) in kJ per kg per day and time spent >3 METs were measured over 5 days. Physical activity level (PAL) was derived from total energy expenditure divided by estimated resting energy expenditure (REE).
2.7.
Table 1 – Background characteristics for rurala and urbanb populations. Rural (n = 1178)
Urban (n = 281)
P-value
Ethnicity (%)c Luo Kamba Maasai Other
34.6 34.5 30.9 0.0
26.2 62.4 2.5 9.0
Gender distribution Female
<0.001d
60.6
37.3
<0.001
38.3 (10.0) 39.9 (10.8)
38.5 (11.7) 34.6 (10.3)
0.787 <0.001
26.8 (5.3) 22.3 (3.8)
<0.001 <0.001
87.0 (13.2) 83.3 (16.6)
<0.001 <0.001
34.1 (5.3) 40.9 (7.3)
0.004 <0.001
48.6 (20.7) 56.2 (24.8)
0.062 <0.001
124.8 (65.2) 117.6 (68.7)
0.736 <0.001
e
Age (yrs) Female Male
Anthropometrye Body mass index (kg/m2) Female 22.2 (4.3) Male 20.8 (3.7) Waist circumference (cm) Female 77.7 (10.0) Male 79.0 (10.4) Physical activitye Cardio-respiratory fitnesskg Female 37.4 (6.8) Male 42.6 (7.5)
1
Activity energy expenditurekg Female 55.9 (20.4) Male 67.7 (25.5) Time spent >3 METs (min) Female 125.4 (72.6) Male 152.3 (80.6)
Statistical analysis
The chi-square test was used to test for differences in proportions, and the two-sample t-test and one-way analysis of variance were used to test for differences in means. An age-standardised prevalence was calculated with the direct method of adjustment using the world population as the standard. Association of risk factors with glucose intolerance was tested independently, controlled for age, by logistic regression. Data on males and females were analysed separately. Results are presented as mean S.D. and odds ratios with 95% confidence intervals. All analyses were done with the Stata 10.0 Intercooled version (Stata Corp, TX, USA).
Results
Mean age of the participants was 38.6 years (range 17–68 years). Of 1486 individuals who initially agreed to participate, 1459 (98.2%) completed the study. Of these, 1178 (80.7%) were rural participants, and 281 (19.3%) were urban participants, and 58.0% were females. Background characteristics for rural and urban populations are presented in (Table 1). Of the 1459 participants examined, 200 (13.7%) met the criteria for glucose intolerance, including 59 individuals with diabetes (4.1%). The age-adjusted prevalence of diabetes was 4.2% (95% CI: 2.0; 7.7), with 4.2% (95%
Questionnaire data
The questionnaires for health assessment were translated from English into Kiswahili and the three local languages Dholuo, Kikamba and Ol-maa and back-translated into English. All interviews were structured and were conducted in Kiswahili, English or the local language by a team investigator or a trained local assistant. Family history of diabetes and previous diagnosis of diabetes, smoking habits and alcohol consumption were determined.
2.8.
3.
Physical activity level <1.6 (%) Female 17.5 Male 15.1 a
(kJ)f
1
(kJ)
15.0 30.2
0.773 0.010
Rural group consists of Luo, Kamba, and Maasai individuals. Urban group consists of Luo, Kamba, Kikuyu, Embu, Meru, Maasai, Kalenjin, Luhya, Kisii, Mijikenda and Teso individuals. c In the urban population, the Kamba consists of the KambaKikuyu-Embu-Meru cluster and the Maasai consists of the MaasaiKalenjin cluster. Other consists of Luhya, Kisii, Mijikenda and Teso individuals. d Chi2-test. e Results are presented as means (S.D.). f Estimated ml O2 1kg 1min 1). b
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found with family history of diabetes, smoking or alcohol consumption except for frequent alcohol intake among males (Table 2). Of those with diabetes, 15 (48%) in the rural and 6 (21%) in the urban area, respectively, were not previously aware of their diabetic status. The association between glucose intolerance and quartiles of body composition, fitness and physical activity controlled for age is shown in (Table 3). Among body composition variables, the highest association was found in increased BMI and visceral fat thickness among females and males, respectively. The association between glucose intolerance and increased AMA was higher in males. When controlled for BMI, the positive effect of AMA disappeared, as the upper quartiles changed from 1.1, 1.2, and 2.1, respectively, to 0.8, 0.8 and 0.6 for the three upper quartiles (data not shown). When using the WHO (16) categories for overweight, the association between BMI 25 kg/m2 and of having glucose intolerance was 1.6 among females, and 6.0 among males. The association between having glucose intolerance and a WC above 88 cm for women and 102 cm for men was 4.9 and 5.8, respectively. In fitness and physical activity variables, the upper quartiles of cardio-respiratory fitness, AEEkg 1, time spent >3 METs, and PAL showed a lower probability of having glucose intolerance in both genders.
4.
Fig. 1 – Prevalence (%) of diabetes and IGT by age-group and gender.
CI: 1.4; 9.4) among females and 4.5% (95% CI: 2.0; 10.2) among males (P = 0.782). The age-adjusted prevalence was 2.2% (95% CI: 0.8; 5.2) in the rural population, and 12.2% in the urban population (95% CI: 5.4; 23.2) (P < 0.001). Of those with diabetes, 21 (36%) were not previously aware of their status as diabetes patients. The group with glucose intolerance included 141 individuals with IGT (9.7%), of which 10 individuals also had IFG. Of the remaining 15 individuals with isolated IFG, two progressed to diabetes after the OGTT. The age-adjusted prevalence of IGT was 12.0% (95% CI: 9.2; 15.2), with 13.1% (95% CI: 7.7; 20.3) among females and 6.1% (95% CI: 2.5; 12.6) among males (P < 0.001). The age-adjusted prevalence was 8.6% (95% CI: 5.1; 14.0) in the rural population, and 13.2% in the urban population (95% CI: 4.6; 26.5) (P = 0.019). The prevalence of diabetes and IGT increased with age (Fig. 1). There was no difference in prevalence of glucose intolerance between the randomly selected and self-selected rural Luo and Kamba participants (16.2% vs. 13.3%, P = 0.433), indicating that selection procedure did not influence the glucose intolerance results. The prevalence of glucose intolerance was highest in the Luo in both genders among the rural ethnic groups, and glucose intolerance was more common with urban residence in both genders. No association with glucose intolerance was
Discussion
We found a relatively low age-standardised prevalence of diabetes of 4.2% with a concurrent high prevalence of IGT of 12.0% in selected Kenyan population groups. The prevalence of diabetes and IGT was highest in the urban population, which is in line with several [10,17], but not all [11] studies from SSA. However, due to differences in methodology, comparisons with other studies conducted in the region are difficult. Few have been based on an OGTT or have calculated age-standardised prevalence. Further, many have included either rural or urban participants, or a different age distribution. Importantly, the IGT prevalence of the current study is the highest to be reported from SSA in populations of ethnic African origin, suggesting that diabetes prevalence will increase further as up to 70% of individuals with IGT may develop diabetes [18]. However, a limitation of this study is the potential selection bias which may have over- or underestimated prevalence assessment. The Luo had the highest glucose intolerance in both genders in the rural population. This finding can partly be explained by the higher total dietary energy intake and the higher glycaemic load from the daily high consumption of cereal grains compared with the Kamba (data not shown) [19]. Based on recent findings on fat accumulation, the relatively low prevalence of glucose intolerance among the rural Maasai was unexpected. They had the highest fat accumulation expressed as BMI, visceral and abdominal subcutaneous fat thickness, WC and AFA with increasing age compared to the Luo and Kamba [20]. On the other hand, this Maasai group had a relatively lower dietary glycaemic load compared to the other groups (data not shown). To what extent the discrepancy between anthropometry and glucose intolerance in this ethnic
diabetes research and clinical practice 84 (2009) 303–310
307
Table 2 – Glucose tolerance status by risk factor categories stratified by gendera. n
% with Glucose intolerance (diabetes + IGT)
OR (95% CI)
Ethnic group Rural Female Luo Kamba Maasai
224 300 193
18.0 12.3 7.8
2.39 (1.27; 4.50) 1.48 (0.78; 2.81) 1.00
0.007 0.231
Male Luo Kamba Maasai
183 106 172
12.9 9.3 5.2
2.63 (1.18; 5.87) 1.64 (0.64; 4.22) 1.00
0.018 0.303
Residency Female Urban Rural
127 717
26.0 9.4
2.50 (1.59; 3.92) 1.00
<0.001
Male Urban Rural
152 463
11.8 10.5
3.67 (2.09; 6.44) 1.00
<0.001
79 734
19.0 8.7
1.12 (0.67; 1.87) 1.00
0.656
54 535
14.8 8.6
1.27 (0.57; 2.85) 1.00
0.563
Smoking status Female Smoker Non-smoker
52 739
17.3 15.2
1.08 (0.74; 1.57) 1.00
0.685
Male Smoker Non-smoker
94 487
14.6 13.0
1.20 (0.83; 1.73) 1.00
0.344
Alcohol consumptionb Female Daily 3–6 days/week <2–3 days/week Never
10 16 18 771
20.0 6.3 22.2 18.1
1.07 (0.45; 2.58) 0.57 (0.22; 1.44) 1.62 (0.85; 3.11) 1.00
0.873 0.234 0.145
Male Daily 3–6 days/week <2–3 days/week Never
16 47 60 466
45.5 17.0 7.1 12.6
3.93 (2.04; 7.57) 2.04 (0.92; 5.09) 0.76 (0.40; 1.45) 1.00
<0.001 0.002 0.401
Family history of diabetes Female Yes No Male Yes No
P-value
Crude prevalence with age-adjusted ORs. Both known and previously unknown cases. b OR age- and weight-adjusted. a
group is caused by lifestyle or genetic protection remains to be determined. The positive association between obesity and glucose intolerance shown in this study has been reported in numerous other studies [21–23]. Further, abdominal obesity – and especially accumulation of visceral fat – has been shown to be the strongest predictor of insulin resistance in the liver and peripheral tissues [24]. When using the WHO [16] categories for overweight based on BMI and WC, the males had risks of glucose intolerance four times higher than women for BMI, while WC
showed a 20% higher risk for men compared to women. Thus, males seem to be at a much higher risk of glucose intolerance with increasing obesity in the current study population. BMI and visceral fat accumulation had the strongest associations with glucose intolerance among women and men, respectively. This indicates that total body fat is more harmful for females, while intra-abdominal fat has the most detrimental effect in males when it comes to glucose intolerance. The latter is supported by the strong association between elevated WC and glucose intolerance in males.
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Table 3 – Glucose intolerance (diabetes + IGT) by gender specific quartiles of body composition, fitness and physical activity variablesa. 1st
2nd
3rd
4th
1 1
2.7 (1.2; 6.2) 0.3 (0.1; 0.9)
2.9 (1.3; 6.4) 1.0 (0.4; 2.1)
3.3 (1.6; 6.8) 2.4 (1.2; 4.6)
0.004 <0.001
WC (cm) Females Males
1 1
1.3 (0.7; 2.5) 1.5 (0.6; 4.0)
1.5 (0.8; 2.8) 1.4 (0.5; 3.8)
2.4 (1.3; 4.3) 5.3 (2.2; 12.9)
0.002 <0.001
AMA (cm2) Females Males
1 1
0.5 (0.3; 0.9) 1.1 (0.5; 2.4)
0.7 (0.4; 1.2) 1.2 (0.6; 2.6)
0.7 (0.4; 1.2) 2.1 (1.0; 4.2)
0.355 0.035
AFA (cm2) Females Males
1 1
1.4 (0.8; 2.6) 0.8 (0.3; 2.0)
2.0 (1.1; 3.6) 1.3 (0.6; 3.0)
2.1 (1.2; 3.7) 3.3 (1.6; 6.9)
0.006 <0.001
Abdominal fat (cm) Visceral fat Females Males
1 1
1.1 (0.6; 2.0) 2.2 (0.8; 6.1)
1.7 (1.0; 3.0) 2.8 (1.1; 7.5)
1.5 (0.9; 2.7) 6.4 (2.6; 15.8)
0.054 <0.001
Subcutaneous fat Females Males
1 1
2.6 (1.4; 4.7) 0.5 (0.2; 1.3)
1.6 (0.9; 3.0) 0.9 (0.4; 2.1)
2.4 (1.3; 4.3) 2.1 (1.0; 4.6)
0.027 0.003
Fitness and physical activity Cardiorespiratory fitnessb Females 1 Males 1
0.7 (0.4; 1.3) 0.8 (0.3; 1.7)
0.5 (0.3; 1.0) 0.5 (0.5; 1.3)
0.4 (0.2; 0.7) 0.4 (0.1; 1.2)
0.003 0.055
1 1
0.7 (0.4;1.2) 0.5 (0.2; 1.1)
0.6 (0.3; 1.0) 0.3 (0.1; 0.9)
0.3 (0.1; 0.6) 0.4 (0.2; 1.2)
<0.001 0.037
Time spent >3 METs (min) Females 1 Males 1
0.7 (0.4; 1.1) 0.4 (0.2; 0.9)
0.5 (0.3; 0.8) 0.5 (0.2; 1.3)
0.4 (0.2; 0.7) 0.4 (0.1; 1.0)
<0.001 0.048
1.1 (0.6; 2.0) 0.5 (0.2; 1.2)
0.5 (0.3; 1.0) 0.5 (0.2; 1.3)
0.4 (0.2; 0.8) 0.5 (0.2; 1.3)
0.002 0.090
Body composition BMI (kg/m2) Females Males
AEEc Females Males
PALd Females Males a b c d
1 1
P-linear trend
Age-controlled. Estimated ml O2kg 1min 1. Activity energy expenditurekg 1. Physical activity level (TEE/REE).
In a crude analysis, AMA was positively associated with glucose intolerance in men. However, the effect of AMA was confounded by the effect of BMI, to an extent in which the upper quartiles of AMA showed a protective effect for having glucose intolerance. For the first time in an African population, this study made it possible to assess the relationship between glucose intolerance and both physical activity and cardio-respiratory fitness based on objective measurements. We found that higher levels of physical activity was inversely associated with glucose intolerance, whether expressed as AEEkg 1, time spent >3 min METs, or PAL. In addition, cardio-respiratory fitness was inversely associated with glucose intolerance. Development of glucose intolerance as a result of low physical activity is a well-documented relationship [25,26]. Our results support current evidence from non-African populations that
even physical activity of moderate intensity and duration is associated with a reduction in risk of type 2 diabetes [27,28]. Frequent alcohol consumption was positively associated with glucose intolerance among males only, but participants may not have reported their true alcohol intake as this behaviour may be regarded as socially unacceptable and thus subject to reporting bias. The same may be true for smoking habits for which no association with glucose intolerance was found. Of the individuals diagnosed with diabetes, 36% of the participants (48% rural and 21% urban) were previously unaware of their status as diabetes patients. This is lower compared to other investigations in SSA [4,7], and could be due to either a rising awareness of diabetes in Kenya, or a selection bias in our study. Further, a relationship between positive family history of diabetes and risk of glucose intolerance could
diabetes research and clinical practice 84 (2009) 303–310
not be found. This could be due to improved diagnostic methods, or it may reflect a relatively recent change of lifestyle based on which diabetes and other chronic diseases have only begun to emerge. In conclusion, this study found relatively low prevalence of diabetes among Kenyans, and ethnic as well as rural–urban differences in prevalence of glucose intolerance were found. To what extent the different associations of ethnicity and glucose intolerance are due to lifestyle changes or genes needs further exploration.
Conflict of interest There are no conflicts of interest.
Acknowledgement We are grateful to all participants, the local chiefs and subchiefs, the local elder councils and district politicians. We are also indebted to laboratory technicians Tobias Oketch (CVBCR), Arthur J. Ukumu (DVBD), Odero Sabiano (DVBD) and Saidi Kisiwa (KEMRI) for skilful collection and analysis of blood samples in the field. Likewise, we owe our sincere thanks to Filista Kingori of KEMRI for collecting health assessment data, and we thank Dr. Søren Brage, MRC Epidemiology Unit, Cambridge, UK, for support with physical activity assessment. We kindly thank all other field staff members from KEMRI, Kenyatta University, and University of Copenhagen, and we sincerely thank all local assistants for their effort in excellent social mobilisation and collection of data. We acknowledge the permission by the Director of KEMRI to publish this manuscript. The study was supported by DANIDA, Cluster of International Health (University of Copenhagen), Steno Diabetes Center, Beckett Foundation, Dagmar Marshall Foundation, Dr. Thorvald Madsen’s Grant, Kong Christian den Tiende’s Foundation, Brdr. Hartmann Foundation.
references
[1] S.R.A. Dodu, The incidence of diabetes mellitus in Accra (Ghana): a study of 4,000 patients, West Afr. Med. J. 7 (1958) 129–134. [2] S.R.A. Dodu, N. de Heer, A diabetes case-finding survey in Ho, Ghana, Ghana Med. J. 3 (1964) 75–80. [3] A.C.B. Wicks, W.M. Castle, M. Gelfand, Effect of time on the prevalence of diabetes in the urban African of Rhodesia, Diabetes 22 (1973) 733–737. [4] D.G. McLarty, A.B. Swai, H.M. Kitange, G. Masuki, B.L. Mtinangi, P.M. Kilima, et al., Prevalence of diabetes and impaired glucose tolerance in rural Tanzania, Lancet 1 (1989) 871–875. [5] N. Marine, A.I. Vinik, I. Edelstein, W.P. Jackson, Diabetes, hyperglycemia and glycosuria among Indians, Malays and Africans (Bantu) in Cape Town, South Africa, Diabetes 18 (1969) 840–857. [6] M.M. Engelgau, K.M.V. Narayan, W.H. Herman, Screening for type 2 diabetes, Diab. Care 23 (2000) 1563–1580.
309
[7] N.S. Levitt, J.M. Katzenellenbogen, D. Bradshaw, M.N. Hoffman, F. Bonnici, The prevalence and identification of risk factors for NIDDM in urban Africans in Cape Town, South Africa, Diab. Care 16 (1993) 601–607. [8] E.A. Nyenwe, O.J. Odia, A.E. Ihekwaba, A. Ojule, S. Babatunde, Type 2 diabetes in adult Nigerians: a study of its prevalence and risk factors in Port Harcourt, Nigeria, Diab. Res. Clin. Pract. 62 (2003) 177–185. [9] M.A. van der Sande, R. Bailey, H. Faal, W.A. Banya, P. Dolin, O.A. Nyan, et al., Nationwide prevalence study of hypertension and related non-communicable diseases in The Gambia, Trop. Med. Int. Health 2 (1997) 1039–1048. [10] T.J. Aspray, F. Mugusi, S. Rashid, D. Whiting, R. Edwards, K.G. Alberti, et al., Rural and urban differences in diabetes prevalence in Tanzania: the role of obesity, physical inactivity and urban living, Trans. R. Soc. Trop. Med. Hyg. 94 (2000) 637–644. [11] J.C. Mbanya, J. Ngogang, J.N. Salah, E. Minkoulou, B. Balkau, Prevalence of NIDDM and impaired glucose tolerance in a rural and an urban population in Cameroon, Diabetologia 40 (1997) 824–829. [12] A. Fedders, C. Salvadori, Peoples and Cultures of Kenya, Transafrica Publications, Nairobi, 1979. [13] World Health Organization, Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications. Part 1: Diagnosis and Classification of Diabetes Mellitus Report of a WHO Consultation, Geneva, 1999. [14] A.R. Frisancho, Anthropometric Standards for the Assessment of Growth and Nutritional Status, The University of Michigan Press, Ann Arbor, MI, 1990. [15] S. Brage, U. Ekelund, N. Brage, M.A. Hennings, K. Froberg, P.W. Franks, et al., Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity, J. Appl. Physiol. 103 (2007) 682–692. [16] World Health Organization, Obesity: Preventing and Managing the Global Epidemic, WHO, Geneva, 2000 (WHO Technical Report Series 894). [17] M.M. Ceesay, M.W. Morgan, M.O. Kamanda, V.R. Willoughby, D.R. Lisk, Prevalence of diabetes in rural and urban populations in southern Sierra Leone: a preliminary survey, Trop. Med. Int. Health 2 (1997) 272–277. [18] International Diabetes Federation, Diabetes Atlas, IDF, Brussels, 2003. [19] W. Willett, J. Manson, S. Liu, Glycemic index, glycemic load, and risk of type 2 diabetes, Am. J. Clin. Nutr. 76 (2002) 274S–280S. [20] D.L. Christensen, J. Eis, A.W. Hansen, M.W. Larsson, D.L. Mwaniki, B. Kilonzo, et al., Obesity and fat distribution in Kenyan populations: impact of ethnicity and urbanisation, Ann. Hum. Biol. 35 (2008) 232–249. [21] G.A. Colditz, W.C. Willett, M.J. Stampfer, J.E. Manson, C.H. Hennekens, R.A. Arky, et al., Weight as a risk factor for clinical diabetes in women, Am. J. Epidemiol. 132 (1990) 501–513. [22] A.V. Astrup, N. Finer, Redefining type 2 diabetes: diabesity or obesity dependent diabetes mellitus? Obes. Rev. 1 (2000) 57–59. [23] A.E. Field, E.H. Coakley, A. Must, J.L. Spadano, N. Laird, W.H. Dietz, et al., Impact of overweight on the risk of developing common chronic diseases during a 10-year period, Arch. Int. Med. 161 (2001) 1581–1586. [24] J.P. Despre´s, A. Nadeau, A. Tremblay, M. Ferland, S. Moorjani, P.J. Lupien, et al., Role of deep abdominal fat and the association between regional adipose tissue distribution and glucose tolerance in obese women, Diabetes 38 (1989) 304–309. [25] S.P. Helmrich, D.R. Ragland, R.W. Leung, R.S. Paffenbarger Jr., Physical activity and reduced occurrence of non-insulin dependent diabetes mellitus, N. Engl. J. Med. 325 (1991) 147–152.
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[26] A.M. Kriska, R.E. LaPorte, D.J. Pettitt, M.A. Charles, R.G. Nelson, L.H. Kuller, et al., The association of physical activity with obesity, fat distribution and glucose intolerance in Pima Indians, Diabetologia 36 (1993) 863–869. [27] F.B. Hu, R.J. Sigal, J.W. Rich-Edwards, G.A. Colditz, C.G. Solomon, W.C. Willett, et al., Walking compared with
vigorous physical activity and risk of type 2 diabetes in women, JAMA 282 (1999) 1433–1439. [28] N.J. Wareham, M.Y. Wong, N.E. Day, Glucose intolerance and physical inactivity: the relative importance of low habitual energy expenditure and cardiorespiratory fitness, Am. J. Epidemiol. 152 (2000) 132–139.
Contents lists available at ScienceDirect
Diabetes Research and Clinical Practice journal homepage: www.elsevier.com/locate/diabres
Prevalence of glucose intolerance and associated risk factors in rural and urban populations of different ethnic groups in Kenya§ D.L. Christensen a,b,*, H. Friis c, D.L. Mwaniki d, B. Kilonzo d, I. Tetens e, M.K. Boit f, B. Omondi d,1, L. Kaduka d, K. Borch-Johnsen b a
Department of International Health, Immunology, and Microbiology, University of Copenhagen, Copenhagen, Denmark Steno Diabetes Center, Gentofte, Denmark c Department of Human Nutrition, University of Copenhagen, Frederiksberg, Denmark d Centre for Public Health Research, KEMRI, Nairobi, Kenya e National Food Institute, Department of Nutrition, Technical University of Denmark, Søborg, Denmark f Department of Exercise, Recreation and Sport Science, Kenyatta University, Nairobi, Kenya b
article info
abstract
Article history:
Objective: To assess the prevalence of glucose intolerance in rural and urban Kenyan
Received 23 April 2008
populations and in different ethnic groups. Further, to identify associations between life-
Received in revised form
style risk factors and glucose intolerance.
12 February 2009
Research design and methods: A cross-sectional study included an opportunity sample of Luo,
Accepted 10 March 2009
Kamba, Maasai, and an ethnically mixed group from rural and urban Kenya. Diabetes and
Published on line 9 April 2009
IGT were diagnosed using a standard OGTT. BMI, WC, AFA, AMA and abdominal subcutaneous and visceral fat thicknesses, physical activity and fitness were measured. Ques-
The study team is indebted to
tionnaires were used to determine previous diabetes diagnosis, family history of diabetes,
the late Benedict Omondi for
smoking habits, and alcohol consumption.
planning and coordinating
Results: Among 1459 participants, mean age 38.6 years (range 17–68 years), the overall age-
laboratory analysis.
standardized prevalence of diabetes and IGT was 4.2% and 12.0%. The Luo had the highest prevalence of glucose intolerance among the rural ethnic groups. High BMI, WC, AFA,
Keywords:
abdominal visceral and subcutaneous fat thickness, low fitness and physical activity,
Diabetes
frequent alcohol consumption, and urban residence were associated with glucose intoler-
Epidemiology
ance.
Luo
Conclusions: The prevalence of diabetes and IGT among different Kenyan population groups
Kamba
was moderate, and highest in the Luo. The role of lifestyle changes and ethnicity on the
Maasai
effect of diabetes in African populations needs further exploration. # 2009 Elsevier Ireland Ltd. All rights reserved.
§ Grant support: DANIDA; Cluster of International Health (University of Copenhagen); Steno Diabetes Center; Beckett Foundation; Dagmar Marshall Foundation; Dr. Thorvald Madsen’s Grant; Kong Christian den Tiende’s Foundation; Brdr. Hartmann Foundation. * Corresponding author at: University of Copenhagen, Øster Farimagsgade 5, P.O.B. 2099, DK-1014 Copenhagen K, Denmark. Tel.: +45 3532 6846; fax: +45 3532 7736. E-mail address: [email protected] (D.L. Christensen). 1 Deceased. 0168-8227/$ – see front matter # 2009 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.diabres.2009.03.007
304 1.
diabetes research and clinical practice 84 (2009) 303–310
Introduction
The first study attempting to estimate the diabetes prevalence in an African population was carried out in 1958 finding a very low prevalence of 0.4% among urban children and adults in Ghana [1]. For the following three decades, prevalence of <3.0% was found in rural and urban participants [2–4], except for studies carried out in South Africa, where a prevalence of 3.6% was found [5]. However, glycosuria was used for screening in most of these early studies, which led to underestimated prevalence rates of diabetes since glycosuria has a low sensitivity as a screening tool [6]. In 1993, an age-adjusted diabetes prevalence of 8.0% in urban South Africans [7] using oral glucose tolerance test (OGTT) was reported, and recently a prevalence of 6.8% was found among urban Nigerians [8]. The latter studies have indicated the beginning of a diabetes epidemic in sub-Saharan Africa (SSA), even though a prevalence of just 0.3% in rural and urban Gambians was found as late as 1997 [9], showing that considerable regional variation continue to exist within the African continent. A rural-urban difference in diabetes prevalence was shown in Tanzania [10], but could not be verified in rural vs. urban populations in Cameroon [11]. Thus, environmental factors may not affect different populations to the same extent when it comes to blood glucose dysregulation. Thus, the aim of the present study was to investigate the impact of urbanisation and ethnicity on the prevalence of diabetes and impaired glucose tolerance (IGT) in Kenyan populations and to identify the major risk factors for developing glucose intolerance.
rural participants, staff and students at Kenyatta University as well as members of two churches. All potential participants were presented with a standard statement by a local social mobiliser, describing the current study as a diabetes investigation. Among the rural populations, the Luo participants were examined at 13 different primary schools in Bondo district around Lake Victoria, The Kamba participants were examined at Mutomo Hospital in Kitui District in eastern Kenya. The Maasai participants were examined at Lolgorian Health Centre in Transmara district. In all, 1178 rural individuals participated. Among the urban population, the study populations included the Luo as well as a cluster of Kamba, Kikuyu, Embu and Meru and a cluster of Maasai and Kalenjin. Each cluster is known to be biologically and culturally closely intra-linked through intermarriage within the groups [12]. Hence, throughout the text the two urban clusters will be described as Kamba and Maasai, respectively. Further, Luhya, Kisii, Teso, and Mijikenda individuals were included in the study as part of the urban group. In all, 281 urban individuals participated. The age of all the participants was taken from their personal ID cards, or by their own account. If the participant did not know his/her age, an estimate according to personal events such as circumcision and age-set membership was made. All participants gave written or oral informed consent. The study was approved by the National Ethical Review Committee in Kenya and the Danish National Committee on Biomedical Research Ethics in Denmark.
2.3.
2.
Materials and methods
2.1.
Study area and population
A cross-sectional study in Kenya was conducted among three rural populations – the Luo, Kamba, and Maasai – and in an urban population of mixed ethnic origin. The Luo are subsisting on mainly cereal foods as staple foods supplemented with fish, the Kamba on cereal foods and tubers, and the Maasai on animal husbandry and maize [12].
2.2.
Selection procedure
Inclusion criteria for the study were age 17 years and Luo, Kamba or Maasai ethnicity in the rural areas and the same ethnicities or biologically and culturally related ethnic groups in the urban area. Exclusion criteria for participation were pregnancy, serious illnesses such as malaria, inability to walk unassisted and severe mental disease. To participate in the urban group residency 2 years in Nairobi was required. Among the Luo and Kamba rural populations, 955 individuals attending local village meetings were randomly selected after signing up for the study. Of these individuals, 11.3% did not show up for the study. Instead, they were replaced by volunteers. All Maasai (n = 365) of 12 different villages were invited and participated. In Nairobi, participants were recruited among biological family members of the
Oral glucose tolerance test
The participants arrived in the morning after an overnight fast. A standard 75-g OGTT was performed beginning with the collection of a venous fasting blood sample which was carried out between 7.30 AM and 11 AM. All blood samples were analysed immediately following collection of the blood. Participants, who had diabetes based on the fasting blood sample or known diabetes, were not asked to consume the glucose solution. Glucose tolerance was classified according to World Health Organization (WHO) criteria [13]. Fasting venous glucose 6.1 mmol/l or 2-h venous glucose 10.0 were taken to indicate diabetes, and IGT was defined as fasting venous glucose <6.1 mmol/l and 2-h venous glucose 6.7 and <10.0 mmol/l, while fasting venous glucose of 5.6 and <6.1 was defined as impaired fasting glycaemia (IFG). Individuals with previous diagnosis of diabetes were categorized as having diabetes, and consequently did not undergo an OGTT. The blood glucose was determined by the glucose dehydrogenase method using haemolysation and deproteinisation on ¨ ngelholm, a HemoCue B-Glucose 201+ device (HemoCue AB, A Sweden) with 5 mL of blood.
2.4.
Anthropometry
Weight and height were measured with the participants wearing undergarments. Body mass index (BMI) was calculated as weight/height2 (kg/m2). On the standing participant, waist circumference (WC) was measured with a body tape
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diabetes research and clinical practice 84 (2009) 303–310
midway between the iliac crest and the costal margin following a quiet expiration and hip circumference at the greater trochanters. Mid-upper arm circumference (MUAC) was measured on the left arm to the nearest 0.1 cm without compressing the tissue and triceps skinfold thickness (TSF) to the nearest 0.1 mm using a Harpenden caliper (Model HSB-BI, West Sussex, UK). Arm muscle area (AMA) and arm fat area (AFA) were calculated, without adjusting for bone area, according to Frisancho [14].
2.5.
Ultrasonography
Abdominal visceral and subcutaneous fat thickness (cm) was measured using ultrasonography (Aquila Basic Unit, Esaote, Pie Medical Equipment, Masstricht, the Netherlands) with a 3.5/5.0 MHz transducer (Probe Article no. 410638 Curved Array HiD probe R40 Pie Medical Equipment, Maastricht, the Netherlands).
2.6.
Physical activity and fitness measurements
The activity measurements were carried out using a combined heart rate (HR) and movement sensor (Actiheart, Cambridge Neurotechnology Ltd, Cambridge, UK) as described by Brage et al. [15]. A step test using a 22-cm high step board (Reebok, Lancaster, UK) was used to estimate VO2max in ml O2kg 1min 1, and to calibrate HR to physiological intensity at the individual level. Monitoring of habitual physical activity (combined HR and accelerometry) to estimate activity energy expenditure (AEE) in kJ per kg per day and time spent >3 METs were measured over 5 days. Physical activity level (PAL) was derived from total energy expenditure divided by estimated resting energy expenditure (REE).
2.7.
Table 1 – Background characteristics for rurala and urbanb populations. Rural (n = 1178)
Urban (n = 281)
P-value
Ethnicity (%)c Luo Kamba Maasai Other
34.6 34.5 30.9 0.0
26.2 62.4 2.5 9.0
Gender distribution Female
<0.001d
60.6
37.3
<0.001
38.3 (10.0) 39.9 (10.8)
38.5 (11.7) 34.6 (10.3)
0.787 <0.001
26.8 (5.3) 22.3 (3.8)
<0.001 <0.001
87.0 (13.2) 83.3 (16.6)
<0.001 <0.001
34.1 (5.3) 40.9 (7.3)
0.004 <0.001
48.6 (20.7) 56.2 (24.8)
0.062 <0.001
124.8 (65.2) 117.6 (68.7)
0.736 <0.001
e
Age (yrs) Female Male
Anthropometrye Body mass index (kg/m2) Female 22.2 (4.3) Male 20.8 (3.7) Waist circumference (cm) Female 77.7 (10.0) Male 79.0 (10.4) Physical activitye Cardio-respiratory fitnesskg Female 37.4 (6.8) Male 42.6 (7.5)
1
Activity energy expenditurekg Female 55.9 (20.4) Male 67.7 (25.5) Time spent >3 METs (min) Female 125.4 (72.6) Male 152.3 (80.6)
Statistical analysis
The chi-square test was used to test for differences in proportions, and the two-sample t-test and one-way analysis of variance were used to test for differences in means. An age-standardised prevalence was calculated with the direct method of adjustment using the world population as the standard. Association of risk factors with glucose intolerance was tested independently, controlled for age, by logistic regression. Data on males and females were analysed separately. Results are presented as mean S.D. and odds ratios with 95% confidence intervals. All analyses were done with the Stata 10.0 Intercooled version (Stata Corp, TX, USA).
Results
Mean age of the participants was 38.6 years (range 17–68 years). Of 1486 individuals who initially agreed to participate, 1459 (98.2%) completed the study. Of these, 1178 (80.7%) were rural participants, and 281 (19.3%) were urban participants, and 58.0% were females. Background characteristics for rural and urban populations are presented in (Table 1). Of the 1459 participants examined, 200 (13.7%) met the criteria for glucose intolerance, including 59 individuals with diabetes (4.1%). The age-adjusted prevalence of diabetes was 4.2% (95% CI: 2.0; 7.7), with 4.2% (95%
Questionnaire data
The questionnaires for health assessment were translated from English into Kiswahili and the three local languages Dholuo, Kikamba and Ol-maa and back-translated into English. All interviews were structured and were conducted in Kiswahili, English or the local language by a team investigator or a trained local assistant. Family history of diabetes and previous diagnosis of diabetes, smoking habits and alcohol consumption were determined.
2.8.
3.
Physical activity level <1.6 (%) Female 17.5 Male 15.1 a
(kJ)f
1
(kJ)
15.0 30.2
0.773 0.010
Rural group consists of Luo, Kamba, and Maasai individuals. Urban group consists of Luo, Kamba, Kikuyu, Embu, Meru, Maasai, Kalenjin, Luhya, Kisii, Mijikenda and Teso individuals. c In the urban population, the Kamba consists of the KambaKikuyu-Embu-Meru cluster and the Maasai consists of the MaasaiKalenjin cluster. Other consists of Luhya, Kisii, Mijikenda and Teso individuals. d Chi2-test. e Results are presented as means (S.D.). f Estimated ml O2 1kg 1min 1). b
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diabetes research and clinical practice 84 (2009) 303–310
found with family history of diabetes, smoking or alcohol consumption except for frequent alcohol intake among males (Table 2). Of those with diabetes, 15 (48%) in the rural and 6 (21%) in the urban area, respectively, were not previously aware of their diabetic status. The association between glucose intolerance and quartiles of body composition, fitness and physical activity controlled for age is shown in (Table 3). Among body composition variables, the highest association was found in increased BMI and visceral fat thickness among females and males, respectively. The association between glucose intolerance and increased AMA was higher in males. When controlled for BMI, the positive effect of AMA disappeared, as the upper quartiles changed from 1.1, 1.2, and 2.1, respectively, to 0.8, 0.8 and 0.6 for the three upper quartiles (data not shown). When using the WHO (16) categories for overweight, the association between BMI 25 kg/m2 and of having glucose intolerance was 1.6 among females, and 6.0 among males. The association between having glucose intolerance and a WC above 88 cm for women and 102 cm for men was 4.9 and 5.8, respectively. In fitness and physical activity variables, the upper quartiles of cardio-respiratory fitness, AEEkg 1, time spent >3 METs, and PAL showed a lower probability of having glucose intolerance in both genders.
4.
Fig. 1 – Prevalence (%) of diabetes and IGT by age-group and gender.
CI: 1.4; 9.4) among females and 4.5% (95% CI: 2.0; 10.2) among males (P = 0.782). The age-adjusted prevalence was 2.2% (95% CI: 0.8; 5.2) in the rural population, and 12.2% in the urban population (95% CI: 5.4; 23.2) (P < 0.001). Of those with diabetes, 21 (36%) were not previously aware of their status as diabetes patients. The group with glucose intolerance included 141 individuals with IGT (9.7%), of which 10 individuals also had IFG. Of the remaining 15 individuals with isolated IFG, two progressed to diabetes after the OGTT. The age-adjusted prevalence of IGT was 12.0% (95% CI: 9.2; 15.2), with 13.1% (95% CI: 7.7; 20.3) among females and 6.1% (95% CI: 2.5; 12.6) among males (P < 0.001). The age-adjusted prevalence was 8.6% (95% CI: 5.1; 14.0) in the rural population, and 13.2% in the urban population (95% CI: 4.6; 26.5) (P = 0.019). The prevalence of diabetes and IGT increased with age (Fig. 1). There was no difference in prevalence of glucose intolerance between the randomly selected and self-selected rural Luo and Kamba participants (16.2% vs. 13.3%, P = 0.433), indicating that selection procedure did not influence the glucose intolerance results. The prevalence of glucose intolerance was highest in the Luo in both genders among the rural ethnic groups, and glucose intolerance was more common with urban residence in both genders. No association with glucose intolerance was
Discussion
We found a relatively low age-standardised prevalence of diabetes of 4.2% with a concurrent high prevalence of IGT of 12.0% in selected Kenyan population groups. The prevalence of diabetes and IGT was highest in the urban population, which is in line with several [10,17], but not all [11] studies from SSA. However, due to differences in methodology, comparisons with other studies conducted in the region are difficult. Few have been based on an OGTT or have calculated age-standardised prevalence. Further, many have included either rural or urban participants, or a different age distribution. Importantly, the IGT prevalence of the current study is the highest to be reported from SSA in populations of ethnic African origin, suggesting that diabetes prevalence will increase further as up to 70% of individuals with IGT may develop diabetes [18]. However, a limitation of this study is the potential selection bias which may have over- or underestimated prevalence assessment. The Luo had the highest glucose intolerance in both genders in the rural population. This finding can partly be explained by the higher total dietary energy intake and the higher glycaemic load from the daily high consumption of cereal grains compared with the Kamba (data not shown) [19]. Based on recent findings on fat accumulation, the relatively low prevalence of glucose intolerance among the rural Maasai was unexpected. They had the highest fat accumulation expressed as BMI, visceral and abdominal subcutaneous fat thickness, WC and AFA with increasing age compared to the Luo and Kamba [20]. On the other hand, this Maasai group had a relatively lower dietary glycaemic load compared to the other groups (data not shown). To what extent the discrepancy between anthropometry and glucose intolerance in this ethnic
diabetes research and clinical practice 84 (2009) 303–310
307
Table 2 – Glucose tolerance status by risk factor categories stratified by gendera. n
% with Glucose intolerance (diabetes + IGT)
OR (95% CI)
Ethnic group Rural Female Luo Kamba Maasai
224 300 193
18.0 12.3 7.8
2.39 (1.27; 4.50) 1.48 (0.78; 2.81) 1.00
0.007 0.231
Male Luo Kamba Maasai
183 106 172
12.9 9.3 5.2
2.63 (1.18; 5.87) 1.64 (0.64; 4.22) 1.00
0.018 0.303
Residency Female Urban Rural
127 717
26.0 9.4
2.50 (1.59; 3.92) 1.00
<0.001
Male Urban Rural
152 463
11.8 10.5
3.67 (2.09; 6.44) 1.00
<0.001
79 734
19.0 8.7
1.12 (0.67; 1.87) 1.00
0.656
54 535
14.8 8.6
1.27 (0.57; 2.85) 1.00
0.563
Smoking status Female Smoker Non-smoker
52 739
17.3 15.2
1.08 (0.74; 1.57) 1.00
0.685
Male Smoker Non-smoker
94 487
14.6 13.0
1.20 (0.83; 1.73) 1.00
0.344
Alcohol consumptionb Female Daily 3–6 days/week <2–3 days/week Never
10 16 18 771
20.0 6.3 22.2 18.1
1.07 (0.45; 2.58) 0.57 (0.22; 1.44) 1.62 (0.85; 3.11) 1.00
0.873 0.234 0.145
Male Daily 3–6 days/week <2–3 days/week Never
16 47 60 466
45.5 17.0 7.1 12.6
3.93 (2.04; 7.57) 2.04 (0.92; 5.09) 0.76 (0.40; 1.45) 1.00
<0.001 0.002 0.401
Family history of diabetes Female Yes No Male Yes No
P-value
Crude prevalence with age-adjusted ORs. Both known and previously unknown cases. b OR age- and weight-adjusted. a
group is caused by lifestyle or genetic protection remains to be determined. The positive association between obesity and glucose intolerance shown in this study has been reported in numerous other studies [21–23]. Further, abdominal obesity – and especially accumulation of visceral fat – has been shown to be the strongest predictor of insulin resistance in the liver and peripheral tissues [24]. When using the WHO [16] categories for overweight based on BMI and WC, the males had risks of glucose intolerance four times higher than women for BMI, while WC
showed a 20% higher risk for men compared to women. Thus, males seem to be at a much higher risk of glucose intolerance with increasing obesity in the current study population. BMI and visceral fat accumulation had the strongest associations with glucose intolerance among women and men, respectively. This indicates that total body fat is more harmful for females, while intra-abdominal fat has the most detrimental effect in males when it comes to glucose intolerance. The latter is supported by the strong association between elevated WC and glucose intolerance in males.
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diabetes research and clinical practice 84 (2009) 303–310
Table 3 – Glucose intolerance (diabetes + IGT) by gender specific quartiles of body composition, fitness and physical activity variablesa. 1st
2nd
3rd
4th
1 1
2.7 (1.2; 6.2) 0.3 (0.1; 0.9)
2.9 (1.3; 6.4) 1.0 (0.4; 2.1)
3.3 (1.6; 6.8) 2.4 (1.2; 4.6)
0.004 <0.001
WC (cm) Females Males
1 1
1.3 (0.7; 2.5) 1.5 (0.6; 4.0)
1.5 (0.8; 2.8) 1.4 (0.5; 3.8)
2.4 (1.3; 4.3) 5.3 (2.2; 12.9)
0.002 <0.001
AMA (cm2) Females Males
1 1
0.5 (0.3; 0.9) 1.1 (0.5; 2.4)
0.7 (0.4; 1.2) 1.2 (0.6; 2.6)
0.7 (0.4; 1.2) 2.1 (1.0; 4.2)
0.355 0.035
AFA (cm2) Females Males
1 1
1.4 (0.8; 2.6) 0.8 (0.3; 2.0)
2.0 (1.1; 3.6) 1.3 (0.6; 3.0)
2.1 (1.2; 3.7) 3.3 (1.6; 6.9)
0.006 <0.001
Abdominal fat (cm) Visceral fat Females Males
1 1
1.1 (0.6; 2.0) 2.2 (0.8; 6.1)
1.7 (1.0; 3.0) 2.8 (1.1; 7.5)
1.5 (0.9; 2.7) 6.4 (2.6; 15.8)
0.054 <0.001
Subcutaneous fat Females Males
1 1
2.6 (1.4; 4.7) 0.5 (0.2; 1.3)
1.6 (0.9; 3.0) 0.9 (0.4; 2.1)
2.4 (1.3; 4.3) 2.1 (1.0; 4.6)
0.027 0.003
Fitness and physical activity Cardiorespiratory fitnessb Females 1 Males 1
0.7 (0.4; 1.3) 0.8 (0.3; 1.7)
0.5 (0.3; 1.0) 0.5 (0.5; 1.3)
0.4 (0.2; 0.7) 0.4 (0.1; 1.2)
0.003 0.055
1 1
0.7 (0.4;1.2) 0.5 (0.2; 1.1)
0.6 (0.3; 1.0) 0.3 (0.1; 0.9)
0.3 (0.1; 0.6) 0.4 (0.2; 1.2)
<0.001 0.037
Time spent >3 METs (min) Females 1 Males 1
0.7 (0.4; 1.1) 0.4 (0.2; 0.9)
0.5 (0.3; 0.8) 0.5 (0.2; 1.3)
0.4 (0.2; 0.7) 0.4 (0.1; 1.0)
<0.001 0.048
1.1 (0.6; 2.0) 0.5 (0.2; 1.2)
0.5 (0.3; 1.0) 0.5 (0.2; 1.3)
0.4 (0.2; 0.8) 0.5 (0.2; 1.3)
0.002 0.090
Body composition BMI (kg/m2) Females Males
AEEc Females Males
PALd Females Males a b c d
1 1
P-linear trend
Age-controlled. Estimated ml O2kg 1min 1. Activity energy expenditurekg 1. Physical activity level (TEE/REE).
In a crude analysis, AMA was positively associated with glucose intolerance in men. However, the effect of AMA was confounded by the effect of BMI, to an extent in which the upper quartiles of AMA showed a protective effect for having glucose intolerance. For the first time in an African population, this study made it possible to assess the relationship between glucose intolerance and both physical activity and cardio-respiratory fitness based on objective measurements. We found that higher levels of physical activity was inversely associated with glucose intolerance, whether expressed as AEEkg 1, time spent >3 min METs, or PAL. In addition, cardio-respiratory fitness was inversely associated with glucose intolerance. Development of glucose intolerance as a result of low physical activity is a well-documented relationship [25,26]. Our results support current evidence from non-African populations that
even physical activity of moderate intensity and duration is associated with a reduction in risk of type 2 diabetes [27,28]. Frequent alcohol consumption was positively associated with glucose intolerance among males only, but participants may not have reported their true alcohol intake as this behaviour may be regarded as socially unacceptable and thus subject to reporting bias. The same may be true for smoking habits for which no association with glucose intolerance was found. Of the individuals diagnosed with diabetes, 36% of the participants (48% rural and 21% urban) were previously unaware of their status as diabetes patients. This is lower compared to other investigations in SSA [4,7], and could be due to either a rising awareness of diabetes in Kenya, or a selection bias in our study. Further, a relationship between positive family history of diabetes and risk of glucose intolerance could
diabetes research and clinical practice 84 (2009) 303–310
not be found. This could be due to improved diagnostic methods, or it may reflect a relatively recent change of lifestyle based on which diabetes and other chronic diseases have only begun to emerge. In conclusion, this study found relatively low prevalence of diabetes among Kenyans, and ethnic as well as rural–urban differences in prevalence of glucose intolerance were found. To what extent the different associations of ethnicity and glucose intolerance are due to lifestyle changes or genes needs further exploration.
Conflict of interest There are no conflicts of interest.
Acknowledgement We are grateful to all participants, the local chiefs and subchiefs, the local elder councils and district politicians. We are also indebted to laboratory technicians Tobias Oketch (CVBCR), Arthur J. Ukumu (DVBD), Odero Sabiano (DVBD) and Saidi Kisiwa (KEMRI) for skilful collection and analysis of blood samples in the field. Likewise, we owe our sincere thanks to Filista Kingori of KEMRI for collecting health assessment data, and we thank Dr. Søren Brage, MRC Epidemiology Unit, Cambridge, UK, for support with physical activity assessment. We kindly thank all other field staff members from KEMRI, Kenyatta University, and University of Copenhagen, and we sincerely thank all local assistants for their effort in excellent social mobilisation and collection of data. We acknowledge the permission by the Director of KEMRI to publish this manuscript. The study was supported by DANIDA, Cluster of International Health (University of Copenhagen), Steno Diabetes Center, Beckett Foundation, Dagmar Marshall Foundation, Dr. Thorvald Madsen’s Grant, Kong Christian den Tiende’s Foundation, Brdr. Hartmann Foundation.
references
[1] S.R.A. Dodu, The incidence of diabetes mellitus in Accra (Ghana): a study of 4,000 patients, West Afr. Med. J. 7 (1958) 129–134. [2] S.R.A. Dodu, N. de Heer, A diabetes case-finding survey in Ho, Ghana, Ghana Med. J. 3 (1964) 75–80. [3] A.C.B. Wicks, W.M. Castle, M. Gelfand, Effect of time on the prevalence of diabetes in the urban African of Rhodesia, Diabetes 22 (1973) 733–737. [4] D.G. McLarty, A.B. Swai, H.M. Kitange, G. Masuki, B.L. Mtinangi, P.M. Kilima, et al., Prevalence of diabetes and impaired glucose tolerance in rural Tanzania, Lancet 1 (1989) 871–875. [5] N. Marine, A.I. Vinik, I. Edelstein, W.P. Jackson, Diabetes, hyperglycemia and glycosuria among Indians, Malays and Africans (Bantu) in Cape Town, South Africa, Diabetes 18 (1969) 840–857. [6] M.M. Engelgau, K.M.V. Narayan, W.H. Herman, Screening for type 2 diabetes, Diab. Care 23 (2000) 1563–1580.
309
[7] N.S. Levitt, J.M. Katzenellenbogen, D. Bradshaw, M.N. Hoffman, F. Bonnici, The prevalence and identification of risk factors for NIDDM in urban Africans in Cape Town, South Africa, Diab. Care 16 (1993) 601–607. [8] E.A. Nyenwe, O.J. Odia, A.E. Ihekwaba, A. Ojule, S. Babatunde, Type 2 diabetes in adult Nigerians: a study of its prevalence and risk factors in Port Harcourt, Nigeria, Diab. Res. Clin. Pract. 62 (2003) 177–185. [9] M.A. van der Sande, R. Bailey, H. Faal, W.A. Banya, P. Dolin, O.A. Nyan, et al., Nationwide prevalence study of hypertension and related non-communicable diseases in The Gambia, Trop. Med. Int. Health 2 (1997) 1039–1048. [10] T.J. Aspray, F. Mugusi, S. Rashid, D. Whiting, R. Edwards, K.G. Alberti, et al., Rural and urban differences in diabetes prevalence in Tanzania: the role of obesity, physical inactivity and urban living, Trans. R. Soc. Trop. Med. Hyg. 94 (2000) 637–644. [11] J.C. Mbanya, J. Ngogang, J.N. Salah, E. Minkoulou, B. Balkau, Prevalence of NIDDM and impaired glucose tolerance in a rural and an urban population in Cameroon, Diabetologia 40 (1997) 824–829. [12] A. Fedders, C. Salvadori, Peoples and Cultures of Kenya, Transafrica Publications, Nairobi, 1979. [13] World Health Organization, Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications. Part 1: Diagnosis and Classification of Diabetes Mellitus Report of a WHO Consultation, Geneva, 1999. [14] A.R. Frisancho, Anthropometric Standards for the Assessment of Growth and Nutritional Status, The University of Michigan Press, Ann Arbor, MI, 1990. [15] S. Brage, U. Ekelund, N. Brage, M.A. Hennings, K. Froberg, P.W. Franks, et al., Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity, J. Appl. Physiol. 103 (2007) 682–692. [16] World Health Organization, Obesity: Preventing and Managing the Global Epidemic, WHO, Geneva, 2000 (WHO Technical Report Series 894). [17] M.M. Ceesay, M.W. Morgan, M.O. Kamanda, V.R. Willoughby, D.R. Lisk, Prevalence of diabetes in rural and urban populations in southern Sierra Leone: a preliminary survey, Trop. Med. Int. Health 2 (1997) 272–277. [18] International Diabetes Federation, Diabetes Atlas, IDF, Brussels, 2003. [19] W. Willett, J. Manson, S. Liu, Glycemic index, glycemic load, and risk of type 2 diabetes, Am. J. Clin. Nutr. 76 (2002) 274S–280S. [20] D.L. Christensen, J. Eis, A.W. Hansen, M.W. Larsson, D.L. Mwaniki, B. Kilonzo, et al., Obesity and fat distribution in Kenyan populations: impact of ethnicity and urbanisation, Ann. Hum. Biol. 35 (2008) 232–249. [21] G.A. Colditz, W.C. Willett, M.J. Stampfer, J.E. Manson, C.H. Hennekens, R.A. Arky, et al., Weight as a risk factor for clinical diabetes in women, Am. J. Epidemiol. 132 (1990) 501–513. [22] A.V. Astrup, N. Finer, Redefining type 2 diabetes: diabesity or obesity dependent diabetes mellitus? Obes. Rev. 1 (2000) 57–59. [23] A.E. Field, E.H. Coakley, A. Must, J.L. Spadano, N. Laird, W.H. Dietz, et al., Impact of overweight on the risk of developing common chronic diseases during a 10-year period, Arch. Int. Med. 161 (2001) 1581–1586. [24] J.P. Despre´s, A. Nadeau, A. Tremblay, M. Ferland, S. Moorjani, P.J. Lupien, et al., Role of deep abdominal fat and the association between regional adipose tissue distribution and glucose tolerance in obese women, Diabetes 38 (1989) 304–309. [25] S.P. Helmrich, D.R. Ragland, R.W. Leung, R.S. Paffenbarger Jr., Physical activity and reduced occurrence of non-insulin dependent diabetes mellitus, N. Engl. J. Med. 325 (1991) 147–152.
310
diabetes research and clinical practice 84 (2009) 303–310
[26] A.M. Kriska, R.E. LaPorte, D.J. Pettitt, M.A. Charles, R.G. Nelson, L.H. Kuller, et al., The association of physical activity with obesity, fat distribution and glucose intolerance in Pima Indians, Diabetologia 36 (1993) 863–869. [27] F.B. Hu, R.J. Sigal, J.W. Rich-Edwards, G.A. Colditz, C.G. Solomon, W.C. Willett, et al., Walking compared with
vigorous physical activity and risk of type 2 diabetes in women, JAMA 282 (1999) 1433–1439. [28] N.J. Wareham, M.Y. Wong, N.E. Day, Glucose intolerance and physical inactivity: the relative importance of low habitual energy expenditure and cardiorespiratory fitness, Am. J. Epidemiol. 152 (2000) 132–139.