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ORIGINAL ARTICLE
Year : 2017  |  Volume : 20  |  Issue : 4  |  Page : 168-172

Prevalence of diabetes mellitus and its risk factors among the suburban population of Northwest Nigeria


1 Departments of Medicine, Usmanu Danfodiyo University Teaching Hospital, Sokoto, Nigeria
2 Department of Medicine, Ahmadu Bello University Teaching Hospital, Zaria, Nigeria
3 Department of Medicine, Federal Medical Center, Gusau, Zamfara, Nigeria
4 Department of Pharmacology, Usmanu Danfodiyo University, Sokoto, Nigeria
5 Department of Medicine, Lagos University Teaching Hospital, Lagos, Nigeria

Date of Web Publication17-Apr-2018

Correspondence Address:
Dr. Anas Ahmad Sabir
Department of Medicine, Usmanu Danfodiyo University Teaching Hospital, Sokoto 234
Nigeria
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DOI: 10.4103/smj.smj_47_16

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  Abstract 


Background: Diabetes mellitus (DM) was previously considered to be rare in sub-Saharan Africa. However, the prevalence is on the increase mainly because of urbanization and changes in lifestyle. Aim: The aim of our study was to determine the prevalence of DM and its correlates in the suburban population of Northwest Nigeria. Materials and Methods: A cross-sectional community-based study was carried out. Two hundred and eighty participants were recruited using a multistage sampling technique. Interviewer-administered questionnaire was utilized in obtaining demographic data from the participants. Anthropometric variables, fasting plasma glucose (FPG), and blood pressure measured using standard guidelines. The diagnosis of DM was based on the WHO guidelines. Results: The mean age was 42.3 ± 10.7 years. The overall prevalence of DM was 4.3% (males 4.5% and females 4.0%). The mean FPG was higher in the females (5.9 ± 1.2 mmol/L) than males (5.8 ± 2.5 mmol/L) though the difference was not statistically significant (P = 0.81). Obesity and increasing age were the major risk factors for DM among the suburban population. Conclusion: DM is common in suburban areas of Northwest Nigeria. We recommend increased awareness of the epidemic potential of this public health problem even in suburban areas.

Keywords: Diabetes mellitus, prevalence, suburban population


How to cite this article:
Sabir AA, Balarabe S, Sani AA, Isezuo SA, Bello KS, Jimoh AO, Iwuala SO. Prevalence of diabetes mellitus and its risk factors among the suburban population of Northwest Nigeria. Sahel Med J 2017;20:168-72

How to cite this URL:
Sabir AA, Balarabe S, Sani AA, Isezuo SA, Bello KS, Jimoh AO, Iwuala SO. Prevalence of diabetes mellitus and its risk factors among the suburban population of Northwest Nigeria. Sahel Med J [serial online] 2017 [cited 2018 Sep 24];20:168-72. Available from: http://www.smjonline.org/text.asp?2017/20/4/168/230263




  Introduction Top


The prevalence of diabetes mellitus (DM) is increasing worldwide, and it is projected that by the year 2030 over 500 million adults will be affected by DM.[1] The increase could be as a result of urbanization and aging of the population.[2] The projected increase in prevalence is expected to be higher in Africa and Asia where there is rapid epidemiological transition.[3] The prevalence of DM is still lower in traditional rural than urban communities.[4],[5],[6] Previous studies by Bakari et al. found the prevalence of 1.6% in a suburban Northern Nigerian city and Erasmus et al. found a prevalence of 1.4% in a rural population of North Central Nigeria (1.4%).[4],[5] Most cases of DM in rural and suburban areas remain undiagnosed, and many patients present for the first time with complications.[7] The aim of our study was to determine the prevalence of DM and its correlates in a suburban population of Northwest Nigeria.


  Materials and Methods Top


Study area

The study was conducted among the suburban population in Sokoto South and Wamakko Local Governments areas of Sokoto State, Northwest Nigeria. Most inhabitants are Muslim Fulani and Hausas.

Participants and sample size

A cross-sectional community-based study was carried out. Two suburban communities from Gagi and Kasarawa districts in Sokoto South and Wamakko Local Government areas of Sokoto State, respectively, were selected using a multistage sampling technique. Fisher's statistical formula for sample size was used to calculate sample size, i.e., N = Z2pq/D2, where N = Minimum sample size, Z = Standard deviation set at 1.96 which corresponds to a 95% confidence interval, P = Prevalence of DM (4.6%) among residents of Sokoto, q = 1 − P, and D = Margin of unacceptable error or measure of precision (0.05). The calculated sample size was 68. However, 280 participants participated in the study. The participants were invited to a designated location, and study questionnaires were administered by trained research assistants. The interviewer-administered questionnaire obtained demographic data. Measurements of weight (to the nearest 0.1 kg), height (to the nearest millimeter), and blood pressure were made following the standard guidelines.[8] Body mass index (BMI) was appropriately derived. Finger prick derived capillary blood was obtained from each participant, and fasting blood glucose test was done to each participant using blood glucometer (Finetest Auto-coding Premium, Infopia Co., Ltd., Kyunggi-Do, Korea).

Consent and permission were duly obtained from all the participants and the local authority concerned, respectively. Ethical approval was obtained from the Ethical Committee of the Usmanu Danfodiyo University Teaching Hospital, Sokoto.

Operational definitions

Participants were diagnosed to have DM if they had fasting plasma glucose level ≥7 mmol/L or casual blood glucose level ≥11.1 mmol/L.[9] Overweight and generalized obesity were defined as BMI ≥25 and 30 kg/m 2, respectively.[10] Underweight is defined as BMI ≤18.5 kg/m 2. Systemic hypertension was defined as systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg.[11]

Statistical analysis

Data were analyzed using the Statistical Package for Social Sciences (SPSS) for Windows, version 17 (IBM SPSS Inc. Chicago Illinois, USA). Frequency distribution tables were constructed, and cross tabulations were done to examine relationship between categorical variables. Chi-square test was used to compare differences between proportions. Student's t-test was used to determine the differences between 2 groups of means. Logistic regression analysis was used to determine the variable that predicts DM. The level of statistical significance was set at P < 0.05.


  Results Top


Sociodemographic and anthropometric characteristics

Two hundred and eighty participants comprising 154 (55%) males and 126 (45%) females were studied. The mean age was 42.3 ± 10.7 years with the females (48.9 ± 14.6 years) being significantly older than the males (37.0 ± 17.7 years) P = 0.002.

The age, anthropometric characteristics, and plasma glucose values of the participants are shown in [Table 1].
Table 1: Age, anthropometric characteristics, and plasma glucose values of the participants

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Although the male had significantly higher weight (60.8 vs. 52.9 kg [P < 0.001]) and height (167.6 vs. 160.1 cm [P < 0.001]) than the female participants, their plasma glucose and blood pressure levels did not differ significantly.

Prevalence of diabetes mellitus

Of the 280 participants studied, 12 had DM giving a prevalence rate of 4.3%. The prevalence was slightly higher in men (4.5% vs. 4.0%). The mean age of participants with diabetes was 49.7 ± 10.3 years.

The distribution of the participants by glycemic status, blood pressure, and BMI category is shown in [Table 2].
Table 2: Comparison of glycemic status according to blood pressure and body mass index category

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The participants with DM had significantly higher prevalence of hypertension than the nondiabetics (66.7% vs. 33.3% [P < 0.001]). The prevalence of DM differed significantly according to weight status or BMI classification.

Relationship between diabetes mellitus and clinical variables

The relationship between DM and clinical variables is as shown in [Table 3]. There was positive relationship between blood glucose and age, weight BMI, SBP, and DBP.
Table 3: Regression analysis showing the relationship between blood glucose and clinical variables

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Relationship between blood glucose and body mass index

The relationship between DM and BMI is shown in [Figure 1]. The prevalence of DM increased with increased BMI.
Figure 1: Curve estimation of the equation of regression to predict blood glucose from body mass index

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Relationship between blood glucose and age

The relationship between DM and age is shown in [Figure 2]. The prevalence of DM increased with advancing age.
Figure 2: Curve estimation of the equation of regression to predict blood glucose from age

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  Discussion Top


The prevalence of DM in the current study (4.3%) is higher than 0.8% previously reported among rural dwellers of the same ethnic group and 1.6% observed by Bakari et al. in a suburban Northern Nigeria.[4],[6] The prevalence rates ranging from 2% to 10% have been observed among suburban populations in previous studies.[12],[13] The high prevalence of DM may be due to rapid urbanization of the suburban regions. The rural sub-Saharan Africa is also at an early stage of epidemiological transition from communicable to noncommunicable diseases because of the gradual adoption of unhealthy lifestyles characterized by increasing intake of high calorie-dense foods and decreased physical inactivity.[14]

We observed that the prevalence of DM increased with advancing age, a finding that is consistent with previous studies.[15],[16] This could be because aging is often accompanied by decline in lean body mass and increase in body fat, particularly visceral adiposity which may contribute to the development of insulin resistance.[17] Aging is also known to induce a decrease of insulin sensitivity and inadequate response of β-cell functional mass when there is insulin resistance.[18] Aging is associated with a decrease of β-cell proliferative capacity and enhances sensitivity to apoptosis.[19]

Obesity is one of the major modifiable risk factors of type 2 DM.[20] The prevalence of DM was high among the obese and overweight and increased with increased BMI. These findings corroborate those of previous studies.[17],[21],[22] Obesity causes increased production of adipokines/cytokines, including tumor necrosis factor-α, resistin, and retinol-binding protein 4, that contribute to insulin resistance as well as reduced levels of adiponectin.[23] Obesity is also associated with ectopic fat deposition, particularly in the liver which leads to dysmetabolic sequelae.[24] Mitochondrial dysfunction is an important underlying defect linking obesity to DM, both by decreasing insulin sensitivity and by compromising β-cell function.[25]


  Conclusion Top


Our data demonstrate that DM is common in suburban areas of Northwest Nigeria. Its major correlates include obesity and hypertension. We recommend research into appropriate preventive interventions.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

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2.
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King H, Aubert RE, Herman WH. Global burden of diabetes, 1995-2025: Prevalence, numerical estimates, and projections. Diabetes Care 1998;21:1414-31.  Back to cited text no. 3
    
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Bakari AG, Onyemelukwe GC, Sani BG. Prevalence of diabetes in suburban Northern Nigeria: Results of a public screening survey. Diabetes Int 1999;9:59-60.  Back to cited text no. 4
    
5.
Erasmus RT, Ebonyi E, Fakeye T. Prevalence of diabetes mellitus in a rural Nigerian population. Niger Med Pract 1988;15:28-38.  Back to cited text no. 5
    
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Sabir A, Ohwovoriole A, Isezuo S, Fasanmade O, Abubakar S, Iwuala S. Type 2 diabetes mellitus and its risk factors among the rural Fulanis of Northern Nigeria. Ann Afr Med 2013;12:217-22.  Back to cited text no. 6
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World Health Organization. WHO STEPwise Approach to Chronic Disease Risk Factor Surveillance-Instrument v2.0. Department of Chronic Diseases and Health Promotion. Available from: http://www.who.int/chp/steps. [Last accessed on 2016 Mar 10].  Back to cited text no. 8
    
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World Health Organization. Definition, diagnosis and classification of diabetes mellitus: Report of a WHO consultation. WHO/NCD/NCS/99-2. Geneva, Switzerland: WHO Publication; 1999.  Back to cited text no. 9
    
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National Institutes of Health, National Heart, Lung, and Blood Institute. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: The evidence report. Obes Res 1998;6 Suppl 2:51S-209S.  Back to cited text no. 10
    
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Hadaegh F, Bozorgmanesh MR, Ghasemi A, Harati H, Saadat N, Azizi F. High prevalence of undiagnosed diabetes and abnormal glucose tolerance in the Iranian urban population: Tehran Lipid and Glucose Study. BMC Public Health 2008;8:176.  Back to cited text no. 12
    
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Sobngwi E, Mbanya JC, Unwin NC, Kengne AP, Fezeu L, Minkoulou EM, et al. Physical activity and its relationship with obesity, hypertension and diabetes in urban and rural Cameroon. Int J Obes Relat Metab Disord 2002;26:1009-16.  Back to cited text no. 14
    
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Harris MI, Flegal KM, Cowie CC, Eberhardt MS, Goldstein DE, Little RR, et al. Prevalence of diabetes, impaired fasting glucose, and impaired glucose tolerance in U.S. adults. The Third National Health and Nutrition Examination Survey, 1988-1994. Diabetes Care 1998;21:518-24.  Back to cited text no. 15
    
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Sabir AA, Isezuo SA, Ohwovoriole AE. Dysglycaemia and its risk factors in an urban Fulani population of northern Nigeria. West Afr J Med 2011;30:325-30.  Back to cited text no. 16
    
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Björntorp P. Metabolic implications of body fat distribution. Diabetes Care 1991;14:1132-43.  Back to cited text no. 17
    
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Maedler K, Schumann DM, Schulthess F, Oberholzer J, Bosco D, Berney T, et al. Aging correlates with decreased beta-cell proliferative capacity and enhanced sensitivity to apoptosis: A potential role for Fas and pancreatic duodenal homeobox-1. Diabetes 2006;55:2455-62.  Back to cited text no. 18
    
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Meneilly GS, Elliott T. Metabolic alterations in middle-aged and elderly obese patients with type 2 diabetes. Diabetes Care 1999;22:112-8.  Back to cited text no. 19
    
20.
Hu FB, Manson JE, Stampfer MJ, Colditz G, Liu S, Solomon CG, et al. Diet, lifestyle, and the risk of type 2 diabetes mellitus in women. N Engl J Med 2001;345:790-7.  Back to cited text no. 20
    
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Schulz LO, Bennett PH, Ravussin E, Kidd JR, Kidd KK, Esparza J, et al. Effects of traditional and western environments on prevalence of type 2 diabetes in Pima Indians in Mexico and the U.S. Diabetes Care 2006;29:1866-71.  Back to cited text no. 21
    
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Ramachandran A, Mary S, Yamuna A, Murugesan N, Snehalatha C. High prevalence of diabetes and cardiovascular risk factors associated with urbanization in India. Diabetes Care 2008;31:893-8.  Back to cited text no. 22
    
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Deng Y, Scherer PE. Adipokines as novel biomarkers and regulators of the metabolic syndrome. Ann N Y Acad Sci 2010;1212:E1-19.  Back to cited text no. 23
    
24.
Larson-Meyer DE, Newcomer BR, Ravussin E, Volaufova J, Bennett B, Chalew S, et al. Intrahepatic and intramyocellular lipids are determinants of insulin resistance in prepubertal children. Diabetologia 2011;54:869-75.  Back to cited text no. 24
    
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Bournat JC, Brown CW. Mitochondrial dysfunction in obesity. Curr Opin Endocrinol Diabetes Obes 2010;17:446-52.  Back to cited text no. 25
    


    Figures

  [Figure 1], [Figure 2]
 
 
    Tables

  [Table 1], [Table 2], [Table 3]



 

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