|Year : 2016 | Volume
| Issue : 2 | Page : 68-74
Obesity among adult Nigerians: Relationship with blood pressure, blood sugar, and proteinuria
Babawale Taslim Bello1, Christiana Oluwatoyin Amira1, Rotimi William Braimoh1, Chioma C Nwizu2
1 Department of Medicine, College of Medicine, University of Lagos, Lagos, Nigeria
2 Department of Medicine, Lagos University Teaching Hospital, Lagos, Nigeria
|Date of Web Publication||13-Jan-2017|
Babawale Taslim Bello
Department of Medicine, College of Medicine, University of Lagos, PMB 12005, Idi-Araba, Lagos
Source of Support: None, Conflict of Interest: None
Introduction Majority of data on the association between obesity and increased cardiovascular risk are from studies conducted in western countries with majority of studies being from Nigeria focusing on prevalence in selected populations. The aim of this study was to determine the relationship between obesity and blood pressure (BP), blood sugar, and proteinuria.
Materials and Methods This was a community-based, cross-sectional study of 526 adults aged 18 years and older in three local government areas in Lagos. Obesity was assessed using the body mass index (BMI). BP, blood glucose (BG), and presence of proteinuria were determined. Obesity was defined as a BMI ≥ 30 kg/m2, elevated blood pressure (EBP) as a systolic blood pressure (SBP) ≥ 140 mmHg or a diastolic blood pressure (DBP) ≥ 90 mmHg, elevated blood glucose (EBG) as a random blood glucose (RBG) ≥ 200 mg/dL, and proteinuria as ≥2+ proteinuria on urinalysis (≥100 mg/dL).
Results The mean age of the study population was 39.2 ± 15.1 years (range 18–83 years) with 51.9% being females. Overall, 116 (22.1%) were obese. The frequency of obesity increased with age and was significantly higher in females (28.9% vs 14.6%; P < 0.01). There was a significant positive correlation between BMI and SBP, DBP, and BG (r = 0.316, P < 0.001; r = 0.316, P < 0.001; and r = 0.245, P < 0.001, respectively). Compared to non-obese individuals, obese individuals in the study were older, more likely to be female, and had eight-fold odds of having EBG and four-fold odds of having proteinuria.
Conclusion Obesity is common among adult Nigerians residing in Lagos. Its prevalence is higher in females and increases with age. It is associated with an increased risk of having EBG and proteinuria.
Keywords: Blood glucose, blood pressure, obesity, proteinuria
|How to cite this article:|
Bello BT, Amira CO, Braimoh RW, Nwizu CC. Obesity among adult Nigerians: Relationship with blood pressure, blood sugar, and proteinuria. Saudi J Obesity 2016;4:68-74
|How to cite this URL:|
Bello BT, Amira CO, Braimoh RW, Nwizu CC. Obesity among adult Nigerians: Relationship with blood pressure, blood sugar, and proteinuria. Saudi J Obesity [serial online] 2016 [cited 2020 Jul 14];4:68-74. Available from: http://www.saudijobesity.com/text.asp?2016/4/2/68/197701
| Introduction|| |
Obesity is a state of excess adiposity defined by World Health Organization (WHO) as a body mass index (BMI) of 30 kg/m2 or more. It has been said to be a “rapidly growing threat to the health of populations” because of the twin problems of exponentially increasing global prevalence and myriad of health risks associated with it. In 2008, the worldwide prevalence of overweight and obesity was estimated to be 34.3% putting the estimated number of affected individuals at about 1.4 billion. By 2013, there were over 2 billion overweight and obese people worldwide. There are regional variations in the prevalence of obesity though, with values as high as 50% in some Middle Eastern and South Pacific countries. In the United States of America, about a third of the population is obese while in Western Europe, values average about 20%. In sub-Saharan Africa, prevalence of obesity ranges from as low as 4.4% in East Africa to about 42% in South African women., In Nigeria, between 8 and 22% of adults are reported to be obese.,,
Both obesity and overweight have been shown to be associated with elevated blood pressure (EBP), an increased risk of developing type 2 diabetes mellitus, and chronic kidney disease as well as increased overall cardiovascular risk.,,, There is also an increased risk of malignancies, asthma, osteoarthritis, and low back pain. Each 5 kg/m2 increase in BMI has been shown to be associated with a 30% higher overall mortality risk and higher mortality from stroke, kidney disease, and diabetes mellitus. In the morbidly obese with BMI over 40 kg/m2, life expectancy reduces by as much as ten years, an effect that is comparable to that of long-term cigarette smoking. Obesity is also associated with increased risk of hospitalizations, higher cost of hospital, and overall medical care and loss of income to the economy from the increased number of days off work for health-related issues.
Majority of the data on the association of obesity with increased cardiovascular risk are from studies conducted in developed countries while most of the studies from Nigeria have focused on its prevalence in selected populations. The aim of this study is to determine the relationship between obesity on the one hand and blood pressure (BP), blood glucose (BG), and proteinuria on the other among adults residing in Lagos, South-west Nigeria.
| Materials and Methods|| |
This was a community-based, cross-sectional study of 526 adult Nigerians residing in three local government areas in Lagos, a cosmopolitan state located in the Southwestern part of the country and which according to current national census figures has a population of about nine million people. Assuming a prevalence of obesity in the population of 22.2%, a five percent error margin, and a 95% confidence level, the minimum representative sample size required is 265 participants. The three local government areas of the state in which patient recruitment was done were selected by simple balloting.
Recruitment into the study was done on separate days at three venues in each local government. Residents of the local government were made aware of the study prior to the day of recruitment and were also sensitized on the day of recruitment. Prior to the day of recruitment, information about the study was disseminated in conjunction with the Local Government Authority, the prominent traditional ruler in the area as well as the market leaders. On the day of recruitment, a parade was held through the streets adjourning the recruitment venue to inform residents of the screening exercise and its venue. Actual recruitment of study participants was done over a period of five hours between 10:00 am and 3:00 pm on each of the days.
All individuals who showed up at the venue of the exercise were screened; however, only data of individuals who were aged 18 years or older were included in the study analysis. Each individual’s age, gender, weight, height, BP, and random blood glucose (RBG) were documented. Weight was determined using a digital LCD electronic tempered glass weighing scale (unbranded), height was measured using a bodymeter measuring tape with wall stop (seca®), BP was measured in the sitting position using a mercury sphygmomanometer (Accusson®), and BG was determined from capillary blood using a glucometer (Accu-check Active®). Urinalysis for proteinuria was performed on freshly voided urine using a urinalysis strip (combo 10®). BMI was calculated by dividing the individual’s weight in kilograms by the square of the height in meters. Obesity was defined as a BMI ≥ 30 kg/m2, EBP as a systolic blood pressure (SBP) ≥ 140 mmHg or a diastolic blood pressure (DBP) ≥ 90 mmHg, elevated blood glucose (EBG) as a random blood sugar ≥200 mg/dL, and proteinuria as ≥2+ proteinuria on urinalysis (≥100 mg/dL).
Data obtained were analyzed using Epi Info® statistical software package version 7.0 (United States Centers for Disease Control and Prevention). Continuous variables are presented as means and standard deviations while categorical variables are presented as percentages. Comparison between means was done using the Student’s t-test while that between percentages was done using the chi-square test. Linear regression analysis was used to determine the relationship between BMI and SBP, DBP, and blood sugar, respectively. Logistic regression analysis was used to determine the association between obesity and the presence of hypertension, diabetes and proteinuria. The level of statistical significance was set at a P-value less than 0.05.
| Results|| |
The mean age of the study population was 39.2 ± 15.1 years (range: 18–83) while 273 (51.9%) of the study participants were females. Compared to male study participants, females in the study had a lower mean height and a higher mean BMI. They also had a higher prevalence of obesity and proteinuria. There were no significant differences in other baseline characteristics between males and females in the study [Table 1]. Overall, 116 (22.1%) of the study participants were obese. [Figure 1]a shows the distribution of the study population according to the WHO classification of BMI. Among participants who were obese, 66.4% had WHO class 1 obesity, 24.1% had class 2 obesity, while 9.5% had class 3 obesity [Figure 1]b.
|Table 1: Comparison of baseline characteristics of male and female study participants|
Click here to view
|Figure 1: (a) Stratification of study population according to WHO body mass index classification. (b) Stratification of obese individuals according to WHO classification of severity of obesity|
Click here to view
[Figure 2]a shows the age range distribution of the study population. Obesity prevalence was highest in the 56–65-year-old age range. There was also a progressive increase in the prevalence of obesity from the 18–25-year-old age range to the 56–65-year-old age range and then a drop-off thereafter [Figure 2]b. There was a significant positive linear correlation between age and BMI (r = 0.374, P < 0.001) [Figure 3]a. There were also significant positive linear correlations between BMI and SBP, DBP, and BG (r = 0.316, P < 0.001; r = 0.316, P < 0.001; and r = 0.245, P < 0.001, respectively) [Figure 3]b, [Figure 3]c, [Figure 3]d. [Table 2] shows the multivariate linear regression of BMI with age, SBP, DBP, and BG.
|Figure 2: (a) Age range distribution of the study population. (b) Prevalence of obesity in the study population stratified according to age range|
Click here to view
|Figure 3: (a) Linear regression of body mass index on age. (b) Linear regression of systolic blood pressure on body mass index. (c) Linear regression of diastolic blood pressure on body mass index. (d) Linear regression of blood sugar on body mass index|
Click here to view
|Table 2: Multivariate linear regression of factors associated with obesity|
Click here to view
The prevalence of proteinuria among non-obese study participants ranged between 3.1 and 4.9% compared to 18.1–27.3% among obese study participants. The highest prevalence (27.3%) was seen among patients with BMI above 40 kg/m2 [Figure 4]. Compared to non-obese study participants, obese individuals in the study had higher mean age, mean weight, mean height, mean BMI, mean SBP, mean diastolic blood, and mean RBG. They also were more likely to be female, and to have EBP, EBG, and proteinuria (P < 0.001 in all cases) [Table 3]. The logistic regression analysis with a model that included age, SBP, DBP, RBG, and proteinuria is shown in [Table 4]. Age ≥ 40 years (OR = 3.9, 95% CI = 2.3–6.6, P < 0.01); female gender (OR = 2.3, 95% CI = 1.4–3.7, P < 0.01); RBG ≥ 200 mg/dL (OR = 8.1, 95% CI = 1.5–42.5, P < 0.01); and proteinuria (OR = 3.9, 95% CI = 1.8–8.4, P < 0.01) were the factors associated with obesity.
|Table 3: Comparison of baseline characteristics of obese and non-obese study participants|
Click here to view
|Table 4: Logistic regression analysis of factors associated with obesity|
Click here to view
| Discussion|| |
Though obesity is generally considered a problem of wealthy Western nations and majority of obese people reside in developed countries, obesity can no longer be seen as a disease of the affluent. As the so-called “westernization” or “urbanization” process sweeps through developing countries, individuals have tended to adopt sedentary lifestyles and consume cheap and readily available fast foods. This has led to more people in these countries becoming obese. This epidemiologic evolution is exemplified by the situation in Lagos, a cosmopolitan city regarded as the economic capital of Nigeria. Less than 50 years ago, the prevalence of obesity among residents in the state was 6.9%; however, we found an overall prevalence of 22.1% in this study, a more than three-fold increase.
The prevalence of obesity documented in this study is consistent with that earlier reported by Amira et al. who found an obesity prevalence of 22.2% in a study involving 1368 residents of the state. As has been the case with earlier studies of adult Nigerians,,,, we found that the prevalence of obesity was significantly higher in females than in males and that both BMI and obesity prevalence increased with increasing age. Females who participated in this study had an approximately 2.5-fold increased odds of being obese compared to males while individuals who were aged 40 years or older had an approximately four-fold increased odds of being obese compared to those less than 40 years old.
The study found a significant positive correlation between BMI on one hand and each of SBP, DBP, and blood glucose on the other, although the correlation coefficients were relatively weak. Significant correlations between BMI and both systolic and diastolic BPs have previously been reported in studies from Tanzania and Nigeria. Tesfaye et al. had also reported the exact finding of a significant but weak positive correlation between BMI and systolic and diastolic BPs across three populations in Africa and Asia. In our study population however, when multivariate linear regression was performed using a model that included age, SBP, DBP, and random blood glucose, the correlation between BMI and SBP became insignificant although that with DBP persisted. This suggests that the effect of BMI on SBP may have been via the effect of age. In addition, compared to non-obese study participants, obese participants in our study had eight-fold increased odds of having elevated blood glucose and four-fold increased odds of having proteinuria.
The association of obesity with proteinuria in this study is noteworthy because it re-emphasizes two long established but often overlooked complications of obesity: proteinuria and kidney disease. Several studies have shown that obesity is a significant risk factor for developing proteinuria and end-stage renal disease in the general population.,,,, Far too often however, clinicians and researchers alike, in portraying the adverse effects of obesity on individuals, tend to focus on BP, blood glucose, and dyslipidaemia, neglecting its effects on kidney function. It is this perception that most likely led Praga to label obesity “a neglected culprit in kidney disease.” In this study, we found an overall prevalence of proteinuria among obese adults of 21%. Significantly, there was an abrupt increase in the prevalence of proteinuria from a BMI of 30 kg/m2. In patients with BMI less than 30 kg/m2, proteinuria prevalence averaged 4% but at BMI of 30 kg/m2 and above, proteinuria prevalence rose to an average of 23%.The association of obesity with proteinuria may not be unexpected. This is because of the known association of obesity with EBP and EBG which are by themselves risk factors for the development of proteinuria. They have a common pathogenic mechanism: microvascular dysfunction and insulin resistance. Obesity is, however, associated with a specific glomerular pathology that has been termed obesity-related glomerulopathy (ORG). It is characterized by glomerulomegally and focal segmental glomerulosclerosis. Although initially reported to occur only in a small proportion of obese individuals, largely those with morbid obesity, ORG is increasingly being diagnosed in obese individuals.
Our study has certain limitations though. The study participants were recruited from among those individuals who showed up for screening on the day of recruitment into the study making the method of patient recruitment liable to some degree of selection bias. However, our finding of a prevalence of obesity that is similar to previously reported values suggests that any selection bias, if at all present, was minimal. In addition, BP and BG recordings were taken on one occasion only. Because the diagnosis of hypertension and diabetes generally requires BP and BG to be taken on more than one occasion, the reported prevalence of EBP and EBG does not necessarily represent the prevalence of hypertension and diabetes, respectively.
In conclusion, the prevalence of obesity in this study is high. It is associated with an increased risk of EBP, EBG, and proteinuria. The findings of this study re-emphasize the need for increased public awareness about the health challenges posed by obesity.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
World Health Organization. Obesity: Preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser 2000;894:i-xii, 1-253.
Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ et al.
National, regional, and global trends in body-mass index since 1980: Systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9 · 1 million participants. Lancet 2011;377:557-67.
Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C et al.
Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: A systematic analysis for the Global Burden of Disease Study 2013. Lancet 2014;384:766-81.
Commodore-Mensah Y, Samuel LJ, Dennison-Himmelfarb CR, Agyemang C. Hypertension and overweight/obesity in Ghanaians and Nigerians living in West Africa and industrialized countries: A systematic review. J Hypertens 2014;32:464-72.
Chukwuonye II, Chuku A, John C, Ohagwu KA, Imoh ME, Isa SE et al.
Prevalence of overweight and obesity in adult Nigerians − A systematic review. Diabetes Metab Syndr Obes 2013;6:43-7.
Abubakari AR, Bhopal RS. Systematic review on the prevalence of diabetes, overweight/obesity and physical inactivity in Ghanaians and Nigerians. Public Health 2008;122:173-82.
Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: A systematic review and meta-analysis. JAMA 2013;309:71-82.
Must A, McKeown NM. The disease burden associated with overweight and obesity. In: De Groot LJ, Beck-Peccoz P, Chrousos G, Dungan K, Grossman A, Hershman JM et al.
, editors. Endotext [Internet]. South Dartmouth, MA: MDText.com, Inc.; 2000-2012.
Prospective Studies Collaboration, Whitlock G, Lewington S, Sherliker P, Clarke R, Emberson J et al.
Body-mass index and cause-specific mortality in 900 000 adults: Collaborative analyses of 57 prospective studies. Lancet 2009;373:1083-96.
Wilson PW, D’Agostino RB, Sullivan L, Parise H, Kannel WB. Overweight and obesity as determinants of cardiovascular risk: The Framingham experience. Arch Intern Med 2002;162:1867-72.
Wang YC, McPherson K, Marsh T, Gortmaker SL, Brown M. Health and economic burden of the projected obesity trends in the USA and the UK. Lancet 2011;378:815-25. Erratum in: Lancet 2011;378:1778. http://www.population.gov.ng/index.php/state-population.
[Last accessed on 2016 July 15].
Kelly T, Yang W, Chen CS, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. Int J Obes (Lond) 2008;32:1431-7.
Johnson TO. Prevalence of overweight and obesity among adult subjects of an urban African population sample. Br J Prev Soc Med 1970;24:105-9.
Amira CO, Sokunbi DO, Sokunbi A. The prevalence of obesity and its relationship with hypertension in an urban community: Data from world kidney day screening programme. Int J Med Biomed Res 2012;1:104-10.
Wahab KW, Sani MU, Yusuf BO, Gbadamosi M, Gbadamosi A, Yandutse MI. Prevalence and determinants of obesity – A cross-sectional study of an adult Northern Nigerian population. Int Arch Med 2011;4:10.
Iloh G, Amadi AN, Nwankwo BO, Ugwu VC. Obesity in adult Nigerians: A study of its pattern and common primary co-morbidities in a rural Mission General Hospital in Imo state, South-Eastern Nigeria. Niger J Clin Pract 2011;14:212-8.
Bakari AG, Onyemelukwe GC, Sani BG, Aliyu IS, Hassan SS, Aliyu TM. Obesity, overweight and underweight in suburban northern Nigeria. Int J Diabetes Metab 2007;15:68-9.
Ahaneku GI, Osuji CU, Anisiuba BC, Ikeh VO, Oguejiofor OC, Ahaneku JE. Evaluation of blood pressure and indices of obesity in a typical rural community in eastern Nigeria. Ann Afr Med 2011;10:120-6.
Njelekela M, Negishi H, Nara Y, Tomohiro M, Kuga S, Noguchi T et al.
Cardiovascular risk factors in Tanzania: A revisit. Acta Trop 2001;79:231-9.
Kadiri S, Walker O, Salako BL, Akinkugbe O. Blood pressure, hypertension and correlates in urbanised workers in Ibadan, Nigeria: A revisit. J Hum Hypertens 1999;13:23-7.
Tesfaye F, Nawi NG, Van Minh H, Byass P, Berhane Y, Bonita R et al.
Association between body mass index and blood pressure across three populations in Africa and Asia. J Hum Hypertens 2007;21:28-37.
Weisinger JR, Kempson RL, Eldridge FL, Swenson RS. The nephrotic syndrome: A complication of massive obesity. Ann Intern Med 1974;81:440-7.
Fox CS, Larson MG, Leip EP, Culleton B, Wilson PW, Levy D. Predictors of new-onset kidney disease in a community-based population. JAMA 2004;291:844-50.
Iseki K, Ikemiya Y, Kinjo K, Inoue T, Iseki C, Takishita S. Body mass index and the risk of development of end-stage renal disease in a screened cohort. Kidney Int 2004;65:1870-6.
Hsu CY, McCulloch CE, Iribarren C, Darbinian J, Go AS. Body mass index and risk for end-stage renal disease. Ann Intern Med 2006;144:21-8.
Gelber RP, Kurth T, Kausz AT, Manson JE, Buring JE, Levey AS et al.
Association between body mass index and CKD in apparently healthy men. Am J Kidney Dis 2005;46:871-80.
Kramer H, Luke A, Bidani A, Cao G, Cooper R, McGee D. Obesity and prevalent and incident CKD: The Hypertension Detection and Follow-Up Program. Am J Kidney Dis 2005;46:587-94.
Praga M. Obesity − A neglected culprit in renal disease. Nephrol Dial Transplant 2002;17:1157-9.
Jonk AM, Houben AJ, de Jongh RT, Serné EH, Schaper NC, Stehouwer CD. Microvascular dysfunction in obesity: A potential mechanism in the pathogenesis of obesity-associated insulin resistance and hypertension. Physiology (Bethesda) 2007;22:252-60.
Kasiske BL, Napier J. Glomerular sclerosis in patients with massive obesity. Am J Nephrol 1985;5:45-50.
Kambham N, Markowitz GS, Valeri AM, Lin J, D’Agati VD. Obesity-related glomerulopathy: An emerging epidemic. Kidney Int 2001;59:1498-509.
[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2], [Table 3], [Table 4]