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 Table of Contents  
ORIGINAL ARTICLE
Year : 2016  |  Volume : 15  |  Issue : 2  |  Page : 111-117

Predicting high blood pressure among adults in Southeastern Nigeria using anthropometric variables


1 Department of Anatomy, Faculty of Medicine Ahmadu Bello University, Zaria, Nigeria
2 Department of Anatomy, Faculty of Basic Health Sciences, Federal University, Lafia, Nasarawa State, Nigeria

Date of Web Publication24-Feb-2017

Correspondence Address:
Monday Nwankwo
Department of Anatomy, Faculty of Basic Health Sciences, Federal University, Lafia, Nasarawa State
Nigeria
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/1596-2393.200912

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  Abstract 

Background: Epidemiological studies on high blood pressure revealed that it has become a global public health concern. Efforts are being made to uncover other indicators of hypertension than the traditional and crude one popularly used, i.e. BMI. Obesity and overweight are common causes of most cardiovascular diseases. Aim: The study examined which anthropometric variable better predicts hypertension among adults in Enugu State of Nigeria. Materials and Methods: Sample of the present cross-sectional study included 540 (288 females and 258 males) adults aged 28-74 years old. Five anthropometric variables (weight, height, chest, waist, and hip circumferences) and blood pressure were measured. Data were collected by means of questionnaires from patients attending Korean Enugu State University Teaching Hospital. Subject is classified as being hypertensive if systolic blood pressure (SBP) >140 mmHg and/or diastolic blood pressure (DBP) >90 mmHg. Results: The overall prevalence rate of hypertension was found to be 26.30% while 24.4% and 28% were recorded for males and females respectively. The prevalence was not significantly associated with gender (χ2 = 0.899, P = 0.343). Hypertension was found to be related to age, weight, chest, waist, and hip circumferences, and waist-hip ratio (P < 0.05) but not height (P > 0.05). Waist circumference with the largest area under the ROC curve was the best predictor of hypertension for all sample population, males and females respectively 0.67, 0.68 and 0.66. Logistic regression model revealed that CC, WC, HC and WHR are predictors of hypertension. Conclusion: Waist circumference appears to be a better predictor of hypertension than other anthropometrics in this sample population.

Keywords: Anthropometry, blood pressure, Enugu, hypertension


How to cite this article:
Okamkpa CJ, Nwankwo M, Danborno B. Predicting high blood pressure among adults in Southeastern Nigeria using anthropometric variables. J Exp Clin Anat 2016;15:111-7

How to cite this URL:
Okamkpa CJ, Nwankwo M, Danborno B. Predicting high blood pressure among adults in Southeastern Nigeria using anthropometric variables. J Exp Clin Anat [serial online] 2016 [cited 2019 Nov 20];15:111-7. Available from: http://www.jecajournal.org/text.asp?2016/15/2/111/200912


  Introduction Top


In Nigeria, a cross-sectional research has shown that hypertension is a potential cardiovascular risk factor irrespective of age group (Adediran et al., 2013; Okpara and Adediran, 2013; Abiodun et al., 2011; Ekwunife and Aguwa, 2011; Adedoyin et al., 2008; Olatunbosun et al., 2000; Rotimi et al., 1999) and has now become a potential public health concern. Heart failure, stroke, and coronary artery disease are just a few of the various cardiovascular diseases associated with hypertension. Hypertension is the most common noncommunicable disease and the leading cause of cardiovascular disease in the world (Khakurel et al., 2009; Kearney et al., 2004). Many people with hypertension are unaware of their condition, and among those with hypertension, treatment is infrequent and inadequate. The global prevalence of hypertension has been on the increase. In 2000 alone, 972 million people are hypertensive with a prevalence rate of 26.4%. These are projected to increase to 1.54 billion affected individuals and a prevalence rate of 29.2% by 2025 (Kearney et al., 2004).

Body mass index (BMI) has long been recognized as the most sensitive epidemiological measure of overweight and obesity. The usefulness of BMI is limited as it does not take account of body fat distribution as seen in different human populations (World Health Organization, 2002). This limitation of BMI necessitates the need for other anthropometric dimensions that take account of regional abdominal fat distribution (Dalton et al., 2003; Welborn et al., 2003; Ko et al., 1999). Previous studies have shown that regional abdominal fat distribution is related to hypertension (Kaufman et al., 1996; Dustan, 1991; Itallie, 1985). For instance, chest circumference (CC), waist circumference (WC), hip circumference (HC), waist-height ratio (WHtR), and waist-hip ratio (WHR) are few anthropometrics that indicate obesity (Yusuf et al., 2004). Overweight has being associated with two- to six-fold increase in the risk of developing hypertension (Deshmukh et al., 2006). According to the World Health Organization (1996) report, 10 kg increase in weight among western population leads to 2–3 mmHg and 1–3 mmHg increase in systolic and diastolic blood pressure (SBP and DBP), respectively. Various studies in Nigerian population have reported the relationship between hypertension and various other factors. Much is not known about which anthropometric measurement better predicts hypertension among adults in Enugu State, Southeast Nigeria. Our aim therefore was to assess which anthropometric measurement better predicts hypertension in a sample of adults from Southeastern Nigeria.


  Materials and Methods Top


Study Population

The present cross-sectional study was conducted in Enugu State University Teaching Hospital in the southeastern region of Nigeria from January 2016 to April 2016. The population pattern of this region is homogenous mainly dominated by the Igbo ethnic group. Data were obtained through questionnaires administered to adult patients visiting the hospital, aged 28–74 years who are not hypertensive at baseline. Only patients who gave their informed consent to voluntarily participate in the study were included in the study. The study protocol was reviewed and approved by the Enugu State Ministry of Health. Questionnaires were administered before anthropometric or blood pressure measurements. The questionnaires have three sections. A demographic section concerned with self-reported individual and family characteristics; age, sex, ethnic group, region of residence (rural/urban). Family history of hypertension section concerning whether any family member has had hypertension previously, whether the subject smokes, takes alcohol, hard drugs, was diabetic, has been diagnosed to be hypertensive previously, and was on antihypertensive drugs.

Anthropometry

Measurements were taken from all the 540 subjects (282 females and 258 males) who constitute the analytic subjects. Simple random sampling technique was used to recruit subjects for the study. Five anthropometric variables were taken: Height, weight, CC, WC, and HC. Two indices were calculated: BMI = weight/height 2 in kg/m 2 and WHR. Height was measured in centimeters using a portable stadiometer with the subjects standing on level ground without shoes. Weight was also measured (to 0.1 kg) with shoes off using a portable scale (Tanita BF680W, Tanita Corp., Tokyo, Japan) placed on the level ground. CC, WC, and HC were measured with an inelastic tape. CC was measured by passing tape around the upper chest, midway between quiet expiration and inspiration. WC was measured with the subject in standing position, from mid-point of the lowest rib and top of the iliac crest at the end of expiration. HC was measured around the largest part of the buttock (Nwankwo et al., 2015). Anthropometric measurements were collected following the World Health Organization (WHO) recommendations (WHO Expert Committee, 1995) based on Lohman et al.'s (1988) methodologies. All measurements were performed by trained research investigators.

Blood Pressure

Blood pressure was measured on the left arm by auscultatory method using mercury sphygmomanometer (Diamond Co., Industrial Electronics and Allied Products, Electronics Cooperative Estate, Pune, Maharashtra). The patient was first seated and made comfortable for at least five minutes in the chair before measurements were taken. Two readings were taken half an hour apart and the average of two was used taken. Hypertension was defined if SBP >140 mmHg and/or DBP >90 mmHg, following the US Seventh Joint National Committee on Detection, Evaluation and Treatment of Hypertension (JNC VII) recommendations (JNC VII, 2003).

Statistical Analyses

Statistical analyses were conducted using Statistical Package for Social Sciences version 22 (IBM SPSS Inc., Chicago, Illinois, USA) and statistical significance was accepted at two-tailed P < 0.05. We assessed the normality of all variables using the Kolmogorov–Smirnov test. Descriptive statistics were calculated as means (standard deviations) or geometric mean (95% confidence interval) if not normally distributed. Group differences were tested for significance using independent sample t-test or Mann–Whitney U-test if the variables failed normality test. Crosstabs (χ2) were computed to calculate prevalence of hypertension with respect to their statistical significance based on gender. Odds ratios were computed to test the magnitude of the relationship between anthropometric variables and being hypertensive or normotensive. Receiver operating characteristic (ROC) curves were plotted to identify the cutoff point (Youden index) of anthropometric variables that delineate the risk of hypertensive and normotensive male and female subjects. Percent sensitivity and specificity were calculated and the value which corresponds to a point on the ROC curve nearest to the upper left corner of the ROC graph was used as the cutoff point for that indicator. Area under the curve and 95% confidence intervals were also calculated.


  Results Top


Anthropometric variables and blood pressure of 540 (282 females, 258 males) subjects were measured in 2016. [Table 1] summarizes the descriptive statistics for age, anthropometric variables, and blood pressure. Male subjects are significantly (P < 0.005) heavier and taller but have proportionally smaller HC than female subjects. The mean age of the study subjects was 48.75 ± 11.46 years and 49.28 ± 10.78 years for females and males, respectively. The mean weight was 63.26 ± 9.68 kg in females and 70.09 ± 12.04 kg in males. The mean CC, WC, and HC were 91.29 ± 11.08 cm, 87.61 ± 11.76 cm, and 98.67 cm, respectively, for females and 90.05 ± 9.47 cm, 86.69 ± 10.96 cm, 96.41 ± 10.09 cm, respectively, for males. The mean SBP and DBP for females and males are, respectively, 128.15 ± 16.25, 91.87 ± 15.61 mmHg and 126.78 ± 15.26, 90.79 ± 14.62 mmHg. However, the mean values for BMI, CC, WC, HC, SBP, and DBP were higher among females when compared to males, but these differences were not statistically significant (P > 0.05).
Table 1: Descriptive statistics of subjects

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[Table 2] compares age, anthropometric variables between hypertensive and normotensive subjects for females, males, and overall subjects. The mean difference in age is statistically significant between hypertensive and normotensive subjects for females, males, and overall subjects (P < 0.05). Again, the mean difference of CC, WC, HC, and WHR was statistically significant between the hypertensive and normotensive females, males, and overall subjects (P < 0.05). Hypertensive subjects showed higher mean age and anthropometric index than normotensive subjects. On the flip side, the difference in mean weight, height, and BMI between hypertensive and normotensive female, male, and overall subjects did not indicate significant difference (P > 0.05).
Table 2: Comparison of age, anthropometric variables between hypertensive and normotensive subjects

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[Table 3] summarizes the prevalence of hypertension among the sampled subjects. The prevalence of hypertension for the overall population is 26.30%. The prevalence of hypertension among females (28.0%) is relatively higher than that of males (24.40%).
Table 3: Prevalence of hypertension

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[Table 4] presents the logistic regression to test the influence of certain anthropometric variables on risk of having hypertension for females, males, and overall study participants. The odds ratios for all the anthropometric variables tested (CC, WC, HC, WHR) are significant (P < 0.01) for females, males, and overall study participants. There is no marked significant difference in the odds ratios for BMI when compared by each sex, for overall sample. Therefore, all other variables are better predictors of hypertension than BMI.
Table 4: Logistic regression of anthropometric variables related to the presence of hypertension

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[Table 5] shows the values for area under the ROC curve and the cutoff points for the anthropometric variables for predicting hypertension. The area of ROC for BMI and WHtR was not significantly different (P > 0.05) for females, males, and all the subjects. However, the area of the ROC for WC, HC, CC, and WHR was significantly different (P < 0.001) for females, males, and all the subjects. The optimal diagnostic points (Youden index) for predicting hypertension were also shown in [Table 5]. WHR has the least cutoff point for females, males, and all the subjects 0.95, 0.90, and 0.95, respectively. On the other hand, HC has highest cutoff point 99.50, 98.50, and 105.50 cm for females, males, and all subjects, respectively.
Table 5: Area under receiver operated characteristic curve, cutoff points, sensitivity, and specificity of different anthropometric variables

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  Discussion Top


Hypertension (high arterial blood pressure) has continued to be one of the major causes of death among cardiovascular disorders. It is at times described as a silent killer as its onset is often not detected early or because it is often idiopathic, hence the use of the term primary (essential) hypertension by many clinicians. Hypertension could also be secondary if its cause (such as renal arterial stenosis) is known. The impacts of hypertension being it to the general population or body are enormous. Many people have lost their loved ones from this condition silently as it is at times not diagnosed early or treated properly. Its deadly effects are either as a result of any or combination of the following; the high pressure will lead to excess workload on the heart which can lead to failure of the heart due to the high throughput. This often leads to death of the patient. If the arterial blood pressure increases, the blood vessels, including those in the brain could be damaged leading to death of the affected part. If the cerebrum, it leads to what is known as cerebral infarction. Finally, at the destination, hypertensive condition can lead to damage to organs of the body such as kidneys, and this could lead to kidney failure.



The present study investigates anthropometric variable best predicts hypertension among adults aged 28–74 years from Enugu in the southeastern part of Nigeria. The mean SBP for this group of adults was high (127.49 ± 15.79) mmHg compared to that reported by Abiodun et al. (2011), lower than that reported by Rotimi et al. (1999) but close to that reported by Adedoyin et al., 2008 in Southwest Nigeria. The prevalence of hypertension in this cross-sectional study was found to be 26.30%. This is about twice the prevalence reported in earlier studies in Nigeria (Olatunbosun et al., 2000; Kadiri et al., 1999; National Expert Committee, 1997; Kaufman et al., 1996; Ogunlesi et al., 1991). In contrast, more recent findings revealed rather higher prevalence of hypertension and this is in keeping to our findings (Adeoye et al., 2016; Adediran et al., 2013). The prevalence of hypertension in this study was slightly higher among females than males. Evidence from previous studies points to the contrary (Ekwunife and Aguwa, 2011; Kearney et al., 2005), Kearney et al. (2005) in the same work however projected that by 2025, women will record higher prevalence rate of hypertension than males by 0.5%. If this prediction turns out to be true, maybe, this is the beginning of it. Our results add credence to their projections. However, our sample size was not large enough either to support or to counter their projection. Relationship between hypertension, body mass index, age, and other anthropometric variables (especially BMI) is quite evident in many studies (Ejike et al., 2008; Mufunda et al., 2006; Cappucio et al., 2004; Franklin et al., 1997). Most hypertensive patients lead a sedentary lifestyle and are overweight (obese). Increased physical exercise has often been recommended as the first line of remedy in the treatment of hypertensive patients. The exercise will help in burning the excess fat, thereby leading to reduction in cardiac output due to mediation of physiological activities that play a role in hypertension.

More so, our results revealed that other than body mass index, other anthropometric variables that are indicators of body fat distribution are also risk factors for hypertension. CC, WC, and HC s are significantly higher among hypertensive subjects than their nonhypertensive counterparts, a finding supported by previous studies (Sola et al., 2013; Janssen et. al., 2002). How can we explain the influence of BMI, body circumferences and WHR (which are all body fat distribution indicators) on hypertension? Excess weight gain in itself does not lead to hypertension rather it leads to additional workload against the excess fat tissue, consequently leading to vascular damage especially in the kidneys leading to kidney failure. The impact of age on hypertension may be due to peripheral resistance (Abiodun et al., 2011). Owing to the significant impact of anthropometric adiposity on hypertension, we recommend increased physical activities and weight loss as the first intervention guideline to patients with hypertension.

The average rate of change for CC, WC, HC, and WHR is greater than that for BMI and WHtR, which is suggestive that these variables are more effective for predicting hypertensive condition. However, some portion of the curve for BMI slightly falls below the diagonal line, indicating a weak predictor (P > 0.05) of hypertensive condition; hence, the area is close to 0.5. WC is the best significant (P < 0.001) predictor of hypertension from the present study since it has the largest area for overall, female, and male subjects, respectively, at 0.67, 0.68, and 0.66. The very small amount of overlap of confidence intervals between CC, HC, and WHtR suggests that WC is a significantly better diagnostic test even though the P values are identical. Our result is in agreement with previous studies (Li et al., 2013; Czernichow et al., 2011; Park et al., 2009; Klein et al., 2007; Hadaegh et al., 2005).


  Conclusion Top


We found that higher WC is the best predictor of hypertension, while CC, HC, and WHR are moderate predictors, and BMI is the least predictor of hypertension among adult males and females in Enugu Metropolis, Southeastern Nigeria. This therefore highlights the need for adults to closely monitor their WC. We also recommend the use of WC as a useful diagnostic tool for predicting hypertension. This is because WC is easy to measure with no any form of technicalities involved. [39]

Acknowledgments

We gratefully appreciate the patience and cooperation of all participants in this study. We also thank the following individuals for assisting us with data collection Uzoigwe Soky, Nneji Ifeanyi, Emeka Kingsley, Chibuoke Ifeoma, and Sopulu.

Financial Support and Sponsorship

Nil.

Conflicts of Interest

There are no conflicts of interest.

 
  References Top

1.
Abiodun G.A., Egwu M.O., Adedoyin R.A. (2011). Anthropometric indices associated with variation in cardiovascular parameters among primary school pupils in Ile-Ife. Int J Hypertens 2011:186194.  Back to cited text no. 1
    
2.
Adediran O.S., Okpara I.C., Adeniyi O.S., Jimoh A.K. (2013). Hypertension prevalence in an urban and rural area of Nigeria. J Med Med Sci 4:149-54.  Back to cited text no. 2
    
3.
Adedoyin A.R., Mbada E.C., Bisiriyu A.L., Adebayo A.R., Balogun O.M., Akintomide O.A. (2008). Relationship of anthropometric indicators with blood pressure levels and the risk of hypertension in Nigerian adults. Int J Gen Med1:33-40.  Back to cited text no. 3
    
4.
Adeoye M.A., Adebiyi A., Owolabi O.M., Lackland T.D., Ogedegbe G., Tayo O.B. (2016). Sex disparity in blood pressure levels among Nigerian health workers. J Clin Hypertens 18:685-9.  Back to cited text no. 4
    
5.
Akinkugbe O.O. (1997). Non-communicable Diseases in Nigeria. Final report of a national survey, Lagos: Federal Ministry of Health-National expert Committee on NCDs. 1-12.  Back to cited text no. 5
    
6.
Cappucio F.P., Micah F.B., Emmitt L., Kerry S.M., Antwi S. Martin-Peprah R., et al. (2004). Prevalence, detection, management, and control of hypertension in Ashanti, West Africa. Hypertension 43:1017-22.  Back to cited text no. 6
    
7.
Czernichow S., Kengne A.P., Stamatakis E., Hamer M., Batty G.D. (2011). Body mass index, waist circumference and waist-hip ratio: Which is the better discriminator of cardiovascular disease mortality risk? Evidence from an individual-participant meta-analysis of 82 864 participants from nine cohort studies. ObesRev 12:680-7.  Back to cited text no. 7
    
8.
Dalton M., Cameron A.J., Zimmet P.Z., Shaw J.E., Jolley D., Dunstan D.W., et al. (2003). Waist circumference, waist-hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults. J Intern Med 254:555-63.  Back to cited text no. 8
    
9.
Deshmukh P.R., Gupta S.S., Dongre A.R., Bharambe M.S., Maliye C., Kaur S., et al., (2006). Relationship of anthropometric indicators with blood pressure levels in rural Wardha. Indian J Med Res 123:657-64.  Back to cited text no. 9
    
10.
Dustan H.P. (1991). Obesity and hypertension. DiabetesCare 15:488-504.  Back to cited text no. 10
    
11.
Ejike C.E., Ugwu C.E., Ezeanyika L.U., Olayemi A.T. (2008). Blood pressure patterns in relation to geographic area of residence: A cross-sectional study of adolescent in Kogi state, Nigeria. BMC Public Health 8:411.  Back to cited text no. 11
    
12.
Ekwunife I.O., Aguwa N.C. (2011). A meta-analysis of prevalence rate of hypertension in Nigerian populations. J Public Health Epidemiol 3:604-7.  Back to cited text no. 12
    
13.
Franklin S.S., Gustin W., Wong N.D., Larson M.G., Weber M.A. Kannel W.B., et al. (1997). Hemodynamic patterns of age-related changes in blood pressure. The Framingham Heart Study. Circulation 96:308-15.  Back to cited text no. 13
    
14.
Hadaegh F., Zabetian A., Sarbakhsh P., Khalili D., James W.P., Azizi F. (2005). Appropriate cutoff values of anthropometric variables to predict cardiovascular outcomes: 7.6 years follow-up in an Iranian population. Int J Obes 33:1437-45.  Back to cited text no. 14
    
15.
Janssen I., Katzmarzyk P.T., Ross R. (2002). Body mass index, Waist circumference, and health risk: Evidence in support of current national institutes of health guide lines. Arch Intern Med 162:2074-9.  Back to cited text no. 15
    
16.
Kadiri S., Walker O., Salako B.L., Akinkugbe O. (1999). Blood pressure, hypertension and correlates in urbanized workers in Ibadan, Nigeria: A revisit. J Hum Hypertens13:23-7.  Back to cited text no. 16
    
17.
Kaufman J.S., Durazo-Arvizu R.A., Rotimi C.N., McGee D.L., Cooper R.S. (1996). Obesity and hypertension in populations of African origin. The investigators of the international collaborative study on hypertension in blacks. Epidemiology 7:398-405.  Back to cited text no. 17
    
18.
Kaufman J.S., Owoaje E.E., James S.A., Rotimi C.N., Cooper R.S. (1996). Determinants of hypertension in West Africa: contribution of anthropometric and dietary factors to urban-rural and socioeconomic gradients. Am J Epidemiol 143:1203-18.  Back to cited text no. 18
    
19.
Kearney P.M., Whelton M., Reynolds K., Muntner P., Whelton P.K. (2005). Global burden of hypertension: Analysis of worldwide data. Lancet 365:217-23.  Back to cited text no. 19
    
20.
Kearney P.M., Whelton M., Reynolds K., Whelton P.K., He J. (2004). Worldwide prevalence of hypertension: A systematic review. J Hypertens 22:11-9.  Back to cited text no. 20
    
21.
Khakurel S., Agrawal R.K., Hada R. (2009). Pattern of end stage renal disease in a tertiary care center. J Nepal Med Assoc 48:126-30.  Back to cited text no. 21
    
22.
Klein S., Allison D.B., Heymsfield S.B., Kelley D.E, Leibel R.L., Nonas C. (2007). Waist circumference and cardiometabolic risk: A consensus from shaping America's Health: Association for weight management and obesity prevention: NAASO. The Obesity Society of America: The American Society for Nutrition and American Diabetes Association. Am J Clin Nutr 85:1197-202.  Back to cited text no. 22
    
23.
Ko G.T., Chan J.C., Cockram C.S., Woo J. (1999). Prediction of hypertension, diabetes, dyslipidaemia or albuminuria using simple anthropometric indexes in Hong Kong Chinese. Int J Obes Relat Metab Disord 23:1136-42.  Back to cited text no. 23
    
24.
Li W.C., Chen I.C., Chang Y.C., Loke S.S., Wang S.H., Hsiao K.Y. (2013). Waist-to-height ratio, waist circumference, and body mass index as indices of cardiometabolic risk among 36,642 Taiwanese adults. Eur J Nutr 52:57-65.  Back to cited text no. 24
    
25.
Lohman T.G., Roche A.F., Martorell R. (1988). Anthropometric Standardization Reference Manual. Human Kinetics Books, Champaign IL.  Back to cited text no. 25
    
26.
Mufunda J., Mebrahtu G., Usman A., Nyarango P., Kosia A., Ghebrat Y., et al. (2006). The prevalence of hypertension and its relationship with obesity: Results from a national blood pressure survey in Eritrea. J Hum Hypertens 20:59-65.  Back to cited text no. 26
    
27.
Nwankwo M., Danborno B., Hamman W.O. (2015). Relationship between body mass index and timing of maturation. J Exp Clin Anat 14:95-100.  Back to cited text no. 27
    
28.
Ogunlesi A., Osotimehin B., Abbiyessuku F., Kadiri S., Akinkugbe O., Liao Y.L., et al. (1991). Blood pressure and educational level among factory workers in Ibadan, Nigeria. J Hum Hypertens 5:375-80.  Back to cited text no. 28
    
29.
Okpara I.C., Adediran O.S. (2013). Dyslipidemia and its relationship with different anthropometric measures in Nigerian adults. J Dent Med Sci 9:7-12.  Back to cited text no. 29
    
30.
Olatunbosun T.S., Kaufman S.J., Cooper S.R., Bella F.A. (2000). Hypertension in a black population: Prevalence and biosocial determinants of high blood pressure in a group of urban Nigerians. J Hum Hypertens 14:249-57.  Back to cited text no. 30
    
31.
Park S.H., Choi S.J., Lee K.S., Park H.Y. (2009). Waist circumference and waist-to-height ratio as predictors of cardiovascular disease risk in Korean adults. Circ J 73:1643-50.  Back to cited text no. 31
    
32.
Rotimi C.N., Cooper R.S., Cao G., Ogunbiyi O., Ladipo M., Owoaje E., et al. (1999). Maximum-likelihood estimate for blood pressure generalized heritability in Nigerian families. Am Heart Assoc 33:874-8.  Back to cited text no. 32
    
33.
Sola O.A., Okpara I.C., Adeniyi O.S., Jimoh A.K. (2013). Hypertension prevalence in an Urban and rural area of Nigeria. J Med Med Sci 4:149-54.  Back to cited text no. 33
    
34.
Itallie T.B.V. (1985). Health implications of overweight and obesity in the United States. Ann Intern Med 103:938-88.  Back to cited text no. 34
    
35.
Welborn T.A., Dhaliwal S.S., Bennett S.A. (2003). Waist-hip ratio is the dominant risk factor predicting cardiovascular death in Australia. Med J Aust 179:580-5.  Back to cited text no. 35
    
36.
WHO Expert Committee. (1995). Physical Status: The Use and Interpretation of Anthropometry. WHO Technical Report Series 854:368-9. WHO, Geneva.  Back to cited text no. 36
    
37.
World Health Organization. (1996). Hypertension Control. (Tech Rep Ser. No. 862). World Health Organization, Geneva, Switzerland.  Back to cited text no. 37
    
38.
World Health Organization. (2002). Reducing risks, promoting healthy life – The World Health Report. World Health Organization, Geneva.  Back to cited text no. 38
    
39.
Yusuf S., Hawken S., Ounpuu S., Dans T., Avezum A., Lanas F., et al. (2004). Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): Case-control study. Lancet 364:937-52.  Back to cited text no. 39
    



 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

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