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Ucheya R. E, Okonofua S. E, Anyanwu L. C, Igweh J. c.Relationship Between Neck Circumference, Waist Circumference, Body Mass Index, Arm Circumference and Waist Hip Ratio as Predictors of Cardiovascular Risk Factors. Biosci Biotechnol Res Asia 2009;6(1).
Manuscript received on : October 02, 2008
Manuscript accepted on : November 24, 2008
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Relationship Between Neck Circumference, Waist Circumference, Body Mass Index, Arm Circumference and Waist Hip Ratio as Predictors of Cardiovascular Risk Factors

R. E. Ucheya*, S. E. Okonofua¹, L. C. Anyanwu² and J. C. Igweh³

1Department of Anatomy, School of Basic Medical Sciences,University of Benin, Benin-city Nigeria. 2Department of Obstetrics and Gynaecology University of Benin Teaching Hospital, Benin, Benin-City Nigeria. 3Faculty of Basic Medical Sciences, Ambrose Alli University, Ekpoma, Edo state Nigeria. 4Department of Physiology, University of Nigeria, Enugu Campus. Enugu Nigeria.    

ABSTRACT: Neck circumference (NC), as an upper body obesity index, is a simple screening measure for identifying overweight and obese patients. Based on the clinical significance of body mass index (BMI), waist circumference (WC), hip circumference (HC) and waist hip ratio (WHR), this study examines a relationship between changes in BMI, WC, WHR and Neck circumference. In a random sample cohort study the study group was comprised of 218 subjects ( Male) with no known major medical conditions who were not receiving any medication therapy. With age (17-34), divided in two age groups (17-25 and 26-34) with mean values (77.06 ± 0.56 and 82.97 ± 1.5 respectively) showed a significant difference (P < 0.05). Main indicators studied included NC, WC, WHR and BMI. Pearson’s correlation coefficients indicated a significant association between changes in WC and changes in NC (r= 0.46 each, P < 0.0001) BMI and NC, (r = 0.51 each, P< 0.0001), Age and NC (r=.127 each, P<0.05) but was insignificant for WHR and NC, (r= 0.1, each, P<0.07). Changes in WC, BMI, and Age, correlated positively with changes in NC but negatively with changes in WHR and the NC was revealed to be double the WC. This might be used as a reliable, simple, quick and cheap method for predicting cardiovascular risk factors for coronary heart diseases and can possibly provide a very useful criterion for fashion designers for predicting waist circumference if a simple measurement of neck circumference is employed.

KEYWORDS: predictors. cardiovascular risk factors. Neck circumference. Waist circumference. Body mass index. Waist hip ratio. Age

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Ucheya R. E, Okonofua S. E, Anyanwu L. C, Igweh J. c.Relationship Between Neck Circumference, Waist Circumference, Body Mass Index, Arm Circumference and Waist Hip Ratio as Predictors of Cardiovascular Risk Factors. Biosci Biotechnol Res Asia 2009;6(1).

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Ucheya R. E, Okonofua S. E, Anyanwu L. C, Igweh J. c.Relationship Between Neck Circumference, Waist Circumference, Body Mass Index, Arm Circumference and Waist Hip Ratio as Predictors of Cardiovascular Risk Factors. Biosci Biotechnol Res Asia 2009;6(1). Available from: http://www.biotech-asia.org/?p=20102

Introduction

In clinical settings Age, tribe, body mass index (BMI), waist circumference (WC), hip circumference, waist hip ratio (WHR) and Arm circumference are of health significance to coronary heart diseases. Overweight is defined as a body mass index (BMI) between 25 and 29.9 kg/m2 and obesity is defined as a BMI of 30 kg/m2 or higher. These conditions pose a major public health problem because they are associated with various chronic diseases (Expert panel on the identification evaluation and treatment of overweight in adults, 1998). It is estimated that more than one-half of adults, 35 to 65 years of age, living in Europe are either obese or overweight. The prevalence of obesity in Europe is estimated to be 10% to 20% of adult men and 15% to 25% of adult women. These figures seem to be increasing (Seidell, 1997). In the United States, the crude prevalence of overweight and obesity (BMI > 25 kg/m2) for age 20 was 59.4% for men, 50.7% for women, and 54.9% overall between 1988 and 1994. The prevalence of obesity (BMI 30 kg/m2) is also on the increase; it was estimated to be 14.5% between 1976 and 1980, and 22.5% between 1988 and 1994 (Flagal 1998).

There are numerous methods of assessing overweight and obesity. Some techniques are applicable at primary care facilities, such as measurements of weight, height, abdominal sagittal diameter, abdominal and hip circumferences, and calculations of waist: hip ratio and BMI. It is not always practical to use these techniques, especially in winter, in busy, everyday primary care practice. Other procedures, such as ultrasound, computed tomography and magnetic resonance imaging are expensive and are primarily used for research purposes. As a first step to achieve obesity control, it is important to develop a reliable, simple, quick method for the assessment of obesity in primary care clinics.

Recently, data clarifying whether or not obesity-related comorbid conditions occur at different levels of (BMI) (weight (kg)/height (m) 2) in different ethnic groups amongst the Caucasians has been documented (Colin et al, 2002).  In his study higher BMI was associated with a higher prevalence of hypertension in all ethnic groups. However, at BMI levels less than 25, prevalence difference figures suggested a stronger association between BMI and hypertension in Chinese men and women but not in Filipino women, compared with non-Hispanic Whites. Non-Hispanic Blacks and Filipino women had a higher prevalence of hypertension at every level of BMI compared with non-Hispanic Whites and Mexican Americans.  Valsamarkis (2003) in his heavily reach work on modest weight loss and reductions in waist circumference after medical treatment are associated with favourable changes in serum adipocytokines. Concluded  that modest weight loss (>5%) after medical treatment in a routine obesity hospital clinic is as Body mass index (BMI) (weight (kg)/height (m)2) is positively and independently associated with morbidity and mortality from hypertension, cardiovascular disease, type II diabetes mellitus, and other chronic diseases(Sunyer,1993). In Caucasian populations, the association between BMI and S25 (Hoffmans et al, 1988, and Stevens et al, 1998). On the basis of this association, the World Health Organization has devised a classification wherein persons with BMIs below 18.5–24.9 are considered underweight, those with BMIs above this range are considered overweight or “at risk,” and those with BMIs greater than or equal to 30 are considered obese (WHO ,1998 and WHO, 1955). Valsamakis (2003) in his cited article  concluded that modest weight loss (>5%) after medical treatment in a routine obesity hospital clinic is associated with improvements in insulin sensitivity and lipid profile. Modest weight loss is also associated with potentially favourably changes in serum adipocytokines, particularly in a rise of serum adiponectin while reduction of waist circumference is associated with a change in serum resistin. George Lunberg (2002) in his detailed work titled “is there a relationship between waist circumference and morality”? Documented that even in persons with a normal body mass index, and unrelated to prevalent diseases, smoking status, and ethnic/racial groupings, a large waist circumference conveyed 20% increase in mortality risk. And suggested that a need for intervention seems pretty obvious.  Jean Vague was the first researcher to realize that different body morphology or types of fat distribution are related to the health risks associated with obesity. He used a neck skinfold in his index of masculine differentiation to assess upper-body fat distribution (Vague, 1956). Although obesity results in metabolic abnormalities, upper-body obesity is more strongly associated with glucose intolerance, hyperinsulinemia, diabetes, hypertriglyceridemia, gout, and uric calculous disease than is lower-body obesity (Vague,1956 and Kissebach et al, 1982). NC, as an index of upper-body subcutaneous adipose tissue distribution, was evaluated in relation to cardiovascular risk factors by (Sjöström et al, 1995). In addition, relationships were examined between changes in body composition, including the neck girth, and changes in cardiovascular risk factors (Sjostrom et al, 1997). Furthermore, the free fatty acid release from upper-body subcutaneous fat was found to be larger than that from lower-body subcutaneous fat (Jenson, 1997), a fact that further strengthens the relevance of measuring upper-body subcutaneous adipose tissue depots. These observations indicate that NC as an index of upper body fat distribution can be used to identify overweight and obese patients. (Mav- andre et al, 2002) in his study on Relationship between waist circumference/ body mass index, and Medical care costs suggested that abdominal adiposity as assessed by WC is associated with increased total health care charges and may be a better predictor of health care charges than the more widely used BMI. They concluded that waist circumference (WC) provides information about regional adiposity and may correlate with health care costs better than body weight or BMI.  Chaoyang et al (1998) in his work on Recent trends in waist circumference and waist height ratio among US children and adolescents reported that Mean waist circumference and waist-height ratio and the prevalence of abdominal obesity among US children and adolescents greatly increased between 1988–1994 and 1999–2004. Tsutomu et al (2002) in his study on Relationship between of upper body obesity to menstrual disorder. Reported that Upper body, but not lower body, obesity is associated with menstrual disorders. Dalton et al (2008) in his highly reference research on waist circumference, waist hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian suggested that given appropriate cut-off points, WHR is the most useful measure of obesity to identify individuals with CVD risk factors. Though undocumented NC as been said to have a positive correlation with WC and is used in determination of waist size for skirts and trousers.

The above study has examined NC, WC, BMI, and WHR as it relates to cardiovascular risk factors and its significance to health. But this present study aims at investigating the relationship between NC, WC, BMI, AC and WHR as simple, cheap, and fast predictors for cardiovascular risk factors. Secondly; to scientifically evaluate if neck circumference can be a criterion for selection of skirts and trouser sizes.

Objectives

The aim of this study was to determine whether a single measure of NC might be used to identify waist circumference and to define NC cut-off levels for waist circumference, body mass index, Arm circumference, and waist hip ratio and according to existing age.

Research Methods and Procedures

The entire cohort studied comprised of 218 Nigeria male undergraduate students within university of Benin, Benin-city, Nigeria. Age range (17 – 34yrs). The sampling method employed was the single-phase random sampling technique. Major converging centres were various departments in university of Benin. Ages and tribe of individuals was determined through oral communication. Major attributes collected and measured (Table 1).

Table 1: Showing major attributes collected and measured.

S/N SEX AGE  (YRS) TRIBE NC (CM) WC (CM) BMI (Kg/m2) HC (cm) WHR WEIGHT (Kg) AC (cm) HEIGHT (cm) HEIGHT (m2)
1 MALE 17 BINI 35 79 19.84 88.00 0.90 58 25.5 171 29241
2 MALE 17 BINI 34.5 74 22.48 87.00 0.85 59 25 162 26244
3 MALE 18 OWAN 34 65.5 16.90 82.00 0.80 49 23 167 27889
4 MALE 18 IKWELE 36 70 17.58 88.00 0.80 52 25 172 29584
5 MALE 18 URHOBO 27 72 18.61 89.00 0.81 57 25 175 30625
6 MALE 18 IDOMA 36 72 18.81 85.00 0.85 55 26 171 29241
7 MALE 18 IGBO 35 73 19.00 85.00 0.86 55 26 170 28900
8 MALE 18 IGBO 35 77.5 19.23 86.50 0.90 63 25.5 181 32761
9 MALE 18 BINI 37 78 19.74 95.00 0.82 72 28 191 36481
10 MALE 18 EFIK 33.5 69.5 20.08 80.00 0.87 54 24.5 164 26896
11 MALE 18 IJAW 36 70 20.99 86.00 0.81 55 26 162 26244
12 MALE 18 ESAN 36.4 77.5 22.23 87.50 0.89 65 28.5 171 29241
13 MALE 18 ESAN 36 87 22.34 100.20 0.87 70 30 177 31329
14 MALE 18 ESAN 40 75 24.50 94.00 0.80 75 29 175 30625
15 MALE 18 IJAW 38 74 58.82 90.00 0.82 170 31.5 170 28900
16 MALE 19 IBO 33 74 17.96 86.00 0.86 55 23 175 30625
17 MALE 19 YORUBA 38 74 18.11 88.00 0.84 61 26 183.5 33672.25
18 MALE 19 BINI 34 73.5 18.61 87.50 0.84 57 26.3 175 30625
19 MALE 19 BINI 35 71 18.72 85.00 0.84 58 24 176 30976
20 MALE 19 OWAN 36 82 18.90 95.20 0.86 64 28.5 184.2 33929.64
21 MALE 19 YORUBA 35 72 19.38 87.00 0.83 58 26.5 173 29929
22 MALE 19 BINI 35 73.5 19.97 92.00 0.80 64 25 179 32041
23 MALE 19 ITSEKIRI 35 79 20.29 98.00 0.81 65 28 179 32041
24 MALE 19 BINI 38 76 20.53 97.00 0.78 68 28 182 33124
25 MALE 19 IGBO 36 78.5 20.81 90.50 0.87 63 25.5 174 30276
26 MALE 19 BINI 35 80 21.77 93.00 0.86 60 29 166 27556
27 MALE 19 AKWA IBOM 38 76 21.97 90.00 0.84 65 28 172 29584
28 MALE 19 ITSEKIRI 36 83 22.23 95.00 0.87 65 31 171 29241
29 MALE 19 BINI 38 77 22.31 92.00 0.84 66 31 172 29584
30 MALE 19 ASABA 38 74 22.44 92.00 0.80 65 27 170.2 28968.04
31 MALE 19 ESAN 38 84 23.24 101.00 0.83 77 30 182 33124
32 MALE 19 IGBO 37 89 24.20 101.00 0.88 70 30.5 170 28900
33 MALE 19 ESAN 40.5 79 24.34 95.00 0.83 78 33.5 179 32041
34 MALE 19 IGBO 40 86 28.00 110.00 0.78 82 38 171 29241
35 MALE 20 OWAN 34 64 17.92 81.50 0.79 53 24.5 172 29584
36 MALE 20 BINI 33.5 69.5 18.64 81.00 0.86 52 26 167 27889
37 MALE 20 ESAN 39 80 19.00 92.50 0.86 70 27.5 182 33124
38 MALE 20 ISOKO 36 73 19.49 86.00 0.85 59 25 174 30276
39 MALE 20 BINI 36 77 19.66 93.00 0.83 63 26 179 32041
40 MALE 20 BINI 37 70 20.20 85.00 0.82 64 24 178 31684
41 MALE 20 IGBO 37.5 73 20.20 85.00 0.86 64 23 178 31684
42 MALE 20 IBO 36 76 20.24 91.00 0.84 62 26 175 30625
43 MALE 20 YORUBA 37 70 20.28 86.00 0.81 60 26.5 172 29584
44 MALE 20 BINI 35.4 82.2 20.30 96.70 0.85 68 30.5 183 33489
45 MALE 20 IGBO 38 81.1 20.40 95.00 0.85 69 28.5 187 34969
46 MALE 20 OWAN 36 81 20.42 93.00 0.87 59 27 170 28900
47 MALE 20 NDOKWA 35 69 20.45 85.00 0.81 53 26 161 25921
48 MALE 20 IKWALE 34 72 20.83 87.00 0.83 52 27 158 24964
49 MALE 20 IKWALE 38 76 21.03 88.00 0.86 65 27 176 30976
50 MALE 20 ESAN 37 77 21.80 96.00 0.80 66 31 174 30276
51 MALE 20 ESAN 41 90 21.98 100.00 0.90 72 35 181 32761
52 MALE 20 NIGER DELTA 37.5 79 22.45 85.50 0.92 64 30 169 28561
53 MALE 20 IBO 36 72 22.50 92.00 0.78 69 29 175 30625
54 MALE 20 ISOKO 39 79 22.60 97.00 0.81 70 30 176 30976
55 MALE 20 IGBO 37.5 75.5 23.03 8.00 9.44 65 31 168 28224
56 MALE 20 NDOKWA 37 78 23.94 93.00 0.84 70 29.5 171 29241
57 MALE 20 ESAN 37 82 24.26 102.00 0.80 76 30 177 31329
58 MALE 21 ANIOCHA 39 71 18.51 90.00 0.79 58 26 177 31329
59 MALE 21 OWAN 37 72 18.90 88.00 0.82 60 25.9 178 31684
60 MALE 21 IBIBIO 33 65 19.00 82.00 0.79 53 25.5 167 27889
61 MALE 21 BINI 36 66 19.03 86.00 0.77 55 24 170 28900
62 MALE 21 IGBO 35 74 19.08 87.00 0.85 66 27 186 34596
63 MALE 21 IKA 38 74 19.62 92.00 0.80 65 28.5 182 33124
64 MALE 21 BINI 38 87 19.70 94.00 0.93 69 28 187 34969
65 MALE 21 ESAN 37.5 75 19.75 89.00 0.84 64 26.5 180 32400
66 MALE 21 IGBIRA 36 76 20.16 90.00 0.84 69 27.5 185 34225
67 MALE 21 IGBO 35.5 74.5 20.60 90.00 0.83 69 26.5 183 33489
68 MALE 21 IGBO 37.5 71 20.75 90.00 0.79 65 26 177 31329
69 MALE 21 ANIOCHA 35.5 80 20.80 95.00 0.84 69 29 182 33124
70 MALE 21 IGBO 35 69.5 20.81 88.00 0.79 63 27.5 174 30276
71 MALE 21 BINI 38.2 82.5 20.91 94.00 0.88 64 28.5 125 15625
72 MALE 21 IGBO 38.5 75 21.01 92.50 0.81 60 29 169 28561
73 MALE 21 ESAN 36 71.5 21.06 91.00 0.79 69 29.5 181 32761
74 MALE 21 BINI 37.5 77 22.00 93.10 0.83 60 30.5 165 27225
75 MALE 21 URHOBO 38 81 22.30 91.00 0.89 69 29.8 170 28900
76 MALE 21 URHOBO 37 80 22.53 94.00 0.85 69 26.5 175 30625
77 MALE 21 AGBOR 37 79 22.60 93.00 0.85 70 30 176 30976
78 MALE 21 IGARRA 41 77 22.60 91.00 0.85 64 26.2 168 28224
79 MALE 21 BINI 38 82 22.80 96.00 0.85 66 29 170 28900
80 MALE 21 IGARRA 39 81 22.86 94.20 0.86 70 28 175 30625
81 MALE 21 ANIOCHA 49 77 23.20 95.00 0.81 76 30.1 181 32761
82 MALE 21 ETSAKO 39 81 23.71 94.00 0.86 76 29 179 32041
83 MALE 21 BINI 37.5 77 23.72 94.00 0.82 71 31 173 29929
84 MALE 21 BINI 39 80.5 23.85 100.00 0.81 79 29 182 33124
85 MALE 21 ESAN 38.7 78 24.00 97.50 0.80 71 28.5 172 29584
86 MALE 21 BINI 40 76 24.06 98.00 0.78 72 32 173 29929
87 MALE 21 IGBO 39.5 76 24.16 93.00 0.82 74 30.5 175 30625
88 MALE 22 IBO 35 74 17.80 84.00 0.88 59 26 182 33124
89 MALE 22 URHOBO 34 70 18.30 83.00 0.84 54 22 171.5 29412.25
90 MALE 22 URHOBO 36 69 19.70 87.00 0.79 57 25 1700 2890000
91 MALE 22 YORUBA 36 74.2 19.71 93.20 0.80 66 27.5 183 33489
92 MALE 22 IKA 34 74 19.84 88.00 0.84 65 27 181 32761
93 MALE 22 IBO 32 78.1 20.00 92.50 0.84 54 26 164.2 26961.64
94 MALE 22 ESAN 35.5 74.5 20.24 89.00 0.84 62 27 175 30625
95 MALE 22 OWAN 36.2 80 20.28 87.40 0.92 60 30 172 29584
96 MALE 22 BINI 35 71 20.31 85.00 0.84 58 29 169 28561
97 MALE 22 IJAW 35 79 20.38 92.00 0.86 69 26 184 33856
98 MALE 22 ORA 37 82 20.56 100.00 0.82 75 27 191 36481
99 MALE 22 IGBANKE 36 74 20.60 87.00 0.85 66 24 179 32041
100 MALE 22 IBO 40.1 84.1 20.76 96.50 0.87 68 30.5 181 32761
101 MALE 22 IGBO 37.5 70 20.76 86.00 0.81 60 27 170 28900
102 MALE 22 URHOBO 38 85.3 21.19 100.10 0.85 66 32.1 176.5 31152.25
103 MALE 22 BINI 36 73 21.20 88.00 0.83 62 27 171 29241
104 MALE 22 DELTA 36 76 21.50 90.00 0.84 60 27.5 167 27889
105 MALE 22 EGUN 37 79 21.60 93.00 0.85 70 27.5 180 32400
106 MALE 22 URHOBO 35 73 21.97 86.00 0.85 65 27 172 29584
107 MALE 22 IKA 37 81 22.09 97.00 0.84 70 29 178 31684
108 MALE 22 OGOJA 39 86.5 22.15 94.00 0.92 70 32.5 178 31684
109 MALE 22 ESAN 37 81 22.22 90.00 0.90 68 29 175 30625
110 MALE 22 IBO 34 85 22.41 95.00 0.89 64 29 169 28561
111 MALE 22 ETSAKO 41.3 82.2 22.70 94.10 0.87 68 30 173.5 30102.25
112 MALE 22 EDO 39.5 76 22.70 89.50 0.85 61 28 164 26896
113 MALE 22 BINI 37.5 77 23.38 92.00 0.84 69 30.8 172 29584
114 MALE 22 HAUSA 36 85.3 23.94 99.00 0.86 75 31.9 177 31329
115 MALE 22 IGBO 40 88 24.30 103.20 0.85 79 31.9 180 32400
116 MALE 22 ETSAKO 39 84 24.52 99.00 0.85 83 29 184 33856
117 MALE 22 URHOBO 39.5 82 25.76 98.00 0.84 78 31.5 174 30276
118 MALE 23 ETSAKO 36 72 18.90 88.00 0.82 64 25 184 33856
119 MALE 23 ETSAKO 36.5 76 18.99 86.00 0.88 65 28 185 34225
120 MALE 23 ESAN 35.5 73 19.23 89.00 0.82 68 26.5 188 35344
121 MALE 23 ESAN 31 73.7 19.44 84.00 0.88 53 24.8 165.1 27258.01
122 MALE 23 NDOKWA 34 74 19.48 81.00 0.91 55 27 168 28224
123 MALE 23 ISOKO 35 75 19.60 93.00 0.81 70 24 189 35721
124 MALE 23 ONDO 37.5 75.5 19.66 91.00 0.83 63 25.5 179 32041
125 MALE 23 YORUBA 36.5 74.5 19.66 91.50 0.81 63 27.5 179 32041
126 MALE 23 IGBO 34 76 19.71 90.10 0.84 59 27 173 29929
127 MALE 23 IBO 36 74 19.83 96.00 0.77 59 27 172.5 29756.25
128 MALE 23 IGBO 36 76 19.88 87.00 0.87 63 24 178 31684
129 MALE 23 ITSEKIRI 34 70 19.96 85.00 0.82 55 27 166 27556
130 MALE 23 URHOBO 35.5 72 20.01 86.00 0.84 67 28 183 33489
131 MALE 23 BINI 39 80 20.37 94.00 0.85 72 27.5 139 19321
132 MALE 23 IKA 34 73 20.43 84.00 0.87 51 26 158 24964
133 MALE 23 ESAN 37 76 21.10 92.00 0.83 69 27 181 32761
134 MALE 23 BINI 37.5 73.5 21.15 89.00 0.83 67 28 178 31684
135 MALE 23 OWAN 37 78 21.38 95.00 0.82 67 28 177 31329
136 MALE 23 IGBANKE 33 77 21.39 100.20 0.77 60 27 167.5 28056.25
137 MALE 23 IBO 42 78 21.40 95.00 0.82 67 30 177 31329
138 MALE 23 UROMI 35.5 75 21.51 86.00 0.87 60 27 167 27889
139 MALE 23 YORUBA 40.5 76 21.60 90.50 0.84 70 30 180 32400
140 MALE 23 ESAN 38 72 21.66 88.00 0.82 65 29 176 30976
141 MALE 23 ETSAKO 37.2 78.5 21.74 91.00 0.86 72 29 182 33124
142 MALE 23 BENIN 37.5 77 21.77 91.00 0.85 69 28 178 31684
143 MALE 23 OWAN 36 76.5 21.97 93.00 0.82 65 27.5 172 29584
144 MALE 23 IGBO 40 77 22.34 95.00 0.81 70 29 177 31329
145 MALE 23 IBO 35.2 83 22.40 93.80 0.88 65 30 170.5 29070.25
146 MALE 23 BINI 38 83 22.47 98.00 0.85 72 29 179 32041
147 MALE 23 IGBO 38.5 76.5 22.60 94.00 0.81 70 28 176 30976
148 MALE 23 ANIOCHA 39 74 22.72 96.00 0.77 72 30 178 31684
149 MALE 23 IKA 39 82 22.79 98.00 0.84 69 31.6 174 30276
150 MALE 23 IKA 39 95 23.21 115.00 0.83 65 38.5 168 28224
151 MALE 23 IBO 40 86 23.50 97.00 0.89 68 32 170 28900
152 MALE 23 IKA 39.5 81 24.00 96.10 0.84 71 28 172 29584
153 MALE 23 IGBO 31.5 82.8 24.42 97.50 0.85 80 32.7 181 32761
154 MALE 23 IBO 38 82 25.20 100.00 0.82 79 32 177 31329
155 MALE 23 IJAW 39.2 92.2 27.91 108.00 0.85 85 38.2 174.5 30450.25
156 MALE 24 IGBO 37 75 23.53 86.50 0.87 61 29 161 25921
157 MALE 24 ESAN 35.8 80.5 19.47 93.00 0.87 61 27.1 177 31329
158 MALE 24 IJAW 36 76.2 19.60 96.20 0.79 60 27 175 30625
159 MALE 24 ESAN 36.5 75.1 19.90 92.00 0.82 66 29.1 182 33124
160 MALE 24 BINI 37.5 75.5 20.20 89.00 0.85 64 29 178 31684
161 MALE 24 ESAN 37.5 77.2 20.76 88.90 0.87 60 30 170 28900
162 MALE 24 AUCHI 36 75 20.89 88.00 0.85 64 26.5 175 30625
163 MALE 24 ESAN 36 74 21.22 93.00 0.80 68 30 179 32041
164 MALE 24 ESAN 37 75.5 21.32 91.50 0.83 73 31.5 185 34225
165 MALE 24 IGBO 37 75 22.23 90.50 0.83 65 39.5 171 29241
166 MALE 24 LAGOS 38 76 22.28 94.00 0.81 73 29 181 32761
167 MALE 24 ESAN 39 80 22.45 93.00 0.86 76 32 184 33856
168 MALE 24 ISOKO 36 79 22.72 91.00 0.87 68 29 173 29929
169 MALE 24 IGARA 37 77 22.79 92.00 0.84 69 30 174 30276
170 MALE 24 BINI 38 77 22.92 92.00 0.84 71 28.5 176 30976
171 MALE 24 YORUBA 42 84 23.40 99.00 0.85 71 32 174 30276
172 MALE 24 IGBO 40 82.5 23.92 100.00 0.83 81 33.5 184 33856
173 MALE 24 ESAN 39 75 23.94 90.00 0.83 70 30 171 29241
174 MALE 24 IGBO 40 87.5 24.21 100.50 0.87 75 30.8 176 30976
175 MALE 24 IGBO 39 86 24.54 95.00 0.91 76 29 176 30976
176 MALE 24 YORUBA 42 83 25.16 96.00 0.86 71 30 168 28224
177 MALE 25 URHOBO 34 70 17.63 81.00 0.86 54 25.5 175 30625
178 MALE 25 ESAN 39 78 18.56 89.50 0.87 67 28.5 190 36100
179 MALE 25 ETSAKO 36 75.5 19.83 88.00 0.86 54 25 165 27225
180 MALE 25 BINI 36.5 78.5 20.00 86.00 0.91 59 28.1 171.5 29412.25
181 MALE 25 ESAN 34 79 20.44 89.00 0.89 52 25.2 159.5 25440.25
182 MALE 25 IJAW 27.1 77.1 20.54 96.00 0.80 60 36 171 29241
183 MALE 25 URHOBO 38 91 21.30 99.00 0.92 69 32 180 32400
184 MALE 25 URHOBO 37 76 21.38 90.00 0.84 67 27 177 31329
185 MALE 25 IGARRA 39.1 84 21.51 97.10 0.87 71 31.1 182 33124
186 MALE 25 IBIBIO 35 78 22.31 86.00 0.91 66 29 172 29584
187 MALE 25 URHOBO 40 88 22.41 99.00 0.89 64 31 169 28561
188 MALE 25 IJAW 36.2 81 22.60 98.00 0.83 55 28 156 24336
189 MALE 25 IZON 40 91 22.99 95.60 0.95 68 32 172 29584
190 MALE 25 IJAW 39 89 23.03 95.00 0.94 65 31 168 28224
191 MALE 25 IGBO 37.5 82.5 23.26 93.00 0.89 68 30.5 171 29241
192 MALE 25 BINI 39.5 80 23.54 100.00 0.80 78 31 182 33124
193 MALE 25 ASAMA 37.5 83.2 25.10 101.00 0.82 70 30.1 167 27889
194 MALE 26 ISOKO 39 74 20.70 88.00 0.84 65 26 177 31329
195 MALE 26 IJAW 34.5 73 21.06 90.00 0.81 57 26.8 164.5 27060.25
196 MALE 26 IGBO 38.5 81.5 21.95 97.00 0.84 68 32.5 176 30976
197 MALE 26 ESAN 37 74 22.58 96.00 0.77 74 30.5 181 32761
198 MALE 26 BINI 40.5 83 22.94 96.00 0.86 76 30.5 182 33124
199 MALE 26 OGONI 39 84 23.70 98.40 0.85 70 29.5 172 29584
200 MALE 26 IJAW 37 92.2 25.65 101.00 0.91 79 34.8 175.5 30800.25
201 MALE 27 IJAW 29 72 20.70 90.00 0.80 60 34 170 28900
202 MALE 27 ESAN 40 82 22.60 96.00 0.85 70 28 176 30976
203 MALE 27 IJAW 34.5 75 23.57 98.00 0.77 66 29 168 28224
204 MALE 27 ESAN 40 81 23.71 101.00 0.80 76 31 179 32041
205 MALE 27 IBO 41 93 27.99 104.00 0.89 79 32 168 28224
206 MALE 28 IJAW 34 77.2 18.18 87.50 0.88 60 26.8 182.5 33306.25
207 MALE 28 IKA 33 76 19.30 84.00 0.90 58 27 173 29929
208 MALE 28 BINI 39.8 93 22.04 101.00 0.92 60 31.1 165 27225
209 MALE 28 ISOKO 40 94 24.51 99.00 0.95 70 32 169 28561
210 MALE 28 IJAW 37 88 25.40 107.00 0.82 57 32 175 30625
211 MALE 29 URHOBO 31 80 22.48 94.00 0.85 54 27.5 155 24025
212 MALE 30 IBO 38 86 21.88 94.00 0.91 56 30 160 25600
213 MALE 30 IBO 40 94 27.04 113.00 0.83 80 36 172 29584
214 MALE 31 ISOKO 36 79 20.45 92.00 0.86 67 29 181 32761
215 MALE 31 IJAW 37 83.2 22.60 93.00 0.89 66 31.8 171.5 29412.25
216 MALE 32 IJAW 39.5 83.5 21.60 97.20 0.86 70 30.8 180 32400
217 MALE 32 IJAW 41.5 96.1 22.48 107.00 0.90 82 35.5 191 36481
218 MALE 34 IJAW 36.9 79.5 21.29 94.00 0.85 69 31.5 180 32400

Anthropometry

All measurements were made by standard techniques (WHO, 1989): weight by digital scales (HANSON, Watford, Hertforshire, England) to within 100 g, without heavy clothing; height barefoot by portable stadiometer (Holtain, Crymmych, Wales) to within 0.5 cm; waist and hip circumferences were calibrated weekly to within 1 mm, using plastic tapes. The waist was measured at the end of a gentle expiration midway between the lowest rib and iliac crest, with the patient standing, and the hips were measured at the greater trochanter. NC was measured in the midway of the neck, between midcervical spine and midanterior neck, to within 1 mm, with plastic tape calibrated weekly. In men with a laryngeal prominence (Adam’s apple), it was measured just below the prominence. All circumferences were taken with the subjects standing upright, with the face directed toward L.B.-N., and shoulders relaxed.

Definitions

Low BMI was defined as <25 kg/m2. High BMI was defined at two levels as 25 or 30 kg/m2 .for both men and women (WHO, 1989). Waist circumference was defined as low: <94 cm for men and <80 cm for women (Lean et al, 1995). High waist circumference was defined at two levels as described previously (Lean et al, 1995), with slight changes, as 94 to 102 cm for men and 80 to 88 cm for women or >102 cm for men and >88 cm for women. Waist: hip ratio was defined as low <0.95 for men and <0.80 for women and high as 0.95 for men and 0.80 for women (Kanaley et al, 1993).  For this work, only definitions applicable to men were employed.

Statistical Analysis

To check for the inter-relationships between NC and Age as dependent variables on WC, BMI and AC as independent variables we made use of the Linear Multiple Regression Model to check for any significant relationship, in it we calculated the Multiple regression Coefficient R = X for age and R = Y for the NC which indicated a significant relationship between the dependent variables and the predictors (Attributes).

To get the pair relationships between any of the two dependent variables (Age and NC) we ran a simple Linear Regression Model which gave us the Pearson correlation coefficient and the P-value at which there is a significant relationship. Also this model gave us an equation wherein we can forecast a cut-off, of which given the value of any of the attributes we can get a corresponding value of the Age or NC.

To further show relationship, the samples were broken into two different age groups (17 – 25yrs and 26 – 35yrs) and the mean values were computed and using a t-test we were able to get the significance in the mean values.

To check for the inter-relationships between NC and Age as dependent variables on WC, BMI and AC as independent variables we made use of the Linear Multiple Regression Model to check for any significant relationship, in it we calculated the Multiple regression Coefficient R = 0.380 for age and R = 0.464 for the NC which indicated a significant relationship between the dependent variables and the predictors (Attributes).

Results

The Pearson’s correlation (Table 2) showed that there is a significant positive relationship of r = 0.307 between NC and WC , r=0.345 between NC and BMI and r =0.415 between NC and AC all at P < 0.001. This shows that an increase in NC will cause a significant increase in the three (3) different attributes, (WC, BMI & AC).

Table 2: Showing the mean and standard deviations of attribute.

ATTRIBUTES Mean Std. Deviation
NECK CIRCUMFERENCE (NC) 36.97 2.53
WAIST CIRCUMFERENCE (WC) 77.73 7.92
BODY MASS INDEX  (BMI) 21.69 3.23
ARM CIRCUMFERENCE (AC) 28.69 2.91

To check if there was a significant difference in the mean a t-test was done (Table 3) and it showed that there was significant difference in the mean values of the BMI. That is as age increase WC and AC increase but this correlation was negative for BMI (Table 4).

Table 3: Showing the descriptive statistics of the age group and different attributes.

  AGE GROUP Mean Std. Deviation Std. Error Mean
WAIST CIRCUMFERENCE

 

17 – 25Yrs 77.05 7.747 0.558
26 – 34Yrs 82.97 7.381 1.476
BMI 17 – 25Yrs 21.5687 3.31797 0.23883
26 – 34Yrs 22.6559 2.25103 0.45021
ARM CIRCUMFERENCE 17 – 25Yrs 28.434 2.8366 0.2042
26 – 34Yrs 30.624 2.7434 0.5487

The above table shows the descriptive statistics of the age group and the different attributes. To check if there is a significant difference in the mean a t-test was done.

Table 4: t-Showing test for the attributes.

t P-value Mean Difference
WAIST CIRCUMFERENCE -3.611 0.000 5.916±1.638
BMI -1.590 0.113 1.08720±0.68379
ARM CIRCUMFERENCE -3.645 0.000 2.1898±0.6008

Mean difference is expressed as mean ± sem.

The t-test shows that there was significant difference in the mean values of the two age groups for WC and AC but no significant difference in the mean values of the BMI. That is as age increase WC and AC increase but this correlation is not shown for BMI.

WHR &WC, WC Pearson’s correlation was statistically significant when the following attributes were compared;& NC, WHR & h, WHR &AC, Age & AC, Age & NC, Age &WC, Age & BMI, Age & HC, age & H, Age & Wt, age & AC, Age & WHR, all showed a statistical significant at P <0.001/ P< 0.0001 (Table 5). On the other hand Pearson’s correlation was statistically insignificant when the following attributes were compared; WHR & NC, WHR &BMI, WHR & HC, WHR & WT, Age & HT.

Table 5: Showing Pearson’s correlation and student t-test for attributes compared.

ATTRIBUTES STUDIED r P value
WHR AND WC 0.54 P<0.0001
WC AND NC 0.46 P<0.0001
WHR AND NC 0.1 p>0.05*
WHR AND BMI 0.074 P >0.05*
WHR AND HC -0.06 P>0.05*
WHR AND HEIGHT -0.112 P<0.05
WHR AND WEIGHT 0.004 P>0.05*
WHR AND AC 0.14 P<0.05
AGE AND AC 0.345 P<0.0001
AGE AND NC 0.127 P< 0.05
AGE AND WC 0.373 P< 0.0001
AGE AND BMI 0.23 P<0.0001
AGE AND HEIGHT 0.01 P=0.439*
AGE AND WEIGHT 0.16 P<0.001
AGE AND AC 0.345 P<0.0001
AGE AND WHR 0.215 P<0.001
AGE AND HC 0.029 P<0.0001

*= not significant

 

 

The scattered diagram of NC and WC was employed as to show the correlation between the two attributes using the regression line equation and the pearson’s correlation coefficient (Fig. 1). The regression line was given as NC=0.098WC + 29.35 which imply that at a particular value of WC we can get the NC.

Figure 1 Figure 1

 

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For correlation between MBI and AC, the regression line was given as NC = 0.270BMI +31.11. This implies that at a particular value of AC we can get the BMI (Fig. 2).

Figure 2 Figure 2

 

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For correlation between NC and AC, the regression line was given as NC= 0.361AC + 26.59, which implies that at a particular value of AC we can get the NC (Fig. 3).

Figure 3 Figure 3

 

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For correlation between age and WC, the regression line was given as Age = 0.094WC +15.08, this implies that at a particular age we can get the WC (Fig. 4).

Figure 4 Figure 4

 

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For correlation between age and BMI, the regression line was given as Age= 0.063BMI + 21.03, which implies that at a given age we can get the BMI (Fig. 5).

Figure 5 Figure 5

 

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For correlation between age and AC, the regression line was given as Age= 0.350X + 12.37, which implies that at a given age we can get the AC (Fig. 6).

Figure 6 Figure 6

 

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Discussion

This present study was performed to examine the relationship between NC, WC, WHR, AC, BMI, HT, WT and age as to determine if they could be of clinical relevance in predicting cardiovascular risk factors. A number of conclusions can be drawn from this study. First, prevalence of obesity among the study age group is not a common feature amongst the south-south and south-east Nigerians. As was evident by 2.3% of the entire cohort presenting with overweight (BMI) between 25 and 29.9kg/m2 while there was no case of obesity among the entire cohort studied BMI of 30kg/m2  or higher (Table 1). Compare with the report by Seidell (1997) it shows that Caucasians have high prevalence of obesity when compared to the Negros. However, this is in agreement with the report by Dicker et al (2008) that the high incidence of overweight and obesity amongst the Caucasians is mainly due to diet type. Secondly, WC showed a positive and significant correlation with NC, WHR, and age, this shows that WC is of great clinical significance, a finding that agrees with a previous report that modest reduction in waist circumference (> 5%) after medical treatment in a routine obesity hospital clinic is as BMI [weight (kg) height (m2) ] is positively and independently associated with morbidity and mortality from hypertension, cardiovascular diseases, type ll diabetes mellitus and other chronic diseases (Valsamarkis,2003; Sunyer, 1993; and Stevens et al, 1998). He concluded that loss/gain in WC are associated with favourable changes in serum adipocytokines. Thirdly, (Figures 1, 2,3,4,5, & 6) respectively, showed strong positive correlation between NC and WC, BMI and AC, NC and AC, Age & WC, Age and BMI, age and AC, (table 5). This however suggest that they can be use as a simple method to asses cardiovascular risk factors based on the evidence that NC, age and BMI has been documented to have a positive correlation with Cardiovascular risk factors in Caucasians (Liubov et al, 2001). Fourthly, this agrees with the findings by Dalton et al (2008) in his highly references article on WC, WHR and BMI and their correlation with cardiovascular risk factors in Australian suggested that given appropriate cut-off points, WHR is the most useful measure of obesity to identify individuals with cardiovascular disease risk factors. Fifthly, greater WC is associated with increased total health care charges and greater BMI is also associated with increased total health care charges although not statistically significant (Marc-Andre Cornier et al, 2002). Finally, this study has been able to indicate that NC can actually be use to determine WC (Y0.198X +21.47= WC) and as such can be use as a criterion by fashion designers to determine skirts and trouser size in normal subjects. Conclusively, it might be of good health value for every person to have a data on measurement of their NC, WC, WHR, AC and BMI as an aid to diagnose early unset of any possibly existing coronary heart diseases.

Conclusion 

Base on the strong correlation evident among the attributes studied, we are of the opinion that NC could be use as a simple, quick, fast and cheap method for early prediction of cardiovascular risk factors and diseases.

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