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.

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.

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.

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.

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.

*= 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   Click here to View figure

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   Click here to View figure

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   Click here to View figure

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   Click here to View figure

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   Click here to View figure

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   Click here to View figure

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.

References

1. Allison, D. B., Fontaine, K. R., Manson, J. E., Stevens, J., VanItallie, T. B. (1999) Annual deaths attributable to obesity in the United States. JAMA 282: 1530–1538.
2. Calle, E. E., Thun, M. J., Petrelli, J. M., Rodrigues, C., Heath, C. W. (1999) Body-mass index and mortality in a prospective cohort of US adults. New Engl J Med 341: 1097–1105.
3. Casimirri, F., Pasquali, R., Cesari, M. P., Melchionda, N., Babara, L. (1989) Interrelationships between body weight, body fat distribution and insulin in obese women before and after hypocaloric feeding and weight loss. Ann Nutr Metab 33: 79–87.
4. Colin  B., linda S., Adair J., Barry M.P. (2002). Ethnic Differences in the association between BMI and Hypertention. American Journal of Epidemiology 155 (4) 346-353.
5. Dalton M., Cameron A.J. Zimmet P.Z. Shaw J.E., Jolly D., Dunstan D.W., Welborn T., (2008). Waist circumference, waist-hip ratio.
6. Dalton, M., Ameron A.J., Zimmet P.Z., shaw J.E., Jolley D., Dunstan D.W., Welborn T.A. (2008). Waist circumference, waist-hip ratio and body mass index and their correlation with cardiovascular disease risk factors.
7. Den Besten, C., Vansant, G., Weststrate, J. A., Deurenberg, P. (1988) Resting metabolic rate and diet induced thermogenesis in abdominal and gluteal femoral obese women before and after weight reduction. Am J Clin Nutr 47: 840–847.
8. Dicker D., Belnic Y., Goldsmith R., Kaluski DN., (2008). Relationship between dietary calcium intake, body mass index, and waist circumference. Isr Med Assoc J. 10 (7): 512-5.
9. Expert Panel on the Identification Evaluation and Treatment of Overweight in Adults (1998). Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: executive summary. Am J Clin Nutr 68: 899–917.
10. Expert panel on the identification evaluation, and treatment of overweight and obesity in adults. (1998). Clinical guide on the identification, Evaluation, and treatment of overweight and obesity in adults: executive summary. AMJ Clin Nutr. 68 (11) 899-917.
11. Flegal K.M., Carrol M.D., Kuczmarski, R.J., Johnson, C.L., (1998). Overweight and obesity in the united states: Prevalence and trends, 1960-1994. Int. J. Obes Relat Metab Disord. 22:39-47.
12. Flegal, K. M., Carrol, M. D., Kuczmarski, R. J., Johnson, C. L. (1998) Overweight and obesity in the United States: prevalence and trends, 1960–1994. Int J Obes Relat Metab Disord 22: 39–47.
13. Fujimoto, W. Y., Bergstrom, R. W., Boyko, E. J., et al. (1999) Visceral adiposity and incident coronary heart disease in Japanese-American men. Diabetes Care 22: 1808–1812.
14. George Lungberg, (2000). Is there a relationship between waist circumference and mortality? Medscape J. Med. Minute 10 (80) 202-209.
15. Hoffmans M.D., Kromhut, D., de lezenne Coilllander C. (1988). The impact of Body Mass. Index year-old dutch mn on 32-year mortality from all cause. Clin Epidemiol 41 (13) 749-56.
16. Jensen, M. D. (1997) Lipolysis: contribution from regional fat. Annu Rev Nutr 17: 127–139.
17. World Health Organization (1989) Measuring Obesity: Classification and Distribution of Anthropometric Data. World Health Organization Copenhagen, Denmark.
18. Kanaley, J. A., Andersen-Reid, M., Oenning, L., Kottle, B. A., Jensen, M. D. (1993) Differential health benefits of weight loss in upper-body and lower body obese women. Am J Clin Nutr 57: 20–26.
19. Kissebach, A. H., Vydelinqum, N., Murray, R., Evans, D. J., Hartz, A. J. (1982) Relation of body fat distribution to metabolic complications of obesity. J Clin Endocrinol Metab 54: 254–260.  Sjöström, C. D., Håkangård, A. C., Lissner, L., Sjöström, L. (1995) Body compartment and subcutaneous adipose tissue distribution-risk factor patterns in obese subjects. Obes Res 3: 9–22.
20. Lean M.E.J., Seidell J.C. (2006). Recent trends in waist circumference and waist  height ratio among US children and adolescents, Pediatrics. 118 (5) 1390-1398
21. Lean, M. E., Han, T. S., Morrison, C. E. (1995) Waist circumference for indicating need for weight management. Br Med J 311: 158–161.
22. Liubov (Louber) Ben-Noun, Ezra Sohar, Arie Laor. ( 2001). Neck circumference as a simple screening measure for  identifying overweight and obese patients. Obesity Research. 24 (9) 470-477.
23. Marc-Andre Cornier, Charles W. Tate Gary K., Grunwald and Daniel h. Bessesen (2002).
24. Obesity Reasearch. Relationship between waist circumference, Body mass Index, and Medical Care Cost. 21 (10) 1167-1172.
25. Mokdad, A. H., Bowman, B. A., Ford, E. S., Vinicor, F., Marks, J. S., Koplan, J. P. (2001) The continuing epidemics of obesity and diabetes in the United States. JAMA 286: 1–195.
26. Must, A., Spadano, J., Coakley, E. H., Field, A. E., Colditz, G., Dietz, W. H. (1999) The disease burden associated with overweight and obesity. JAMA 282: 1523–1529.
27. Rexrode, K. M., Carey, V. J., Hennekens, C. H., et al. (1998) Abdominal adiposity and coronary heart disease in women. JAMA 280: 1843–1848
28. SAS Institute Inc (1989) SAS/STAT User’s Guide, Version 6 4th ed. SAS Institute Inc Cary, NC.
29. Seidell, J. C., Flegal, K. M. (1997) Assessing obesity: classification and epidemiology. Br Med Bull 53: 238–252.
30. Seideu, J.C., Flagal K.M., (1997). Assessing obesity: Classification and epidemiology. Br Med Bull 53: 238-252.
31. Sjöström, C. D., Lissner, L., Sjöström, L. (1997) Relationship between changes in body composition and changes in cardiovascular risk factors: the SOS Intervention Study: Swedish obese subjects. Obes Res 5: 519–530.
32. Stevens J. Pamuk ER, (1998). The effect of age on the association between body-mass index and ortality. N Engl J Med, 338 (56) 1-7.
33. Sunyer G. (1993). Medical harzads of obesity. Ann Interm. Med., 19 (4) 655-60.
34. Tsutomu Douchi,  Riki Kuwahata,  Shinako Yamamoto;  Toshimichi Oki;  Hideki Yamasaki; Yukihiro Nagata (2002). Relationship of upper body obesity to menstrual disorders. ACTA Obstetricia et Gyneocologica Scandivica. 81(2) 147-150
35. Tsutomu N. T., Chaoyang L.I., obesity and menstrual disorders. Acta obstetrician et Gyneocologica scandivica. 81 (2) 147-150.
36. Vague, J. (1956) The degree of masculine differentiation of obesities: a factor determining predisposition to diabetes, atherosclerosis, gout, and uric calculous disease. Am J Clin Nutr 4: 20–34.
37. Vague, J. (1956). The degree of masculine differenciation of obesities: a factor determining Earls M.D., Ford, M.D., (2002): Relationship of upper body predisposition to diabeties, atherosclerosis,gout, and uric calculous disease. Am J. Clin. Nutr. 24 (4) 20-34.
38. Valsamakis (2003). Modest weight loss and reduction in waist circumference after medical treatment are associated with favourable changes in serum adipocytokines. Healtj Sciences Journal 53 (6) 430-434.
39. Wolf, A. M., Colditz, G. A. (1996) Social and economic effects of body weight in the United States. Am J Clin Nutr 63: 466S–469S.
40. Wolf, A. M., Colditz, G. A. (1998) Current estimates of the economic cost of obesity in the United States. Obes Res 6: 97–106.
41. World health organisation, (1995). Report of a WHO Expert Comitte. Physical status: the use and interpretation of anthropometry Geneva, Switzerland (WHO technical report series No. 854)
42. World health organisation, (1998). Obesity preventing and managing the global epidemic. Report of a Who consultation on obesity, 3-5. Geneva, Swtzerland.