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Correlation of anthropometric indices with lipid profile in adult females.

INTRODUCTION

Dyslipidemia is an abnormal amount of lipids in the blood. In developed countries, most dyslipidemias are hyperlipidemias. It may be manifested as an elevation of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) concentration and a decrease in high-density lipoprotein cholesterol. This is most often due to changes in diet and lifestyle. Overweight and obesity are the two major modifiable risk factors in the causation of dyslipidemia. Serum lipid level as a cardiometabolic risk factor has been well known. Abnormal visceral fat produces physiological changes that alter lipid profile, leading to dyslipidemia and hyperlipidemia, which in turn increases the risk of cardiovascular events. This is particularly true of alterations in LDL-C, an independent causal factor in atherosclerosis. [1]

Obesity is a major public health problem, the prevalence of which has increased worldwide and it significantly increases morbidity and mortality of any given population. Indians have a considerably higher prevalence of premature coronary artery disease compared with Europeans, Chinese, and Malaysians. [2] Within the Indian subcontinent, a dramatic increase in the prevalence of coronary artery disease has been predicted in the next 20 years due to rapid changes in lifestyle consequent to economic development. Obesity is an excessive accumulation of body fat and in its gross manifestation possesses a real threat to health. [3] Alterations in body fat distribution are associated with changes in lipids and lipoproteins and associated with increased coronary heart disease. [4]

Obese individuals differ not only in the amount of excess fat they store but also in the regional distribution of fat within the body. It is useful therefore to be able to distinguish between those at increased risk as a result of "abdominal fat distribution" or "android obesity" from those with the less serious "gynoid" fat distribution, in which fat is more evenly and peripherally distributed around the body. [1]

Different methods are used for the measurement of obesity - they include simple anthropometric measurements such as body mass index (BMI), waist circumference (WC), waist hip ratio (WHR), skinfold thickness, and body density. It is unclear which anthropometric measure is the most important predictor of risk of cardiovascular disease in adults. BMI has traditionally been the chosen indicator by which to measure body size and composition and to diagnose underweight and overweight. BMI does not account for factors such as body fat distribution, specifically abdominal obesity, and cannot distinguish between lean and fat body mass. [5] WC reflects abdominal fat, which contains a higher amount of visceral fat. Therefore, measures that reflect abdominal adiposity and which have an influence on blood lipid profile such as WC, WHR, and WHR are considered superior to BMI in predicting cardiovascular disease risk. Anthropometric indices are simpler and non-invasive tests. It can be applied to predict lipid profile abnormality and at-risk population for future cardiovascular and other endocrine events. Hence, this study aims to correlate and understand the association between measures of adiposity such as WC, WHR, and BMI with serum lipid levels and to determine the best predictor of deranged serum lipid profile among them.

MATERIALS AND METHODS

The study was a hospital-based cross-sectional, descriptive study. The participants of the study were adult females attending Kannur Medical College, Anjarakandy, for executive checkup. The study was done on 306 female subjects. The inclusion criteria were individuals willing to enroll in the study, individuals undergoing executive checkup, and individuals with no known previous history of hypertension, hyperlipidemia, diabetes mellitus, liver diseases, and endocrine diseases. The exclusion criteria were individuals with the previous history of hypertension, diabetes mellitus, liver disease, and endocrine diseases, individuals on lipid-lowering drugs, anti-tubercular drugs, and herbal medications and pregnant and lactating women. The Institutional ethical committee approval was obtained, written informed consent was taken from all subjects, thorough history was taken from all subjects, and all the participants underwent complete general physical examination for the presence of pallor, icterus, clubbing, cyanosis, lymphadenopathy, and edema. Vital parameters such as pulse rate, blood pressure, and temperature were checked. Systemic examination including respiratory, cardiovascular, abdominal, and central nervous system examination was done thoroughly in all subjects. Routine blood investigations such as hemoglobin and random blood sugar were done. Patients who were on medications for tuberculosis, herbal medications, and lipid-lowering drugs and who were on thyroid medications were excluded based on history. Blood investigations done were LDL-C, high-density lipoprotein cholesterol (HDL-C), TC, TGs, very LDL-C, LDL/HDL, and TC/HDL. The anthropometric measurements done were height, weight, BMI, WC, hip circumference, and WHR. Lipid profile tests were done in fully automated analyzer.

BMI

Body weight was measured in kg by a mechanical scale to the nearest kg. Height was measured to the nearest 1 cm. BMI was calculated using Quetelet's index-BMI = weight in kg/height ([m.sup.2]).

WC and WHR

WC was measured midway between the lowest rib and the iliac crest and hip circumference at the level of the greater trochanters with legs close together, using a non-stretchable measuring tape by an average of three measurements nearest to 0.5 cm. The WHR equals WC divided by hip circumference. [6]

Statistical Analysis

Data were entered into Microsoft Excel after coding the data and were analyzed using SPSS 17 version software. Data are presented in the form of frequencies and proportions. Bar diagrams are used to show the graphical representation of data. Pearson correlation was done to calculate the correlation coefficient for quantitative variables. Scatter plots are used to demonstrate correlation. Association between qualitative data was done by Chi-square test, and for quantitative data, analysis of variance (ANOVA) was used. A P < 0.05 was considered as statistically significant.

RESULTS

A total of 306 subjects were included in the study. According to Table 1, the mean age of the subjects was 47 years (standard deviation 10.92), the mean WC was 85.09 cm [+ or -] 10.53 cm, BMI was 23.35 [+ or -] 4.09 kg/[m.sup.2], and the mean WHR was 0.87 [+ or -] 0.05. The mean values for lipid profile parameters were as follows: TC - 194.61 [+ or -] 40.11 mg/dl, LDL - 125.67 [+ or -] 36.04 mg/dl, HDL - 46.75 [+ or -] 2.57 mg/dl, 23.53 [+ or -] 9.88 mg/dl, TGs - 116.58 [+ or -] 48.32 mg/dl, LDL/HDL - 2.67 [+ or -] 0.81, and that of TC/HDL - 4.16 [+ or -] 0.9.

Majority of the subjects were in the age group of 40-59 years [Table 2]. In summary of the results, it was seen that there was significant positive correlation between BMI and TC, LDL, LDL/HDL, and TC/HDL ratio [Table 3], there was a significant positive correlation between WC and TC, LDL, TGs, LDL/HDL, and TC/HDL ratio [Table 4], there was a significant positive correlation between WHR and TC, LDL, VLDL, TGs, LDL/HDL ratio, and TC/HDL ratio [Table 5], there was no significant positive correlation observed with BMI, WC and WHR and HDL-C. WHR correlated best with lipid profile parameters.

DISCUSSION

Dyslipidemia is one of the most important known and modifiable risk factors for the development of coronary artery disease and other complications. Obesity, being one of the important reversible causes of dyslipidemia, is simply a condition of excess body fat. This study was done to correlate simple anthropometric measurements with lipid profile parameters and hence to signify the importance of implementing anthropometry in routine screening procedures.

However, due to the difficulty in obtaining accurate measures of body fatness in the population, measures of height and weight have been widely used to identify overweight and obesity. Obesity is currently defined using BMI. BMI does not, however, measure the proportion of weight which is related to increased muscle or the uneven distribution of abnormal excess fat within the body, which seriously affect the health risks associated with overweight and obesity. It is a good but not a perfect surrogate for body fatness. For the above-mentioned reason, a measure of obesity and overweight that takes into account increased incidence of obesity-related morbidity because of accumulation of abdominal and visceral fat is more desirable. WHR and WC are simple measures and give a better measure of abdominal and visceral fat. In this study, it was observed that there was a significant positive correlation between BMI, TC, LDL, LDL/HDL ratio, and TC/HDL ratio. It was also observed that there was a significant positive correlation between WC, TC, LDL, TGs, LDL/HDL ratio, and TC/HDL ratio and a significant positive correlation between WHR, TC, LDL, VLDL, TGs, LDL/HDL ratio, and TC/HDL. Pearson's correlations (r) between lipid profile and anthropometric measurements were done. It was also observed that, on quantitative analysis using ANOVA, there was a significant association between anthropometric indices (BMI, WC, and WHR) with lipid profile parameters which were similar to the findings seen with Pearson's correlation. BMI, WC, and WHR, all the three parameters correlated well with the parameters of lipid profile. However, WHR best correlated with lipid profile parameters and hence was the better indicator of deranged lipid profile, abdominal obesity, and its adverse effects. The finding of the study was in accordance to so many other studies.

In a study done by Zhang et al., "Anthropometric predictors of coronary heart disease in Chinese women," it was concluded that WHR was positively associated with the risk of coronary artery disease in both younger and older women, while other anthropometric indices, including BMI, were related to cardiovascular disease risk primarily among younger women. [7]

In another study done by Parvin et al., it was concluded that WHR, as compared to BMI, WC, and WHR, may be a better indicator of cardiovascular risk factors. [8]

In this study, BMI, WC, and WHR and lipid parameters were high in subjects in the age group of 40-59 years, which shows that menopause has great effect of body fat and lipid profile. WHR which measures central and abdominal obesity is thus a better predictor of dyslipidemia and its observed consequences. In a study done by Lee et al., it was concluded that measures of centralized obesity proved superior over BMI for detecting cardiovascular risk factors in both men and women. [9] In another study done by Dixon et al., it was said that smaller hip and larger WC are associated with dyslipidemia and the metabolic syndrome in obese women. [10] In a study done by Chu et al., on premenopausal Taiwanese women, it was concluded that WHR had the best performance in predicting hypertension and diabetes mellitus. [11] Liu et al., in a study "utility of obesity indices in screening Chinese post-menopausal women for metabolic syndrome," concluded that WHR and waist height ratio are the best indicators of metabolic syndrome development. It was also said in that study that a WHR of 0.85 or higher should be incorporated into the identification of metabolic syndrome risk in Chinese post-menopausal women. [12]

The utility of WHR as an effective screening measure of obesity has been observed in a study done by Hadaegh et al. in which it was revealed that a high WHR and general obesity are the important predictors of type-2 diabetes mellitus. [13]

A similar finding was observed in a study done by Kaur et al., in which it was concluded that WHR was the best predictor of type-2 diabetes mellitus and that it should be used as a routine measurement along with BMI for screening. [14]

WHR is a more reliable tool than WC when ethnic differences are taken into account. In such a situation, WHR proves superior. In a study done by Herrera et al., it was concluded that WHR was the most accurate anthropometric indicator to screen for high-risk coronary artery disease in the presence of interethnic differences. It was also seen that BMI was almost uninformative and WC was less reliable. [15]

In a study done by Farrag et al., it was demonstrated that WHR had the best association with coronary artery disease severity. [16]

Several factors may account for the discrepancy in findings. First, the predictive power of WC is population dependent, [17] and second, it also varies from race to race. A study done by Lear et al. [18] also reported that ethnic descent modifies the relationship between WC and metabolic risk factors. Although most studies showed that WC may be a better reflection of the accumulation of visceral fat than WHR, it should be noted that WHR has been introduced as an appropriate index for the evaluation of chronic disease risk and it has been suggested that an increased WHR may reflect both relative abundance of abdominal fat (increased WC) and a relative lack of gluteal muscle (decreased hip circumference). [19] WHR not only shows body fat distribution but also reflects most of the lifestyle-related factors of a person. It is also independently associated with cardiovascular risk factors. Therefore, using WHR as a screening measure could definitely provide much more useful information to identify subjects with cardiovascular risk factors.

The principal limitation of this study is that it was done on a smaller population and also the fact that the causes for dyslipidemia are multifactorial. Hence, besides anthropometric measures, other factors such as heredity and lifestyle changes should also be considered.

A small amount of error can be attributed to the measurements of WC and WHR done on extremely obese subjects, in whom the exact site of waist and hip circumference becomes difficult to measure. However, the problem with the measurement of WC and WHR is restricted to the very obese population, for whom further investigation of dyslipidemia and other cardiovascular disease risk factors is done as a routine in any case. Therefore, considering that the measurement of obesity in the clinical setting is usually conducted primarily to inform further investigations, these measurements (WC and WHR) can be easily used to screen people for dyslipidemia and obesity-related complications.

CONCLUSION

Obesity and dyslipidemia are key independent modifiable risk factors for many non-communicable chronic diseases. Both obesity and dyslipidemia appear to develop from an interaction of genotype and the environment. Using simple anthropometric methods, diagnosing obesity as a possible predictor of dyslipidemia is expected to be helpful in efforts to prevent and diagnose both morbidities. This study was done to correlate anthropometric indicators of obesity such as WC, WHR, and BMI with lipid profile parameters and to identify the best indicator in predicting individuals at risk of future complications of obesity and overweight. On analyzing and comparing the data collected, I have come to the conclusion that WHR was a better indicator of dyslipidemia when compared to WC and BMI. WHR had the highest correlation signifying the importance of measuring abdominal and visceral fat in predicting dyslipidemia and associated complications. It can be used as an effective screening tool to predict dyslipidemia and the grave complications which it can lead to. Finally, it can be said that obesity is a health epidemic across the world and we have a responsibility as a society to do all we can to promote good nutrition, healthy eating, and physical activity so that we can stop the rising trend.

REFERENCES

[1.] Park K. Park`s Textbook of Preventive and Social Medicine. Jabalpur: Bhanot Publishers; 2013. p. 335.

[2.] Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation on Obesity. Geneva, Switzerland: WHO; 1997.

[3.] WHO Study Group on Diabetes Mellitus. WHO Technical Report Series.

[4.] Brenner DR, Tepylo K, Eny KM, Cahill LE, El-Sohemy A. Comparison of body mass index and waist circumference as predictors of cardio metabolic health in a population of young Canadian adults. Diabetol Metab Syndr 2010;2:28.

[5.] Holanda MM, Filizola RG, Costa MJ, Andrade EM, Silva JA. Anthropometric evaluation in diabetic patients with ischemic stroke. Arq Neuropsiquiatr 2006;64:14-9.

[6.] Garg S, Vinutha S, Karthiyanee K, Nachal A. Relation between anthropometric measurements and serum lipid profile among cardio-metabolically healthy subjects: A pilot study. Indian J Endocrinol Metab 2012;16:857-8.

[7.] Zhang X, Shu XO, Gao YT, Yang G, Matthews CE, Li Q, et al. Anthropometric predictors of coronary heart disease in Chinese women. Int J Obes Relat Metab Disord 2004;28:734-40.

[8.] Esmaeilzadeh A, Mirmiran P, Mehrabi Y, Azizi F. Waist-to-hip ratio as the best predictor of cardiovascular risk factors compared to waist circumference and body mass index in adult men, district-13, Tehran. Tehran Univ Med J 2004;62:63-74.

[9.] Lee CM, Huxley RR, Wildman RP, Woodward M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: A meta-analysis. J Clin Epidemiol 2008;61:646-53.

[10.] Dixon JB, Strauss BJ, Laurie C, O'Brien PE. Smaller hip circumference is associated with dyslipidemia and the metabolic syndrome in obese women. Obes Surg 2007;17:770-7.

[11.] Chu FL, Hsu CH, Jeng C. Lowered cutoff points of obesity indicators are better predictors of hypertension and diabetes mellitus in premenopausal taiwanese women. Obes Res Clin Pract 2015;9:328-35.

[12.] Liu P, Ma F, Lou H, Zhu Y. Utility of obesity indices in screening Chinese postmenopausal women for metabolic syndrome. Menopause 2014;21:509-14.

[13.] Hadaegh F, Zabetian A, Harati H, Azizi F. The prospective association of general and central obesity variables with incident type 2 diabetes in adults, Tehran lipid and glucose study. Diabetes Res Clin Pract 2007;76:449-54.

[14.] Kaur P, Radhakrishnan E, Sankarasubbaiyan S, Rao SR, Kondalsamy-Chennakesavan S, Rao TV, et al. A comparison of anthropometric indices for predicting hypertension and Type 2 diabetes in a male industrial population of Chennai, south India. Ethn Dis 2008;18:31-6.

[15.] Herrera VM, Casas JP, Miranda JJ, Perel P, Pichardo R, Gonzalez A, et al. Interethnic differences in the accuracy of anthropometric indicators of obesity in screening for high risk of coronary heart disease. Int J Obes (Lond) 2009;33:568-76.

[16.] Farrag A, Hassan A, Zarief BE, Ahmad D, AI-Haj A. Efficiency of Various Parameters of Obesity for Identifying Severity of Coronary Artery Disease in Egyptian Patients; 2012.

[17.] Molarius A, Seidell JC. Selection of anthropometric indicators for classification of abdominal fatness - A critical review. Int J Obes Relat Metab Disord 1998;22:719-27.

[18.] Lear SA, Chen MM, Frohlich JJ, Birmingham CL. The relationship between waist circumference and metabolic risk factors: Cohorts of European and Chinese descent. Metabolism 2002;51:1427-32.

[19.] Seidell JC, Han TS, Feskens EJ, Lean ME. Narrow hips and broad waist circumferences independently contribute to increased risk of non-insulin-dependent diabetes mellitus. J Intern Med 1997;242:401-6.

Ramya Reddy R, Swarnalatha Nambiar

Department of Physiology, Kannur Medical College, Kannur, Kerala, India

Correspondence to: Ramya Reddy R, E-mail: ramyamayudhar@gmail.com

Received: October 16, 2017; Accepted: November 04, 2017

How to cite this article: Reddy RR, Nambiar S. Correlation of anthropometric indices with lipid profile in adult females. Natl J Physiol Pharm Pharmacol 2018;8(4):512-516.

Source of Support: Nil, Conflict of Interest: None declared.

DOI: 10.5455/njppp.2018.8.1042004112017
Table 1: Mean values of quantitative variables among the subjects

Parameters                 Mean (n=306)  SD      SEM

Age (years)                 47.28         10.92  0.62
Height (cm)                163.8           9.42  0.71
Weight (kg)                 62.79         12.78  0.73
Waist circumference (cm)    85.09         10.53  0.60
Hip circumference (cm)      97.01        923     0.52
BMI (kg/[m.sup.2])          23.35          4.09  0.23
WHR                          0.87          0.05  0.01
Total cholesterol (mg/dl)  194.61         40.11  2.29
LDL (mg/dl)                125.67         36.04  2.06
HDL (mg/dl)                 46.75         2.57   0.14
VLDL (mg/dl)                23.53          9.88  0.56
Triglycerides (mg/dl)      116.58         48.32  2.76
LDL/HDL                      2.67          0.81  0.04
TC/HDL                       4.16          0.90  0.05

SD: Standard deviation, BMI: Body mass index, LDL: Low-density
lipoprotein, HDL: High-density lipoprotein, VLDL: Very low-density
lipoprotein, TC: Total cholesterol, SEM: Standard error mean, WHR:
Waist hip ratio

Table 2: Age distribution of the subjects

Age (years)  Frequency (%)

20-29         13 (42)
30-39         70 (22.9)
40-49         85 (27.8)
50-59         84 (27.5)
60-69         54 (17.6)
Total        306 (100.0)

Table 3: Correlation between BMI (kg/[m.sup.2]) and lipid parameters
(mg/dl)

Parameters          TC      LDL     HDL    VLDL   TGs    LDL/HDL  TC/HDL

BMI (kg/[m.sup.2])
Pearson
correlation          0.258   0.231  0.049  0.045  0.072   0.217    0.235
P                   <0.001  <0.001  0.390  0.436  0.208  <0.001   <0.001

BMI: Body mass index, TC: Total cholesterol, LDL: Low-density
lipoprotein, HDL: High-density lipoprotein, VLDL: Very low-density
lipoprotein, TGs: Triglycerides

Table 4: Correlation between WC (cm) and lipid parameters (mg/dl)

Parameters   TC      LDL     HDL    VLDL   TGs    LDL/HDL  TC/HDL

WC (cm)
Pearson
correlation   0.457   0.431  0.061  0.092  0.128   0.405    0.424
P            <0.001  <0.001  0.291  0.109  0.025  <0.001   <0.001

WC: Waist circumference, TC: Total cholesterol, LDL: Low-density
lipoprotein, HDL: High-density lipoprotein, VLDL: Very low-density
lipoprotein, TGs: Triglycerides

Table 5: Correlation between WHR and lipid parameters (mg/dl)

Parameters   TC      LDL     HDL    VLDL   TGs    LDL/HDL  TC/HDL

WHR
Pearson
correlation   0.587   0.560  0.092  0.137  0.178   0.508    0.538
P            <0.001  <0.001  0.106  0.017  0.002  <0.001   <0.001

TC: Total cholesterol, LDL: Low-density lipoprotein, HDL: High-density
lipoprotein, VLDL: Very low-density lipoprotein, TGs: Triglycerides,
WHR: Waist hip ratio
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Title Annotation:RESEARCH ARTICLE
Author:R., Ramya Reddy; Nambiar, Swarnalatha
Publication:National Journal of Physiology, Pharmacy and Pharmacology
Article Type:Report
Geographic Code:9INDI
Date:Apr 1, 2018
Words:3564
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