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Year : 2018  |  Volume : 11  |  Issue : 2  |  Page : 152-157  

Effect of central obesity on lipid profile in healthy young adults

Department of Physiology, Medical College and Hospital, Kolkata, West Bengal, India

Date of Web Publication18-May-2018

Correspondence Address:
Shaikat Mondal
Department of Physiology, Medical College and Hospital, Kolkata, West Bengal
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Source of Support: None, Conflict of Interest: None


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Background: Increased abdominal obesity is related to adverse metabolic markers. Waist circumference (WC) alone has been shown to correlate more strongly to direct measures of abdominal fat accumulation. Waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) are other parameters to estimate abdominal obesity. Increase in total cholesterol (TC), and triglyceride (TG) increases health risks; whereas, decrease in high density lipoprotein cholesterol (HDL-C) increases future health risk. Aim: The aim of the present study was to find if any correlation exist between central obesity and serum lipid profile in otherwise healthy young adults. Materials and Methods: A cross-sectional study was conducted with 76 (male = 41, female = 35) apparently healthy young adults. Weight, height, WC, and hip circumference were measured. WHR and WHtR were calculated from measured parameters. Serum lipid profile parameters were obtained from venous blood collected after overnight fasting (i.e., 12 h fasting). Pearson's correlation (with α = 0.05) was used to obtain a correlation between central obesity parameters and lipid profile parameters. Statistical analyses were performed in GraphPad Prism 6.01 windows based software. Results: Mean age of the subjects was 18.83 ± 0.85 years. Correlation of WC with TC (r = 0.08, P = 0.45), TG (r = 0.21, P = 0.05) and HDL-C (r = −0.06, P = 0.56) was insignificant. Correlation of WHR with TC (r = 0.07, P = 0.49), TG (r = 0.26, P = 0.02) and HDL-C (r = 0.07, P = 0.50) and WHtR with TC (r = 0.09, P = 0.41), TG (r = 0.17, P = 0.12) and HDL-C (r = 0.03, P = 0.74) also showed insignificant correlation. Conclusion: Indirectly measured central obesity has an insignificant correlation with serum lipid profile in healthy young adults.

Keywords: Abdominal obesity, cholesterol, lipid profile, visceral fat, waist-to-height ratio

How to cite this article:
Mondal S, Mukhopadhyay SK. Effect of central obesity on lipid profile in healthy young adults. Med J DY Patil Vidyapeeth 2018;11:152-7

How to cite this URL:
Mondal S, Mukhopadhyay SK. Effect of central obesity on lipid profile in healthy young adults. Med J DY Patil Vidyapeeth [serial online] 2018 [cited 2022 Jun 25];11:152-7. Available from: https://www.mjdrdypv.org/text.asp?2018/11/2/152/232640

  Introduction Top

Global burden of obesity is increasing at an alarming rate, and it is the most neglected epidemic according to the World Health Organization (WHO).[1],[2] It is well-documented that obesity is associated with increased risk of cardiovascular and metabolic diseases.[3],[4] Between subcutaneous fat and visceral fat, latter is associated with higher risk of cardiometabolic diseases.[5],[6] Serum lipids (e.g., triglyceride (TG), total cholesterol (TC)) are markers which are commonly used to estimate the risk of cardiometabolic diseases.[7]

In clinical practice, obesity is diagnosed by simple height and weight measurement of the subject to calculate body mass index (BMI).[8] However, BMI has a major limitation that it cannot stratify people according to central obesity. Waist circumference (WC), a simple parameter can indirectly estimate the level of visceral fat or central obesity. Waist-to-hip ratio (WHR) ratio is also used as a parameter for measurement of central obesity.[9],[10] In addition, it is documented that waist-to-height ratio (WHtR) ratio is more acceptable predictor variable than WC or WHR for cardiometabolic diseases.[10],[11],[12],[13]

Several previous studies were conducted in India and abroad to find out the correlation between central obesity and lipid profile with diverse result.[14],[15],[16],[17],[18],[19] In majority of the studies, the sample was chosen either from elder age group or from a wide range of age group. However, our research question was for the young adult age group. The question was whether we can have a rough estimation of dyslipidemia from simple anthropometric parameters or not.

With this background, the aim of this study was fixed to find out if any correlation exists between central obesity and serum lipid profile in healthy young adults.

  Materials and Methods Top

After designing the study protocol, clearance from institutional ethics committee was obtained (MC/KOL/IEC/NON-SPON/134/11-2015). This was a cross-sectional study which was conducted during January 2016–May 2017 in the postgraduate research laboratory of the Department of Physiology.

Sampling technique

For sample size estimation, we considered waist circumference as the predictor variable and total cholesterol as the outcome variable. After reviewing related published research papers, we expected a correlation coefficient 0.35 between WC and TC. With this expected correlation coefficient, chances of type I error = 0.05 and chances of type II error = 0.20, the sample size was calculated as 62.[20] However, we intended to include more than this number according to available logistics. The age range was minimized to limit age-related change in body fat. The convenience sample was taken from otherwise healthy male and female between 18 years and 22 years of age. An inclusion criterion was age range only. Exclusion criteria were any acute or chronic disease or deformity and subjects on any medication or with any type of addiction. All the subjects were included after taking written informed consent for participation. They were also informed that they can exit from the study at any point without stating any reason.

Anthropometric parameters measurement

Weight was measured by a digital weighing scale Omron HBF-375 (OMRON Healthcare Pvt., Ltd., Japan) with a sensitivity of 0.1 kg. Standing height was measured by portable stadiometer to the nearest 0.1 cm. From this two parameters, BMI was calculated according to Quetelet's equation (BMI = weight in kg/height in m2). WC was measured by stretch resistant fiberglass measuring tape to the nearest 0.1 cm. A female attendant was appointed during measurements on female subjects. Measurements were taken while subjects were in an erect posture with hands by the sides and feet positioned closed together. WC of male subjects was taken without upper body clothing. However, for female subjects, it was taken with light clothing in front of female attendant. WC measurement was taken at the approximate midpoint between the lower palpable rib and upper border of iliac crest with measuring tape placed horizontally to the floor after a normal expiration. Hip circumference (HC) was also measured in this position at the maximum girth of the hip to the nearest 0.1 cm.

Serum lipid profile measurement

A volume of 5 ml venous blood was collected from an antecubital vein in the morning with overnight fasting condition (i.e., 12 h fasting). Blood sample was labeled and carried immediately to the Department of biochemistry for serum lipid testing. TC, TG, and high-density lipoprotein cholesterol (HDL-C) were determined from the collected blood by automatic analyzer Sclavo Konelab – 600i Prime (Sclavo Diagnostic International SRL, Italy). Reports were stored for analysis.

Statistical analysis

Data were entered into a spreadsheet application OpenOffice Calc (Apache Software Foundation, Maryland, USA) according to sex to calculate BMI, WHR, and WHtR from the raw data. For categorization of subjects with health risks according to their WC, level of WC >78 cm for male and WC >72 cm for female were considered.[21] For categorization of subjects with health risks according to their WHR, level of WHR >0.90 for male and WHR >0.85 for female were considered according to the WHO.[22] For categorizing subjects with health risks according to WHtR, for both male and female, WHtR >0.5 was considered.[23]

For both the sex, TC >200 mg/dL, TG >150 mg/dL, and HDL-C <40 mg/dL were considered marker for increased health risks.[24] Subjects with health risk or without health risk were compared using Chi-square test. Unpaired t-test was used to compare the mean between male and female. Pearson's correlation was used to find the correlation between anthropometric parameters (predictor variable) and serum lipid profile parameters (outcome variable). These 3 statistical tests were carried out in GraphPad Prism 6.01 (GraphPad Software, Inc., La Jolla, USA). For all statistical test, two tailed α was 0.05. Hence, P < 0.05 was considered as statistically significant.

  Results Top

Mean age of sample (n = 76) was 18.83 ± 0.85 years. Age, anthropometric parameters for central obesity, and serum lipid profile are shown in [Table 1].
Table 1: Age, anthropometric parameters, and lipid profile parameters in study sample (n=76) according to sex expressed in mean and standard deviation

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Distribution of subjects with health risk according to central obesity parameters is shown in [Table 2]. Distribution of subjects with health risk according to serum lipid profile is shown in [Table 3].
Table 2: Distribution of study subjects according to central obesity parameters

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Table 3: Distribution of study subjects according to serum lipid profile parameters

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The correlation coefficient between central obesity parameters and serum lipid profile parameters is shown in [Table 4].
Table 4: Pearson's correlation coefficient (r) between anthropometric parameters for central obesity and serum lipid profile parameters in healthy young adult (n=76)

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  Discussion Top

Measurement of central obesity by direct method requires extensive laboratory facility, time, expertise, and high expense. Hence, in clinical settings, indirect measurement is gaining popularity. Being easy to measure with simple measuring tape, circumference measurement can serve as a quick method to estimate health risks. However, there are different opinions about the appropriate method to estimate central obesity. A study by Grundy et al. found that WC is the most useful parameter to measure central obesity.[25] Rodea-Montero et al. showed that WHtR is more appropriate than WC for cardiometabolic risk estimation.[26] Hence, we took all 3 common parameters (viz., WC, WHR, WHtR) suggested for estimation of central obesity.

Among the subjects of this study, WC in female was less than male subjects [Table 1], whereas, HC in both sexes was almost equal. Hence, WHR was less in female. This supports the gynoid pattern of fat distribution in female subjects.

If we compare overall health risks according to 3 parameters (viz., WC, WHR, WHtR), there is discordant distribution of the subjects in each category [Table 2]. According to measured WC, 65.79% of study subjects were in health risk category, however, according to WHtR, 44.74% were in health risk group. WHR showed that only 19.74% of subjects were in health risk category. Hence, use of these anthropometric parameters for central obesity measurement in young adult may give different results across different parameters. Hence, combination of these parameters may provide a better health risk profile.[27],[28]

Increase in serum lipids above a certain level carries the risk of cardiovascular diseases. In this study, subjects were measured for TC, TG, and HDL-C. Among these, increase in TG and TC above the normal level, and decrease in HDL-C below the desired level carry health risks. Subjects who participated in the study lead a sedentary life style. None of them were having regular exercise. Subjects in health risk category according to their different serum lipids were as follows: 10.53% according to TC, 22.37% according to TG, and 21.05% according to HDL-C [Table 3]. However, if we consider the level of health risk according to anthropometric parameters, the level was far more than these percentages. The anthropometric cutoff for central obesity has been established after considering multiple health-related risk factors for a wide range of age group. The difference in the percentage of the population in health risk according to anthropometric parameters and lipid profile may be due to this reason.

When Pearson's correlation was carried out between the central obesity parameters and serum lipid profile parameters, the result showed statistically insignificant correlation coefficient between predictor and outcome variable in all sets except WHR and TG [Table 4]. Although it was statistically significant, the level of correlation coefficient was quantitatively insignificant (r = 0.26).

Mishra et al. conducted a study on 60 subjects between 18 and 56 years age range and found highest correlation coefficient as r = −0.35 between WHR and HDL-C. Rest of the coefficient was less than this level. With this level of correlation, they stated that central obesity is associated with abnormal lipid profile. However, the sample size and level of r square does not indicate any significant correlation.[14] Chadha et al. conducted a study on healthy aviator in different age groups, and they found a quantitatively insignificant correlation between central obesity and lipid profile in the age group between 20 and 29 years.[17] Similarly, Chinyere and Sola also reported an insignificant correlation between central obesity as determined by WC and WHtR with lipid profile.[29] Results of these studies are supportive to our study though the age range was different than that of the current study.

In contrast, Darmawan and Irfanuddin reported the relatively higher level of correlation between central obesity and lipid profile in 22–55 years subjects. They found a correlation between WC and TG as r = 0.369 in male and r = 0.535 in female. Furthermore, correlation found between WHR and TG was r = 0.543 in male and r = 0.271 in female.[30] Chehrei et al. reported that HDL has no correlation with central obesity, but TG is correlated with WHtR with a correlation coefficient of r = 0.309 and WC with a correlation coefficient of r = 0.308. Mean age of their study was around 40 years.[19] Manjareeka et al. reported a significant correlation between WC and TG (r = 0.44), WC and HDL (r = −0.62), WHR with TG (r = 0.31), WHR with HDL (r = −0.24). They took a sample from 18 to 65 years subjects.[16] Rocha et al. found no correlation between central obesity and lipid profile in elderly male above 60 years. However, they found weak correlation between central obesity and lipid profile in female (r < 0.29).[15] Saghafi-Asl et al. conducted study on overweight and obese 63 patients and stated that WHtR has a strong correlation with TC (r = 0.37).[31] These findings, however, are not supported by the result of our study. The reason for this discordance may lie in the sample itself, as all of them took sample from a wide age range. The difference in life style and ethnicity may be other contributing factors.

The result of this study indicates that, at normal physiological state, increase in abdominal obesity does not increase serum lipids in parallel. Other metabolic risk factors associated with central obesity were beyond the scope of our study. However, increase in central obesity has its ill effect on overall cardiovascular health.[32] Increase in central obesity also decrease maximal aerobic capacity in young adults.[33] Hence, the result of this study should be interpreted with caution.

Limitations of the study

This study has several limitations. For estimation of central obesity, we used simple anthropometric parameters, commonly used in clinical settings. However, this method is indirect one, and it does not quantify visceral fat directly. The sample size was relatively small. A larger sample size would reflect more accurate result. Intra-observer variation in anthropometric measurements may be a confounding factor which was beyond our control. According to logistics, we could not take low-density lipoprotein-cholesterol -C among serum lipid profile.

  Conclusion Top

Central obesity, measured by anthropometric parameters has an insignificant correlation with serum lipid profile in otherwise healthy young adults. Hence, in young adults, only an increase in central obesity does not indicate underlying dyslipidemia.


We would like to thank the 1st year medical students of 2015–2016 and 2016–2017 batches who actively participated in this study.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.

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


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