Research articles

By Dr. Shyamal Koley
Corresponding Author Dr. Shyamal Koley
Department of Sports Medicine and Physiotherapy, Guru Nanak Dev University, Amritsar, India, - India
Submitting Author Dr. Shyamal Koley

Serum cholesterol, Triglycerides, HDL-C, LDL-C, VLDL-C. Subcutaneous fat.

Koley S. Association Of Subcutaneous Fat With Some Anthropometric Characteristics And Lipid Profile In Vegetarian And Non-vegetarian Middle Aged Menopausal Women Of Central India. WebmedCentral SPORTS MEDICINE 2010;1(10):WMC00941
doi: 10.9754/journal.wmc.2010.00941
Submitted on: 09 Oct 2010 11:36:53 AM GMT
Published on: 09 Oct 2010 12:59:08 PM GMT


The purpose of this cross-sectional study was to search the correlations of ultrasonographically estimated subcutaneous fat with some anthropometric characteristics and lipid profile of middle aged vegetarian and non-vegetarian menopausal women of central India. To solve this purpose, 47 purposively selected middle aged vegetarian (n=30) and non-vegetarian (n=17) menopausal women between the age group 40 - 60 years were collected from Jabalpur, Madhya Pradesh, Central India. The measurements included were height, weight, BMI,  subcutaneous abdominal fat, five skinfold measurements, viz. biceps, triceps, subscapular, suprailiac and abdominal and seven lipid profile components, viz. serum cholesterol, High Density Lipoprotein-C (HDL-C),  Low Density Lipoprotein-C (LDL-C), Triglycerides, VLDL-C, Total Cholesterol : High Density Lipoprotein-C ratios and Low Density Lipoprotein - C : High Density Lipoprotein–C ratios. Results indicated statistically significant (p


Subcutaneous abdominal fat, a component of central obesity, has a strong association with metabolic profiles (1). Women begin to increase visceral as well as subcutaneous fat deposited at the onset of menopause (2). An android fat distribution (abdominal obesity, or “apple shaped” body) is related to an increased risk of cardiovascular disease (3). Subcutaneous abdominal fatincreases insulin resistance and the related cluster of metabolicrisk factors (glucose intolerance or diabetes mellitus, lowHDL-cholesterol concentrations, elevated trigylglycerol concentrations,hypertension, and obesity) (4-6). This cluster was first describedby Reaven (4) as "syndrome X" and is also referred to as the"insulin resistance syndrome" or "metabolic syndrome" (7).
Indian population has a very high incidence of ischemic heart disease with lipid profile is one of the risk factors which is different from those seen in western populations. Elevated levels of triglyceride, cholesterol and LDL-C are documented as risk factors for atherogenesis. Blood level of HDL-C in contrast, bears an inverse relationship for the risks of atherosclerosis and coronary heart disease. Different plasma lipids vary significantly in various population groups due to differences in geographical, cultural, economical, social conditions.
Diabetes mellitus has become a widespread disease nowadays. According to World Health Organization report (8), around 171,000,000 people were affected with diabetes worldwide by the year 2000 and will reach around 366,000,000 by the year 2030. The prevalence of diabetes is on the rise, more alarmingly in the developing nations. In India alone 31,705,000 people were affected by the year 2000 and will reach around 79,441,000 by the year 2030. Due to the high degree of genetic predisposition and high susceptibility to environmental conditions, characterized by a low BMI, high upper body adiposity, a high body fat percentage and a high level of insulin resistance, Indian population faces higher risk for diabetes and its complications (9). Evidence in the literature has suggested that the visceral fat thickness measured by ultrasonography could be more reliable method to quantify subcutaneous visceral fat as compared with other methods (10-11).
Association of lipid profile is reported with lifestyle (12, 13), age (14), intra-abdominal adiposity (15-16), obesity (17-20), BMI (21) and waist to hip ratios (22-24). In the present study, an attempt has been made to investigate the relationship of subcutaneous abdominal fat with lipid profile along with some anthropometric variables in vegetarian and non-vegetarian middle aged menopausal women of central India.


The study was conducted within the framework of an ongoing prospective cohort study of vegetarian and non-vegetarian middle aged menopausal women of Jabalpur, Madhya Pradesh, central India. Women were recruited from a health check-up camp organized by Digambar Jain Mahila Samiti and Punjabi Mahila Samiti at the Gorakhpur gurudwara, Jabalpur, India, between 27th June -27th July, 2007. A total of 47 middle aged (between 40-60 years) menopausal (due to small sample size pre, peri and post menopausal samples were pooled) women participated in the study. A total of 30 vegetarian Jain middle aged women and 17 non-vegetarian Punjabi middle aged women with a body mass index (BMI) ³ 30kg/m2 were screened for inclusion in the study. Exclusion criteria included self reported pregnancy, any chronic infectious disease, weight loss >6kgs during past 6 months. The study was approved by Institutional Ethical Committee and a written informed consent was obtained by all the participants.
Anthropometric measurements
The height was recorded during inspiration using a stadiometer (Holtain Ltd., Crymych, Dyfed, UK) to the nearest 0.1 cm. The subject was asked to stand erect on the stadiometer with bare foot. The horizontal bar of the stadiometer was placed on the vertex of the subject and the readings were recorded. Weight was measured by digital standing scales (Model DS-410, Seiko, Tokyo, Japan) to the nearest 0.1 kg. The subject was asked to stand erect on the digital weighing machine with minimum cloths and bare foot. The readings were recorded from the scales of the digital weighing machine. BMI was then calculated using the formula weight (kg)/height2 (m)2. Five skinfold measurements were taken from the sites, biceps (vertical skinfold raised on the anterior aspect of the biceps muscle), triceps (vertical skinfold raised on the posterior aspect of the triceps muscle, exactly halfway between the olecranon process and the acromion process when the hand is supinated), subscapular (oblique skinfold raised 1 cm below the inferior angle of the scapula at approximately 450 to the horizontal plane following the natural cleavage lines of the skin), suprailiac (diagonal fold raised immediately above the crest of the ilium on a vertical line from the mid-axilla) and abdominal (vertical fold raised at a lateral distance of approximately 2 cm from the umbilicus) using Harpenden skinfold caliper (Holtain Ltd, Crosswell, Crymych, UK) to the nearest 0.2 mm. All the anthropometric measurements were taken following the standard techniques (25). A pre-tested semi structured questionnaire was developed to obtain information on the demographic, nutritional and lifestyle profiles of the participants.
Estimation of lipid profiles
Venous blood samples were taken from all the subjects in the morning after fasting overnight. Plasma levels of fasting plasma glucose, total cholesterol, triglycerides, High Density Lipoprotein- Cholesterol (HDL-C), Low Density Lipoprotein-Cholesterol (LDL-C) and Very Low Density Lipoprotein (VLDL) were analyzed. Total cholesterol and triglyceride concentrations were determined with a semi-automated enzymatic analyzer (RA 50, Semi-auto Chemistry Analyzer, Thyrocare India Ltd, India). HDL- Cholesterol serum level was measured by using phosphotungstate precipitation method. The ratio of total cholesterol-to-high density lipoprotein cholesterol (HDL-C) was considered to be the best predictor of heart disease and has been used in our study. Exclusion factors were confirmed from the subject’s personal physician report and a detailed history.
Estimation of subcutaneous fat
The subject was asked to report in fasting position in the morning. They were made to lie in supine position on the table keeping her heels, buttock and shoulders in contact with the table. The abdominal fat distribution by ultrasonography was estimated by a real time US scanner (Sonoline Prima, Siemens, Germany) according to the standard procedure (10). Ultrasound gel was applied and a convex 3.5 MHz transducer was applied at a distance of 1 cm cranially from the umbilicus on the xypho-umbilical line. Transverse scans were performed during mid-inspiration. The subcutaneous fat thickness was measured as the distance between the skin fat and fat muscle interfaces.
Statistical analysis
Standard descriptive statistics (mean ± standard deviation) were determined for directly measured and derived variables. Student’s t-test was used for the comparison of various anthropometric variables between middle aged vegetarian and non-vegetarian menopausal women.. Pearson’s correlation coefficients were applied to establish the relationships among the variables measured. Data were analyzed using SPSS (Statistical Package for Social Science) version 17.0. A 5% level of probability was used to indicate statistical significance.


Table 1 shows the descriptive statistics of 16 variables in middle aged vegetarian and non-vegetarian menopausal women of central India. The middle aged vegetarian women have higher mean values in all the variables studied, except BMI, subscapular skinfold and serum HDL-C than their non-vegetarian counterparts. However, statistically no significant differences were found in any case between those two sets of populations.
The correlation coefficients (r) of subcutaneous fat and 15 other variables in vegetarian and non-vegetarian middle aged menopausal women of central India were shown in Table 2. In vegetarian middle aged women, statistically significant positive correlations (p£0.05) were noted between subcutaneous fat and weight, BMI, subscapular.


Early post menopausal status is associated with a preferential increase in subcutaneous abdominal fat that is independent of age and total body fat mass. This increased abdominal fat accumulation in women can be attributed to the increased androgenic activity in the postmenopausal women as the hormones are known to affect the fat distribution (2,3). Preliminary studies suggest that the menopause transition is associated with deleterious changes in body composition and body fat distribution. The association between body fat distribution and lipid profile has been shown to be the important predictor for metabolic disturbances including dyslipidemia, hypertension, diabetes, cardio vascular disease etc.
 In the present study, anthropometric parameters, viz. height, weight, BMI, biceps skinfold, triceps skinfold, subscapular skinfold, suprailiac skinfold and abdominal skinfold were not the affected factors among vegetarian and non-vegetarian middle aged women (Table 1). Weight was one such factor that affects greatly towards metabolic risk. In fact, it was reported earlier too, that weight loss and/or gain was related to increased risk for abdominal fat distribution and therefore metabolic risk profile (20). No marked mean differences were found in the distribution of  serum cholesterol, serum HDL-C, serum triglyceride, serum LDL-C, serum VLDL, ratio of total cholesterol: HDL-C and ratio of LDL-C: HDL-C in vegetarian and non-vegetarian middle aged women as data was collected from same geographical and socio-economic background. Elevation in any one of the components of lipid, except HDL-C, plays an important role in development of coronary heart diseases. The present study showed statistically no marked differences among all the lipid components as obesity was frequently present among middle aged vegetarian and non-vegetarian menopausal women, thus blood lipid levels altered homogenously. It showed that the increase in LDL-C and total cholesterol makes the individual more prone to metabolic risk profile (26).
In this study, subcutaneous abdominal fat was found to be negatively related to HDL-C only in the vegetarian middle aged women (Table 2). HDL-C is known to be good cholesterol (27). Reduction in plasma HDL-C impairs the normal clearance from arterial wall thereby accelerating the development of atherosclerosis (28). Improvement of HDL-C helps provides protective effect on heart. The present study is in agreement with the findings of earlier studies (29, 30) on relationship between fat distribution and serum lipids. One of the limitations of the study was the small sample size, especially in the non-vegetarian group. Future study is required considering larger sample size to draw the generalized statement.


1. Goodpaster BH, Thaete FL, Simoneaux JA, Kelley DE. Subcutaneous abdominal fat and thigh muscle composition predicting insulin sensitivity independently of visceral fat. Diabetes 1997; 46:1579-85.
2. Kotani KK. Sexual dimorphism of age related changes in whole body fat distribution             in the obese. Int J Metab Disord 1994; 18: 207-11.
3. Vague J. The degree of masculine differentiation of Obesities: A factor determining predisposition to diabetes, atherosclerosis, gout and uric calculus disease. Am J Clin Nutr 1956; 4: 20-34.
4. Reaven GM. Role of insulin resistance in human disease. Diabetes 1988; 37: 1595–1607.
5. Pascot A, Despres JP, Lemieux I. Contribution of visceral obesity to the deterioration of the metabolic risk profile in men with impaired glucose tolerance. Diabetologia 2000; 43: 1126–35.
6. Arora M, Koley S, Gupta S, Sandhu JS. A study on lipid profile and body fat in patients with diabetes mellitus. The Anthropologist 2007; 9: 295-8.
7. Després JP. Abdominal obesity as important component of insulin-resistance syndrome. Nutrition 1993; 9: 452– 9.
8. World Health Organization (1998) Obesity: preventing and managing the global epidemic: Report of a WHO consultation on obesity, Geneva.
9. Rosenson RS (2005) HDL-C and the diabetic patients: Target for therapeutic intervention. Diabetes Res Clin Pract, 68: 36-42.
10. Armellini F, Zamboni M, Robbi R, Todesco T, Rigo L, et al. Total and intra-abdominal fat measurements by ultrasound and computerized tomography. Int J Obes Relat Metab Disord 1993; 17: 209–14.
11. Tornaghi G, Raiteri R, Pozzato C, Rispoili A, Bramani M et al. Anthropometric or ultrasonic measurements in assessment of visceral fat? A comparative study. Int J Obes Relat Metab Disord 1994; 18: 771– 5.
12. Twisk JW, Kemper HC, Van Mechelen W, Post GB, Van Lenthe FJ. Body fatness: longitudinal relationship of BMI and the sum of skinfolds with other risk factors for CHD. Int J Obes Relat Metab Disord 1998; 22: 915- 22.
13. Serter R, Demirbas B, Korukluoslu B, Culha C, Cakal E et al. The effect of  L-thyroxine replacement therapy on lipid based cardiovascular risk in             subclinical hypothyroidism. J Endocrinol Invest 2004; 27: 897-903.
14. Maki KC, Kritsch K, Foley S, Soneru I, Davidson MH. Age dependence of the relationship between adiposity and serum LDL-C in men. J Am Clin Nutr,1997;  16: 578- 83.
15. Mannabe E, Aoyagi K, Tachibana H, Takemoto T. Relationship of intra- abdominal adiposity and peripheral fat distribution to lipid metabolism in an island population in western Japan: gender differences and effect of menopause. Tohoku  J Exp Med 1999; 188: 189-202.
16. Mari O, Furuya R, Ohkawa S, Yoneyama T, Nisikino M et al. Altered abdominal fat distribution and its association with serum lipid profile in non-diabetic haemo-dialysis patients. Nephrol Dial Transplant 1999; 14: 2427- 32.
17. Despres JP, Allard C, Tremblay A, Talbot J, Bouchard C. Evidence for a             regional component of body fatness in association with serum lipids in men and women. Metabolism 1985; 34: 967- 73.
18. Denke MA, Sempos CT, Grundy SM. Excess body weight: An under- contributor to high blood cholesterol in White American men. Arch Int Med 1993; 153: 1093-1103.
19. Dongsheng H, Judy H, Stuart R, Gray K, Jablonski A et al. Effects of obesity and body fat distribution on lipids and lipoproteins in nondiabetic American Indians: The Strong Heart Study. Obesity Research 2000; 8: 411- 21.
20. Pihl E, Jurimae T. Relationship between body weight change and cardiovascular risk factors in male former athletes. Int J Obes Relat Metab Disord 2001; 25: 1057- 62.
21. Bertolli A, Di-Daniele N, Ceccobelli M, Ficara A, Girasoli C et al. Lipid profile, BMI, body fat distribution and aerobic fitness in men with metabolic syndrome. Acta Diabetol 2003; 40: S130- 3.
22. Okosun S, Richard ICS, Richard BO, Cooper S, Forrester T. Association of waist circumference with risk of hypertension and type 2 Diabetes in Nigerians, Jamaicans and African-Americans. Diabetes Care 2000;  21: 1836- 42.
23. Dalton M, Cameron AJ, Zimmet PZ, Shaw JE, Jolley J et al. Waist circumferences, waist-hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults. J Intrn Med 2003; 254: 555- 63.
24. Lopatynski J, Mardarowixz G, Szczesniak G. A comparative evaluation of waist circumference, waist to hip ratio, waist to height ratio and body mass index as indicators of impaired glucose tolerance and as risk factors for type 2 diabetes mellitus. Ann Univ Mariae Cuire Sklodowska 2003;  58: 413- 9.
25. Lohmann TG, Roche AF, Martorell R. Anthropometric Standardization Reference Manual. Champaign, IL: Human Kinetics Books.1988.
26. Martinez CA, Ramos R, Gonzalez MT, Castineiras MJ. Dyslipidemia and cardiovascular risk factors in type 2 diabetes mellitus patients with associated diabetic nephropathy. Nefrologia 2002; 22:  51- 8.
27. Castelli WP. Cardiovascular diseases in women. Am J Obstet Gynecol 1988; 158: 1553- 60.
28. Miller GJ, Miller NE. Plasma high density lipoprotein concentration and development of ischaemic heart disease. Lancet 1975; 1:16- 9.
29. Freedman DS, Burke GL, Harsha DW, Srinivasan SR, Cresanta JL, Webber LS, Berenson GS. Relationship of changes in obesity to serum lipids and lipoproteins changes in childhood and adolescence.  JAMA 1985; 254: 515-20.
30. Imamura T, Kano H, Shin K, Konjiki O, Ohsawa Y et al. Relationship between abdominal fat distribution assessed by computed tomography and serum lipids in elderly. Nippon Ronen Igakkai Zasshi 1993; 30: 123- 9.

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