Revistas Fase R003 Id Materia 4032

Artigo Original
Body fat topography as a predictor of an increase in blood lipids
Topografia da gordura corporal como preditor do aumento de lipídios sanguíneos

William Alves Lima
Doutorando no Programa de Pós-Graduação Strictu Sensu em Educação Física da Universidade Católica de Brasília (UCB/DF).
Maria Fátima Glaner
Professora adjunta no Programa de Pós-Graduação Strictu Sensu em Educação Física da Universidade Católica de Brasília (UCB/DF).
Address for correspondence: Maria Fátima Glaner – Quadra 201, Lote 6, Bloco B – apt. 803 – Águas Claras – ZIP Code 71937-540 – Brasília – DF – Brazil – E-mail: [email protected] / [email protected] – Tel.: 55 (61) 3435 3464 and 55 (61) 9623-9996.

Recebido para publicação em 03/2008. Aceito em 10/2008.

Copyright Moreira Jr. Editora. Todos os direitos reservados.

Unitermos: body fat distribution, coronary disease, triglycerides, cholesterol, lipoproteins LDL and HDL.
Unterms: distribuição da gordura corporal, coronariopatia, triglicerídeos, colesterol, lipoproteínas LDL e HDL.

Numeração de páginas na revista impressa: 3 à 9


Excess body fat is related to metabolic disorders such as an increase in lipids and circulating glucose, favoring the development of cardiovascular diseases. The objective of the present study was to determine whether body fat topography and relative body fat (%BF) predict plasma concentrations of glucose, total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL) and low-density lipoprotein (LDL) and the atherogenic index (AI) in adult men. For this, fasting blood metabolites were measured in 79 men. The AI was calculated using three different formulas. %BF and fat topography (leg, trunk and arm %BF) were estimated by dual-energy X-ray absorptiometry. Stepwise multiple linear regression was used for statistical analysis (p £ .05). The results showed that glucose concentration was not predicted by total or regional %BF. Trunk %BF alone explained 19.6% of TC concentrations, 16.3% of HDL, 16.0% of LDL and 13.4% of TG. TG was better explained by the inverse relationship between trunk %BF and leg %BF (22.7%). The same was observed for HDL (21.8%). %BF trunk accounted for 25.7% of the variation in AI1 and the inverse relationship between trunk %BF and leg %BF accounted for 30.3% of this variation. Trunk %BF accounted for 26.4% of the variation in AI2 and for 25.0% in AI3. In conclusion, trunk %BF was the best predictor of TC, TG, HDL, LDL and AI. Glucose concentration was not predicted by the total or localized accumulation of body fat.


Excessive accumulation of body fat and body fat topography have been associated with a series of risk factors that lead to the development of chronic noncommunicable diseases such as cardiovascular diseases, hypertension, and diabetes mellitus, among others(1-3). The development of these diseases is characterized by an increase in the blood concentrations of total cholesterol (TC), low-density lipoprotein (LDL), triglycerides (TG) and fasting glucose, and a concomitant reduction of high-density lipoprotein (HDL), thus increasing the chances of the formation of fatty plaques in the blood vessel wall(4-7). Furthermore, some investigators have suggested that the endogenous concentrations of these blood lipid metabolites (TC, LDL, TG and HDL) vary according to body fat topography which, together with physical inactivity, can lead to diseases such as atherosclerosis and the formation of gallstones(8-10).

Alterations in the lipid profile are asymptomatic and may remain asymptomatic for long periods of time however, they may have negative consequences such as high blood pressure, infarcts, and strokes, among others(6,10,11).

A better understanding of the association between percent fat, body fat topography and risk factors of cardiovascular diseases is necessary to precisely define these associations in each specific population(7,13-14).
The analysis of body fat distribution is important because the quantity of intra-abdominal fat is a much more important factor than obesity itself since it is correlated with the atherogenic profile(2). Another approach to identify risk situations based on the concentrations of cholesterol and lipoproteins is to determine the proportion between these metabolites, which is called the atherogenic index (AI). The higher the proportion between “bad” cholesterol (LDL) and “good” cholesterol (HDL), the higher the chances of establishing an atherosclerotic disease(15,16).

In view of the above considerations, the objective of the present study was to determine whether body fat topography and relative body fat (%BF) predict the plasma concentrations of fasting glucose, TC, TG, HDL and LDL and the AI in men older than 25 years.



The present sample consisted of employees of a metallurgy company in the Federal District, Brazil, comprising the headquarters and two branch offices. A total of 168 male employees were ed but, after application of the exclusion criteria, only 79 employees remained, who comprised the present sample. These subjects formed a heterogeneous group in terms of age which ranged from 25 to 45 years. Both employees directly involved in the manufacturing, transport and sale of the metal and employees performing administrative activities were invited to participate in the study. All volunteers who agreed to participate signed a free informed consent form containing detailed information regarding the procedures for data collection and guaranteeing complete anonymity as required by the ethical norms of resolution 196/October, 1996 (National Health Council). The study was approved by the Ethics Committee of the Catholic University of Brasília.

Exclusion criteria were a TG concentration ³ 400 mg/dL which does not permit the use of the Friedewald formula for the estimation of LDL, lack of compliance with the preestablished fasting period of 12 to 14 h, use of any pharmacological medication for the control of dyslipidemia or glycemia, use of hormone ment therapy or hormone supplements, and presence of liver or thyroid diseases that may cause secondary dyslipidemias.

Instruments and procedures
Body weight and height were measured as described by Gordon et al. (17). Body fat topography and %BF were estimated by dual-energy X-ray absorptiometry (DEXA) according to manufacturer instructions (Lunar, model DPX-IQ, software version 4.7e). Body weight and height measurements and DEXA were always performed by the same investigator in order to standardize the data collection procedures. Body fat topography and %BF were analyzed by a single technician with three years of experience in this area.

The DEXA apparatus was subjected to two calibration procedures as recommended by the manufacturer, one of them once a week and the other once a day. Section lines were traced for separation of the body regions (trunk, arms and legs), one on the glenohumeral joint at the point that comprises the junction between the humerus and acromion for the estimation of arm %BF, and one from the lateral portion of the iliac crest, perpendicularly dividing the femoral cervix and thus separating the lower limbs for the estimation of leg %BF. Excluding the limbs and head, the remaining region thus comprised the trunk for the estimation of trunk %BF.

Blood was collected from the volunteers after a 12-h fast at the infirmary of the company on a predetermined day. The participants were also asked not to consume any alcoholic beverages on the day preceding blood collection or caffeine during the last meal. The interval between blood collection and body fat measurement by DEXA did not exceed 5 days.

Venous blood samples (6 mL) were collected from one of the vessels in the antecubital fossa of the volunteer, with 4 mL being collected into a vacuum tube containing EDTA for the determination of TC, HDL and TG, and approximately 2 mL into a vacuum tube containing fluoride for the measurement of fasting glycemia. After each individual collection, the tubes with the blood samples were stored in a metal support at room temperature (about 25oC) in the dark, and then transported to the laboratory for immediate analysis. The maximum time interval between collection and arrival at the laboratory for centrifugation was 120 min. About 25 blood samples were collected daily always between 6:30 and 7:30 am.

All blood analyses were performed in duplicate on the same day of collection by the same examiner. Glucose, TG, TC and HDL were measured by serological exams using Doles commercial kits according to manufacturer instructions. LDL was calculated based on TG, TC and HDL using the formula of Friedewald(18):
LDL = TC – (HDL + TG : 5)

Note: %BF = relative body fat DEXA = dual-energy X-ray absorptiometry s = standard deviation CV = coefficient of variation.

The following three formulas were used to calculate the AI: AI1) division of TC concentration by HDL concentration(12,19) AI2) subtraction of HDL from TC(20,21) AI3) division of LDL by HDL(22-24).

A semi-automatic spectrophotometer (Bioplusâ, model BIO-2000) was used for biochemical analysis of the blood samples. All quality control procedures recommended by the National Sanitary Surveillance Agency, Brazil, were followed.

Statistical analysis
Stepwise multiple linear regression was applied to determine whether body fat topography and %BF (independent variables) separately explained the TC, HDL, LDL, TG, glucose and AI results (dependent variables). A level of significance of p £ .05 was adopted. Statistical analysis was performed using the SPSS software release 12.0, licensed for use by the Catholic University of Brasília.

The characteristics of the sample are shown in Table 1, with the respective mean, standard deviation and range of each variable studied. As can be seen, the volunteers had a mean %BF slightly above that accepted by the OPS/WHO(25), which is up to 19% for men below the age of 50. The blood metabolites analyzed were on average within borderline reference values. The same was observed for mean AI (AI2 and AI3), except for AI1 which was on average above the reference values, indicating a low proportion of HDL in relation to TC, a fact favoring the development of some cardiovascular diseases.

The mean blood parameters did not indicate health risks. However, analysis of the standard deviation and range revealed the presence of subjects in the sample who were at a higher risk of developing cardiovascular diseases, especially because of the elevated lipid profile and the consequent possible formation of atheroma plaques (Table 1).

The coefficients of variation demonstrated a relative homogeneity of the sample in terms of age, body weight, height and glucose. However, the sample presented marked heterogeneity in terms of body fat deposits and most lipid variables, except for TC.

Table 2 shows the stepwise multiple linear regression parameters used to predict the extent to which the different fat deposits contribute to an increase in the fasting blood concentrations of glucose, TC, HDL, LDL and TG.

Regression analysis showed that glucose concentration was not predicted by %BF and therefore did not enter the regression model. The other blood variables analyzed were significantly explained by body fat topography, especially trunk %BF. As can be seen in Table 3, fasting HDL and TG were better explained when the subjects presented a concomitant increase in trunk %BF and lower stores of leg %BF (typical android profile).

Note: TC = total cholesterol HDL = high-density lipoprotein LDL = low-density lipoprotein TG = triglycerides R = multiple correlation coefficient R² = coefficient of determination p = probability.

Note: AI = atherogenic index R = multiple correlation coefficient R² = coefficient of determination p = probability.

The stepwise multiple linear regressions indicating to what extent body fat topography contributes to the AI. Combined trunk and leg %BF better accounted for AI1 (30.3%) than trunk %BF alone accounted for AI1 (25.7%) (Table 3). However, leg %BF alone contributed little to blood TC, HDL and LDL concentrations (Table 2), suggesting that individuals with higher amounts of fat accumulated in the trunk are more predisposed to increases in circulating LDL and very low-density lipoprotein (VLDL) concentrations, with a consequent reduction of HDL, events characterizing a high AI1. Table 3 shows.

With respect to AI2, 26.4% of the variations in this risk factor were accounted for by trunk %BF, with trunk %BF thus being the best predictor of this index (Table 3). Since the AI2 comprises all circulating atherogenic cholesterol, this result indicates that the larger the amounts of fat accumulated in the trunk, the higher the levels of circulating cholesterol and, consequently, the greater the chances of cholesterol deposition in the vessel walls and of the formation of atheroma plaques. These results emphasize the importance of the control of body fat, mainly of fat accumulation in the trunk, due to its great influence on the concentrations of “bad” cholesterol, with a consequent increase in the AI.

As also shown in Table 3, trunk %BF (25.0%) better explained the variation in AI3. Thus, an increase of fat stores localized in the trunk may contribute more to the increase of this atherogenic index (AI3), which indicates how much more LDL is present compared to HDL, without considering VLDL.


In the present study, we investigated the effects of body fat topography and %BF on plasma concentrations of fasting glucose, TC, TG, HDL and LDL and the AI in men aged 25 to 45 years. The objective was to determine to what extent body fat predicts glucose and blood lipid concentrations and, therefore, to evaluate the risk of developing cardiovascular diseases.

The heterogeneity of the sample in terms of the risk factors analyzed is demonstrated in Table 1 by the coefficient of variation. This finding indicates the need for studies involving larger samples so that these results can be extrapolated to the general population.

Although several investigators(5,12,19,23,26) have found high positive correlations between fat accumulated in the trunk and glucose, the same was not observed in the present sample. This difference might be due to the fact that some of the cited studies have used other techniques for the quantification of %BF such as computed tomography or magnetic resonance imaging (measurement of fat located between the first and fourth lumbar vertebrae corresponding to trunk fat, in addition to leg fat), or even quantified %BF based on the waist-to-hip ratio. This fact limits the comparison of the present results with those reported in the other studies due to possible variations in the estimates.

Some volunteers presented manifestations of hypercholesterolemia or hypertriglyceridemia, with elevated concentrations of these variables being observed even during fasting (Table 1). However, the objective of this study was to investigate a sample that is truly heterogenous in terms of these parameters in order to determine the influence of body fat.

Among the different fat stores, %BF in the trunk is the best indicator that an individual is at risk to develop hypercholesterolemia (19.6%), as shown in Table 2. This chance is even higher in the case of visceral fat stores(27,28). Thus, excessive fat stores, especially in the trunk, increase the chances of elevation in blood LDL concentrations, with a consequent increased risk for the formation of atheroma plaques and development of cardiovascular disease.
Trunk and leg %BF together best explained TG concentrations (22.7%), with leg %BF acting in an inversely proportional manner compared to trunk %BF alone (13.4%). However, although TG concentrations could be explained by the different body fat topography regions, trunk fat showed the highest correlation (Table 3).

The fact that trunk %BF best accounts for circulating TG concentrations also agrees with the results reported by other investigators(6,10-12,26,29,30). This finding demonstrates that excess fat in the trunk region is also associated with a higher chance of hypertriglyceridemia, which also increases the risk of cardiovascular diseases, especially when the higher TG concentration is related to low HDL and high LDL concentrations. Thus, among the different body fat stores, trunk %BF best accounts for the variations in plasma TC, HDL, LDL and TG concentrations. However, trunk %BF should not be regarded as an absolute contributor to the increase of these risk factors because the magnitude of its contribution, although significant, was small. Therefore, this parameter cannot be used to safely predict the concentrations of these metabolites.

However, trunk %BF continues to be a good indicator of possible alterations in the concentrations of these metabolites and, consequently, of the development of cardiovascular diseases. Several investigators have emphasized that these variables might be better indicators of possible cardiovascular diseases when combined with other factors such as lifestyle and level of physical activity, which also markedly predict the concentrations of these metabolites(31-33).

%BF did not enter any of the regression models, a finding indicating that this variable as a whole did not explain the concentrations of the metabolites analyzed, probably because it involves both more and less expressive body fat deposits. This finding emphasizes the importance of the evaluation of body fat topography as a possible indicator for the development of cardiovascular diseases.

Even individuals with TC and lipoprotein concentrations within the reference range might be subject to the formation of atheroma plaques. This occurs because HDL levels are in their lower limit and are unable to carry all excess cholesterol to the liver for metabolization. Since this compound cannot accumulate in blood, LDL concentrations increase to transport this cholesterol, which is then deposited in all other tissues. As a result, an individual may present TC levels within normal limits but a much higher proportion of LDL and VLDL in relation to HDL, thus indicating a high chance of deposition of this cholesterol in the vessel wall. This proportional difference between HDL, LDL and, sometimes, VLDL is called AI. The AI is thus calculated to identify the proportion between these metabolic variables in order to determine the risk of installation of cardiovascular disease.

It is important to point out that %BF did not enter any of the regression models for either of the three calculated AI, possibly because this parameter comprises all three body fat topography regions where fat is stored. Since these regions explain each metabolic variable differently, %BF as a whole is a weak predictor of these variables. We found no studies using the three formulas for the calculation of the AI or correlating these AI with body fat topography.

Trunk %BF was the best predictor of the AI calculated using the three different formulas, i.e., the higher the accumulation of %BF in the trunk, the greater the chances of an increase in “bad” cholesterol and a decrease in “good” cholesterol, with a concomitant increased risk of cardiovascular diseases. Since men tend to show an android body fat topography, with larger amounts of fat stored in the central region of the body, they are more predisposed to elevations in blood metabolite concentrations as well as in the AI as trunk %BF increases, thus increasing the risk of cardiovascular diseases.

Although the present volunteers are of different ethnic origin and have distinct lifestyles and dietary and social habits compared to populations investigated in other studies(8,12,29,30,34), our results agree with those reported in these studies in which trunk fat best explained the variations in the concentrations of circulating lipid metabolites. This indicates that, irrespective of factors such as ethnic origin, lifestyle, dietary habits and social practices, trunk fat is a good predictor of the risks associated with the development of cardiovascular diseases such as the formation of atheroma plaques, heart diseases and strokes, among others.


The results of the present study lend some support to the idea that trunk fat is a better predictor of risk factors associated with the development of cardiovascular diseases, such as increases in TC, HDL, LDL and TG concentrations and in the proportion of the AI, than arm and leg fat. Glucose concentration was not predicted by the total or localized accumulation of body fat. It is therefore important to prevent excessive accumulation of body fat, especially in the trunk, which otherwise may lead to chronic metabolic disorders in the individual such as increases in circulating TC, TG and LDL concentrations, diabetes, and the formation of atheroma plaques.

Further studies with a similar design but involving distinct samples in terms of lifestyle, men and women from adolescence to third age or different ethnic groups, and even employing different methods for the estimation of body fat and body fat topography are necessary. In addition, investigations determining the influence of these fat deposits on other risk factors such as markers of cardiac or hepatic injury should also be conducted.

1. Bouchard, C., Bray, G. A., & Hubbard, V. S. (1990). Basic and clinical aspects of regional fat distribution. American Journal of Clinical Nutrition, 52, 946-950.
2. Dresprés, J. P., Moorjani, S., Lupien, P. J., Tremblay, A., Nadeau, A., & Bouchard, C. (1990). Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis, 10(4), 497-511.
3. Lazarus, R., Gore, C. J., Booth, M., & Owen, N. Effects of body composition and fat distribution on ventilatory function in adults. (1998). American Journal of Clinical Nutrition, 68, 35-41.
4. Drewnowski, A., & Specter, S. E. (2004). Poverty and obesity: the role of energy density and energy costs. American Journal of Clinical Nutrition, 79, 6-16.
5. Goldstone A. P, Thomas, E. L., Brynes, A. E., Bell. J. D., Frost, G., Saeed, N., et al. (2001). Visceral adipose tissue and metabolic complications of obesity are reduced in Prader-Willi syndrome female adults: evidence for novel influences on body fat distribution. The Journal of Clinical Endocrinology & Metabolism, 86(9), 4330-4338.
6. Hu, D., Hannah, J., Gray, R. S., Jablonski, K. A., Henderson, J. A., Robbins, D. C., et al. (2000). Effects of obesity and body fat distribution on lipids and lipoproteins in nondiabetic American Indians: the strong heart study. Obesity Research, 8(6), 411-421.
7. Nicklas, J. B., Penninx, B. W. J. H., Cesari, M., Kritchevisky, S. B., Newman, A. B., Kanaya, A. M., et al. (2004). Association of visceral adipose tissue with incident myocardial infarction in older men and women. American Journal of Epidemiology, 160(8), 741-749.
8. Eyben, F. E., Mouritsen, J., H., & Montvilas, P. (2003). Intra-abdominal obesity and metabolic risk factors: a study of young adults. International Journal of Obesity, 27, 941-949.
9. Whitney, K. D., Watson, M. A., Collins, J. L., Benson, W. G., Stone, T. M., Numerick, M. J., et al. (2002). Regulation of cholesterol homeostasis by the liver X receptors in the central nervous system. Molecular Endocrinology, 16(6), 1378-1385.
10. Wu, C. H., Yao, W. J., Lu, F. H., Yang, Y. C., Wu, J. S., & Chang, C. J. (2001). Sex differences of body fat distribution and cardiovascular dysmetabolic factors in old age. Age and Ageing, 30, 331-336.
11. Bacha, F., Saad, R., Gungor, N., Janosky, J., & Arslanian, S. A. (2004). Obesity, regional fat distribution, and syndrome X in obese black versus white adolescents: race differential in diabetogenic and atherogenic risk factors. Journal of Clinical Endocrinology and Metabolism, 88(6), 2534-2540.
12. Goh, V. H. H., Tain, C. F., Tong. T. Y. Y., Mok, H. P. P., & Wong, M. T. (2004). Are BMI and other anthropometric measures appropriate as indices for obesity? A study in an Asian population. Journal of Lipid Research, 45, 1892-1898.
13. Stranges, S., Trevisan. M., Dorn, J. M., Dmochowski, J., & Donahue, R .P. (2005). Body Fat Distribution, Liver Enzymes, and Risk of Hypertension: Evidence From the Western New York Study. Hypertension, 46, 1186 – 1193.
14. Teixeira, P. J., Sardinha, L. B., Going, S. B., & Lohman, T. G. (2001). Total and regional fat and serum cardiovascular disease risk factors in lean and obese children and adolescents. Obesity Research, 9(8), 432-442.
15. Levine, J. A., Abboud, L., Barry, M., Reed, J. E., Sheedy, P. F., & Jensen, M. D. (2000). Measuring leg muscle and fat mass in humans comparison of CT and dual-energy X-ray absorptiometry. Journal Applied of Physiology, 88, 452-456.
16. Perón, J. M. R., González, S. R. M., Cabrera, E. B. A., Salido, J. A. P., Rosa, R. F., Sandoval, M. H., & Álvares, J. L. A. (2002). Utilización de un índice pronóstico de morbilidad y mortalidad por enfermedad cardiovascular asociada con factores de riesgo aterogênico. Revista Cubana de Medicina Militar, 31(2): 87-93.
17. Gordon, C. C., Chumlea, W. C., & Roche, A. F. Stature, recumbent length, and weight. In: Lohman, T. G., Roche, A. F., Martorell, R. (1991). (Eds.). Anthropometric standardization reference manual (pp 3-8). Abridged Edition. Champaign: Human Kinetics Books.
18. Friedewald, W. T., Levy, R. I. & Fredrickson, D. S. (1972). Estimation of the concentration of low-density lipoprotein cholesterol in plasma without use of preparative ultracentrifuge. Clinical Chemistry, 18(6), 499-502.
19. Chang, C., Wu, C., Lu, F., Wu, J., Chiu, N., & Yao, W. (1999). Discriminating glucose tolerance status by regions of interest of dual-energy X-ray absorptiometry. Diabetes Care, 22(12), 1938-1943.
20. Lu, W., Resnick, H. E., Jablonski, J. A. Jones, K. L., Jain, A. K., Howard, W. J., et al. (2003). Non-HDL cholesterol as a predictor of cardiovascular disease in type-2 diabetes. Diabetes Care, 26(1), 16-23.
21. Tuomilehto, J., Marti, B., Kartovaara, L., Korhonen, H. J., & Pietnen, P. (1990). Body fat distribution, serum lipoproteins and blood pressure in middle-aged Finnish men and women. Revisté Epidémiologie et Santé Public, 38, 507-515.
22. Bouchard, C., Dresprés, J. P., & Mauriège, P. (1993). Genetic and nongenetic determinants of regional fat distribution. Endocrine Reviews, 14(1):72-92.
23. Ferland, M., Después, J. P., Nadeau, A., Moorjani, S., Tremblay, A., Lupien, P. J., et al. (1991). Contribution of glucose tolerance and plasma insulin levels to the relationships between body fat distribution and plasma lipoprotein levels in women. International Journal of Obesity, 15, 677-688.
24. Svendsen, O. L., Hassager, C., & Christiansen, C. (1993). Relationships and independence of body composition, sex hormones, fat distribution and other cardiovascular risk factors in overweight postmenopausal women. International Journal of Obesity, 17, 459-463.
25. OPS/WHO – Organização Pan-americana de Saúde, & World Health Organization. (2004). Doenças crônico degenerativas e obesidade: estratégia mundial sobre alimentação saudável, atividade física e saúde. Brasília: Ed. Ministerio da Saúde.
26. Caprio, S., Hyman, L. D., Mccarthy, S., Lange, R., Bronson, M., & Tamborlane, W. V. (1996). Fat distribution and cardiovascular risk factors in obese adolescent girls: importance of the intra abdominal fat depot. American Journal of Clinical Nutrition, 64, 12-17.
27. Mauriège, P., Imbeault, P., Prud‘Homme, D., Tremblay, A., Nadeau, A., & Després, J. P. (2000). Subcutaneous adipose tissue metabolism at menopause: importance of body fatness and regional fat distribution. Journal of Clinical Endocrinology & Metabolism, 85(7), 2446-2454.
28. Tatsukawa, M., Kurokawa, M., Tamari, Y., Yoshimatsu, H., & Sakata, T. (2000). Regional fat deposition in the legs is useful as a presumptive marker of antiatherogenesity in Japanese. Proceedings of the Society for Experimental Biology and Medicine, 223, 156-162.
29. Anderson, A. J., Sobocinski, K. A., Freedman , D. S., Barboriak, J. J., Rimm, A. A., & Gruchow, W. H. (1988). Body fat distribution, plasma lipids and lipoproteins. Arteriosclerosis, 8(1), 88-94.
30. Dresprés, J. P., Moorjani, S., Ferland, M., Tremblay, A., Lupien, P. J., Nadeau, A., et al. (1989). Adipose tissue distribution and plasma lipoprotein levels in obese women: importance of intra-abdominal fat. Arteriosclerosis, 9(2), 203-210.
31. Church, T. S., Cheng, Y. J., Earnest, C. P., Barlow, C. E., Gibbons, L. W., Priest, E. L., & Blair, S. N. (2004). Exercise capacity and body composition as predictors of mortality among men with diabetes. Diabetes Care, 27(1), 83-88.
32. Hunter, G. R., Bryan, D. R., Wetzstein, C. J., Zuckerman, P. A., & Bamman, M. M. (2002). Resistance training and intra-abdominal adipose tissue in older men and women. Medicine and Science in Sports and Exercise, 34(6), 1023-1028.
33. Martins, I. S., Coelho, L. T., Casajus, M. I., & Okani, E. T. (1995). Smoking, consumption of alcohol and sedentary life style in population grouping and their relationships with lipemic disorders. Revista de Saúde Pública, 29(1), 38-45.
34. Price, T. M., O’Brien, S. N., Welter, B. H., George, R., Anandjiwala, J., & Kilgore, M. (1998). Estrogen regulation of adipose tissue lipoprotein lipase – possible mechanism of body fat distribution. American Journal of Obstetrics and Gynecology, 178(1), 101-107.