Impact of ethnicity on body fat patterning in Asian Indians and blacks: relation with insulin resistance

Nutrition Volume 19, Number 9, 2003

REFERENCES 1. Druml W. Protein metabolism in acute renal failure. Miner Electrolyte Metab 1998;24:47 2. Monson P, Mehta RL. Nutrition in acute renal failure: a reappraisal for the 1990s. J Ren Nutr 1994;4:58 3. Klein CJ, Moser-Veillon PB, Schweitzer A, et al. Magnesium, calcium, zinc, and nitrogen loss in trauma patients during continuous renal replacement therapy. JPEN 2002;26:77 4. Fiaccadori E, Lombardi M, Leonardi S, et al. Prevalence of clinical outcome associated with preexisting malnutrition in acute renal failure: a prospective cohort study. J Am Soc Nephrol 1999;10:581 5. Klein CJ, Gettings LG, Reynolds HN. Nitrogen balance is not a requirement for survival in adults receiving continuous renal replacement therapy after traumatic injury (abstract). Blood Purif 1999;17:21. 6. K/DOQI. Clinical practice guidelines for nutrition in chronic renal failure. Am J Kidney Dis 2000;35(suppl 2):S51 7. Frankenfield DC, Reynolds HN. Nutritional effect of continuous haemodiafiltration. Nutrition 1995;11:388 8. Druml W. Metabolic aspects of continuous renal replacement therapies. Kidney Int 1999;56(suppl 72):S56 9. Macias WL, Alaka KJ, Murphy MH, et al. Impact of the nutritional regimen on protein catabolism and nitrogen balance in patients with acute renal failure. JPEN 1996;20:56 10. Bellomo R, Tan HK, Bhonagiri S, Opal I, Daskalakis BN. High protein intake during continuous hemodiafiltration: impact on amino acids and nitrogen balance. Int J Artif Organs 2002;25:261 11. Bellomo R, Seacombe J, Daskalakis M, et al. A prospective comparative study of moderate versus high protein intake for critically ill patients with acute renal failure. Ren Fail 1997;19:111 12. Scheinkestel CD, Adams F, Mahony L, et al. Impact of increasing parenteral protein loads on amino-acid levels and balance in critically-ill anuric patients on continuous renal replacement therapy (CRRT). Nutrition 2003;19:733 13. Rennie MJ, Bohe J, Wolfe RR. Latency, duration and dose response relationships of amino acid effects on human muscle protein synthesis. J Nutr 2002;132:3225S 14. Sigler MH, Teehan BP. Solute transport in continuous hemodialysis: a new treatment for acute renal failure. Kidney Int 1987;32:562 15. Crook MA, Hally V, Panteli JV. The importance of the refeeding syndrome. Nutrition 2001;17:632 16. Druml W. Nutritional support in acute renal failure. In: Mitch WE, Klahr S, eds. Nutrition and the kidney, 3rd ed. London: Little, Brown & Co, 1998:213 17. Brunori G. Nutrition support in renal disease. In: Payne-James J, Grimble G, Silk D, eds. Artificial nutrition support in clinical practice, 2nd ed. London: GMM, 2001:523 18. Schwartz IF, Schwartz D, Traskonov M, et al. L-arginine transport is augmented through up-regulation of tubular CAT-2 mRNA in ischemic acute renal failure in rats. Kidney Int 2002;62:1700 19. Efron DT, Barbul A. Arginine and nutrition in renal disease. J Ren Nutr 1999:142 20. Jerkic´ M, Varagic´ J, Jovovic´ D, et al. L-arginine reduces tubular cell injury in acute post-ischaemic renal failure. Nephron Dial Transpl 1999;14:1398

doi:10.1016/S0899-9007(03)00139-4

Impact of Ethnicity on Body Fat Patterning in Asian Indians and Blacks: Relation With Insulin Resistance Body fat patterning and its relations to metabolic disorders and cardiovascular risk are extremely important issues, given the high and rapidly increasing prevalence rates of obesity and diabetes mellitus globally. Although body fat patterning has been loosely applied to “android” and “gynoid” obesity phenotypes and abdom-

This editorial was supported by the Department of Science and Technology, Ministry of Science and Technology, Government of India, New Delhi. Correspondence to: Anoop Misra, MD, Department of Internal Medicine, All India Institute of Medical Sciences, New Delhi 110 029, India. E-mail: [email protected]

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inal obesity, many of these also may be influenced by body composition factors other than body fat. For example, skeletal shape (due to sex) and abdominal contour (due to fat-free mass) may contribute to “body fat patterning.” Body fat patterning has been defined as the “relative distribution of subcutaneous fat in relation to total body fat”1 as opposed to regional adiposity (e.g., abdominal obesity that may be due to a combination of excess of subcutaneous and intra-abdominal fat). More objectivity can be given to this term if the subcutaneous fat is measured with skinfold calipers, ultrasound, computed tomography, or magnetic resonance imaging. In line with arguments provided by Stewart,2 I and my colleagues support skinfold thickness measurement as a simple and inexpensive tool to determine body fat patterning. Measurement of subcutaneous fat with skinfold calipers has shown good correlations with that measured with computed tomography or magnetic resonance imaging.3,4 Although measurements of individual skinfolds such as subscapular and triceps are useful, ratios of individual skinfolds (subscapular to triceps) and the sum of skinfolds (central and peripheral) may provide additional valuable information of body fat patterning. Measurement of multiple skinfolds, at 8 to 14 body sites, may elicit more information of fat patterning and help diagnose certain disorders that cause generalized or partial fat loss, such as generalized and partial lipodystrophies. The sum of multiple skinfolds can be used for the calculation of total body fat by using available generalized prediction equations. Caveats in the measurement of skinfold thickness include interobserver variations and difficulty in assessing skinfold thickness with calipers in morbidly obese people. Further, it is difficult to measure skinfold thickness over certain areas of the body and in certain pathologic conditions: the cheeks in facial lipodystrophy and over the area of excess dorsocervical fat, also called the “buffalo hump,” which is seen in Cushing’s syndrome, and lipodystrophy related to highly active antiretroviral therapy in patients positive with the human immunodeficiency virus. Stewart has argued most of the important points succinctly2; however, the relation of body fat patterning with one of its important determinants, ethnicity, needs discussion. Body fat patterning in black and white subjects has been elegantly reviewed by Wagner and Heyward5; however, a few issues are emphasized here. Most of the data show that blacks have less subcutaneous fat in the extremities and more subcutaneous fat over the truncal region as compared with whites, as shown in boys,6 men,7 and women.7 Further, blacks have more subcutaneous truncal fat posteriorly and posterolaterally, whereas whites have more subcutaneous truncal fat anteriorly.8,9 Importantly, these ethnic differences in the fat patterning persist in highly trained athletes, as recorded by Malina et al.10 in the athletes participating in 1976 Montreal Olympics. Based on their data, Malina et al. argued that sports and training primarily affect the percentage of body fat, whereas biological factors affect fat patterning. Whether such body fat patterning affects cardiovascular risk factors such as the high prevalence of hypertension in blacks needs more investigation. Although less investigated, body fat patterning may be more critical to Asian Indians who have high prevalence rates of coronary heart disease, insulin resistance syndrome, and type 2 diabetes mellitus11–13 and are predicted to have a sharp rise in the prevalence of type 2 diabetes mellitus over the next two decades.12 Importantly, Asian Indians have a rather distinctive body composition: excess body fat, abdominal adiposity, and less lean mass at the range of body mass index considered as “normal.”14 –16 Further, excess relative risks of diabetes mellitus and dyslipidemia in Asian Indians have been shown to occur at a “normal” body mass index and waist circumference.17 The difference in body fat patterning has been clearly shown for migrant Asian Indians as compared with whites.18,19 Interestingly, Asian Indian men with body fat content similar to that of white men had higher truncal skinfolds (117 ⫾ 37 mm versus 92.4 ⫾ 38 mm, respectively; P ⫽ 0.03) and truncal-to-peripheral skinfold ratios (2.83 ⫾ 0.66 versus 2.22 ⫾ 0.52, respectively; P ⫽ 0.002).19 Asian Indians in this

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study also were significantly more insulin resistant than were whites despite having body mass indices in the upper normal or slightly overweight range. Similarly, the central-to-peripheral skinfold ratio was significantly higher in Asian Indian and Pakistani premenopausal women than in white women (1.35 ⫾ 0.26 versus 1.12 ⫾ 0.33, respectively; P ⫽ 0.0003).18 Greater abdominal subcutaneous fat content of Asian Indians as compared with whites also has been reported using magnetic resonance imaging.20 Subscapular, not triceps, skinfolds in Asian Indian neonates were comparatively preserved despite lower birth weight and were accompanied by higher insulin levels as compared with white neonates.21 These data suggest that preserved truncal subcutaneous fat in Asian Indians is influenced by genetic or biological factors occurring during the intrauterine period. Overall, it appears that the greater truncal fat in Asian Indians may be an important contributor to insulin resistance syndrome and cardiovascular risk, but more data are needed. Blacks and Asian Indians share similar body fat patterning, i.e., thicker truncal skinfolds and thinner peripheral skinfolds as compared with whites. However, the other body composition characteristics of blacks are radically different from those of Asian Indians: higher body mass index, greater lean mass, less total body fat, and less intra-abdominal fat content.5,22 Although much data suggest that intra-abdominal fat is important for the pathogenesis of insulin resistance and dyslipidemia, several investigators have shown that abdominal subcutaneous adipose tissue is a better correlate of insulin sensitivity.23–25 Subcutaneous adipose tissue in a particular region of the body may be more metabolically active. My colleagues and I previously reported that abdominal subcutaneous adipose tissue located posteriorly and posterolaterally particularly influences insulin sensitivity, significantly more so than the anterior subcutaneous abdominal adipose tissue and intra-abdominal adipose tissue. In those with upper body obesity as indicated by a waist-to-hip ratio of greater than 0.85, non-splanchnic upper body free fatty acid delivery was greater, indicating that release of free fatty acid originated from non-splanchnic upper body fat and not from visceral fat.24 These data further emphasize that body fat patterning, i.e., regional excess of subcutaneous adipose tissue, may exert important metabolic effects which may be quantitatively equal to or greater than those due to intra-abdominal fat.25 A possible explanation for these observations is that the greater fat mass is in the truncal subcutaneous region than in the intra-abdominal region. Even in epidemiologic studies, separate relationships of adipose tissue depots have been shown. Subscapular-to-triceps skinfolds and waist-to-hip ratios independently predicted type 2 diabetes mellitus and high-density lipoprotein cholesterol in males and serum triglycerides in females.26,27 It is also likely that many obese individuals, in particular Asian Indians, may have truncal obesity in addition to abdominal obesity. These research studies have relevance for clinical practice. Use of simple anthropometric parameters such as body mass index and waist circumference (or waist-to-hip ratio) would provide an estimate of generalized and abdominal obesity, and the subscapularto-triceps ratio would provide an estimate of truncal obesity. These anthropometric parameters should be the minimum necessary to assess the metabolic and cardiovascular risks for all and in particular for Asian Indians and blacks. For the developing countries, the use of these simple anthropometric parameters would be cost effective and could be carried out in remote rural areas.

Anoop Misra, MD Department of Internal Medicine All India Institute of Medical Sciences New Delhi, India

Nutrition Volume 19, Number 9, 2003

REFERENCES 1. Malina RM. Physical anthropology. In: Lohman TG, Roche AF, Martorell R, eds. Anthropometric standardization reference manual. Champaign, IL: Human Kinetics, 1988:99 2. Stewart A. Fat patterning: indicators and implications. Nutrition 2003;19:559 3. Orphanidou C, McCargar L, Birmingham CL, Mathieson J, Goldner E. Accuracy of subcutaneous fat measurement: comparison of skinfold calipers, ultrasound, and computed tomography. J Am Diet Assoc 1994;94:855 4. Hayes PA, Sowood PJ, Belyavin A, Cohen JB, Smith FW. Sub-cutaneous fat thickness measured by magnetic resonance imaging, ultrasound, and calipers. Med Sci Sports Exerc 1988;20:303 5. Wagner DR, Heyward VH. Measures of body composition in blacks and whites: a comparative review. Am J Clin Nutr 2000;71:1392 6. Harsha DW, Frerichs RR, Berenson GS. Densitometry and anthropometry of black and white children. Hum Biol 1978;50:261 7. Zillikens MC, Conway JM. Anthropometry in blacks: applicability of generalized skinfold equations and differences in fat patterning between blacks and whites. Am J Clin Nutr 1990;52:45 8. Harsha DW, Voors AW, Berenson GS. Racial differences in subcutaneous fat patterns in children aged 7–15 years. Am J Phys Anthropol 1980;53:333 9. Robson JR, Bazin M, Soderstrom R. Ethnic differences in skin-fold thickness. Am J Clin Nutr 1971;24:864 10. Malina RM, Mueller WH, Bouchard C, Shoup RF, Lariviere G. Fatness and fat patterning among athletes at the Montreal Olympic Games, 1976. Med Sci Sports Exerc 1982;14:445 11. Reddy KS, Yusuf S. Emerging epidemic of cardiovascular disease in developing countries. Circulation 1998;97:596 12. King H, Aubert RE, Herman WH. Global burden of diabetes, 1995–2025: prevalence, numerical estimates, and projections. Diabetes Care 1998;21:1414 13. Misra A, Vikram NK. Insulin resistance syndrome (metabolic syndrome) and Asian Indians. Curr Sci 2002;83:1483 (available at http://tejas.serc.iisc.ernet.in/ ~currsci) 14. Misra A. Body composition and the metabolic syndrome in Asian Indians: a saga of multiple adversities. Natl Med J India 2003;16(1):3 15. Dudeja V, Misra A, Pandey RM, et al. BMI does not accurately predict overweight in Asian Indians in northern India. Br J Nutr 2001;86:105 16. Misra A, Athiko D, Sharma R, Pandey RM, Khanna N. Non-obese hyperlipidemic Asian northern Indian males have adverse anthropometric profile. Nutr Metab Cardiovasc Dis 2002;12:178 17. Vikram NK, Pandey RM, Misra A, et al. ‘Non-obese’ (BMI ⬍25 kg/m2) Asian Indians with ‘normal’ waist circumference have high cardiovascular risk. Nutrition 2003;19:503 18. Kamath SK, Hussain EA, Amin D, et al. Cardiovascular disease risk factors in 2 distinct ethnic groups: Indian and Pakistani compared with American premenopausal women. Am J Clin Nutr 1999;69:621 19. Chandalia M, Abate N, Garg A, Stray-Gundersen J, Grundy SM. Relationship between generalized and upper body obesity to insulin resistance in Asian Indian men. J Clin Endocrinol Metab 1999;84:2329 20. Raji A, Seely EW, Arky RA, Simonson DC. Body fat distribution and insulin resistance in healthy Asian Indians and Caucasians. J Clin Endocrinol Metab 2001;86:5366 21. Yajnik CS, Lubree HG, Rege SS, et al. Adiposity and hyperinsulinemia in Indians are present at birth. J Clin Endocrinol Metab 2002;87:5575 22. Banerji MA, Faridi N, Alturi R, Chaiken RL, Lebovitz HE. Body composition, visceral fat, leptin and insulin resistance in Asian Indian men. J Clin Endocrinol Metab 1999;84:137 23. Misra A, Garg A, Abate N, et al. Relationship of anterior and posterior subcutaneous abdominal fat to insulin sensitivity in nondiabetic men. Obes Res 1997;5:93 24. Guo Z, Hensrud DD, Johnson CM, Jensen MD. Regional postprandial fatty acid metabolism in different obesity phenotypes. Diabetes 1999;48:1586 25. Misra A, Vikram NK. Clinical and pathophysiological consequences of abdominal adiposity and adipose tissue depots. Nutrition 2002;19:457 26. Haffner SM, Stern MP, Hazuda HP, Pugh J, Patterson JK. Do upper-body and centralized adiposity measure different aspects of regional body-fat distribution? Relationship to non-insulin-dependent diabetes mellitus, lipids, and lipoproteins. Diabetes 1987;36:43 27. Seidell JC, Cigolini M, Charzewska J, et al. Fat distribution in European men: a comparison of anthropometric measurements in relation to cardiovascular risk factors. Int J Obes Relat Metab Disord 1992;16:17

doi:10.1016/S0899-9007(03)00141-2