Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Paper
  • Published:

Generalized abdominal visceral fat prediction models for black and white adults aged 17–65 y: the HERITAGE Family Study

International Journal of Obesity volume 28, pages 925–932 (2004)Cite this article

Abstract

OBJECTIVE: To determine if the relationship between abdominal visceral fat (AVF) and measures of adiposity are different between Black and White subjects and to develop valid field prediction models that accurately identify those individuals with AVF levels associated with high risk for chronic disease.

DESIGN: Cross-sectional measurements obtained from 91 Black men, 137 Black women, 227 White men, and 237 White women subjects, ages 17–65 y, who were participants in the HERITAGE Family Study, both at baseline and following 20 weeks of endurance training.

MEASURMENTS: AVF, abdominal subcutaneous fat (ASF), abdominal total fat (ATF), and sagittal diameter (SagD) were measured by computed tomography (CT). Body density was determined by hydrostatic weighing and was used to estimate relative body fat. Arm, waist (WC), and hip circumferences and skinfold thickness measures were taken, and BMI was calculated from weight (kg) and height (m2). Since CT abdominal fat variables were skewed, a natural log transformation (Ln) was used to produce a normal distribution. The General Linear Model (GLM) procedure was used to test the relationship between AVF and two different groups of variables—CT and anthropometric.

RESULTS: The AVF of White men and women was significantly higher than that of Black men and women, independent of BMI, WHR, WC, and age, and was greater for men than for women. The CT model showed that the combination of SagD, Ln (ASF), age, and race accounted for 84 and 75% of the variance in AVF in men and women, respectively. The anthropometric model provided two valid generalized field AVF prediction equations. The Field-I equation, which included BMI, WHR, age and race, had an r2 of 0.78 and 0.73 for men and women, respectively. The Field-II equation, which included BMI (women only), WC, age, and race, had an r2 of 0.78 and 0.72 for men and women, respectively. The field model equations became less accurate as the estimated AVF increased.

CONCLUSIONS: (1) At the same age and level of adiposity, Black men and women have less AVF than White men and women. These differences are greater in men than in women. (2) The field regression equations can be generalized to the diverse group of adults studied, both in an untrained and trained state. However, their accuracy decreases with increasing levels of AVF.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

References

  1. Bouchard C, Bray GA, Hubbard VS . Basic and clinical aspects of regional fat distribution. Am J Clin Nutr 1990; 52: 946–950.

    Article  CAS  Google Scholar 

  2. Grauer WO, Moss AA, Cann CE, Goldberg HI . Quantification of body fat distribution in the abdomen using computed tomography. Am J Clin Nutr 1984; 39: 631–637.

    Article  CAS  Google Scholar 

  3. Kvist H, Chowdhury B, Grangård U, Tylén U, Sjöström L . Total and visceral adipose-tissue volumes derived from measurements with computed tomography in adult men and women: predictive equations. Am J Clin Nutr 1988; 48: 1351–1361.

    Article  CAS  Google Scholar 

  4. Kvist H, Sjöström L, Tylén U . Adipose tissue volume determinations in women by computed tomography: technical consideration. Int J Obes 1986; 10: 53–67.

    CAS  Google Scholar 

  5. Rössner S, Bo WJ, Hiltbrandt E, Hinson W, Karstaedt N, Santago P, Sobol WT, Crouse JR . Adipose tissue determinations in cadavers—a comparison between cross-sectional planimetry and computed tomography. Int J Obes Relat Metab Disord 1990; 14: 893–902.

    Google Scholar 

  6. van der Kooy K, Seidell JC . Techniques for the measurement of visceral fat: a practical guide. Int J Obes Relat Metab Disord 1993; 17: 187–196.

    CAS  Google Scholar 

  7. Ross R, Léger L, Morris D, de Guise J, Guardo R . Quantification of adipose tissue by MRI: relationship with anthropometric variables. J Appl Physiol 1992; 72: 787–795.

    Article  CAS  Google Scholar 

  8. Ross R, Shaw KD, Martel Y, de Guise J, Avruch L . Adipose tissue distribution measured by magnetic resonance imaging in obese women. Am J Clin Nutr 1993; 57: 470–475.

    Article  CAS  Google Scholar 

  9. Seidell JC, Bakker CJG, van der Kooy K . Imaging techniques for measuring adipose-tissue distribution—a comparison between computed tomography and 1.5-T magnetic resonance. Am J Clin Nutr 1990; 51: 953–957.

    Article  CAS  Google Scholar 

  10. Sjöström L, Kvist H, Cederblad A, Tylén U . Determination of total adipose tissue and body fat in women by computed tomography, 40 K, and tritium. Am J Physiol 1986; 250: E736–E745.

    Google Scholar 

  11. Albu JB, Murphy L, Frager DH, Johnson JA, Pi-Sunyer FX . Visceral fat and race-dependent health risks in obese nondiabetic premenopausal women. Diabetes 1997; 46: 456–462.

    Article  CAS  Google Scholar 

  12. Bonora E, Micciolo R, Ghiatas AA, Lancaster JL, Alyassin A, Muggeo M, DeFronzo RA . Is it possible to derive a reliable estimate of human visceral and subcutaneous abdominal adipose tissue from simple anthropometric measurements? Metabolism 1995; 44: 1617–1625.

    Article  CAS  Google Scholar 

  13. Conway JM, Yanovski SZ, Avila NA, Hubbard VS . Visceral adipose tissue differences in black and white women. Am J Clin Nutr 1995; 61: 765–771.

    Article  CAS  Google Scholar 

  14. Conway JM, Chanetsa FF, Wang P . Intraabdominal adipose tissue and anthropometric surrogates in African American women with upper-and lower-body obesity. Am J Clin Nutr 1997; 66: 1345–1351.

    Article  CAS  Google Scholar 

  15. Després JP, Prud'homme D, Pouliot MC, Tremblay A, Bouchard C . Estimation of deep abdominal adipose-tissue accumulation from simple anthropometric measurements in men. Am J Clin Nutr 1991; 54: 471–477.

    Article  Google Scholar 

  16. Ferland M, Després JP, Tremblay A, Pinault S, Nadeau A, Moorjani S, Lupien PJ, Thériault G, Bouchard C . Assessment of adipose tissue distribution by computed axial tomography in obese women: association with body density and anthropometric measurements. Br J Nutr 1989; 61: 139–148.

    Article  CAS  Google Scholar 

  17. Hill JO, Sidney S, Lewis CE, Tolan K, Scherzinger AL, Stamm ER . Racial differences in amounts of visceral adipose tissue in young adults: the CARDIA (Coronary Artery Risk Development in Young Adults) Study. Am J Clin Nutr 1999; 69: 381–387.

    Article  CAS  Google Scholar 

  18. Janssen I, Heymsfield SB, Allison DB, Kotler DP, Ross R . Body mass index and waist circumference independently contribute to the prediction of nonabdominal, abdominal, subcutaneous, and visceral fat. Am J Clin Nutr 2002; 75: 683–688.

    Article  CAS  Google Scholar 

  19. Kekes-Szabo T, Hunter GR, Nyikos I, Williams M, Blaudeau T, Snyder S . Anthropometric equations for estimating abdominal adipose tissue distribution in women. Int J Obes Relat Metab Disord 1996; 20: 753–758.

    CAS  PubMed  Google Scholar 

  20. Koester RS, Hunter GR, Snyder S, Khaled MA, Berland LL . Estimation of computerized tomography derived abdominal fat distribution. Int J Obes Relat Metab Disord 1992; 16: 543–554.

    CAS  Google Scholar 

  21. Lovejoy JC, de la Bretonne JA, Klemperer M, Tulley R . Abdominal fat distribution and metabolic risk factors: effects of race. Metabolism 1996; 45: 1119–1124.

    Article  CAS  Google Scholar 

  22. Lovejoy JC, Smith SR, Rood JC . Comparison of regional fat distribution and health risk factors in middle-aged white and African American women: The Healthy Transitions Study. Obes Res 2001; 9: 10–16.

    Article  CAS  Google Scholar 

  23. Seidell JC, Oosterlee A, Thijssen MAO, Burema J, Deurenberg P, Hautvast JGAJ, Ruijs JHJ . Assessment of intra-abdominal and subcutaneous abdominal fat: relation between anthropometry and computed tomography. Am J Clin Nutr 1987; 45: 7–13.

    Article  CAS  Google Scholar 

  24. Weits T, van der Beek EJ, Wedel M, Ter Haar Romeny BM . Computed tomography measurement of abdominal fat deposition in relation to anthropometry. Int J Obes Relat Metab Disord 1988; 12: 217–225.

    CAS  Google Scholar 

  25. Després JP, Couillard C, Gagnon J, Bergeron J, Leon AS, Rao DC, Skinner JS, Wilmore JH, Bouchard C . Race, visceral adipose tissue, plasma lipids, and lipoprotein lipase activity in men and women : The Health, Risk Factors, Exercise Training, and Genetics (HERITAGE) Family Study. Arterioscler. Thromb. Vasc. Biol. 2000; 20: 1932–1938.

    Article  Google Scholar 

  26. Després JP, Lamarche B . Effects of diet and physical activity on adiposity and body fat distribution: implications for the prevention of cardiovascular disease. Nutr Res Rev 1993; 6: 137–159.

    Article  Google Scholar 

  27. Hunter GR, Snyder SW, Kekes-Szabo T, Nicholson C, Berland LL . Intra-abdominal adipose tissue values associated with risk of possessing elevated blood lipids and blood pressure. Obes Res 1994; 2: 563–568.

    Article  CAS  Google Scholar 

  28. Rankinen T, Kim SY, Perusse L, Després JP, Bouchard C . The prediction of abominal visceral fat level from body composition and anthropometry: ROC analysis. Int J Obes Relat Metab Disord 1999; 23: 801–809.

    Article  CAS  Google Scholar 

  29. Bouchard C, Leon AS, Rao DC, Skinner JS, Wilmore JH, Gagnon J . The HERITAGE Family Study: aims, design, and measurement protocol. Med Sci Sports Exerc 1995; 27: 721–729.

    Article  CAS  Google Scholar 

  30. Wilmore JH, Després JP, Stanforth PR, Mandel S, Rice T, Gagnon J, Leon AS, Rao DC, Skinner JS, Bouchard C . Alterations in body weight and composition consequent to 20 wk of endurance training: the HERITAGE Family Study. Am J Clin Nutr 1999; 70: 346–352.

    Article  CAS  Google Scholar 

  31. Sjöström L . Measurement of fat distribution. In: Bouchard C, Johnston FE (eds). Fat distribution during growth and later health outcomes. Alan R Liss: New York, 1988, pp 43–61.

    Google Scholar 

  32. Lohman TG, Roche AF, Martorell F (eds). Anthropometric standardization reference manual. Human Kinetics Books: Champaign, IL, 1988.

    Google Scholar 

  33. Jackson AS, Pollock ML . Factor analysis and multivariate scaling of anthropometric variables for the assessment of body composition. Med Sci Sports Exerc 1976; 8: 196–203.

    Article  CAS  Google Scholar 

  34. Wilmore JH, Stanforth PR, Domenick MA, Gagnon J, Daw EW, Leon AS, Rao DC, Skinner JS, Bouchard C . Reproducibility anthropometric and body composition measurements: The HERITAGE Family Study. Int J Obes Relat Metab Disord 1997; 21: 297–303.

    Article  CAS  Google Scholar 

  35. Gagnon J, Province MA, Bouchard C, Leon AS, Skinner JS, Wilmore JW, Rao DC . The HERITAGE Family Study: quality assurance and quality control. Ann Epidemiol 1996; 6: 520–529.

    Article  CAS  Google Scholar 

  36. SAS. StatView reference. SAS Institute Inc.: Cary, 1998.

  37. Pedhauzur EJ . Multiple regression in behavioral research: explanation and prediction. Harcourt Brace College Publishers: New York, 1997.

    Google Scholar 

  38. Guo SS, Chumlea WC . Statistical methods for the development and testing of predictive equations. In: Roche AF, Heymsfield SB, Lohman TG (eds). Human body composition. Human Kinetics: Champaign IL, 1996, pp 191–202.

    Google Scholar 

  39. Holiday DB, Ballard JE, McKeown BC . PRESS-related statistics: regression tools for cross-validation and case diagnostics. Med Sci Sports Exerc 1995; 27: 612–620.

    Article  CAS  Google Scholar 

  40. Hosmer DW, Lemeshow S . Applied logistic regression. John Wiley: New York, 1989.

    Google Scholar 

  41. Lemieux S, Prud'homme D, Bouchard C, Tremblay A, Després JP . A single threshold value of wasit girth identfies normal-weight and overweight subjects with excess visceral adipose tissue. Am J Clin Nutr 1996; 64: 685–693.

    Article  CAS  Google Scholar 

  42. Räikkönen K, Matthews KA, Kuller LH . Anthropometric and psychosocial determinants of visceral obesity in healthy postmenopausal women. Int J Obes Relat Metab Disord 1999; 23: 775–782.

    Article  Google Scholar 

  43. Clasey JL, Bouchard C, Teates CD, Riblett JE, Thorner MO, Hartman ML, Weltman A . The use of anthropometric and dual-energy X-ray absorptiometry (DXA) to estimate total abdominal and abdominal visceral fat in men and women. Obes Res 1999; 7: 256–264.

    Article  CAS  Google Scholar 

  44. Pouliot MC, Després JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A, Nadeau A, Lupien PJ . Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol 1994; 73: 460–468.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The HERITAGE Family Study is supported by the National Heart, Lung and Blood Institute through the following grants: HL45670 (C Bouchard, PI); HL47323 (AS Leon, PI); HL47317 (DC Rao, PI); HL47327 (JS Skinner, PI); and HL47321 (JH Wilmore, PI). Claude Bouchard is partially supported by the George A Bray Chair in Nutrition. Further, Art Leon is partially supported by the Henry L Taylor Professorship in Exercise Science and Health Enhancement. Thanks are also expressed to all of the co-principal investigators, investigators, co-investigators, local project coordinators, research assistants, laboratory technicians, and secretaries who have contributed to this study (see Bouchard et al29). Finally, the HERITAGE consortium is very thankful to those hard-working families whose participation has made these data possible.

Author information

Authors and Affiliations

  1. Department of Kinesiology and Health Education, The University of Texas, Austin, TX, USA

    P R Stanforth

  2. Department of Health and Human Performance, University of Houston, Houston, TX, USA

    A S Jackson

  3. Department of Health and Kinesiology, Texas A&M University, College Station, TX, USA

    J S Green & J H Wilmore

  4. Laboratory of Molecular Endocrinology, Laval University, Québec City, Québec, Canada

    J Gagnon

  5. Pennington Biomedical Research Center, Baton Rouge, LA, USA

    T Rankinen & C Bouchard

  6. Québec Heart Institute, Laval Hospital Research Center, Laval University, Québec City, Québec, Canada

    J-P Desprès

  7. School of Kinesiology and Leisure Studies, University of Minnesota, Minneapolis, MN, USA

    A S Leon

  8. Division of Biostatistics, Washington University School of Medicine, St Louis, MO, USA

    D C Rao

  9. Department of Kinesiology, Indiana University, Bloomington, IN, USA

    J S Skinner

Authors
  1. P R Stanforth
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A S Jackson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J S Green
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J Gagnon
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. T Rankinen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. J-P Desprès
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. C Bouchard
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. A S Leon
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. D C Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. J S Skinner
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. J H Wilmore
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P R Stanforth.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Stanforth, P., Jackson, A., Green, J. et al. Generalized abdominal visceral fat prediction models for black and white adults aged 17–65 y: the HERITAGE Family Study. Int J Obes 28, 925–932 (2004). https://doi.org/10.1038/sj.ijo.0802563

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/sj.ijo.0802563

Keywords

This article is cited by

Search

Advanced search

Quick links