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.

  • Original Article
  • Published:

Accuracy of body mass index in diagnosing obesity in the adult general population

International Journal of Obesity volume 32, pages 959–966 (2008)Cite this article

Abstract

Background:

Body mass index (BMI) is the most widely used measure to diagnose obesity. However, the accuracy of BMI in detecting excess body adiposity in the adult general population is largely unknown.

Methods:

A cross-sectional design of 13 601 subjects (age 20–79.9 years; 49% men) from the Third National Health and Nutrition Examination Survey. Bioelectrical impedance analysis was used to estimate body fat percent (BF%). We assessed the diagnostic performance of BMI using the World Health Organization reference standard for obesity of BF%>25% in men and>35% in women. We tested the correlation between BMI and both BF% and lean mass by sex and age groups adjusted for race.

Results:

BMI-defined obesity (⩾30 kg m−2) was present in 19.1% of men and 24.7% of women, while BF%-defined obesity was present in 43.9% of men and 52.3% of women. A BMI⩾30 had a high specificity (men=95%, 95% confidence interval (CI), 94–96 and women=99%, 95% CI, 98–100), but a poor sensitivity (men=36%, 95% CI, 35–37 and women=49%, 95% CI, 48–50) to detect BF%-defined obesity. The diagnostic performance of BMI diminished as age increased. In men, BMI had a better correlation with lean mass than with BF%, while in women BMI correlated better with BF% than with lean mass. However, in the intermediate range of BMI (25–29.9 kg m−2), BMI failed to discriminate between BF% and lean mass in both sexes.

Conclusions:

The accuracy of BMI in diagnosing obesity is limited, particularly for individuals in the intermediate BMI ranges, in men and in the elderly. A BMI cutoff of⩾30 kg m−2 has good specificity but misses more than half of people with excess fat. These results may help to explain the unexpected better survival in overweight/mild obese patients.

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

Figure 1
Figure 2
Figure 3

Similar content being viewed by others

References

  1. Lancet T . Curbing the obesity epidemic. Lancet 2006; 367: 1549.

    Article  Google Scholar 

  2. National Institutes of Health Consensus Development Panel. Health implications of obesity: National Institutes of Health Consensus Conference Statement. Ann Intern Med 1985; 103: 1073–1077.

    Article  Google Scholar 

  3. Pi-Sunyer FX . The obesity epidemic: pathophysiology and consequences of obesity. Obes Res 2002; 10 (Suppl 2): 97S–104S.

    Article  Google Scholar 

  4. Krauss RM, Winston M, Fletcher RN, Grundy SM . Obesity: impact of cardiovascular disease. Circulation 1998; 98: 1472–1476.

    Article  Google Scholar 

  5. Poirier P, Giles TD, Bray GA, Hong Y, Stern JS, Pi-Sunyer FX et al. Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss. Arterioscler Thromb Vasc Biol 2006; 26: 968–976.

    Article  CAS  Google Scholar 

  6. Haslam DW, James WP . Obesity. Lancet 2005; 366: 1197–1209.

    Article  Google Scholar 

  7. Quetelet A . A Treatise on Man and the Development of His Faculties.Originally published in 1842. Reprinted in 1968 by. Burt Franklin: New York.

  8. National Institutes of Health. National Heart, Lung, and Blood Institute. Clinical Guidelines on the Identification, and Treatment of Overweight and Obesity in Adults: The Evidence Report. NIH Publication no. 98-4083, September 1998.

  9. World Health Organization. Physical status: the use and interpretation of anthropometry. Report of a WHO expert committee. World Health Organ Tech Rep Ser 1995; 854: 1–452.

    Google Scholar 

  10. Seidell JC, Kahn HS, Williamson DF, Lissner L, Valdez R . Report from a Centers for Disease Control and Prevention Workshop on use of adult anthropometry for public health and primary health care. Am J Clin Nutr 2001; 73: 123–126.

    Article  CAS  Google Scholar 

  11. Romero-Corral A, Montori VM, Somers VK, Korinek J, Thomas RJ, Allison TG et al. Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies. Lancet 2006; 368: 666–678.

    Article  Google Scholar 

  12. Franzosi MG . Should we continue to use BMI as a cardiovascular risk factor? Lancet 2006; 368: 624–625.

    Article  Google Scholar 

  13. McGee DL . Body mass index and mortality: a meta-analysis based on person-level data from twenty-six observational studies. Ann Epidemiol 2005; 15: 87–97.

    Article  Google Scholar 

  14. Flegal KM, Graubard BI, Williamson DF, Gail MH . Cause-specific excess deaths associated with underweight, overweight, and obesity. JAMA 2007; 298: 2028–2037.

    Article  CAS  Google Scholar 

  15. Yusuf S, Hawken S, Ounpuu S, Bautista L, Franzosi MG, Commerford P et al. Obesity and the risk of myocardial infarction in 27 000 participants from 52 countries: a case–control study. Lancet 2005; 366: 1640–1649.

    Article  Google Scholar 

  16. Price GM, Uauy R, Breeze E, Bulpitt CJ, Fletcher AE . Weight, shape, and mortality risk in older persons: elevated waist–hip ratio, not high body mass index, is associated with a greater risk of death. Am J Clin Nutr 2006; 84: 449–460.

    Article  CAS  Google Scholar 

  17. Romero-Corral A, Somers VK, Sierra-Johnson J, Jensen MD, Thomas RJ, Squires RW et al. Diagnostic performance of body mass index to detect obesity in patients with coronary artery disease. Eur Heart J 2007; 28: 2087–2093.

    Article  Google Scholar 

  18. Frankenfield DC, Rowe WA, Cooney RN, Smiths JS, Becker D . Limits of body mass index to detect obesity and predict body composition. Nutrition 2001; 17: 26–30.

    Article  CAS  Google Scholar 

  19. Piers LS, Soares MJ, Frandsen SL, O’Dea K . Indirect estimates of body composition are useful for groups but unreliable in individuals. Int J Obes Relat Metab Disord 2000; 24: 1145–1152.

    Article  CAS  Google Scholar 

  20. Kuczmarski RJ . Bioelectrical impedance analysis measurements as part of a national nutrition survey. Am J Clin Nutr 1996; 64: 453S–458S.

    Article  CAS  Google Scholar 

  21. National Center for Health Statistics. Plan and Operation of the Third National Health and Nutrition Examination Survey, 1988–1994. Vital Health Stat 1 1994; 1–407.

  22. US Department of Health and Human Services. National Center for Health Statistics. The Third National Health and Nutrition Examination Survey (NHANES III, 1988–1994). Centers for Disease Control and Prevention: Washington DC, 1996.

  23. US Department of Health and Human Services. National Center for Health Statistics. NHANES III Anthropometric Procedure Video Stock no. 017-022-01355. Goverment Printing Office: Washington DC, 1996.

  24. Guo SS, Wu W, Chumlea WC, Roche AF . Predicting overweight and obesity in adulthood from body mass index values in childhood and adolescence. Am J Clin Nutr 2002; 76: 653–658.

    Article  CAS  Google Scholar 

  25. Chumlea WC, Guo SS, Kuczmarski RJ, Flegal KM, Johnson CL, Heymsfield SB et al. Body composition estimates from III NHANES bioelectrical impedance data. Int J Obes Relat Metab Disord 2002; 26: 1596–1609.

    Article  CAS  Google Scholar 

  26. SAS Version 9.1. SAS Institute: Cary, NC.

  27. SUDAAN Research Triangle Release 9.0.3. Research Triangle Institute: Research Triangle Park, NC, 2007.

  28. Blew RM, Sardinha LB, Milliken LA, Teixeira PJ, Going SB, Ferreira DL et al. Assessing the validity of body mass index standards in early postmenopausal women. Obes Res 2002; 10: 799–808.

    Article  Google Scholar 

  29. Smalley KJ, Knerr AN, Kendrick ZV, Colliver JA, Owen OE . Reassessment of body mass indices. Am J Clin Nutr 1990; 52: 405–408.

    Article  CAS  Google Scholar 

  30. McGinn TG, Guyatt GH, Wyer PC, Naylor CD, Stiell IG, Richardson WS . Users’ guides to the medical literature: XXII: how to use articles about clinical decision rules. Evidence-Based Medicine Working Group. JAMA 2000; 284: 79–84.

    Article  CAS  Google Scholar 

  31. Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ . Overweight, obesity, and mortality from cancer in a prospectively studied cohort of US adults. N Engl J Med 2003; 348: 1625–1638.

    Article  Google Scholar 

  32. Adams KF, Schatzkin A, Harris TB, Kipnis V, Mouw T, Ballard-Barbash R et al. Overweight, obesity, and mortality in a large prospective cohort of persons 50–71 years old. N Engl J Med 2006; 355: 763–778.

    Article  CAS  Google Scholar 

  33. O’Donovan G, Owen A, Kearney EM, Jones DW, Nevill AM, Woolf-May K et al. Cardiovascular disease risk factors in habitual exercisers, lean sedentary men and abdominally obese sedentary men. Int J Obes (London) 2005; 29: 1063–1069.

    Article  Google Scholar 

  34. Goldberg AP, Busby-Whitehead MJ, Katzel LI, Krauss RM, Lumpkin M, Hagberg JM . Cardiovascular fitness, body composition, and lipoprotein lipid metabolism in older men. J Gerontol A Biol Sci Med Sci 2000; 55: M342–M349.

    Article  CAS  Google Scholar 

  35. Yataco AR, Busby-Whitehead J, Drinkwater DT, Katzel LI . Relationship of body composition and cardiovascular fitness to lipoprotein lipid profiles in master athletes and sedentary men. Aging (Milano) 1997; 9: 88–94.

    CAS  Google Scholar 

  36. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR, Flegal KM . Prevalence of overweight and obesity among US children, adolescents, and adults, 1999–2002. JAMA 2004; 291: 2847–2850.

    Article  CAS  Google Scholar 

  37. Snijder MB, Kuyf BE, Deurenberg P . Effect of body build on the validity of predicted body fat from body mass index and bioelectrical impedance. Ann Nutr Metab 1999; 43: 277–285.

    Article  CAS  Google Scholar 

  38. Fogelholm M, van Marken Lichtenbelt W . Comparison of body composition methods: a literature analysis. Eur J Clin Nutr 1997; 51: 495–503.

    Article  CAS  Google Scholar 

  39. Snijder MB, van Dam RM, Visser M, Seidell JC . What aspects of body fat are particularly hazardous and how do we measure them? Int J Epidemiol 2006; 35: 83–92.

    Article  CAS  Google Scholar 

  40. Khaodhiar L, Blackburn GL . Results of expert meetings: obesity and cardiovascular disease. Obesity assessment. Am Heart J 2001; 142: 1095–1101.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

Dr Abel Romero-Corral was supported by a postdoctoral fellowship from the American Heart Association. Dr Somers was supported by NIH grants HL-65176, HL-70302, HL-73211 and M01-RR00585. Dr Justo Sierra-Johnson was partially supported by faculty funds from the Board of Post-Graduate Education of the Karolinska Institute (KID Award) and by the European Foundation for the Study of Diabetes through a research fellowship. Dr Lopez-Jimenez was a recipient of a Clinical Scientist Development Award from the American Heart Association.

Author information

Authors and Affiliations

  1. Division of Cardiovascular Diseases, Mayo Clinic College of Medicine, MN, USA

    A Romero-Corral, V K Somers, J Sierra-Johnson, R J Thomas, J Korinek, T G Allison, F H Sert-Kuniyoshi & F Lopez-Jimenez

  2. Department of Medicine, Atherosclerosis Research Unit, Karolinska Institutet, Sweden

    J Sierra-Johnson

  3. Division of Endocrinology, Mayo Clinic College of Medicine, Rochester, MN, USA

    M L Collazo-Clavell

  4. Division of Primary Care Internal Medicine, Mayo Foundation, Rochester, MN, USA

    J A Batsis

Authors
  1. A Romero-Corral
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V K Somers
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J Sierra-Johnson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. R J Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. M L Collazo-Clavell
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. J Korinek
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. T G Allison
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. J A Batsis
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. F H Sert-Kuniyoshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. F Lopez-Jimenez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to F Lopez-Jimenez.

Additional information

Disclosure/Conflict of interest

All authors have read and approved submission of the manuscript and the mention of their names. Abel Romero-Corral, Virend K Somers and Francisco Lopez-Jimenez are recipients of a grant from Select Research Ltd.

Supplementary Information accompanies the paper on International Journal of Obesity website (http://www.nature.com/ijo)

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Romero-Corral, A., Somers, V., Sierra-Johnson, J. et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes 32, 959–966 (2008). https://doi.org/10.1038/ijo.2008.11

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ijo.2008.11

Keywords

This article is cited by

Search

Advanced search

Quick links