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:

Techniques and Methods

Optimal scaling of weight and waist circumference to height for maximal association with DXA-measured total body fat mass by sex, age and race/ethnicity

International Journal of Obesity volume 37, pages 1154–1160 (2013)Cite this article

Subjects

Abstract

BACKGROUND:

Body mass index (BMI; weight (Wt)/height (Ht) (in kg m−2) and waist circumference (WC) are widely used as proxy anthropometric measures for total adiposity. Little is known about what scaling power of ‘x’ in both Wt(kg)/Ht(m)x and WC(m)/Ht(m)x is maximally associated with measured total body fat mass (TBFM). Establishing values for x would provide the information needed to create optimum anthropometric surrogate measures of adiposity.

OBJECTIVE:

To estimate the value of ‘x’ that renders Wt/Htx and WC/Htx maximally associated with DXA-measured TBFM.

SUBJECTS:

Participants of the NHANES 1999–2004 surveys, stratified by sex (men, women), race/ethnicity (non-Hispanic whites, non-Hispanic blacks, Mexican-Americans), and age(18–29, 30–49, 50–84years).

METHODS:

We apply a grid search by increasing x from 0.0–3.0 by increments of 0.1 to the simple regression models, TBFM=b0+b1*(Wt/Htx) and TBFM=b0+b1*(WC/Htx) to obtain an estimate of x that results in the greatest R2, taking into account complex survey design features and multiply imputed data.

RESULTS:

R2’s for BMI are 0.86 for men (N=6544) and 0.92 for women (N=6362). The optimal powers x for weight are 1.0 (R2=0.90) for men and 0.8 (R2=0.96) for women. The optimal power x for WC is 0, that is, no scaling of WC to height, for men (R2=0.90) or women (R2=0.82). The optimal powers for weight across nine combinations of race/ethnicity and age groups for each sex vary slightly (x=0.8–1.3) whereas the optimal scaling powers for WC are all 0 for both sexes except for non-Hispanic black men aged 18–29y (x=0.1). Although the weight-for-height indices with optimal powers are not independent of height, they yield more accurate TBFM estimates than BMI.

CONCLUSION:

In reference to TBFM, Wt/Ht and Wt/Ht0.8 are the optimal weight-for-height indices for men and women, respectively, whereas WC alone, without Ht adjustment, is the optimal WC-for-height index for both sexes. Thus, BMI, an index independent of height, may be less useful when predicting TBFM.

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

Similar content being viewed by others

References

  1. Quetelet A . Recherches sur le poids de l'homme aux different ages, Nouveaux Memoire de l'Academie Royale des Sciences et Belles-Lettres de Bruxelles, 1832, t. VII.

  2. Eknoyan G . Adolphe Quetelet (1796–1874)—the average man and indices of obesity. Nephrol Dial Transplant 2008; 23: 47–51.

    Article  PubMed  Google Scholar 

  3. Keys A, Fidanza F, Karvonen MJ, Kimura N, Taylor HL . Indices of relative weight and obesity. J Chronic Dis 1972; 25: 329–343.

    Article  CAS  PubMed  Google Scholar 

  4. National Institues of Health. National Heart, Lung, and Blood Institute Clinical Guidelines on the Identification, and Treatment of Overweight and Obesity in Adults: The Evidence Report 1998.

  5. Barba C, Cavalli-Sforza T, Cutter J, Darnton-Hill I, Deurenberg P, Deurenberg-Yap M et al. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004; 363: 157–163.

    Article  Google Scholar 

  6. Gallagher D, Visser M, Sepulveda D, Pierson RN, Harris T, Heymsfield SB . How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol 1996; 143: 228–239.

    Article  CAS  PubMed  Google Scholar 

  7. Garn SM, Leonard WR, Hawthorne VM . 3 limitations of the body-mass index. Am J Clin Nutr 1986; 44: 996–997.

    Article  CAS  PubMed  Google Scholar 

  8. Romero-Corral A, Lopez-Jimenez F, Sierra-Johnson J, Somers VK . Differentiating between body fat and lean mass—how should we measure obesity? Nat Clin Pract Endocrinol Metab 2008; 4: 322–323.

    Article  PubMed  Google Scholar 

  9. Heymsfield SB, Gallagher D, Mayer L, Beetsch J, Pietrobelli A . Scaling of human body composition to stature: new insights into body mass index. Am J Clin Nutr 2007; 86: 82–91.

    Article  CAS  PubMed  Google Scholar 

  10. Okorodudu DO, Jumean MF, Montori VM, Romero-Corral A, Somers VK, Erwin PJ et al. Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. Int J Obes 2010; 34: 791–799.

    Article  CAS  Google Scholar 

  11. Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes 2008; 32: 959–966.

    Article  CAS  Google Scholar 

  12. de Gonzalez AB, Hartge P, Cerhan JR, Flint AJ, Hannan L, MacInnis RJ et al. Body-Mass Index and Mortality among 1.46 Million White Adults. N Engl J Med 2010; 363: 2211–2219.

    Article  PubMed Central  Google Scholar 

  13. Zhu SK, Heo M, Plankey M, Faith MS, Allison DB . Associations of body mass index and anthropometric indicators of fat mass and fat free mass with all-cause mortality among women in the first and second national health and nutrition examination surveys follow up studies. Ann Epidemiol 2003; 13: 286–293.

    Article  PubMed  Google Scholar 

  14. Allison DB, Faith MS, Heo M, Kotler DP . Hypothesis concerning the U-shaped relation between body mass index and mortality. Am J Epidemiol 1997; 146: 339–349.

    Article  CAS  PubMed  Google Scholar 

  15. Allison DB, Zhu SK, Plankey M, Faith MS, Heo M . Differential associations of body mass index and adiposity with all-cause mortality among men in the first and second National Health and Nutrition Examination Surveys (NHANES I and NHANES II) follow-up studies. Int J Obes 2002; 26: 410–416.

    Article  CAS  Google Scholar 

  16. Allison DB, Zannolli R, Faith MS, Heo M, Pietrobelli A, VanItallie TB et al. Weight loss increases and fat loss decreases all-cause mortality rate: results from two independent cohort studies. Int J Obes 1999; 23: 603–611.

    Article  CAS  Google Scholar 

  17. Florey Cdu V . The use and interpretation of ponderal index and other weight-height ratios in epidemiological studies. J Chronic Dis 1970; 23: 93–103.

    Article  PubMed  Google Scholar 

  18. Benn RT . Some mathematical properties of weight-for-height indices used as measures of adiposity. Br J Prev Soc Med 1971; 25: 42–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Flegal KM . Ratio of actual to predicted weight as an alternative to a power-type weight-height index (Benn index). Am J Clin Nutr 1990; 51: 540–547.

    Article  CAS  PubMed  Google Scholar 

  20. Wang J, Thornton JC, Bari S, Williamson B, Gallagher D, Heymsfield SB et al. Comparisons of waist circumferences measured at 4 sites. Am J Clin Nutr 2003; 77: 379–384.

    Article  PubMed  Google Scholar 

  21. Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey Data. 1999-2004http://www.cdc.gov/nchs/nhanes/nhanes_questionnaires.htm Hyattsville, MD: US Department of Health and Human Services, Centers for Disease Control and Prevention.

  22. Centers for Disease Control and Prevention (CDC). National Center for Health Statisitic (NCHS). National Health and Nutrition Examination Survey, 2007-2008: Overviewhttp://www.cdc.gov/nchs/data/nhanes/nhanes_07_08/overviewbrochure_0708.pdf Hyattsville, MD: US Department of Health and Human Services, Centers for Disease Control and Prevention 2007.

  23. Schoeller DA, Tylavsky FA, Baer DJ, Chumlea WC, Earthman CP, Fuerst T et al. QDR 4500A dual-energy X-ray absorptiometer underestimates fat mass in comparison with criterion methods in adults. Am J Clin Nutr 2005; 81: 1018–1025.

    Article  CAS  PubMed  Google Scholar 

  24. National Center for Health Statisitic(NCHS). National Health and Nutrition Examination Survey: Technical Documentation for the 1999–2004 Dual Energy X-ray Absorptiometry (DXA) Multiple Imupation Data Files, Internethttp://www.cdc.gov/nchs/data/nhanes/dxa/dxa_techdoc.pdfFebruary, 2008.

  25. Schenker N, Borrud LG, Burt VL, Curtin LR, Flegal KM, Hughes J et al. Multiple imputation of missing dual-energy X-ray absorptiometry data in the National Health and Nutrition Examination Survey. Stat Med 2011; 30: 260–276.

    Article  PubMed  Google Scholar 

  26. Raghunathan TE, Lepkowski JM, Van Hoewyk J, Solenberger P . A multivariate technique for multiply imputing missing values using a sequence of regression models. Surv Methodol 2001; 27: 85–95.

    Google Scholar 

  27. White IR, Royston P, Wood AM . Multiple imputation using chained equations: Issues and guidance for practice. Stat Med 2011; 30: 377–399.

    Article  PubMed  Google Scholar 

  28. National Center for Health Statisitic. Anthropometry Procedures Manualhttp://www.cdc.gov/nchs/data/nhanes/nhanes_03_04/BM.pdf Hyattsville, MD, USA Department of Health and Human Services, Center for Disease Control and Prevention 2004.

  29. Heo M, Faith MS, Pietrobelli A, Heymsfield SB . Percentage of body fat cutoffs by sex, age, and race-ethnicity in the US adult population from NHANES 1999–2004. Am J Clin Nutr 2012; 95: 594–602.

    Article  CAS  PubMed  Google Scholar 

  30. Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y . Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 2000; 72: 694–701.

    Article  CAS  PubMed  Google Scholar 

  31. Jackson AS, Ellis KJ, McFarlin BK, Sailors MH, Bray MS . Cross-validation of generalised body composition equations with diverse young men and women: the Training Intervention and Genetics of Exercise Response (TIGER) Study. Br J Nutr 2009; 101: 871–878.

    Article  CAS  PubMed  Google Scholar 

  32. Fernandez JR, Heo MS, Heymsfield SB, Pierson RN, Pi-Sunyer FX, Wang ZM et al. Is percentage body fat differentially related to body mass index in Hispanic Americans, African Americans, and European Americans? Am J Clin Nutr 2003; 77: 71–75.

    Article  CAS  PubMed  Google Scholar 

  33. He Q, Heo M, Heshka S, Wang J, Pierson RN, Albu J et al. Total body potassium differs by sex and race across the adult age span. Am J Clin Nutr 2003; 78: 72–77.

    Article  CAS  PubMed  Google Scholar 

  34. Guo SS, Zeller C, Chumlea WC, Siervogel RM . Aging, body composition, and lifestyle: the Fels Longitudinal study. Am J Clin Nutr 1999; 70: 405–411.

    Article  CAS  PubMed  Google Scholar 

  35. National Center for Health Statisitic. Analytic and Reporting Guidelines: The National Health and Nutrition Examination Survey (NHANES), September 2006, Internet:http://www.cdc.gov/nchs/data/nhanes/nhanes_03_04/nhanes_analytic_guidelines_dec_2005.pdf 2006.

  36. Little RJA, Rubin DB . Statistical Analysis with Missing Data 2nd edn Wiley: Hoboken, NJ, USA, 2002).

    Book  Google Scholar 

  37. Heymsfield SB, Heo MS, Thomas D, Pietrobelli A . Scaling of body composition to height: relevance to height-normalized indexes. Am J Clin Nutr 2011; 93: 736–740.

    Article  CAS  PubMed  Google Scholar 

  38. Schreiner PJ, Terry JG, Evans GW, Hinson WH, Crouse JR, Heiss G . Sex-specific associations of magnetic resonance imaging-derived intra-abdominal and subcutaneous fat areas with conventional anthropometric indices—the atherosclerosis risk in communities. Am J Epidemiol 1996; 144: 335–345.

    Article  CAS  PubMed  Google Scholar 

  39. Heo M, Faith MS, Mott JW, Gorman BS, Redden DT, Allison DB . Hierarchical linear models for the development of growth curves: an example with body mass index in overweight/obese adults. Stat Med 2003; 22: 1911–1942.

    Article  PubMed  Google Scholar 

  40. Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet 2011; 377: 557–567.

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

  42. Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH . The disease burden associated with overweight and obesity. J Am Med Assoc 1999; 282: 1523–1529.

    Article  CAS  Google Scholar 

  43. Zhu SK, Wang ZM, Heshka S, Heo MS, Faith MS, Heymsfield SB . Waist circumference and obesity-associated risk factors among whites in the third National Health and Nutrition Examination Survey: clinical action thresholds. Am J Clin Nutr 2002; 76: 743–749.

    Article  CAS  PubMed  Google Scholar 

  44. Heo M, Pietrobelli A, Wang D, Heymsfield SB, Faith MS . Obesity and functional impairment: influence of comorbidity, joint pain, and mental health. Obesity 2010; 18: 2030–2038.

    Article  PubMed  Google Scholar 

  45. Davidson LE, Wang J, Thornton JC, Kaleem Z, Silva-Palacios F, Pierson RN et al. Predicting fat percent by skinfolds in racial groups: durnin and womersley revisited. Med Sci Sports Exerc 2011; 43: 542–549.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Heymsfield S, Lohman TG, Wang Z, Going SB (eds) Human Body Composition. Human Kinetics: USA, 2005.

    Google Scholar 

  47. Bergman RN, Stefanovski D, Buchanan TA, Sumner AE, Reynolds JC, Sebring NG et al. A better index of body adiposity. Obesity 2011; 19: 1083–1089.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This study is supported in part by the Albert Einstein College of Medicine funds.

Author information

Authors and Affiliations

  1. Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA

    M Heo, G C Kabat & T E Rohan

  2. New York Nutrition and Obesity Research Center, St Luke’s-Roosevelt Hospital, College of Physicians and Surgeons, Columbia University, New York, NY, USA

    D Gallagher

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

    S B Heymsfield

Authors
  1. M Heo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. G C Kabat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D Gallagher
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S B Heymsfield
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. T E Rohan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M Heo.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies the paper on International Journal of Obesity website

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Heo, M., Kabat, G., Gallagher, D. et al. Optimal scaling of weight and waist circumference to height for maximal association with DXA-measured total body fat mass by sex, age and race/ethnicity. Int J Obes 37, 1154–1160 (2013). https://doi.org/10.1038/ijo.2012.201

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ijo.2012.201

Keywords

This article is cited by

Search

Advanced search

Quick links