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Body composition estimates from NHANES III bioelectrical impedance data

International Journal of Obesity volume 26, pages 1596–1609 (2002)Cite this article

Abstract

BACKGROUND: Body composition estimates for the US population are important in order to analyze trends in obesity, sarcopenia and other weight-related health conditions. National body composition estimates have not previously been available.

OBJECTIVE: To use transformed bioelectrical impedance analysis (BIA) data in sex-specific, multicomponent model-derived prediction formulae, to estimate total body water (TBW), fat-free mass (FFM), total body fat (TBF), and percentage body fat (%BF) using a nationally representative sample of the US population.

DESIGN: Anthropometric and BIA data were from the third National Health and Nutrition Examination Survey (NHANES III; 1988–1994). Sex-specific BIA prediction equations developed for this study were applied to the NHANES data, and mean values for TBW, FFM, TBF and %BF were estimated for selected age, sex and racial-ethnic groups.

RESULTS: Among the non-Hispanic white, non-Hispanic black, and Mexican-American participants aged 12–80 y examined in NHANES III, 15 912 had data available for weight, stature and BIA resistance measures. Males had higher mean TBW and FFM than did females, regardless of age or racial-ethnic status. Mean TBW and FFM increased from the adolescent years to mid-adulthood and declined in older adult age groups. Females had higher mean TBF and %BF estimates than males at each age group. Mean TBF also increased with older age groups to approximately 60 y of age after which it decreased.

CONCLUSIONS: These mean body composition estimates for TBW, FFM, TBF and %BF based upon NHANES III BIA data provide a descriptive reference for non-Hispanic whites, non-Hispanic blacks and Mexican Americans in the US population.

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Acknowledgements

We gratefully acknowledge Katalin Losonczy and Margaret Carroll for their computer programming to calculate the reported means and complex sample variance estimates. Supported by funding from the Centers for Disease Control and Prevention, the US Army Medical Research and Materiel Command, the Nutritional Services Branch, the National Institute of Diabetes and Digestive and Kidney Diseases and grants HD-27063, HD-12252 and HL-53404 from the National Institutes of Health. Mention of a trademark or proprietary product does not constitute a guarantee of warranty of the product by the United States Department of Agriculture and does not imply its approval to the exclusion of other products that may also be suitable. US Department of Agriculture, Agricultural Research Service, Northern Plains Area is an equal opportunity/affirmative action employer and all agency services are available without discrimination.

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Authors and Affiliations

  1. Department of Community, Health Wright State University School of Medicine, Dayton, Ohio, USA

    WC Chumlea & SS Guo

  2. National Institutes of Health Division of Digestive Diseases and Nutrition, NIDDK, Bethesda, Maryland, USA

    RJ Kuczmarski

  3. Division of Health Examination Statistics, Centers for Disease Control and Prevention, National Center for Health Statistics, Hyattsville, Maryland, USA

    KM Flegal & CL Johnson

  4. Obesity Research Center, St Lukes-Roosevelt Hospital, Columbia University, New York, USA

    SB Heymsfield

  5. US Department of Agriculture, Agriculture Research Service, Grand Forks HNRC, Grand Forks, North Dakota, USA

    HC Lukaski

  6. Military Operational Medicine Program, Military Medical Research and Materiel Command, Frederick, Maryland, USA

    K Friedl

  7. Division of Nutrition Research Coordination and Nutritional Sciences Branch, National Institutes of Health, NIDDK, Bethesda, Maryland, USA

    VS Hubbard

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  1. WC Chumlea
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Correspondence to SS Guo.

Appendix

Appendix

Conversion of NHANES III BIA values

The NHANES III BIA data were obtained with a Valhalla impedance analyzer. Before applying the TBW and FFM prediction equations to these data, the Valhalla Res value for each NHANES III subject was converted to an equivalent RJL Res value using equations developed from a separate, independent sample. Res data collected at the same visit between the right wrist and ankle using a Valhalla 1990B and an RJL 101 BIA instrument were available from 197 male and 235 female participants, 12–65 y of age in the Fels Longitudinal Study.43 These Fels, RJL Res values were regressed on corresponding Fels Valhalla Res values separately for each sex.

The BIA Res conversion equations are as follows for males and females:

For males: RJL Res=2.5+0.98 Val Res; r2=0.996, RMSE=5.0 ohms

For females: RJL Res=9.6+0.96 Val Res; r2=0.993, RMSE=5.3 ohms

Variance estimation

The means presented in this report are based on a complex sample design, and techniques that account for this design were used to estimate the standard errors of these means (ie the square root of their variance). Variance estimates based on the complex sample design are different from and generally larger than those obtained under the assumption of simple random sampling. The design effect (Deff) measures the influence of the complex sample upon the variance and is defined as the ratio of the complex samples variance, VarCS, to the variance based on a simple random sample of the same size (ie the weighted simple random sample estimate of the variance), VarSRS:

Because of the wide variability of the design effect across age groups within gender and race-ethnicity, the application of an average design effect stabilizes estimates of standard errors of the mean.49 More specifically:

  • Each of the six race/ethnic (re=1, 2, 3) and gender (g=1, 2) specific subgroups was partitioned into seven subgroups.

  • Design effects for each of these seven subgroups were estimated for reth and gth group:

  • DEFF(BIA)re;g;a a = . . . ; 7

  • A mean design effect across the seven age groups was calculated:

  • The complex sample standard error of the mean BIA for the reth race/ethnic, gth gender and ath subdomain sem(BIA)re,g,a,CS was estimated by multiplying the weighted simple random estimate of the standard error of the mean for that domain s.e.m.(BIA)re,g,a,SRS by the square root of the corresponding race/ethnic and gender specific mean design effect given in equation (2).

SUDAAN, a statistical software package that incorporates the sample weights and accounts for the complex sample design through Taylor Series linearization was used to estimate the design effects.26 The complex sample standard deviation, σCS, was estimated by adding the square of the complex sample estimate of the standard error of the mean given in equation (3) to the square of the simple random sample estimate of the standard deviation σ^SRS2 and taking the square root of this sum:

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Chumlea, W., Guo, S., Kuczmarski, R. et al. Body composition estimates from NHANES III bioelectrical impedance data. Int J Obes 26, 1596–1609 (2002). https://doi.org/10.1038/sj.ijo.0802167

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