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The Use of Bioelectrical Impedance Analysis for Body Composition in Epidemiological Studies

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European Journal of Clinical Nutrition (2013) 67, S79–S85 & 2013 Macmillan Publishers Limited All rights reserved 0954-3007/13 www.nature.com/ejcn ORIGINAL ARTICLE The use of bioelectrical impedance analysis for body composition in epidemiological studies ABo¨ hm and BL Heitmann BACKGROUND/OBJECTIVES: Bioelectrical impedance analysis (BIA) is a relatively simple, inexpensive and non-invasive technique to measure body composition and is therefore suitable in field studies and larger surveys. SUBJECTS/METHODS: We performed an overview of BIA-derived body fat percentages (BF%) from 55 published studies of healthy populations aged 6–80 years. In addition, the relationship between body mass index (BMI) and body composition is documented in the context of BIA as a good alternative to closely differentiate which composition of the body better relates to the risk of cardiovascular diseases (CVDs)and all-cause mortality. RESULTS AND CONCLUSIONS: BIA-estimated percentage of BF varies greatly with population and age. BIA-estimated BF% is directly and closely related to various health outcomes such as CVDs, which is in contrast to BMI where both high and low BMIs are associated with increased risk of developing chronic diseases. Studies, among others using BIA, suggest that low BMI may reflect low muscle and high BMI fat mass (FM). BIA-derived lean and FM is directly associated with morbidity and mortality. To the contrary, BMI is rather of limited use for measuring BF% in epidemiological studies. European Journal of Clinical Nutrition (2013) 67, S79–S85; doi:10.1038/ejcn.2012.168 Keywords: bioelectrical impedance; percentage body fat; body mass index; mortality INTRODUCTION diabetes, which are associated with hydration alterations of FFM, Obesity is a condition in which fat accumulates in the body1 and may not be sufficiently accurate in estimating BF, and that four- or the body fat percentage (BF%) is therefore the relevant measure three-compartment models are a better alternative under these 12 of obesity. In the 1980s, bioelectrical impedance analysis (BIA) was circumstances. However, in field studies, or surveys including introduced as a new method to be used for estimating body many subjects, clearly such advanced three- or four-compartment composition,2 and since then many studies have investigated its models are not useful and the simpler methods, such as validity—indeed, because then BIA has been widely applied for impedance, are needed. predicting body composition (for example, fat-free mass (FFM), A number of studies have measured BIA in random population total body water and BF) in healthy subjects with normal fluid samples to either derive reference values for BF and FFM or relate distribution,3–5 and the method is considered useful in relation to such body composition measures to subsequent development of estimating BF% in both epidemiological and clinical research.6 disease and/or mortality. Body mass index (BMI) measures the Moreover, Wells and Fewtrell7 described BIA as the ‘only predictive degree of relative overweight and does not differentiate between 13,14 technique that estimates lean mass’. lean and fat mass. Previous studies suggest that this may be It is widely recognised that calculation of body composition part of the reason for the general finding of a U-formed measures from BIA requires population-specific equations, as also association between BMI and cardiovascular, as well as total, illustrated by the results by Deurenberg et al.8 examining validity mortality. The present review extracts estimations of average BF% of BIA among various European population groups where from published studies among healthy populations and discusses significant differences in biases for the prediction of BF% among the relationship between overweight and body composition in the participants from the European centres were reported. However, context of using BIA to measure body composition. relatively few studies have in fact developed their own specific equations, and suitable equations therefore often have to be looked for from other validation studies. In addition, BIA seems to APPLICATION OF BIA FOR BODY COMPOSITION be a good method to estimate BF% in healthy subjects with BIA has been used in large cohort studies, such as NHANES (USA), normal BF distribution;9 however, several studies9,10 have NUGENOB (EU) or MONICA (DK), to predict body composition (for suggested that this may not be the case in obese individuals, example, fat mass (FM) and BF%) in individuals, and has been where BIA tends to underestimate BF% (BF% 430%). Further, BIA found to be useful in large-scale epidemiological studies.15 measurements allow the determination of anatomic locations of Consequently, BIA has an important part in contributing to develop BF depositions (for example, central and peripheral), and thus are and compare body composition across populations, but a summary also applied to compare BF proportions.11 of results from such studies has rarely been presented. Table 1 gives It has also been argued that composition measurement an overview of such studies published in the period 1991–2009. instruments such as BIA that rely on constant body hydration However, comparison of BIA-derived body composition and thus do not regard health inequalities such as obesity and measures must be done with care, as BIA measures are dependent Institute of Preventive Medicine, Research Unit for Dietary Studies, Frederiksberg Hospital, Frederiksberg, Denmark. Correspondence: Dr BL Heitmann, Institute of Preventive Medicine, Research Unit for Dietary Studies, Frederiksberg Hospital, Nordre Fasanvej 57, Hovedvejen, Entrance 5, Ground floor, DK-2000 Frederiksberg, Denmark. E-mail: [email protected] BIA and epidemiology ABo¨hm and BL Heitmann S80 Table 1. Percentage of BF% from BIA by gender and age from published studies across different populations Study Population Country of Number of Gender Age Mean implementation subjects (years) BF% þ s.d. Antal et al.28 Healthy, randomly selected, Hungary 1928 m 7 12.6±7.4 Caucasian schoolchildren 8 13.8±7.4 9 17.1±7.6 10 19.4±9.2 11 19.3±9.0 12 19.4±8.0 13 16.7±8.4 14 15.6±7.3 f 7 14.3±6.5 8 16.5±8.6 9 19.9±8.0 10 21.3±8.7 11 20.8±7.7 12 20.3±7.0 13 23.3±7.6 14 23.5±7.2 Sung et al.49 Healthy, randomly selected Hong Kong 14 842 m 6 17.3±4.9 Hong Kong schoolchildren 7 17.9±5.4 8 18.7±5.9 9 19.7±6.7 10 20.6±6.9 11 19.7±7.4 12 18.0±7.1 13 17.1±6.4 14 17.4±6.1 15 18.9±6.3 16 19.7±6.0 17 20.2±6.0 18 19.1±4.0 f 6 14.1±4.8 7 15.4±5.7 8 16.2±5.9 9 17.2±6.1 10 18.3±6.5 11 19.3±6.6 12 21.1±6.9 13 23.5±6.8 14 24.3±6.4 15 25.4±6.5 16 25.5±6.3 17 25.7±6.2 18 25.5±5.7 Chumlea et al.50 Healthy, randomly selected USA 2880 m 12–13.9 18.4±7.3 non-Hispanic white Americans 14–15.9 18.4±8.3 16–17.9 17.7±6.8 18–19.9 19.6±6.9 20–29.9 21.8±6.2 30–39.9 23.6±5.8 40–49.9 24.2±5.7 50–59.9 25.1±6.0 60–69.9 26.2±5.5 70–79.9 25.1±5.5 3277 f 12–13.9 24.8±9.7 14–15.9 29.1±6.5 16–17.9 30.7±6.9 18–19.9 30.8±7.9 20–29.9 31.1±7.5 30–39.9 33.0±8.5 40–49.9 35.4±6.9 50–59.9 37.3±7.1 60–69.9 36.9±6.9 70–79.9 35.9±6.9 USA 2348 m 12–13.9 19.5±8.9 European Journal of Clinical Nutrition (2013) S79 – S85 & 2013 Macmillan Publishers Limited BIA and epidemiology ABo¨hm and BL Heitmann S81 Table 1. (Continued ) Study Population Country of Number of Gender Age Mean implementation subjects (years) BF% þ s.d. Healthy, randomly selected non-Hispanic black Americans 14–15.9 17.8±7.5 16–17.9 18.6±6.4 18–19.9 19.9±6.0 20–29.9 23.7±7.0 30–39.9 23.6±6.7 40–49.9 24.9±6.1 50–59.9 25.1±6.7 60–69.9 24.9±6.6 70–79.9 24.3±6.3 2606 f 12–13.9 26.9±8.8 14–15.9 30.9±8.0 16–17.9 32.6±8.5 18–19.9 33.3±8.7 20–29.9 35.5±7.5 30–39.9 38.0±7.7 40–49.9 39.4±7.0 50–59.9 40.0±7.5 60–69.9 39.8±6.9 70–79.9 38.5±6.7 Heitmann51 Healthy, randomly selected Denmark 1527 m 35 20.7 Caucasians 45 23.6 55 25.7 65 26.9 1467 f 35 26.2 45 30.2 55 33.8 65 35.7 Pichard et al.52 Healthy, randomly selected Switzerland 1838 m 15–24 14.5±4.3 Caucasians 25–34 16.3±4.9 35–44 17.8±5.8 45–54 19.2±6.0 55–64 20.9±7.2 1555 f 15–24 24.5±4.2 25–34 24.6±4.7 35–44 25.2±5.1 45–54 25.9±5.5 55–64 30.1±5.8 Kyle et al.53 Healthy, randomly selected Switzerland and USA 3714 m 20–29 17.3±4.7 Caucasians 30–39 19.0±4.9 40–49 20.1±5.1 50–49 20.7±5.6 60–69 22.5±5.4 70–79 24.6±5.1 3199 f 20–29 26.3±5.1 30–39 26.1±5.5 40–49 26.9±5.7 50–49 29.3±5.6 60–69 32.6±6.6 70–79 35.9±5.7 Lahmann et al.54 Healthy, randomly selected Sweden 5464 f 45–49 28.8±4.9 Caucasian women 50–54 29.4±4.8 55–59 31.1±5.0 60–64 31.9±4.9 65–69 32.5±4.7 70–73 31.5±4.8 Nagaya et al.55 Healthy Japanese adults Japan 12 287 m 30–34 21.7±5.0 35–39 21.0±4.7 40–44 21.2±4.5 & 2013 Macmillan Publishers Limited European Journal of Clinical Nutrition (2013) S79 – S85 BIA and epidemiology ABo¨hm and BL Heitmann S82 Table 1.
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