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The use of bioelectrical impedance analysis for body composition in epidemiological studies

Abstract

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.

Introduction

Obesity is a condition in which fat accumulates in the body1 and the body fat percentage (BF%) is therefore the relevant measure of obesity. In the 1980s, bioelectrical impedance analysis (BIA) was introduced as a new method to be used for estimating body composition,2 and since then many studies have investigated its validity—indeed, because then BIA has been widely applied for predicting body composition (for example, fat-free mass (FFM), total body water and BF) in healthy subjects with normal fluid distribution,3, 4, 5 and the method is considered useful in relation to estimating BF% in both epidemiological and clinical research.6 Moreover, Wells and Fewtrell7 described BIA as the ‘only predictive technique that estimates lean mass’.

It is widely recognised that calculation of body composition measures from BIA requires population-specific equations, as also illustrated by the results by Deurenberg et al.8 examining validity of BIA among various European population groups where significant differences in biases for the prediction of BF% among participants from the European centres were reported. However, 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 be a good method to estimate BF% in healthy subjects with normal BF distribution;9 however, several studies9, 10 have suggested that this may not be the case in obese individuals, where BIA tends to underestimate BF% (BF% >30%). Further, BIA measurements allow the determination of anatomic locations of BF depositions (for example, central and peripheral), and thus are also applied to compare BF proportions.11

It has also been argued that composition measurement instruments such as BIA that rely on constant body hydration and thus do not regard health inequalities such as obesity and diabetes, which are associated with hydration alterations of FFM, may not be sufficiently accurate in estimating BF, and that four- or three-compartment models are a better alternative under these circumstances.12 However, in field studies, or surveys including many subjects, clearly such advanced three- or four-compartment models are not useful and the simpler methods, such as impedance, are needed.

A number of studies have measured BIA in random population samples to either derive reference values for BF and FFM or relate such body composition measures to subsequent development of disease and/or mortality. Body mass index (BMI) measures the degree of relative overweight and does not differentiate between lean and fat mass.13, 14 Previous studies suggest that this may be part of the reason for the general finding of a U-formed association between BMI and cardiovascular, as well as total, mortality. The present review extracts estimations of average BF% from published studies among healthy populations and discusses the relationship between overweight and body composition in the context of using BIA to measure body composition.

Application of BIA for body composition

BIA has been used in large cohort studies, such as NHANES (USA), NUGENOB (EU) or MONICA (DK), to predict body composition (for example, fat mass (FM) and BF%) in individuals, and has been found to be useful in large-scale epidemiological studies.15 Consequently, BIA has an important part in contributing to develop and compare body composition across populations, but a summary of results from such studies has rarely been presented. Table 1 gives an overview of such studies published in the period 1991–2009.

Table 1 Percentage of BF% from BIA by gender and age from published studies across different populations

However, comparison of BIA-derived body composition measures must be done with care, as BIA measures are dependent on several factors such as, for instance, age, gender, ethnicity and the presence of medical conditions.16 In this regard, differences in limb-to-trunk length contribute greatly to variations in BIA for more or less the same body composition. Indeed, one study17 compared four groups of different ethnic identities, including the Aboriginals from Australia, and found that, except for the Aboriginals, the associations between body weight and resistance (impedance) were generally constant in the different ethnic groups, once height and age differences had been considered. Thus, this study indicated that the relationship between body size and body composition (total body water or FFM) after all may involve a certain universality that is independent of the population specificity for impedance measurement.

Table 1 gives BF% data from European, Asian and US studies published previously, and shows that there are marked differences in BF% between groups of the same age and gender. Such differences are also seen for BMI. Figures 1 and 2 show the distribution of BF% with age in men and women from some of the larger cohorts published between 1991 and 2009. The figure shows that BF% generally is higher the higher the age, especially within the European population, whereas the Japanese and US populations tend to level off above the age of 60. between the Asian and European populations—with higher BF% values for the US population, particularly among black Americans and in the age group of 20–60 years. Thereafter, differences appear to be smaller between the Caucasian populations in Europe and the US, whereas only little difference in BF% is seen with age for the Asian population. In fact, it appears that among Asian men, BF% is lower for older men compared with the BF% of younger men.

Figure 1
figure1

BF% distribution with age in women from published cohort studies: Denmark;51 Switzerland;53 USA;50 Japan;55 and Hong Kong.49 For illustration of BF% from data of Sung et al.,49 average of BF% was calculated for subjects aged 14–15.9, 16–17.9 and 18–19.9 years.

Figure 2
figure2

BF% distribution with age in men from published cohort studies: Denmark;51 Switzerland;53 USA;50 Japan;55 and Hong Kong.49 For illustration of BF% from data of Sung et al.,49 average of BF% was calculated for subjects aged 14–15.9, 16–17.9 and 18–19.9 years.

BF% and BMI

BMI has been used as a key component for measuring risk related to obesity in a variety of epidemiological studies, and is generally considered to be a good indicator of obesity. As BMI is calculated by total body mass (FM and FFM),14 it allows no differentiation between adipose tissue and lean mass. BMI correlates with total BF content18 and BF%.13, 19 However, some studies6, 20, 21 have suggested that BMI is better correlated with BF (kg) than BF% because BMI does not differentiate between BF and body lean mass. Other studies6, 22, 23, 24, 25 have found that BMI was not very good at quantifying either BF or BF%.

Moreover, although BMI in reality is somewhat of limited use in relation to correctly assessing BF and obesity,26 BMI is widely used as a marker for risk of disease and premature death because of its simplicity, and because a high BMI generally associates with increased risk of disease compared with average levels of BMI, at least before the age of 60–65 years.1, 13, 18, 21, 27 However, in reality, the association between BMI and morbidity or mortality is U-shaped, and compared with a low BMI, a high BMI is generally not related to excess morbidity or mortality. See below.

Thus, BMI is a crude surrogate for obesity,13 and may introduce a bias in relation to understanding the importance of obesity for health outcomes.23 For instance, a cross-sectional study with 1928 schoolchildren categorised 17.9% of boys and 12.8% of girls as obese using BF% from BIA, whereas only 7.4% of boys and 6.3% of girls were considered obese based on BMI, and the authors concluded that determination of BF% in addition to BMI seems to be a necessity to correctly identify childhood obesity.28 These results are in accordance with those of Dugas et al.,29 who suggested that BMI alone may not be an equivalent method to determine adiposity in a group of multi-ethnic (non-Hispanic white, non-Hispanic black and Mexican-American) adolescents. It was argued that BMI standard cut-off points for a healthy weight range of 18.5–<25 kg/m2 that had been developed for international use,1 but mostly refer to data from Caucasian populations, are not to be used for populations other than Caucasians.30, 31

Variations in BF% vs BMI are large, and such variations are, as indicated earlier, dependent on age, ethnicity and degree of obesity. At higher levels of obesity, fatness is generally underestimated when using BMI formulas, and because body composition varies with age BMI also tends to underestimate BF% in younger subjects and overestimate it in older subjects.8

In addition, even when on average the same individuals are characterised as using BIA and BMI, variation is often greater for the estimates derived from BMI, as evidenced by a study by Heitmann,6 who compared different methods for evaluating BF based on population-specific equations derived from either BMI, skinfold or BIA, and showed that all three techniques provided reliable average BF estimates (as expected relating to the population-specific equations). However, fat estimated from BIA showed a lower variance than that from BMI and skinfolds, and both the s.e. of estimate and the s.d. were higher, and the R value lower when BF was estimated from BMI and skinfolds than from BIA.

Figures 3 and 4 show the correlation between BMI and BF% estimated from BIA vs BF% estimated from a four-compartment reference model, which was derived from the measurement of total body water by dilutometry and whole-body potassium counting by scintigraphy. The data presented represent information from a subset of Danes from the Danish MONICA study conducted in 1987–1988 among 72 men and 67 women aged 35–65 years.6

Figure 3
figure3

Correlation between BMI and a four-compartment reference model in 72 men and 67 women.6

Figure 4
figure4

Correlation between BF% estimated from BIA and a four-compartment reference model in 72 men and 67 women.6

The figures show, as expected, markedly greater variation in BF% by BMI than by BIA-estimated BF%. However, it also shows that men and women are largely separated into two groups, with either generally higher (women) or lower (men) BF% when BMI is expressed as the function of BF% measured by the reference method, whereas men and women are clustered in two distinct groups across the full range of BF% when BF% by BIA is expressed as a function of the reference measure. The large variation in BF% for the given BMI is further illustrated in Figure 5, which shows the scatter of BF% vs BMI, here for the entire sample of 1528 men aged 35–65 years from the Danish MONICA study conducted in 1987–1988.6 As can be seen, there were large variations in BF% for a given BMI—for instance, BF% varied between 7 and almost 40% for men with a BMI of 25 kg/m2. This observation is furthermore in agreement with several other studies,20, 23, 26, 29 for instance, the NHANES cohort, showing that for a BMI of 25 kg/m2, BF% in men ranged widely from 13.8 to 35.3% and from 26.4 to 32.8% in women, respectively.13 On the contrary, a population-based study32 including 26 942 subjects found that BF% measured from BIA was strongly correlated with BMI and waist circumference more in women than in men.

Figure 5
figure5

Distribution of BF% for any given BMI value.51

Relation between BMI and BF% to CVD and mortality

A large number of studies14, 19, 22, 33, 34, 35, 36have demonstrated the J- or U-shaped relation between BMI and mortality, where both a high and a low BMI is significantly associated with increased risk of death.37 It has been hypothesised that the J- or U-shaped association was dependent on smoking status and pre-existing diseases in individuals.19, 38 However, a recent study39 of almost 900 000 participants (aged 35–89 years) based on 57 prospective studies and four continents found that the U-shaped relationship persisted when excluding smokers and even all subjects with a presence of chronic pre-existing disease. Similar findings were noted in a large Chinese40 and a large American cohort.41 Heitmann et al.22 and also Allison et al.33 explained this phenomenon of the U-shaped association by the fact that BMI is a compound of mainly FM and FFM which have opposite effects on mortality, and where the relationship between FM and mortality increases monotonically, whereas that between FFM and mortality monotonically decreases. Until this date, remarkably little research has been carried out on how body composition, rather than BMI, influences mortality risk. However, a few studies should be mentioned. For instance, the long-term population-based study of Calling et al.32 that showed that especially a high BF% was associated with increased cardiovascular risk as BF% emerged as an independent risk factor for coronary events and cardiovascular disease (CVD) death in men, respectively, and coronary event and ischemic stroke in women. Another study42 found significant positive correlations between BF% and coronary heart disease risk factors, in white non-Hispanic and Hispanic male and Hispanic female college students.

The group of Marques-Vidal et al.,43 in their paper, discussed whether BF%, BMI or waist:hip ratio (WHR) was the better predictor of high (>5%) 10-year risk of fatal CVD, and found stronger associations between BF% and risk than between BMI and risk. In fact, in this study, development of CVD over 10 years was three times higher for those with the higher BF% than for those with high BMI. These results are also in accordance with those of Singh et al.,44 who found a strong association between BF% and coronary artery disease and the coronary risk factors hypertension, hypercholesterolemia, diabetes mellitus and sedentary lifestyle in Indian men.

In addition, Heitmann et al.45 reported significant associations between FM or FFM and total mortality, which were furthermore found to vary with the level of physical activity. Finally, Lahmann et al.19 assessed the effect of body composition on all-cause mortality in a Swedish cohort of 28 098 men and women, aged 45–73 years, and found a larger hazard associated with a high BF% than a high BMI in both sexes. BF% of >35% among middle-aged individuals (45- to 59-year-olds) was found to entail the highest estimated hazard ratio. Further, men with a BF% ranging from 17 to 20% were found to have the lowest mortality risk, whereas a BF% of >25% was associated with the highest mortality. Comparison of mortality with lean body mass showed that the higher the lean body mass, the lower the mortality risk in men.

However, not all studies find an advantage of using body composition measures over BMI. For instance, Dolan et al.41 demonstrated no obvious advantage of predicting mortality in women aged 65 years and older BIA-measured BF% compared with BMI or waist circumference, and Simpson et al.46 concluded that waist circumference and WHR were better predictors of risk of all-cause mortality than BIA in the Melbourne Collaborative Cohort Study, carried out with 41 313 men and women (aged 27–75 years, with most (99.3%) aged 40–69 years). The same cohort found WHR, body height and waist circumference to be better predictors of colon cancer risk than general adiposity, expressed as BMI or BF% among 24 072 women.47 Further, a prospective study with 10 564 men of the Malmö Diet and Cancer cohort found no association between general adiposity and risk of prostate cancer, but did find a relationship with WHR and body height.48

There is also discussion about rather focusing on lean mass parameters (lean mass or lean mass index) than on BMI, as lean mass parameters, but not other body composition parameters (for example, FM), showed a significant association with risk factors for all-cause mortality in the elderly Asian population examined by Han et al.27

Conclusion

The present review shows that BIA is a valid and precise method for predicting body composition under controlled conditions in healthy subjects. BIA can be used to estimate fat and lean mass, both of which associate linearly with morbidity and mortality in contrast to BMI where a U-formed association is found. BMI, however, is a simple but inaccurate method to estimate both BF% and risk of diseases/mortality, because BMI neither allows distinctions between FM and FFM nor considers changes that occur with age. BF% measured by BIA increases with age, with marked differences for a given age and gender between different populations.

However, further research may be necessary to conclude firmly whether BIA measurements are more effective than BMI, waist circumference and WHR for predicting all-cause mortality.

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Böhm, A., Heitmann, B. The use of bioelectrical impedance analysis for body composition in epidemiological studies. Eur J Clin Nutr 67, S79–S85 (2013). https://doi.org/10.1038/ejcn.2012.168

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Keywords

  • bioelectrical impedance
  • percentage body fat
  • body mass index
  • mortality

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