Skip to main content

Thank you for visiting 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.

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



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.


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.


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

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

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

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

Figure 4

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

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


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.


  1. 1

    Eveleth PB . Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. Am J Hum Biol 1996; 86: 786–787.

    Article  Google Scholar 

  2. 2

    Baumgartner RN, Chumlea WC, Roche AF . Estimation of body composition from bioelectric impedance of body segments. Am J Clin Nutr 1989; 50: 221–226.

    CAS  Article  Google Scholar 

  3. 3

    Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gomez JM et al. Bioelectrical impedance analysis--part I: review of principles and methods. Clin Nutr 2004; 23: 1226–1243.

    Article  Google Scholar 

  4. 4

    Heitmann BL . Impedance: a valid method in assessment of body composition? Eur J Clin Nutr 1994; 48: 228–240.

    CAS  Google Scholar 

  5. 5

    Segal KR, Van LM, Fitzgerald PI, Hodgdon JA, Van Itallie TB . Lean body mass estimation by bioelectrical impedance analysis: a four-site cross-validation study. Am J Clin Nutr 1988; 47: 7–14.

    CAS  Article  Google Scholar 

  6. 6

    Heitmann BL . Evaluation of body fat estimated from body mass index, skinfolds and impedance. A comparative study. Eur J Clin Nutr 1990; 44: 831–837.

    CAS  PubMed  Google Scholar 

  7. 7

    Wells JC, Fewtrell MS . Measuring body composition. Arch Dis Child 2006; 91: 612–617.

    CAS  Article  Google Scholar 

  8. 8

    Deurenberg P, Andreoli A, Borg P, Kukkonen-Harjula K, de LA, van Marken Lichtenbelt WD et al. The validity of predicted body fat percentage from body mass index and from impedance in samples of five European populations. Eur J Clin Nutr 2001; 55: 973–979.

    CAS  Article  Google Scholar 

  9. 9

    Sun G, French CR, Martin GR, Younghusband B, Green RC, Xie YG et al. Comparison of multifrequency bioelectrical impedance analysis with dual-energy X-ray absorptiometry for assessment of percentage body fat in a large, healthy population. Am J Clin Nutr 2005; 81: 74–78.

    CAS  Article  Google Scholar 

  10. 10

    Pimentel GD, Bernhard AB, Frezza MR, Rinaldi AE, Burini RC . Bioelectric impedance overestimates the body fat in overweight and underestimates in Brazilian obese women: a comparison with Segal equation 1. Nutr Hosp 2010; 25: 741–745.

    CAS  PubMed  Google Scholar 

  11. 11

    Risica PM, Ebbesson SO, Schraer CD, Nobmann ED, Caballero BH . Body fat distribution in Alaskan Eskimos of the Bering Straits region: the Alaskan Siberia Project. Int J Obes Relat Metab Disord 2000; 24: 171–179.

    CAS  Article  Google Scholar 

  12. 12

    Ritz P, Salle A, Audran M, Rohmer V . Comparison of different methods to assess body composition of weight loss in obese and diabetic patients. Diabetes Res Clin Pract 2007; 77: 405–411.

    CAS  Article  Google Scholar 

  13. 13

    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 (Lond) 2008; 32: 959–966.

    CAS  Article  Google Scholar 

  14. 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.

    CAS  Article  Google Scholar 

  15. 15

    Sun SS, Chumlea WC, Heymsfield SB, Lukaski HC, Schoeller D, Friedl K et al. Development of bioelectrical impedance analysis prediction equations for body composition with the use of a multicomponent model for use in epidemiologic surveys. Am J Clin Nutr 2003; 77: 331–340.

    CAS  Article  Google Scholar 

  16. 16

    Dehghan M, Merchant AT . Is bioelectrical impedance accurate for use in large epidemiological studies? Nutr J 2008; 7: 26.

    Article  Google Scholar 

  17. 17

    Heitmann BL, Swinburn BA, Carmichael H, Rowley K, Plank L, McDermott R et al. Are there ethnic differences in the association between body weight and resistance, measured by bioelectrical impedance? Int J Obes Relat Metab Disord 1997; 21: 1085–1092.

    CAS  Article  Google Scholar 

  18. 18

    Pi-Sunyer FX, Becker DM, Bouchard C, Carleton RA, Colditz GA, Dietz WH et al. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: executive summary. Am J Clin Nutr 1998; 68: 899–917.

    Article  Google Scholar 

  19. 19

    Lahmann PH, Lissner L, Gullberg B, Berglund G . A prospective study of adiposity and all-cause mortality: the Malmo Diet and Cancer study. Obes Res 2002; 10: 361–369.

    Article  Google Scholar 

  20. 20

    Pasco JA, Nicholson GC, Brennan SL, Kotowicz MA . Prevalence of obesity and the relationship between the body mass index and body fat: cross-sectional, population-based data. PLoS One 2012; 7: e29580.

    CAS  Article  Google Scholar 

  21. 21

    Garrow JS, Webster J . Quetelet’s index (W/H2) as a measure of fatness. Int J Obes 1985; 9: 147–153.

    CAS  Google Scholar 

  22. 22

    Heitmann BL, Erikson H, Ellsinger BM, Mikkelsen KL, Larsson B . Mortality associated with body fat, fat-free mass and body mass index among 60-year-old swedish men-a 22-year follow-up. The study of men born in 1913. Int J Obes Relat Metab Disord 2000; 24: 33–37.

    CAS  Article  Google Scholar 

  23. 23

    Rothman KJ . BMI-related errors in the measurement of obesity. Int J Obes (Lond) 2008; 32 (Suppl 3), S56–S59.

    Article  Google Scholar 

  24. 24

    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.

    CAS  Article  Google Scholar 

  25. 25

    Roubenoff R, Dallal GE, Wilson PW . Predicting body fatness: the body mass index vs estimation by bioelectrical impedance. Am J Public Health 1995; 85: 726–728.

    CAS  Article  Google Scholar 

  26. 26

    Luke A, Durazo-Arvizu R, Rotimi C, Prewitt TE, Forrester T, Wilks R et al. Relation between body mass index and body fat in black population samples from Nigeria, Jamaica, and the United States. Am J Epidemiol 1997; 145: 620–628.

    CAS  Article  Google Scholar 

  27. 27

    Han SS, Kim KW, Kim KI, Na KY, Chae DW, Kim S et al. Lean mass index: a better predictor of mortality than body mass index in elderly Asians. J Am Geriatr Soc 2010; 58: 312–317.

    Article  Google Scholar 

  28. 28

    Antal M, Peter S, Biro L, Nagy K, Regoly-Merei A, Arato G et al. Prevalence of underweight, overweight and obesity on the basis of body mass index and body fat percentage in Hungarian schoolchildren: representative survey in metropolitan elementary schools. Ann Nutr Metab 2009; 54: 171–176.

    CAS  Article  Google Scholar 

  29. 29

    Dugas LR, Cao G, Luke AH, Durazo-Arvizu RA . Adiposity is not equal in a multi-race/ethnic adolescent population: NHANES 1999-2004. Obesity (Silver Spring) 2011; 19: 2099–2101.

    Article  Google Scholar 

  30. 30

    Craig P, Halavatau V, Comino E, Caterson I . Differences in body composition between Tongans and Australians: time to rethink the healthy weight ranges? Int J Obes Relat Metab Disord 2001; 25: 1806–1814.

    CAS  Article  Google Scholar 

  31. 31

    Gurrici S, Hartriyanti Y, Hautvast JG, Deurenberg P . Relationship between body fat and body mass index: differences between Indonesians and Dutch Caucasians. Eur J Clin Nutr 1998; 52: 779–783.

    CAS  Article  Google Scholar 

  32. 32

    Calling S, Hedblad B, Engstrom G, Berglund G, Janzon L . Effects of body fatness and physical activity on cardiovascular risk: risk prediction using the bioelectrical impedance method. Scand J Public Health 2006; 34: 568–575.

    Article  Google Scholar 

  33. 33

    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 Relat Metab Disord 2002; 26: 410–416.

    CAS  Article  Google Scholar 

  34. 34

    Troiano RP, Frongillo EA, Sobal J, Levitsky DA . The relationship between body weight and mortality: a quantitative analysis of combined information from existing studies. Int J Obes Relat Metab Disord 1996; 20: 63–75.

    CAS  Google Scholar 

  35. 35

    Chen Z, Yang G, Offer A, Zhou M, Smith M, Peto R et al. Body mass index and mortality in China: a 15-year prospective study of 220 000 men. Int J Epidemiol 2012; 41: 472–481.

    Article  Google Scholar 

  36. 36

    Song X, Pitkaniemi J, Gao W, Heine RJ, Pyorala K, Soderberg S et al. Relationship between body mass index and mortality among Europeans. Eur J Clin Nutr 2012; 66: 156–165.

    CAS  Article  Google Scholar 

  37. 37

    Calle EE, Thun MJ, Petrelli JM, Rodriguez C, Heath CW . Body-mass index and mortality in a prospective cohort of U.S. adults. N Engl J Med 1999; 341: 1097–1105.

    CAS  Article  Google Scholar 

  38. 38

    Manson JE, Stampfer MJ, Hennekens CH, Willett WC . Body weight and longevity. a reassessment. JAMA 1987; 257: 353–358.

    CAS  Article  Google Scholar 

  39. 39

    Whitlock G, Lewington S, Sherliker P, Clarke R, Emberson J, Halsey J et al. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet 2009; 373: 1083–1096.

    Article  Google Scholar 

  40. 40

    Lin WY, Tsai SL, Albu JB, Lin CC, Li TC, Pi-Sunyer FX et al. Body mass index and all-cause mortality in a large Chinese cohort. CMAJ 2011; 183: E329–E336.

    Article  Google Scholar 

  41. 41

    Dolan CM, Kraemer H, Browner W, Ensrud K, Kelsey JL . Associations between body composition, anthropometry, and mortality in women aged 65 years and older. Am J Public Health 2007; 97: 913–918.

    Article  Google Scholar 

  42. 42

    Koutoubi S, Huffman FG . Body composition assessment and coronary heart disease risk factors among college students of three ethnic groups. J Natl Med Assoc 2005; 97: 784–791.

    PubMed  PubMed Central  Google Scholar 

  43. 43

    Marques-Vidal P, Bochud M, Mooser V, Paccaud F, Waeber G, Vollenweider P . Obesity markers and estimated 10-year fatal cardiovascular risk in Switzerland. Nutr Metab Cardiovasc Dis 2009; 19: 462–468.

    CAS  Article  Google Scholar 

  44. 44

    Singh RB, Niaz MA, Beegom R, Wander GS, Thakur AS, Rissam HS . Body fat percent by bioelectrical impedance analysis and risk of coronary artery disease among urban men with low rates of obesity: the Indian paradox. J Am Coll Nutr 1999; 18: 268–273.

    CAS  Article  Google Scholar 

  45. 45

    Heitmann BL, Hills AP, Frederiksen P, Ward LC . Obesity, leanness, and mortality: effect modification by physical activity in men and women. Obesity (Silver Spring) 2009; 17: 136–142.

    Article  Google Scholar 

  46. 46

    Simpson JA, MacInnis RJ, Peeters A, Hopper JL, Giles GG, English DR . A comparison of adiposity measures as predictors of all-cause mortality: the Melbourne Collaborative Cohort Study. Obesity (Silver Spring) 2007; 15: 994–1003.

    Article  Google Scholar 

  47. 47

    MacInnis RJ, English DR, Hopper JL, Gertig DM, Haydon AM, Giles GG . Body size and composition and colon cancer risk in women. Int J Cancer 2006; 118: 1496–1500.

    CAS  Article  Google Scholar 

  48. 48

    Wallstrom P, Bjartell A, Gullberg B, Olsson H, Wirfalt E . A prospective Swedish study on body size, body composition, diabetes, and prostate cancer risk. Br J Cancer 2009; 100: 1799–1805.

    CAS  Article  Google Scholar 

  49. 49

    Sung RY, So HK, Choi KC, Li AM, Yin J, Nelson EA . Body fat measured by bioelectrical impedance in Hong Kong Chinese children. Hong Kong Med J 2009; 15: 110–117.

    PubMed  Google Scholar 

  50. 50

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

    CAS  Article  Google Scholar 

  51. 51

    Heitmann BL . Body fat in the adult Danish population aged 35-65 years: an epidemiological study. Int J Obes 1991; 15: 535–545.

    CAS  PubMed  Google Scholar 

  52. 52

    Pichard C, Kyle UG, Bracco D, Slosman DO, Morabia A, Schutz Y . Reference values of fat-free and fat masses by bioelectrical impedance analysis in 3393 healthy subjects. Nutrition 2000; 16: 245–254.

    CAS  Article  Google Scholar 

  53. 53

    Kyle UG, Genton L, Lukaski HC, Dupertuis YM, Slosman DO, Hans D et al. Comparison of fat-free mass and body fat in Swiss and American adults. Nutrition 2005; 21: 161–169.

    Article  Google Scholar 

  54. 54

    Lahmann PH, Lissner L, Gullberg B, Berglund G . Sociodemographic factors associated with long-term weight gain, current body fatness and central adiposity in Swedish women. Int J Obes Relat Metab Disord 2000; 24: 685–694.

    CAS  Article  Google Scholar 

  55. 55

    Nagaya T, Yoshida H, Takahashi H, Matsuda Y, Kawai M . Body mass index (weight/height2) or percentage body fat by bioelectrical impedance analysis: which variable better reflects serum lipid profile? Int J Obes Relat Metab Disord 1999; 23: 771–774.

    CAS  Article  Google Scholar 

Download references


Publication of this article was supported by a grant from seca Gmbh & Co. KG, Hamburg, Germany.

Author information



Corresponding author

Correspondence to B L Heitmann.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Böhm, A., Heitmann, B. The use of bioelectrical impedance analysis for body composition in epidemiological studies. Eur J Clin Nutr 67, S79–S85 (2013).

Download citation


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

Further reading


Quick links