Body mass index and percent body fat: a metaanalysis among different ethnic groups

P Deurenberg1, M Yap2 and WA van Staveren1

1Division of Human Nutrition and Epidemiology, Wageningen Agricultural University, 6703 HD Wageningen, The Netherlands and2Department of Nutrition, Level 5, Institute of Health, Singapore

OBJECTIVE: To study the relationship between percent body fat and body mass index (BMI) in different ethnic groupsand to evaluate the validity of the BMI cut-off points for obesity.DESIGN: Meta analysis of literature data.SUBJECTS: Populations of American Blacks, Caucasians, Chinese, Ethiopians, Indonesians, Polynesians and Thais.MEASUREMENTS: Mean values of BMI, percent body fat, gender and age were adapted from original papers.RESULTS: The relationship between percent body fat and BMI differs in the ethnic groups studied. For the same levelof body fat, age and gender, American Blacks have a 1.3 kg=m2 and Polynesians a 4.5 kg=m2 lower BMI compared toCaucasians. By contrast, in Chinese, Ethiopians, Indonesians and Thais BMIs are 1.9, 4.6, 3.2 and 2.9 kg=m2 lowercompared to Caucasians, respectively. Slight differences in the relationship between percent body fat and BMI ofAmerican Caucasians and European Caucasians were also found. The differences found in the body fat=BMI relation-ship in different ethnic groups could be due to differences in energy balance as well as to differences in body build.CONCLUSIONS: The results show that the relationship between percent body fat and BMI is different among differentethnic groups. This should have public health implications for the de®nitions of BMI cut-off points for obesity, whichwould need to be population-speci®c.

Keywords: body fat; body mass index; body build; obesity; cut-off values; ethnic; race

Introduction

Obesity is a global problem,1 as the prevalence ofobesity is increasing in developed, as well as in lessdeveloped, countries.1,2 Obesity is characterised by anincreased amount of body fat, de®ned in young adultsas body fat > 25% in males and > 35% in females,3,4

corresponding to a body mass index (BMI) of30 kg=m2 in young Caucasians.3 The amount ofbody fat can be determined in vivo, by a number ofmethods such as underwater weighing, deuteriumdilution, dual energy X-ray absorptiometry (DEXA)or skinfold thickness measurements. For epidemiolo-gical studies the BMI or Quetelet-index,5 de®ned asbody weight divided by height squared (kg=m2), isregarded as the most suitable indicator for overweightand obesity. Based on Garrow,6 the World HealthOrganisation (WHO) recommends a BMI cut-offpoint for overweight at 25 kg=m2, whereas for obesitya cut-off point of 30 kg=m2 is recommended.1,3

From the physiological point of view, it is not thedegree of excess weight (as is measured by, forexample, the BMI), but the degree of body fatnessthat is important as a risk factor. This has already been

recognised by Behnke et al 7 who showed, usingunderwater weighing, that overweight does not neces-sarily coincide with an excess of body fat.

It is known that the relationship between BMI andbody fat is age- and gender- dependent.8 ± 13 Althoughthere are a number of publications in which nodifferences in the relationship between body fat andBMI of ethnic groups were found,12,13 some recentstudies indicate that in some populations these differ-ences may well exist.14 ± 22 Those differences may bedue to differences in body build,14,15 as well asdifferences in energy intake and physical activity.20

Therefore the use of different cut-off points fordifferent population groups may be necessary. Thereis, however, a lack of adequate information world-wide, but especially in less developed countries. Thislack of adequate information is recognised by theWHO in its recent report, in which it is stated thatthe BMI cut-off values for overweight and obesitymay not correspond with the same degree of fatnessacross different populations.1

The use of adequate cut-off points is of greatimportance in establishing reliable prevalence ®guresfor obesity and consequent public health policies.Based on the calculations of Rose,23 it can be con-cluded that lowering the cut-off point for obesity from30 kg=m2 to 27 kg=m2 could increase the prevalenceof obesity in a population by as much as 14 percentagepoints. Based on data from a representative study inSingapore, a lowering of the BMI cut-off point from30 kg=m2 to 27 kg=m2 would result in an increase in

Correspondence: Dr Paul Deurenberg, Division of HumanNutrition and Epidemiology, Wageningen AgriculturalUniversity, Bomenweg 2, 6703 HD Wageningen, TheNetherlands.Received 30 January 1998; revised 26 May 1998; accepted1 July 1998

International Journal of Obesity (1998) 22, 1164±1171ß 1998 Stockton Press All rights reserved 0307±0565/98 $12.00

http://www.stockton-press.co.uk/ijo

the prevalence of obesity from 6.2% to 15.3% (1993,unpublished results).

The aim of the present paper was to get moreinsight into differences among different ethnicgroups of the relationship between percent body fatand BMI. For this, available data from studies on bodyfat and BMI were collected and analysed. In theanalysis, age and gender differences were accountedfor.

Subjects and methods

Data from 32 studies9 ± 12,16 ± 22,24 ± 45 were analysed,consisting (in total) of 11 924 subjects, 5563 femalesand 6361 males. Criteria for inclusion of these studiesfrom the literature using Medline were based onadequate information on (mean) body fat, (mean)BMI, gender and ethnic group. In addition, the meth-odology used for body fat determination should ide-ally be a `reference method'. Key words that havebeen used were body composition, body fat, BMI,Quetelet index, race and ethnicity. Some of the usedstudies could not be found with the computer searchand were added from personal records.

Caucasians were used as a `reference' group, forcomparison with data of other ethnic groups. Amer-ican, Australian and European Caucasians were ana-lysed as one group, as no information was available ontheir ancestry, but they were also separately studied.Available data on Blacks were generally data on`American' Blacks. They were separately analysedfrom Ethiopians. Despite the small numbers of Ethio-pians, they were included in the analysis as theirresults clearly differed from the other Black groups.

Chinese were treated as one group, although theremight be differences among Chinese of differentorigin. Here also, only limited information was avail-able about ancestry.

For 28 data points, body fat was determined bydensitometry (underwater weighing), in 26 studies bydual energy X-ray absorptiometry (DEXA), in 13studies by dilution techniques (deuterium oxide dilu-tion), in 13 studies by a three or four-compartmentmodel and in 14 studies using bioimpedance analysis

or skinfold thickness measurements. The principles ofthe used methodologies are described in detail else-where.46 It is assumed that densitometry, DEXA,deuterium dilution and a multi-compartment model,give valid and comparable results within 2 ± 3% bodyfat at a group level.21,33,34,44 The studies in whichskinfolds or impedance were used to determine bodyfat were included in the analysis because they con-cerned speci®c ethnic groups for which no otherinformation was available.

The mean values of BMI, body fat and age fromeach study were used in the statistical analysis assingle data points. If BMI was not reported in theoriginal study, the index was calculated from the(mean) weight and height.

As most data were from studies in Caucasians,these data were used to develop a prediction equationfor body fat from BMI, taking the effects of genderand age into account. Different models were tested,but stepwise multiple linear regression techniques47

with gender coded as dummy variable (females� 0,males� 1), appeared to have a higher explainedvariance and=or lowest prediction error compared tocurvilinear models (using BMI squared). This predic-tion equation was applied to the different ethnicpopulation groups and the residuals (measuredminus predicted percent body fat) were calculatedand tested for signi®cance from zero. Differences inslope and=or intercept between regression lines weretested using the technique described by Kleinbaum etal. 47 Males and females were generally analysedtogether using a dummy variable to increase thepower of statistical tests. Analysis of (co)variance(AN(C)OVA) was used to compare differences inBMI between different ethnic groups, taking differ-ences in gender distribution, age and body fat intoaccount. For the statistical calculations SPSS forWindows48 was used. Data are presented as mean�standard error (s.e.m.).

Results

Table 1 shows the distribution of males and femalesparticipating in the different (ethnic) groups as well as

Table 1 Characteristics of the subjects in the different ethnic groups

Used referencemethod for body fatAge Body fat BMI

n Females=males (y) (%) (kg=m2) UWW D2O DEXA 3(4)C Skinfolds Impedance

mean s.e.m. mean s.e.m. mean s.e.m.Caucasian 2516=1976 44.1 2.1 27.2 1.1 24.1 0.3 25 4 10 7Black 1109=849 43.0 1.9 26.6 2.0 25.7 0.5 1 2 4 4 6Ethiopian 20=24 34.0 2.5 30.7 5.5 21.3 0.2 2Chinese 926=684 51.1 4.5 26.1 1.4 22.2 0.3 2 3 10Thai 838=2703 43.3 2.2 32.0 1.8 23.0 0.3 2 6Indonesian 74=77 31.3 4.3 29.3 3.7 22.4 0.7 2 2Polynesian 80=48 38.3 1.8 28.2 6.5 29.7 0.1 2

BMI�body mass index (kg=m2); UWW�underwater weighing; D2O�Deuterium oxide dilution; DEXA�dual energy X-rayabsorptiometry; 3(4)C�3(4)-compartment model.

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1165

their age, percent body fat and BMI, and the body fatreference techniques that were used. Mean age in theseparate studies ranged from 18.4 ± 76.6 y, mean bodyfat from 11.4 ± 43.9% and mean BMI from 19.3 ±36.0 kg=m2. The unadjusted relationship between per-cent body fat and BMI is presented in Figure 1 formales and females. It is obvious that differences inpercent body fat for the same BMI can be remarkable.Part of this difference is due to difference in genderand difference in age.

In Table 2, the regression coef®cients of the step-wise multiple linear regression for the total Caucasianpopulation are given. Gender alone explains 52% ofthe variance in body fat. The complete regressionequation includes gender, age and BMI as indepen-dent variables. Combined, these variables explain88% of the variation in body fat within the Caucasianpopulations. There was no interaction between BMIand gender, and BMI and age. The residuals (mea-sured minus predicted values) were not related to ageand gender, but were positively correlated with bodyfat (r� 0.35, P< 0.05), meaning that at higher levelsof percent body fat the prediction formula under-estimates body fat.

Table 3 gives the differences between measuredbody fat (as reported in the original paper) andpredicted body fat for the different male and femalegroups, using the prediction formula from Table 2.For Ethiopians and Polynesians no s.e.m. is provided

in the table, as there were only data from one study ineach subgroup.

Figure 2a,b,c,d show plots of measured vs predictedvalues in relation to the line of identity (measuredvalue equals predicted value). As expected, the Cau-casian data points are all located around the line ofidentity (Figure 2a). Also Chinese data points tend tobe around the line of identity (Figure 2b), but body fatseems to be underestimated at lower BMI levels andoverestimated at higher BMI levels. Polynesian andBlack data points (Figure 2c) are generally below theline of identity, meaning that for the same BMI, ageand gender, these ethnic groups have lower body fat.In Indonesians, Thais and Ethiopians the Caucasianprediction formula underestimates body fat, thus inthese population groups, body fat is relatively high incomparison with the BMI (Figure 2d). Only theChinese data points are not parallel with the line ofidentity (P< 0.05). Separate multiple regression ana-lysis using the 15 Chinese data points, showed that theregression coef®cient for age is slightly lower(0.137� 0.028) and that the regression coef®cientfor BMI (0.774� 0.490) is much lower, compared tothe Caucasians. The regression coef®cient for genderwas not different when compared to the Caucasians.

Figure 3 shows that European Caucasians andAmerican Caucasians also differ slightly in theirrelationship between body fat and BMI. The twoindicated regression lines are not different in slope(P� 0.16), but are different in intercept (3.8% bodyfat (P< 0.05)). When not corrected for age andgender, the overall difference between measured andpredicted body fat was 3.6% between Europeans andAmericans. This difference was consistent for thedifferent methodologies used for the body fat mea-surements and was 0.4� 0.3% for multi-compartmentmodels (P� 0.05), 3.3� 1.8% for deuterium dilution(P� 0.2), 4.0� 0.7% for underwater weighing(P< 0.01) and 5.3� 1.3% for DEXA (P< 0.01).Although the subjects in the European studies hadhigher (P< 0.05) body fat (28.4� 8.7) compared tothe Americans (23.4� 4.7), this difference was not

Figure 1 Percent body fat and body mass index (BMI) in males(solid squares) and females (open squares) of all ethnic groups

Table 2 Regression of percent body fat as dependent variable,and body mass index (BMI), age and gender as independentvariables for the Caucasian studies

Age BMI InterceptGender (y) (kg=m2) (%) SEE

b s.e.m. b s.e.m. b s.e.m. Mean s.e.m. (%) R2

710.5 1.5 ± ± ± ± 32.4 1.1 5.2 0.52710.5 0.9 0.28 0.03 ± ± 20.0 1.6 3.2 0.81711.4 0.8 0.20 0.03 1.294 0.253 78.0 5.6 2.5 0.88

b� regression coef®cient; Gender: males�1, females� 0; s.e.m.standard error of the mean, SEE� standard error of estimate.All b's signi®cant (P<0.05).

Table 3 Differences between measured and predicted body fatfrom body mass index (BMI), in the different ethnic groups,using the Caucasiana prediction equation

Females Males

Ethnic group mean s.e.m. mean s.e.m.

Caucasian 0.1 0.5 0.1 0.5American Black 71.9* 0.8 71.9 1.0Ethiopianb 10.0 ± 9.9 ±Chinese 70.0 0.7 1.0 0.7Thai 5.9* 0.3 7.6* 0.6Indonesian 8.8* 1.2 6.7 1.3Polynesianb 73.9 ± 74.1 ±

aPrediction equation (Table 2): Percent body fat (%BF)�1.294�BMI� 0.20�Age ± 11.4�Gender ± 8.0bNo s.e.m. provided because only one set of data for males andfemales*P< 0.05 from zero, otherwise not signi®cant or too small to betested (Indonesian, Ethiopian, Polynesian)

Ethnic differences in BMI and fatnessP Deurenberg et al

1166

responsible for the difference between measured andpredicted value (ANCOVA, results not shown). Also,age and gender difference between European andAmerican Caucasians did not explain the difference.

In Figure 4 the mean difference in BMI correctedfor age, gender and body fat, in the different ethnicgroups compared to Caucasians (as a reference), isgiven. The differences in BMI for a given value ofbody fat, age and gender are in some populationslarge. The BMI of Indonesians for example is3.2 kg=m2 lower for the same level of body fatness.

Discussion

The assessment of body fat in populations is importantfor public health policies related to overweight andobesity. Government and responsible institutionsrequire accurate ®gures of obesity prevalence, to

Figure 2 Relationship between measured and predicted body fat in different ethnic groups using a Caucasian prediction formula.

Figure 3 Relationship between measured and predicted bodyfat in Caucasians of different origin.

Figure 4 Adjustments to be made in body mass index (BMI) tore¯ect equal levels of body fat compared to Caucasians of thesame age and gender (mean, 95% con®dence interval). Differ-ence in BMI (kg/m2) differences from BMI cut-off point assuggested by the World Health Organisation (WHO) 1; ns�notsigni®cant; *P< 0.05.

Ethnic differences in BMI and fatnessP Deurenberg et al

1167

adequately address the issue of obesity-related mor-bidity and mortality. For population studies, the BMIis generally accepted as a measure of body fatness,1,3

although there is evidence that BMI may re¯ectdifferent levels of body fatness in different ethnicgroups.14 ± 22 However, not all studies among differentethnic groups showed differences in the relationshipbetween body fat and BMI.12,13 It has been longestablished that age and gender8,10,11,12 determinethe relationship between body fat and BMI. Thusthese parameters have to be controlled for whenstudying the relationship between body fat and BMIamong different groups. Other factors could beinvolved, such as differences in body build,14,15 butfor most populations this information is not available.

In the present paper, data from the literature wereused. For this it was assumed that the methods used todetermine body fat in the different studies providedvalid estimates of body fat, at least at a populationlevel. Although there are studies showing that thedifferences between reference methods for measuringbody fat among populations are not verylarge,21,33,34,44 it cannot be excluded that some differ-ences in the present analysis may be due partly todifferences in methodology employed. These differ-ences could be due to technical error as well as toviolations of assumptions, such as for example, hydra-tion of fat free mass (FFM) and density of FFM. Itseems, however, unlikely that differences larger than2 ± 3% body fat are due to methodological differences.For this paper, only studies using reference methods(densitometry, deuterium oxide dilution, multi-com-partment models and DEXA) were used, with theexception of a few studies which used skinfolds orimpedance.17,20,28

The differences in body fat and BMI between thedifferent ethnic groups, as shown in Table 1, do notre¯ect real differences in these parameters betweenthe populations. The subjects in the selected studieswere by no means representative of their ethnicgroups. For the purpose of this study, validatingBMI as a measure of body fat, this criterion is alsonot necessary.

Most data were available from Caucasians andtherefore these data were used as reference points.Stepwise multiple regression (Table 2) resulted in aprediction equation for body fat of which the regres-sion coef®cients did not differ signi®cantly from theregression coef®cients found in an earlier study8 andas recently reported by Gallagher et al 12 in a studyamong Blacks and Whites. Curvilinear regression didnot result in better predicted values (results notshown). It is remarkable that there are differencesbetween the American Caucasians and the European(mainly UK and Netherlands) Caucasians, in therelationship between body fat and BMI (Figure 3),with the Europeans having a 3.8% higher body fat atthe same BMI level (after correction for age andgender). This is in line with results of Gallagher etal, 12 that the prediction equation developed in our

laboratory8 did overestimate body fat in CaucasianNew Yorkers. The difference is not due to differencesin age and gender distribution (these are controlled forin the prediction equation) between Americans andEuropeans, nor by differences in level of body fatbetween the two groups as was tested by ANCOVA.Different levels in energy intake and energy expendi-ture20 and=or differences in body build14,15 could bepossible explanations. No data were available aboutthe ancestry of the Caucasians and there are knowndifferences in body build within Caucasian popula-tions.

The prediction equation was applied to other ethnicgroups, and as can be seen in Table 3 and in Figures2a,b,c,d, there are in some populations rather largedifferences between measured and predicted body fat.The fact that these differences are not always statis-tically different (Table 3) is in part due to relativelysmall numbers of data points in subgroups. Thedifferences are comparable in males and females.

For Chinese, there were no signi®cant differencesbetween measured and predicted body fat values. It isnotable (see Figure 2b) that the Chinese data pointsare not parallel with the prediction line (line ofidentity). The regression between measured and pre-dicted value was, in the Chinese population, signi®-cantly different for the slope (P< 0.05) and intercept(P< 0.05) from the line of identity. Also the relation-ship between body fat, the BMI and age was differentin Chinese compared to Caucasians, showing a lowerregression coef®cient for BMI and a lower age effect,but a much larger intercept. For this, it seems likelythat body fat in the Chinese is over predicted by theprediction formula, especially at lower BMI values,where the impact of the (larger) intercept is greater.When BMI in the Chinese was corrected for body fat,taking age and gender into account, the Chinese had alower BMI for the same body fat (see also Figure 4).This is in accordance with ®ndings of Wang and co-workers,8,19 but in contrast with ®ndings of a study inBeijing Chinese.13

Differences and=or inconsistencies in the relation-ship between body fat and BMI in the Chinese couldbe due to differences in body build, northern Chinese(Beijing) having a bigger body build than southernChinese. It is known that Chinese have generallythinner bones than Caucasians.49 Bone mineral den-sity, however, is not signi®cantly different from Cau-casians, when corrected for weight, height and age.50

Also, Asians seem to have less muscle mass comparedto Whites, also after correction for differences in BMIor differences in FFM=height2.51 The studies of Wangand co-workers,18,19 only indicate `Asians,' but thesubjects were mainly Chinese (J Wang, personalcommunication). No information is, however, avail-able about region of origin, which could have beenhelpful in interpreting the results.

In American Blacks, as well as in Polynesians, theprediction equation overestimated body fat, meaningthat Blacks have lower body fat for the same BMI

Ethnic differences in BMI and fatnessP Deurenberg et al

1168

compared to Caucasians. When comparing AmericanBlacks and Caucasians, the differences between mea-sured and predicted body fat were not signi®cant,con®rming the ®ndings of Gallagher et al. 12 How-ever, Luke et al 20 found differences in the relation-ship of body fat=BMI also among Blacks living indifferent areas. As a possible explanation, Luke etal 20 discussed differences in dietary pattern andphysical activity. It is remarkable that the African(Nigerian) Blacks in the study of Luke et al 20 have alow body fat for their BMI compared to other Blacks(outlier in Figure 2c).

In Indonesians, Thais and Ethiopians, the predictionequation largely underestimated body fat. In Indone-sians the underestimation was of the same magnitudein males and females independent of the body fatmethodology used (3-compartment model based ondensitometry and deuterium oxide dilution,21 anddeuterium dilution alone16). In Thais, there was nodifference whether skinfolds28 or DEXA22 was usedas the reference method. The reason for this under-estimation remains unclear, but body build of someAsians is rather slender 49 and earlier we reported aslender body build in Ethiopians31 and also recently inIndonesians.16,21 Subjects with a small frame, but thesame body height, are likely to have a relatively lowerFFM (due to lower muscle mass) and hence BMI islikely to underestimate their body fat when usingprediction formulae developed in subjects with abigger body build.

The reason for the different relationships betweenbody fat and BMI in the different populations isunknown. Apart from differences between dietarypatterns and differences in physical activity,20 differ-ences in body build may be an important contributor.

Norgan 14,15 has discussed the importance of rela-tive leg length for the interpretation of the BMI, and itis known that there are differences in relative sittingheight between Caucasians and Blacks, and betweenCaucasians and Asians, with Blacks having relativelylonger legs and Asians having relatively shorterlegs.52 Apart from relative leg length, a stocky orslender body build may also have an impact. A stockyperson is likely to have more muscle mass=connectivetissue than a slender person with the same bodyheight. Thus, at the same BMI, the slender personwill have more body fat. Additional anthropometricmeasures may be necessary to improve the quality ofthe BMI as an indicator of body fatness among ethnicgroups.

Conclusions

The consequences of the different relationshipsbetween body fat and BMI are evident. As increasedbody fat and not increased weight or BMI is the riskfactor for excess mortality,1,3 cut-off points for

obesity (based on the BMI) could be different fordifferent populations and as a result, population-spe-ci®c (rather than general) cut-off points have to bede®ned. This meta-analysis shows, like some otherpapers in the literature, that in some populations thelevel of obesity in terms of percent body fat is reachedat a much lower BMI compared to the cut-off valuessuggested by the WHO. In Indonesians, Thais andEthiopians the cut-off values for obesity based onBMI could be as low as 27 kg=m2, whereas, forexample, in Blacks and Polynesians, the cut-offpoint could be slightly higher than the now usedvalue of 30 kg=m2. Data from this study do notallow de®nitive conclusions about any ethnic group,as in many populations the number of data points wastoo limited. More research into different ethnic groupsis necessary and, as the differences between theAmerican Caucasians and the European Caucasiansin this study show, also within one `ethnic' groupthere could be differences that could be large enoughto be important. The inclusion of body build para-meters, such as (relative) sitting height and skeletalwidths, in such studies would be advisable.

Summary

There are differences in BMI among populations ofthe same age, gender and level of body fatness.Consequently the prevalence of obesity in populationswill be over- or underestimated using general cut-offpoints. The relationship between body fat and BMI ina speci®c population should be studied before theWHO cut-off point of 30 kg=m2 for obesity is applied.

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