Brief Communication: Body Mass Index, Body AdiposityIndex, and Percent Body Fat in Asians

Dapeng Zhao,1,2* Yonglan Li,3 Lianbin Zheng,1 and Keli Yu1

1Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University,Tianjin 300387, China2College of Life Sciences, Northwest University, Xi’an 710069, China3College of Life Sciences and Technology, Inner Mongolia Normal University, Hohhot 010022, China

KEY WORDS body mass index; body adiposity index; percent body fat; obesity; Asians

ABSTRACT Human obesity is a growing epidemicthroughout the world. Body mass index (BMI) is com-monly used as a good indicator of obesity. Body adiposityindex (BAI 5 hip circumference (cm)/stature (m)1.5 218), as a new surrogate measure, has been proposedrecently as an alternative to BMI. This study, for thefirst time, compares BMI and BAI for predicting percentbody fat (PBF; estimated from skinfolds) in a sample of302 Buryat adults (148 men and 154 women) living inChina. The BMI and BAI were strongly correlated withPBF in both men and women. The correlation coefficientbetween BMI and PBF was higher than that between

BAI and PBF for both sexes. For the linear regressionanalysis, BMI better predicted PBF in both men andwomen; the variation around the regression lines foreach sex was greater for BAI comparisons. For thereceiver operating characteristic (ROC) analysis, thearea under the ROC curve for BMI was higher than thatfor BAI for each sex, which suggests that the discrimina-tory capacity of the BMI is higher than the one ofBAI. Taken together, we conclude that BMI is a morereliable indicator of PBF derived from skinfold thicknessin adult Buryats. Am J Phys Anthropol 000:000–000,2013. VC 2013 Wiley Periodicals, Inc.

With the rapid socioeconomic development and theconcomitant nutrition transition in daily diets, humanobesity has become a growing worldwide epidemic, forboth developed countries and developing countries (Pop-kin and Gordon-Larsen, 2004; James, 2008; Weitz et al.,2012). Obesity is one important health risk factor formany diseases such as Type 2 diabetes mellitus, cardio-vascular disease, hypertension, and cancer (Donohoeet al., 2010; Gade et al., 2010). Therefore, it is necessaryto properly quantify body fat at both the individual andthe population levels (Stein and Colditz, 2004).

Body mass index (BMI 5 body weight (kg)/stature(m2)) is commonly used as a good indicator to quantifyadiposity in field research and clinical applications (Keyset al., 1972; Alvero-Cruz et al., 2010; Wilson et al.,2011). The limitation of BMI is that it does not differen-tiate between fat and lean mass, and may be influencedby age, sex, and ethnicity thereby limiting its usefulness(Garn et al., 1986; Jackson et al., 2002; Nevill et al.,2006). To address this limitation, Bergman et al. (2011)developed the body adiposity index (BAI 5 hip circum-ference (cm)/stature (m)1.5 2 18) as an alternative toBMI and found a strong association between BAI andpercent body fat (PBF) as determined by dual-energy X-ray absorptiometry in a sample of Mexican–Americanand Black individuals. Compared with BMI, BAI can bemeasured without weight data and is more practical toassess in field studies (Heymsfield and Shen, 2011).However, it is found that original bias in BAI is con-founded by sex (Barreira et al., 2011; Schulze and Ste-fan, 2011; Freedman et al., 2012; Vinknes et al., 2013).

Recently, some validation studies comparing BAI withBMI for estimating PBF have been conducted onEuropean-American, African-American, Hispanic,Whites, and Caucasian samples (Barreira et al., 2011;Gibson et al., 2011; Appelhans et al., 2012; Freedman

et al., 2012; Godoy-Matos et al., 2012; Johnson et al.,2012; L�opez et al., 2012; Schulze et al., 2012; Sun et al.,2012; Geliebter et al., 2013). However, it is stillunknown how well BAI performs in Asian individuals.Based on the above background, and considering theobvious sexual dimorphism on body composition (See-man, 2001; Li et al., 2009; Schulze and Stefan, 2011),the main purpose of this study is to do the following:investigate the sex-specific relationship between bothBMI and BAI and PBF in a sample of obese Asian adultswith the same ethnicity; analyze which index is a betterpredictor for each sex for predicting PBF; and explorethe proximate causes behind that.

METHODS

Study subjects

For this study, we investigated adult participants (N 5302, 148 men and 154 women) of Buryats ethnicity

Grant sponsor: Natural Science Foundation of China; Grant num-bers: 31200293, 30830062, 31271283; Grant sponsor: Talent Intro-duction Fund of Tianjin Normal University; Grant numbers:5RL115.

*Correspondence to: Dapeng Zhao, Tianjin Key Laboratory of Ani-mal and Plant Resistance, College of Life Sciences, Tianjin NormalUniversity, Binshui West Road 393, Xiqing District, Tianjin 300387,China. E-mail: dpzhao329@gmail.com, skyzdp@mail.tjnu.edu.cn

Received 24 November 2012; accepted 21 June 2013

DOI: 10.1002/ajpa.22341Published online 00 Month 2013 in Wiley Online Library

(wileyonlinelibrary.com).

� 2013 WILEY PERIODICALS, INC.

AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 00:00–00 (2013)

recruited from Xinihe town, Evenk autonomous banner,Hulun Buir grasslands, Inner Mongolia, China. As thelargest indigenous group in Siberia, Buryats are the mainnorthern subgroup of the Mongols (Shimizu et al., 2006).Buryats share many customs with other Mongols, includ-ing nomadic herding, and currently live in China, Mongo-lia, and Russia. Buryats immigrated into China in theearly 20th Century and settled in Hulun Buir grasslands.In China, Han is the largest ethnic group (Zhao et al.,2012), whereas Buryats are considered an ethnic minority(Yang, 2003). Buryats in this study from Inner Mongoliaare similar in stature but markedly heavier than Buryatsin southern Siberia (Leonard et al., 2009). This disparitymay be attributable to more general differences betweenthe two communities, such as dietary and living habits.

Buryats were chosen in this study because BMI isinfluenced by factors such as body proportionalityshaped by climate/environment (Norgan, 1994a,b; Katz-marzyk and Leonard, 1998; Leonard and Katzmarzyk,2010). Given that Buryats are a stoutly built, cold-adapted group, our results could possibly be extrapolatedto other ethnic populations living in areas that havesimilar climatic conditions in Asia as well as in othercontinents. Permission to conduct this research wasobtained from local governments and voluntary partici-pants before the onset of the study.

Data collection

Stature was measured to the nearest centimeter (cm)with a Martin stadiometer, and weight to the nearestkilogram (kg) with a digital scale for each participant.BMI was then calculated based on the formula: BMI 5body weight (kg)/stature (m2). We measured hip circum-ference with a tape measure and calculated the BAI fol-lowing the formula: BAI 5 (hip circumference (cm)/stature (m)1.5) 2 18.

We measured four classic skinfold thicknesses (triceps,biceps, subscapular, and suprailiac) with a skinfold cali-per. Raw skinfold data were converted into data on bodydensity via the published equations of Durnin andWomersley (1974), using sex-specific equations to con-vert body density to PBF (Siri, 1961; Yao et al., 2002;Wells, 2012). We defined PBF of >25% in men and >35%in women as belonging to obese samples, based on thestandard among previous studies (WHO, 1995; Deuren-berg et al., 1998; De Lorenzo et al., 2003).

Data analysis

We applied commonly used statistical tests in thisstudy. We adopted t-tests to examine sex differences oneach measure (L�opez et al., 2012). Pearson’s correlation

coefficients (r) were used to examine correlations amongBMI, BAI, and PBF (Geliebter et al., 2013; L�opez et al.,2012). The linear regression was conducted to assess thebest predictor of PBF based on the method described byGeliebter et al. (2013). The coefficient of determination(r2) and standard error of the estimate (SEE) were calcu-lated. In addition, receiver operating characteristic(ROC) curves were employed in this study (L�opez et al.,2012). Cutoff values were calculated based on the pointon the ROC curve with the lowest value for the formula:(1 2 sensitivity)2 1 (1 2 specificity)2. The area underthe ROC curve was a good measure to display the dis-criminatory capacity of one predictor (L�opez et al.,2012). SPSS for Windows (version 16.0) was used to con-duct all the analyses, which were two-tailed with a sig-nificance level of P � 0.05.

RESULTS

Results for this study generally showed that men hadsignificantly higher scores than women on body weightand stature. Women displayed significantly higher scoresthan men on skinfold thicknesses, hip circumference,PBF, BMI, and BAI (Table 1). Based on the PBF stand-ard, 26.35% of men (N 5 39) and 43.50% of women (N 567) were classified as obese individuals.

BMI and BAI showed a significant correlation for eachsex (Table 2). The correlation coefficient between BMIand PBF was higher than that between BAI and PBFfor both sexes. Correlation coefficients in BMI–PBF andBAI–PBF correlations were higher in men than inwomen, whereas they were the opposite in BMI–BAI cor-relation (Table 2).

The linear regression analysis showed that BMI wasthe best predictor of PBF in both men (r2 5 0.704, P <0.01) and women (r2 5 0.596, P < 0.01). The variation(SEE) around the regression lines for each sex wasgreater for BAI comparisons than for BMI comparisonswith PBF (Fig. 1).

The area under the ROC for BMI was higher thanthat for BAI for men and women, respectively (Fig. 2).

TABLE 1. Sex differences in anthropometric dimensions between Buryat men and women

Men (N 5 148) Women (N 5 154)Sex difference

Range Mean 6 SD Range Mean 6 SD P-value

Age (years) 18–77 36.36 6 12.77 19–71 40.46 6 13.28Weight (kg) 45.80–141.00 74.98 6 17.06 41.50–118.80 66.32 6 16.95 <0.05Stature (m) 1.53–1.82 1.70 6 0.06 1.33–1.68 1.56 6 0.06 <0.05Skinfolds (mm) 14.50–111.00 45.71 6 21.79 26.00–120.00 67.05 6 20.41 <0.05Hip circumference (cm) 82.50–148.00 98.71 6 9.73 79.00–139.00 101.76 6 12.65 <0.05BMI (kg/m2) 17.85–44.06 26.00 6 5.26 17.23–47.59 27.38 6 6.67 <0.05BAI (cm/m1.5) 19.97–45.02 26.74 6 4.16 22.84–56.79 34.54 6 6.81 <0.05PBF (%) 4.64–37.83 19.95 6 7.31 18.19–43.90 33.12 6 5.85 <0.05

TABLE 2. Pearson’s correlations of BMI and BAI with PBF forBuryat men and women

Men Women

r P-value r P-value

BMI–PBF 0.839 <0.01 0.772 <0.01BAI–PBF 0.772 <0.01 0.729 <0.01BMI–BAI 0.878 <0.01 0.911 <0.01

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ROC curves showed that the cutoff point of the BMI inmen is 26.89 (95% CI: 87.7–96.4%) and provided a sensi-tivity of 89.7% and specificity of 80.7%. In women, thecutoff point of the BMI is 27.67 (95% CI: 86.2–95.3%)and provided a sensitivity of 77.6% and specificity of89.7%. The cutoff point of the BAI in men is 27.80 (95%CI: 84.6–95.2%) and provided a sensitivity of 87.2% andspecificity of 81.7%. In women, the cutoff point of theBAI is 36.02 (95% CI: 84.2–94.4%) and provided a sensi-tivity of 76.1% and specificity of 90.8%.

DISCUSSION

To our knowledge, this study is the first to compareBMI and BAI for predicting PBF in Asians. Participantswith the same ethnicity were chosen, and analysis wasconducted for men and women, respectively, so as toremove the potential impact of race and gender. Wefound that BMI and BAI showed a significant correlationfor each sex, which is consistent with related findings inother populations (e.g., Caucasian population: L�opezet al., 2012). BMI and BAI are more similarly correlatedwith PBF in women than men. This may be owing to thefact that, as one important variable of the BAI formula,

hip circumference is more informative to predict PBF inwomen than in men (Flegal et al., 2009; Schulze andStefan, 2011).

The main results from a multianalysis in this studyare as follows: 1) for Pearson’s correlation analysis, thecorrelation coefficient between BMI and PBF was higherthan that between BAI and PBF for both sexes; 2) forthe linear regression analysis, BMI was the best predic-tor of PBF in both sexes: the variation around theregression lines for each sex was greater for BAI com-parisons; and 3) for the ROC analysis, the area underthe ROC curve for BMI was higher than that for BAI foreach sex, which suggests that the discriminatorycapacity of the BMI is higher than the one of the BAI.All these results consistently show that BMI is a betterpredictor of PBF derived from skinfold thickness inAsian adults.

Currently, validation studies have shown inconsistentresults on whether BAI is better than BMI for predictingPBF. Some researchers found that BMI performed simi-larly or better than BAI for estimating PBF (Barreiraet al., 2011; Gibson et al., 2011; Freedman et al., 2012;L�opez et al., 2012; Schulze et al., 2012; Geliebter et al.,2013; Vinknes et al., 2013), whereas others present a

Fig. 1. Regression between BMI/BAI and PBF: the variation (SEE) around the regression lines for each sex was greater forBAI comparisons than for BMI comparisons with PBF. (a) Men: BMI and PBF; (b) men: BAI and PBF. (c) Women: BMI and PBF;(d) women: BAI and PBF.

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converse finding (Appelhans et al., 2012; Godoy-Matoset al., 2012; Johnson et al., 2012; Sun et al., 2012). Thisstudy does not lend support to the viewpoint by Berg-man et al. (2011) that BAI is better than BMI for esti-mating PBF. Besides the possible effect of differingsample sizes, the discrepancy among all related findingsmay result from two main differences.

First, different PBF examination standards have beenadopted including dual-energy X-ray absorptiometry(e.g., Bergman et al., 2011), bioelectrical impedanceanalysis (L�opez et al., 2012), air displacement plethys-mography (Geliebter et al., 2013), and skinfold anthrop-ometry (this study). Differences in methods influence therelationship between BMI/BAI and the correspondingPBF. For example, among clinically severe obese women,BAI was significantly correlated with PBF examined bybioelectrical impedance analysis and air displacementplethysmography, but not significantly correlated withPBF examined by dual-energy X-ray absorptiometry(Geliebter et al., 2013). Although we first adopted thePBF standard derived by skinfold thickness, it is found

that direct skinfold anthropometry could also provide anaccurate PBF estimate and potentially help in obesitydiagnosis (Pongchaiyakul et al., 2005). One advantage ofthis PBF standard in this study may be that it is moreconvenient and propitious in field study.

Second, various ethnic groups were investigatedamong these validation studies. It is well documentedthat body composition and obesity levels among differentethnic groups are not the same (Cornelisse-Vermaat andMaassen van den Brink, 2007; EI-Sayed et al., 2011).The relationship between BMI and PBF is ethnic-specific. For example, it was found that Asians showed ahigher PBF at a lower BMI compared to Caucasians(Deurenberg et al., 1998). Therefore, when comparedwith BMI and BAI for predicting PBF, we suggest pay-ing more attention to maintaining the consistency ofPBF measurement and considering ethnic-specific fac-tors on the validity of these indices.

On the whole, this study presents the first evidence oncomparing BMI and BAI for predicting PBF in Asiansand finds that BMI is a better predictor of PBF derived

Fig. 2. ROC analysis for BMI/BAI: the ROC curve is represented by the solid line; the diagonal reference line is represented bythe dotted line; the area under the ROC for BMI was higher than that for BAI for men and women, respectively. (a) Men: BMI andPBF; (b) men: BAI and PBF. (c) Women: BMI and PBF; (d) women: BAI and PBF.

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from skinfold thickness. Current evidence has shownthat BAI overestimates PBF at lower levels of adiposity,predicts PBF well when BMI falls within the normalrange, and underestimates PBF at higher levels of adi-posity (Johnson et al., 2012; Vinknes et al., 2013). Ourresults should be treated with caution in that the skin-fold prediction equation originally developed in Cauca-sian populations (Durnin and Womersley, 1974) may notapply equally to different ethnic groups across the world(Davidson et al., 2011) although this Caucasian-basedequation has been tested to support use in Asian sub-jects (Yao et al., 2002). It is necessary to make furtherinvestigations on the validity of BAI among various eth-nic groups in Asia as well as other continents. Further-more, additional studies are needed to further determinethe clinical significance of the BAI (e.g., Schulze et al.,2012; Snijder et al., 2012).

ACKNOWLEDGEMENTS

The authors are grateful to the staff of local govern-ments and to their participants for permission tomeasure.

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