Skip to main content Accessibility help
×
Home
Hostname: page-component-59b7f5684b-2bkkj Total loading time: 1.236 Render date: 2022-09-29T14:09:04.749Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "displayNetworkTab": true, "displayNetworkMapGraph": false, "useSa": true } hasContentIssue true

Body mass index bias in defining obesity of diverse young adults: the Training Intervention and Genetics of Exercise Response (TIGER) Study

Published online by Cambridge University Press:  06 April 2009

Andrew S. Jackson*
Affiliation:
Department of Health and Human Performance, The University of Houston, 3855 Holman Street, Houston, TX77204-6015, USA
Kenneth J. Ellis
Affiliation:
USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA
Brian K. McFarlin
Affiliation:
Department of Health and Human Performance, The University of Houston, 3855 Holman Street, Houston, TX77204-6015, USA
Mary H. Sailors
Affiliation:
USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA
Molly S. Bray
Affiliation:
USDA/ARS Children's Nutrition Research Center, Baylor College of Medicine, Houston, TX, USA
*
*Corresponding author: Dr Andrew S. Jackson, fax +1713 743 9860, email udde@mac.com
Rights & Permissions[Opens in a new window]

Abstract

The BMI cut-score used to define overweight and obesity was derived primarily using data from Caucasian men and women. The present study evaluated the racial/ethnic bias of BMI to estimate the adiposity of young men and women (aged 17–35 years) using dual-energy X-ray absorptiometry (DXA) determination of percentage body fat (DXA-BF%) as the referent standard. The samples were 806 women and 509 men who were tested from one to three times over 9 months providing 1300 observations for women and 820 observations for men. Linear mixed models (LMM) regression showed that with age and BMI controlled, DXA-BF% of African-American (AA) men and women, Asian-Indian men and women, Hispanic women and Asian women significantly differed from non-Hispanic white (NHW) men and women. For the same BMI of NHW women, the DXA-BF% of AA women was 1·76 % lower, but higher for Hispanic (1·65 %), Asian (2·65 %) and Asian-Indian (5·98 %) women. For the same BMI of NHW men, DXA-BF% of AA men was 4·59 % lower and 4·29 % higher for Asian-Indian men. Using the recommended BMI cut-scores to define overweight and obesity systematically overestimated overweight and obesity prevalence for AA men and women, and underestimated prevalence for Asian-Indian men and women, Asian women and Hispanic women. The present study extends the generalisability of research documenting the racial/ethnic bias of the universal overweight and obesity BMI cut-scores.

Type
Full Papers
Copyright
Copyright © The Authors 2009

BMI, the ratio of weight and height, is an accepted public health method to define overweight and obesity(Reference Kuczmarski and Flegal1, 2). In an invited commentary to the British Journal of Nutrition, Deurenberg stated his opposition to using a universal BMI cut-score to define obesity(Reference Deurenberg3). He maintained that the practice of defining obesity with a BMI of 30 kg/m2 was based on data from Caucasian men and women from Europe and the USA and represented a percentage body fat (BF%) of about 25 % for young Caucasian men and 35 % for young Caucasian women(4). Evidence supporting Deurenberg's position comes from research documenting age, sex and race/ethnic group bias in predicting BF% from BMI(Reference Chang, Wu and Chang5Reference Jackson, Stanforth and Gagnon13). These studies show that the BF% for BMI systematically varies due to age, sex and race/ethnic group.

Obesity has become a pandemic in the USA and the American population is growing in ethnic and racial diversity. Data from the National Health and Nutrition Examination Survey (NHANES) 2003–4(Reference Ogden, Carrol and Curtin14) show that the percentage of all Americans over the age of 20 years who exceeded the overweight BMI standard of ≥ 25 kg/m2 was 66·3 %, while 30·5 % were considered obese (BMI ≥ 30 kg/m2). These high prevalence levels are linked to overweight and obesity in young adulthood. The NHANES data documented that 57·1 % of young adults (aged 20–39 years) were overweight, while obesity among this group approaches 30 %(Reference Ogden, Carrol and Curtin14). The prevalence of overweight and obesity of African-American (AA) and Mexican American men and women aged 20–39 years is higher than non-Hispanic white (NHW) men and women, linking the prevalence not only to sex, but also racial/ethnic group.

Recent NHANES data(Reference Flegal, Graubard and Williamson15, Reference Flegal, Graubard and Williamson16) showed that excess deaths were associated with obesity and underweight, but not overweight. Fontaine et al. (Reference Fontaine, Redden and Wang17) reported that the association between longevity and BMI of AA and NHW adults differed. The optimal BMI associated with longevity was higher for AA men and women than NHW men and women. Stevens et al. (Reference Stevens, Plankey and Williamson18) reported that BMI was a less potent risk factor in AA women than NHW women when educational status was controlled. Manson & Bassuk(Reference Manson and Bassuk19) report that the ethnic difference in longevity associated with a BMI may in part be due to race bias of BMI to estimate adiposity. The present study was designed to evaluate the fidelity of BMI to estimate adiposity of racially/ethnically diverse young men and women. The goal was to examine the race/ethnic group effect on the BMI prediction of BF% using dual-energy X-ray absorptiometry (DXA) determination of BF% (DXA-BF%) as the referent adiposity standard.

Methods

Subjects

Subjects were recruited from the Training Intervention and Genetics of Exercise Response (TIGER) study. The TIGER subjects were students enrolled at the University of Houston (Houston, TX, USA). The target subject was a sedentary individual under the age of 35 years who exercised less than 30 min/week for the previous 6 months, and was not actively limiting energy intake by dietary modification. Subjects were excluded from the study if they had a physical or physiological contraindication to aerobic exercise, a known metabolic disorder that may alter body composition, or were pregnant or lactating.

The TIGER study subjects engaged in 30 weeks (two semesters) of exercise training, 3 d/week for 30 min/d at 65–85 % of heart rate-defined VO2max. The data came from five yearly cohorts. The sample consisted of 806 females and 509 males who ranged in age from 17 to 35 years. These data included men and women who had from one to three measurement visits over the 9-month study duration. The measurement visits were at baseline, after 15 weeks, and at the end of the 30-week exercise programme. The total number of observations for the visits was 1300 for women and 820 for men. The race/ethnic group composition of the male and female samples differed slightly. The composition of the 509 men at baseline was: NHW, 37 %; Hispanic, 26 %; AA, 22 %; Asian Indian, 5 %; Asian, 10 %. The composition of the 806 women at baseline was: NHW, 29 %; Hispanic, 25 %; AA, 35 %; Asian-Indian, 3 %; Asian, 8 %.

Measurements

Height was determined with a stadiometer (SECA Road Rod; SECA, Hanover, MD, USA) and weight was measured with a digital scale (SECA 770). Subjects reported their birth date, sex and race/ethnicity using a standard, coded self-report demographic form. DXA was used to measure BF%. The DXA data for the first two cohorts were only measured at baseline and 30-week time points. DXA data were available for all three test visits for cohorts 3 to 5 if the subject completed the exercise programme. All individuals had baseline data. The number of individuals who had repeat tests were: 15 weeks, 311 women and 217 men; 30 weeks, 183 women and ninety-four men.

Whole-body DXA scans were completed on a Hologic Delphi-A unit (adult whole body software v. 11.2; Hologic, Inc., Bedford, MA, USA) and a Hologic Discovery W instrument (adult whole body software QDR v. 12.3). The same trained technicians administered the DXA scans. The instruments were calibrated daily with a spine standard and weekly with a step calibrator, as described by the manufacturer. All female participants completed a standard urine pregnancy test before DXA testing to ensure that they were not pregnant before scanning. Subjects were asked to lie in the supine position and remain still. The entire scan was completed in less than 6 min. Software supplied from the manufacturer was used to calculate whole-body (minus the head) fat mass, lean mass and bone mineral mass. Total DXA weight was computed by summing the DXA parts and used to compute DXA-BF%. As recommended by Lohman & Chen(Reference Lohman, Chen, Heymsfield, Lohman, Wang and Going20), DXA weight and scale-measured body weight were compared with linear regression. The R 2 between measured and DXA weight was >0·99. The slope of the measured–DXA weight regression line of 1·01 (95 % CI 1·00, 1·02) and the intercept of − 0·08 (95 % CI − 0·37, 0·20) were within chance variation of 1·0 and 0. The standard error of the estimate for scale-measured weight was 1·5 kg.

Statistical methods

Maximum likelihood, linear mixed models (LMM) regression was used to model the data with a random intercept growth model(Reference Rabe-Hesketh and Skrondal21). A major advantage of LMM is that all data can be used with subjects who have a different number of observations over time(Reference Rabe-Hesketh and Skrondal21, Reference Twisk22). Stata version 10 (xtmixed program) was used for data analysis(23). The first LMM examined the linearity of the BMI and DXA-BF% relationship. The second model examined the effect of race/ethnicity group on DXA-BF% with age and BMI statistically controlled. The dependent variable for the LMM analyses was DXA-BF% and the fixed effects variables were BMI and race/ethnic group, which was dummy coded using NHW women and men as the referent groups. The repeat tests were the random part of the model. Each fixed effect LMM regression coefficient was tested with a z-test to determine if it was significantly different from zero(23). A log-ratio test(Reference Rabe-Hesketh and Skrondal2123) was used to determine if the model that included race/ethnic group provided a better fit of the data than the BMI model. It has been previously documented(Reference Jackson, Stanforth and Gagnon13) that for the same BMI, the BF% of women is ≥ 10·4 % higher than that of men, and that age accounts for BF% independently of BMI and sex. For these reasons, the men and women's data were analysed separately and age was used as a covariate.

Results

Table 1 gives the descriptive statistics for the women and men and the number of observations for the total sample and for race/ethnicity group. The women and men were similar in age and the data exhibited the well-documented sex differences in physical characteristics for height, weight and body composition. The NHW women were significantly taller than Hispanic, Asian-Indian and Asian women and heavier than Asian-Indian and Asian women, while AA women were heavier than NHW women. Compared with NHW women, the BMI of Hispanic (P = 0·057) and AA women was higher and the BMI for Asian women was lower. The DXA bone mineral mass and lean mass of all race/ethnic groups were significantly different from NHW women. The DXA-BF% of Hispanic and Asian-Indian women was significantly higher than that of NHW women, while DXA-BF% did not differ between NHW, AA and Asian women. The NHW men were significantly taller than Hispanic, Asian-Indian and Asian men. There were no significant race/ethnic group differences for weight or BMI in males. The DXA bone mineral and lean mass of all groups were significantly different from those of the NHW men. The mean DXA fat mass of AA men was significantly (P = 0·023) lower than that of NHW men. The mean DXA-BF% of AA men was 3·5 % lower than that for NHW men.

Table 1 Characteristics of the Training Intervention and Genetics of Exercise Response (TIGER) women (n 806) and men (n 509) for the total sample and contrasted by race/ethnic groups

(Mean values and standard deviations)

BMC, bone mineral content; DXA-BF%, body fat percentage determined by dual-energy X-ray absorptiometry.

* Mean value was significantly different from that of white men or women (P < 0·01).

The LMM analyses are provided in Table 2 (women) and Table 3 (men). Provided are the LMM regression coefficients (estimates), the standard errors of the coefficient (se) and the probability (P) that the coefficient was significantly different from 0. LMM I showed that, with age controlled, the BMI relationship with DXA-BF% for men was linear, but quadratic for women. Fig. 1 gives the bivariate relationship for all observations contrasted by sex. Also provided in Fig. 1 are the quadratic and linear lines of best fit for the women and men's bivariate distributions. These graphs show that the DXA-BF% of women was higher than that of men for the same BMI through the range studied.

Table 2 Maximum likelihood linear mixed models (LMM) analyses of the Training Intervention and Genetics of Exercise Response (TIGER) women (1300 total observations)

(Estimates with their standard errors)

Table 3 Maximum likelihood linear mixed models (LMM) analyses of the Training Intervention and Genetics of Exercise Response (TIGER) men (820 total observations)

(Estimates with their standard errors)

Fig. 1 The bivariate relationship between BMI and body fat percentage determined by dual-energy X-ray absorptiometry (DXA-BF%) using 1300 total observations for women and 820 observations for men. (●), Women's data; (), men's data. The relationship (regression line) for women was quadratic (); for men it was linear (- - -).

LMM II in Tables 2 and 3 included race/ethnic group with BMI. The log-ratio test confirmed that LMM II provided a better fit for both women and men (P < 0·001). The women's LMM regression weights for all race/ethnic groups were significantly different from the referent group, NHW women. For the same age and BMI, the DXA-BF% of AA women was 1·76 % lower than that of NHW women and the DXA-BF% of Hispanic, Asian-Indian and Asian women were significantly higher. The race/ethnic group bias for the same age and BMI ranged from 1·65 % for Hispanic women to nearly 6 % for Asian-Indian Women. The analysis of the men's data showed that the LMM II regression weights of Hispanic and Asian men were within random variation of NHW men. The LMM regression weights for AA and Asian-Indian men were significantly different from that of NHW men and the effects were substantial. For the same age and BMI, the DXA-BF% of AA men was 4·57 % lower than that of NHW men while the DXA-BF% of Asian-Indian men was 4·29 % higher. A final LMM analyses examined the race/ethnic group and BMI interaction. A log likelihood-ratio test confirmed that adding the BMI × race/ethnic group interaction term for both men (P = 0·093) and women (P = 0·384) did not improve the fit over LMM II. This confirmed that the systematic race/ethnic bias was constant over the entire BMI range.

Figs. 2 and 3 show the significant ethnic group bias for women and men. The graphs include solid and dashed lines representing the NHW women and men DXA-BF% for the BMI overweight and obesity cut-scores. These DXA-BF% NHW cut-scores for overweight and obesity were: women, 31·2 and 36·9 %; men, 18·7 and 24·6 %. The overweight and obese lines in the figures show that the BMI for Hispanic, Asian and Asian-Indian women is lower than the NHW women values of 25 and 30 kg/m2 while the BMI for AA women is higher. Figure 3 shows that the BMI of AA men is about 4 kg/m2 lower than NHW men and the BMI cut-score for Asian-Indian men is nearly 4 kg/m2 higher. Table 4 gives these race/ethnic group BMI specific cut-scores (95 % CI) equated to the NHW DXA-BF% values.

Fig. 2 Body fat percentage estimates determined by dual-energy X-ray absorptiometry (DXA-BF%) for selected BMI values of women contrasted by race/ethnic groups. (—), DXA-BF% value of 31·2 %, which equates to the BMI cut-score for overweight; (- - -), DXA-BF% value of 36·9 %, which equates to the BMI cut-score for obese; (⋄), non-Hispanic whites; (●), Hispanic whites; (■), African-Americans; (♦), Asian-Indians; (▲), Asians. The graphs were constructed using linear mixed model (LMM) II to model the data (see Table 2).

Fig. 3 Body fat percentage estimates determined by dual-energy X-ray absorptiometry (DXA-BF%) for selected BMI values of men contrasted by non-Hispanic white (⋄), African-American (■) and Asian-Indian (♦) groups. (—), DXA-BF% value of 18·7 %, which equates to the BMI cut-score for overweight; (- - -), DXA-BF% value of 24·6 %, which equates to the BMI cut-score for obese. The graphs were constructed using linear mixed model (LMM) II to model the data (see Table 3).

Table 4 Overweight and obese BMI cut-scores for race/ethnic groups equivalent to body fat percentage determined by dual-energy X-ray absorptiometry for 25 and 30 kg/m2 of non-Hispanic white women and men

(Mean values and 95 % confidence intervals)

The LMM III random model residuals were examined graphically two ways: normal curves superimposed on the histogram of residuals; standardised normal quartile probability plots of the residuals(Reference Rabe-Hesketh and Skrondal21, 23, Reference West, Welch and Gatecki24). These graphs showed that the residuals were normally distributed (graphics not shown) with no serious outliers. The excellent fit of the LMM II data is further documented by the model error estimates provided in Tables 2 and 3. The error estimates were < 1·2 %, which is close to the measurement error that would be expected when measuring BF% with DXA technology.

Discussion

These TIGER results documented that the relationship between BMI and BF% differs for men and women. Similar to results reported by other investigators(Reference Deurenberg, Yap and van Staveren7, Reference Deurenberg, Weststrate and Seidell10, Reference Gallagher, Visser and Sepulveda11, Reference Jackson, Stanforth and Gagnon13), these TIGER data found that, for a given BMI, the BF% of women was higher than of men. These results with young adults documented that the relationship between BMI and BF% of men was linear, but quadratic for women. For a BMI < 40 kg/m2 the sex difference in BF% was ≥ 10 %. The non-linear relationship between BMI and BF% has been documented with men and women from the Heritage Family Study(Reference Jackson, Stanforth and Gagnon13). The major difference between samples was that the Heritage study subjects ranged in age from 17 to 65 years, compared with the TIGER age range of 17 to 35 years, and the Heritage study BF% was measured with a two-component method, not DXA.

An important finding of the present study was that BMI overestimated the DXA-BF% of AA men and women. These TIGER results are consistent with the meta-analysis results reported by Deurenberg et al. (Reference Deurenberg, Yap and van Staveren7) and Jackson et al. (Reference Jackson, Stanforth and Gagnon13) but differed from the results reported by Gallagher et al. (Reference Gallagher, Visser and Sepulveda11). Deurenberg et al. found that using a BMI prediction equation derived with Caucasians overestimated the measured BF% of AA men and women by nearly 2 %. The Heritage Family Study included 665 AA and NHW men and women who ranged in age from 17 to 65 years. In a second Heritage study(Reference Stanforth, Jackson and Green25) a race effect for abdominal visceral fat was found for both men and women. Controlling for age, BMI and waist circumference, the abdominal visceral fat measured by computed tomography of AA men and women was significantly lower than that of NHW men and women.

Other investigators have reported a race/ethnic group bias when estimating BF% from BMI with equations derived from Caucasian men and women. Using samples of older Hispanic Americans and controlling for age, Fernandez et al. (Reference Fernandez, Heo and Heymsfield9) reported that for BMI values below 30 kg/m2, Hispanic American women had a higher BF% than either Caucasian or AA women, but, like these results, did not find a significant race/ethnic group effect for men. Several investigators(Reference Chang, Wu and Chang5Reference Deurenberg-Yap, Schmidt and van Staveren8) reported that BMI systematically underestimated the BF% of Asian subjects. Deurenberg-Yap et al. (Reference Deurenberg-Yap, Schmidt and van Staveren8) recommended that, for the same Caucasian BF%, the obesity BMI cut-score for Chinese and Malays was 27 kg/m2. These TIGER data showed that the obesity BMI cut-score for Asian women was 27·6 (95 % CI 26·6, 28·9) kg/m2, within random variation of the Deurenberg-Yap et al. recommendation. Deurenberg-Yap et al. (Reference Deurenberg-Yap, Schmidt and van Staveren8) also reported a race bias for Singaporean Indians. Their data showed that, for the same BF% of Caucasians, the BMI cut-score for obesity was 26 kg/m2, which is within chance variation of the values we found with these young Asian-Indian men (26·5 (95 % CI 24·9, 28·1) kg/m2) and slightly higher than what we found for Asian-Indian women (24·8 (95 % CI 23·6, 25·9) kg/m2).

These TIGER results identified a significant Asian group effect for women, but not men, whereas Chang et al. (Reference Chang, Wu and Chang5) and Deurenberg-Yap et al. (Reference Deurenberg-Yap, Schmidt and van Staveren8) reported a significant effect for both men and women. A likely reason for this difference was research design. Chang et al. and Deurenberg-Yap et al. combined the data of men and women and controlled for sex by including it as an independent variable of their multiple regression model. To examine this methodological issue, we combined the TIGER men and women's data and used sex as an independent variable (male = 0, female = 1). This LMM analysis (results not shown) revealed that, with age and sex statistically controlled, the Asian group effect was 1·72 (P < 0·001). This demonstrated that for the same BMI, the DXA-BF% of Asian men and women would be 1·7 % higher than that of NHW men and women. This suggested that the difference between studies was linked to combining men and women into the same statistical model. A strength of the present study was the large number of observations for men and women. This not only provided our sex-specific models with sufficient statistical power, but also controlled the effect of sex, which has been shown to be substantial. For the same BMI, the BF% of women has consistently been found to be ≥ 10·4 % higher than that of men(Reference Jackson, Stanforth and Gagnon13).

The recommended obesity cut-score of 30 kg/m2(Reference Kuczmarski and Flegal1, 2) was determined from the general trends in the relationship between BMI and morbidity and mortality rates, not on the accuracy of BMI to estimate BF%. Recent NHANES results(Reference Flegal, Graubard and Williamson15, Reference Flegal, Graubard and Williamson16) found that, compared with the normal-weight category (BMI 18·5 to < 25 kg/m2), obesity (BMI ≥ 30 kg/m2) was associated with excess mortality or CVD, whereas overweight (BMI 25 to < 30 kg/m2) was not. Fontaine et al. (Reference Fontaine, Redden and Wang17) reported that the BMI of young adults was related to longevity and the effect on AA men and women differed from that on NHW men and women. They reported that NHW men and women had a reduced longevity if their BMI was ≥ 30 kg/m2 at age 20 to 30 years. The pattern for AA men and women differed. A consistent reduction in longevity did not take place with AA women until the BMI reached ≥ 37 kg/m2 and ≥ 33 kg/m2 for AA men. Stevens et al. (Reference Stevens, Plankey and Williamson18) reported that the reference BMI for a 40 % higher risk of all-cause mortality was 35·9 kg/m2 in AA women compared with 27·3 kg/m2 for NHW women. These TIGER results are consistent with the observation made by Manson & Bassuk(Reference Manson and Bassuk19) who stated that the ethnic variation in the relationship between BMI and total adiposity or visceral adiposity might partly account for the observed racial discrepancies reported by Fontaine et al. (Reference Fontaine, Redden and Wang17).

A limitation of the present study is that the TIGER subjects are not a random sample of the US population, but the TIGER men and women are representative of the overweight and obesity prevalence of the national population of young adults(Reference Ogden, Carrol and Curtin14). The self-reported definition of racial/ethnic group membership was another limitation in the present study. The genetic technique of admixture analysis provides an objective method of defining racial heritage. Research has shown that admixture mapping cannot only localise, but also fine map, a phenotypically important variant(Reference Reich, Patterson and Ramessh26). The genetic examination of these TIGER data with admixture analysis is planned and will provide a clearer understanding of the source of the race/ethnic group bias found with these diverse young men and women.

These TIGER results support the findings of others(Reference Chang, Wu and Chang5Reference Fernandez, Heo and Heymsfield9, Reference Jackson, Stanforth and Gagnon13, Reference Stanforth, Jackson and Green25) that standardised obesity cut-points based on BMI do not represent equivalent levels of body fat across different race/ethnic groups. Our data indicate that, based on DXA-BF%, overweight and obesity cut-points should be higher in AA men and women and lower in Asian-Indian, Asian and Hispanic women and Asian-Indian men. These findings document the inherent bias of using BMI as an indicator of obesity in diverse men and women. These results extend the generalisability of race/ethnic group bias of BMI to young adults. The large number of observations and separate models for women and men support the fidelity of these findings.

Acknowledgements

Support for the present study was provided by the National Institute of Diabetes & Digestive & Kidney Diseases/National Institutes of Health grant R01-DK062148 and by USDA/ARS contract 6250–51 000–046.

The present study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the institutional review committees on the use of human subjects for research at both the Baylor College of Medicine and the University of Houston, Houston, TX. Written informed consent was obtained from all subjects.

A. S. J. designed the study, performed the data analyses and drafted the paper. K. J. E. was responsible for all DXA measurements. B. K. M. contributed to the drafting of the paper and aided with DXA measurements. M. H. S. was responsible for database management and helped with data analysis. M. S. B. designed the TIGER study and contributed to drafting the manuscript.

The authors have no professional relationships with companies or manufacturers who may benefit from the results of the present study.

References

1Kuczmarski, RJ & Flegal, KM (2000) Criteria for definition of overweight in transition: background and recommendations for the United States. Am J Clin Nutr 72, 10751081.CrossRefGoogle ScholarPubMed
2World Health Organization (1998) Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation on Obesity. Geneva: WHO.Google Scholar
3Deurenberg, P (2001) Invited Commentary: Universal cut-off BMI points for obesity are not appropriate. Br J Nutr 85, 135136.CrossRefGoogle Scholar
4World Health Organization (1995) Physical Status: The Use and Interpretation of Anthropometry. WHO Technical Report Series no. 854. Geneva: WHO.Google Scholar
5Chang, CJ, Wu, CH, Chang, CS, et al. (2003) Low body mass index but high percent body fat in Taiwanese subjects: implications of obesity cutoffs. Int J Obes 27, 253259.CrossRefGoogle ScholarPubMed
6Chung, S, Song, J, Shin, H, et al. (2005) Korean and Caucasian overweight premenopausal women have different relationship of body mass index to percent body fat with age. J Appl Physiol 99, 103107.CrossRefGoogle ScholarPubMed
7Deurenberg, P, Yap, M & van Staveren, WA (1998) Body mass index and percent body fat: a meta analysis among different ethnic groups. Int J Obes 22, 11641171.CrossRefGoogle ScholarPubMed
8Deurenberg-Yap, M, Schmidt, G, van Staveren, WA, et al. (2000) The paradox of low body mass index and high body fat percentage among Chinese, Malays and Indians in Singapore. Int J Obes 24, 10111017.CrossRefGoogle ScholarPubMed
9Fernandez, JR, Heo, M, Heymsfield, SV, et al. (2003) Is percentage body fat differentially related to body mass index in Hispanic Americans, African Americans, and European Americans? Am J Clin Nutr 77, 7175.CrossRefGoogle ScholarPubMed
10Deurenberg, P, Weststrate, JA & Seidell, JC (1991) Body mass index as a measure of body fatness: age- and sex-specific prediction formulas. Br J Nutr 65, 105114.CrossRefGoogle ScholarPubMed
11Gallagher, D, Visser, M, Sepulveda, D, et al. (1996) How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? Am J Epidemiol 143, 228239.CrossRefGoogle ScholarPubMed
12Gallagher, D, Heymsfield, SB, Heo, M, et al. (2000) Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 72, 694701.CrossRefGoogle ScholarPubMed
13Jackson, AS, Stanforth, PR, Gagnon, J, et al. (2002) The effect of sex, age, and race on estimating percent body fat from BMI: the Heritage Family Study. Int J Obes 26, 789796.CrossRefGoogle ScholarPubMed
14Ogden, CL, Carrol, MD, Curtin, LR, et al. (2006) Prevalence of overweight and obesity in the United States, 1999–2004. JAMA 295, 15491555.CrossRefGoogle ScholarPubMed
15Flegal, KM, Graubard, BI, Williamson, DF, et al. (2005) Excess deaths associated with underweight, overweight, and obesity. JAMA 293, 18611867.CrossRefGoogle ScholarPubMed
16Flegal, KM, Graubard, BI, Williamson, DF, et al. (2007) Cause-specific excess deaths associated with underweight, overweight, and obesity. JAMA 298, 20282037.CrossRefGoogle ScholarPubMed
17Fontaine, KR, Redden, DT, Wang, C, et al. (2003) Years of life lost due to obesity. JAMA 289, 187193.CrossRefGoogle ScholarPubMed
18Stevens, J, Plankey, MW, Williamson, DF, et al. (1998) The body mass index–mortality relationship in white and African American women. Obes Res 6, 268277.CrossRefGoogle ScholarPubMed
19Manson, JE & Bassuk, SS (2003) Obesity in the United States: a fresh look at its high toll. JAMA 289, 229230.CrossRefGoogle Scholar
20Lohman, TG & Chen, Z (2005) Dual energy X-ray absorptiometry. In Human Body Composition, chapter 4, pp. 6378 [Heymsfield, SB, Lohman, TG, Wang, Z and Going, SB, editors]. Champaign, IL: Human Kinetics.Google Scholar
21Rabe-Hesketh, S & Skrondal, A (2008) Multilevel and Longitudinal Modeling Using Stata, 2nd ed.College Station, TX: Stata Press.Google Scholar
22Twisk, J (2003) Applied Longitudinal Data Analysis for Epidemiology: A Practical Guide. Cambridge, UK: Cambridge University Press.Google Scholar
23StataCorp (2007) User's Guide: Release 10. College Station, TX: StataCorp LP.Google Scholar
24West, BT, Welch, KB & Gatecki, AT (2007) Linear Mixed Models. New York: Chapman & Hall/CRC.Google Scholar
25Stanforth, PR, Jackson, AS, Green, JS, et al. (2004) Generalized abdominal visceral fat prediction models for black and white adults age 17–65: The Heritage Family Study. Int J Obes 28, 925932.CrossRefGoogle ScholarPubMed
26Reich, D, Patterson, N, Ramessh, V, et al. (2007) Admixture mapping of an allele affecting interleukin 6 soluble receptor and interleukin 6 levels. Am J Hum Genet 80, 716726.CrossRefGoogle ScholarPubMed
Figure 0

Table 1 Characteristics of the Training Intervention and Genetics of Exercise Response (TIGER) women (n 806) and men (n 509) for the total sample and contrasted by race/ethnic groups(Mean values and standard deviations)

Figure 1

Table 2 Maximum likelihood linear mixed models (LMM) analyses of the Training Intervention and Genetics of Exercise Response (TIGER) women (1300 total observations)(Estimates with their standard errors)

Figure 2

Table 3 Maximum likelihood linear mixed models (LMM) analyses of the Training Intervention and Genetics of Exercise Response (TIGER) men (820 total observations)(Estimates with their standard errors)

Figure 3

Fig. 1 The bivariate relationship between BMI and body fat percentage determined by dual-energy X-ray absorptiometry (DXA-BF%) using 1300 total observations for women and 820 observations for men. (●), Women's data; (), men's data. The relationship (regression line) for women was quadratic (); for men it was linear (- - -).

Figure 4

Fig. 2 Body fat percentage estimates determined by dual-energy X-ray absorptiometry (DXA-BF%) for selected BMI values of women contrasted by race/ethnic groups. (—), DXA-BF% value of 31·2 %, which equates to the BMI cut-score for overweight; (- - -), DXA-BF% value of 36·9 %, which equates to the BMI cut-score for obese; (⋄), non-Hispanic whites; (●), Hispanic whites; (■), African-Americans; (♦), Asian-Indians; (▲), Asians. The graphs were constructed using linear mixed model (LMM) II to model the data (see Table 2).

Figure 5

Fig. 3 Body fat percentage estimates determined by dual-energy X-ray absorptiometry (DXA-BF%) for selected BMI values of men contrasted by non-Hispanic white (⋄), African-American (■) and Asian-Indian (♦) groups. (—), DXA-BF% value of 18·7 %, which equates to the BMI cut-score for overweight; (- - -), DXA-BF% value of 24·6 %, which equates to the BMI cut-score for obese. The graphs were constructed using linear mixed model (LMM) II to model the data (see Table 3).

Figure 6

Table 4 Overweight and obese BMI cut-scores for race/ethnic groups equivalent to body fat percentage determined by dual-energy X-ray absorptiometry for 25 and 30 kg/m2 of non-Hispanic white women and men(Mean values and 95 % confidence intervals)

You have Access
37
Cited by

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Body mass index bias in defining obesity of diverse young adults: the Training Intervention and Genetics of Exercise Response (TIGER) Study
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Body mass index bias in defining obesity of diverse young adults: the Training Intervention and Genetics of Exercise Response (TIGER) Study
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Body mass index bias in defining obesity of diverse young adults: the Training Intervention and Genetics of Exercise Response (TIGER) Study
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *