Lower BMI Cutoffs to Define Overweight and Obesity in China

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1 Lower BMI Cutoffs to Define Overweight and in China Wei He 1,2, Qingqing Li 1, Min Yang 1,3, Jingjing Jiao 1,3, Xiaoguang Ma 1,3, Yunjie Zhou 1, Aihua Song 1, Steven B. Heymsfield 4, Shanchun Zhang 5, and Shankuan Zhu 1,3 Objective: To investigate ethnic difference in the associations of BMI with comorbidity, mortality, and body composition between mainland Chinese and U.S. whites. Methods: Ethnic-comparison study using data from China Health and Nutrition Survey, U.S. National Health and Nutrition Examination Survey, and data from Zhejiang University (China) and Columbia University (U.S.). Results: Chinese people experienced greater odds of comorbidities than whites for a given BMI after standardizing for age and sex: 43 for diabetes, 30 for dyslipidemia, 28 for hypertension, 38 for metabolic syndrome, and 48 for hyperuricemia. Comparisons of BMI-mortality associations found that the U-shaped BMI-mortality curve shifted 1-2 kg m 22 to the left in Chinese compared to whites. Compared to whites at BMIs of 25 and 30 kg m 22, corresponding cutoffs in Chinese were 22.5 and 25.9 kg m 22 in men, and 22.8 and 26.6 kg m 22 in women after both fat and fat distribution were taken into account. Conclusions: Comorbidity, mortality, and body composition data consistently support the use of lower BMI cutoffs in Chinese than those in whites. (2015) 23, doi: /oby Introduction Whether Chinese should use ethnic-specific BMI cutoffs to define overweight and obesity is still open to debate. The World Health Organization (WHO) defines overweight as BMI 25 kg m 22 and obesity as BMI 30 kg m 22. This definition is based primarily on data from whites thus may not be applicable to other ethnic populations. In China, consistent with the lower cutoff values recommended by WHO experts for Asians (1), China Task Force defines overweight as BMI 24 kg m 22 and obesity as BMI 28 kg m 22. This definition, however, has been challenged by several recent studies that support using the WHO definition in Chinese (2-4). Because obesity is defined as abnormal or excess fat accumulation that impairs health (1), ethnic comparison in the association of BMI with comorbidities, mortality, and body composition can provide evidence-based information regarding whether ethnic-specific BMI cutoffs should be used in China. However, few data were available to allow such a direct comparison between Chinese and whites, and those in existence often suffer from limitations such as small sample sizes (5), limited outcome indicators (6,7), and/or biased populations (Chinese immigrants living in Western countries or whites living in China) (8,9). To address this gap, we linked the data from Zhejiang University (China) and Columbia University (U.S.), and leveraged the resources of two nationwide studies: China Health and Nutrition Survey (CHNS) and the U.S. National Health and Nutrition Examination Survey (NHANES). -related outcomes, including comorbidity, mortality, and body composition (fat and fat distribution) all of which have been used as bases to derive BMI cutoffs are compared between mainland Chinese and U.S. whites. Methods Data sources China Health and Nutrition Survey (CHNS) is an ongoing cohort conducted by the University of North Carolina at Chapel Hill and 1 and Body Composition Research Center, Chronic Disease Research Institute, Zhejiang University School of Public Health, School of Medicine, Hangzhou, China. Correspondence: Shankuan Zhu (zsk@zju.edu.cn) 2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden 3 Department of Nutrition and Food Hygiene, Zhejiang University School of Public Health, School of Medicine, Hangzhou, China 4 Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA 5 Department of Epidemiology and Biostatistics, Zhejiang University School of Public Health, School of Medicine, Hangzhou, China. Funding agencies: This study was supported, in part, by funding from Zhejiang University, the China Medical Board (CMB) (grant and ), and a Scholarship Award for Excellent Doctoral Student granted by the Ministry of Education, China. Disclosure: The authors declared no conflict of interest. Author contributions: WH and SKZ designed research; WH, SKZ, and SBH conducted research and acquired the data; WH and SKZ analyzed data; WH wrote the article; WH, QL, MY, JJ, XM, YZ, AS, SBH, SCZ, and SKZ critically revised the manuscript for important intellectual content; WH and SKZ had primary responsibility for final content. All authors read and approved the final manuscript. Additional Supporting Information may be found in the online version of this article. Received: 11 September 2014; Accepted: 13 November 2014; Published online 22 January doi: /oby VOLUME 23 NUMBER 3 MARCH

2 the Chinese Center for Disease Control and Prevention. CHNS uses a multistage, random cluster sampling design, with a response rate of about 88 (10). The CHNS rounds have been completed in 1989, 1991, 1993, 1997, 2000, 2004, 2006, and Details on the survey can be found at U.S. National Health and Nutrition Examination Survey (NHANES), which began in the early 1960s, is a nationally representative survey administered by the National Center for Health Statistics. NHANES uses a complex, multistage, probability sampling design, with a response rate >75 in different years. The sample for the survey is selected to represent the U.S. population of all ages. Details on the survey are available at In these analyses, we included 8,322 Chinese from CHNS 2009 and 16,244 whites from NHANES for the ethnic comparison of obesity-related comorbidities. Data from CHNS 2009 were used because fasting blood were collected for the first time in The BMI-mortality study included 9,379 Chinese from CHNS followed through 2009 and 6227 whites from NHANES III followed through CHNS and NHANES III were used to make sure that the two populations were comparable on time period and that the follow-up was long enough for mortality events to occur. CHNS 1989 was not included because of the lack of data on smoking. Furthermore, 1,016 Chinese from Zhejiang University (China) and 190 whites from Columbia University (U.S.) were included to compare the ethnic difference in fat and fat distribution (11). Written informed consent was obtained from each participant. The studies were approved by the institutional review boards of the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China, and St. Luke s-roosevelt Hospital Center, New York, NY. Data sets from CHNS and NHANES were publicly available. Outcome definitions -related comorbidities. -related comorbidities were defined as follows: (1) hypertension: systolic blood pressure 140 mmhg or diastolic blood pressure 90 mmhg or currently taking antihypertensive medications; (2) diabetes: the use of insulin or oral hypoglycemic medications or a fasting glucose level 7.0 mmol l 21 ; (3) dyslipidemia: triglyceride 2.26 mmol l 21, total cholesterol 6.22 mmol l 21, low-density lipoprotein cholesterol (LDLC) 4.14 mmol l 21, or high-density lipoprotein cholesterol (HDLC) <1.03 mmol l 21 ; (4) hyperuricemia: serum uric acid lmol l 21 (7.0 mg dl 21 ) in men or lmol l 21 (6.0 mg dl 21 ) in women; and (5) metabolic syndrome: the presence of at least two of the following risk factors according to ATP III criteria (without waist circumference): fasting glucose level 5.6 mmol l 21 or taking anti-diabetic medications, blood pressure 130/85 mmhg or taking anti-hypertensive drugs, serum triglyceride 1.7 mmol l 21, or HDLC<1.03 mmol l 21 for men or <1.29 mmol l 21 for women. All-cause mortality. Mortality ascertainment from NHANES III was based upon a probabilistic match between NHANES III and National Death Index (NDI) death certificate records. Identical matching methodology applied to the NHANES I Epidemiological Follow-up Study for validation purposes found that 96.1 of deceased participants and 99.4 of living participants were correctly classified (12). In CHNS, at the time of each follow-up, each individual was listed as alive and still present, having moved, or having died, during household survey. The month in which a move or death occurred was recorded (13). The lost-to-follow-up rate for this study is complex to determine, because the participants who left in one survey may have moved back in a later year. If we define response rate based on those who participated in and remained in the last round in 2009, the cumulative loss to follow-up rate was in this study. Several major causes of loss to follow-up have been discussed elsewhere (10). Body composition. Total, trunk, and legs fat mass was measured by the same dual-energy X-ray absorptiometry system (Lunar Prodigy, WI) in Zhejiang University and Columbia University (11). To investigate the inter-machine variation between the two sites, the body composition of one man and one woman was measured at both Zhejiang University and Columbia University within a month. A mean coefficient of variation of 3.05 was found for body fat. In the present study, we defined percent body fat (BF) as body fat divided by body weight, percent trunk fat (TF) as trunk fat divided by body weight, and trunk to legs fat ratio (TLFR) as trunk fat divided by legs fat. Statistical analysis Comorbidity study. Age- and sex-standardized prevalence of comorbidities was calculated within each BMI category by standardizing both Chinese and white populations to the China 2010 Census population (14). We calculated the average odds ratios for Chinese relative to whites for having comorbidities by further standardizing whites to the Chinese BMI distributions. The ethnic differences were also further examined by using logistic regression analyses, adjusting for age, sex, BMI, and smoking, either with or without including the interaction term between ethnicity and BMI. The BMI cutoffs for predicting the presence of obesity-related comorbidities were obtained and compared in Chinese and whites by using receiver operating characteristic () curve analyses. The BMI values with the maximum sensitivity and specificity (15), calculated as the square root of [(1-sensitivity) 2 1(1-specificity) 2 ], were viewed as appropriate cutoffs. Mortality study. The U-shaped association between BMI and mortality was examined by linear-quadratic methods by including both BMI and BMI 2 in the Cox model after adjusting for age, sex, and smoking (never, former, current) (16,17), Subjects who died within 3 years were excluded to avoid possible reverse causation. The predicted hazard ratios for different BMI levels relative to the BMI with minimum death probability were obtained and fitted by the following equation: HR5expða 0 1a 1 BMI1a 2 BMI 2 Þ where a 1 and a 2 were the coefficients derived from the Cox regression models, and a 0 5 a2 1 4a 2. The BMI with minimum mortality rate was estimated by BMI nadir 52 a 1 2a 2 Analyses were performed separately in Chinese and whites. The Chinese-white differences in BMI-mortality association were then VOLUME 23 NUMBER 3 MARCH

3 Chinese BMI Cutoffs He et al. TABLE 1 Subject characteristics by sex and ethnicity a Men Women Chinese White Chinese White Comorbidities study b Data sources CHNS 2009 NHANES CHNS 2009 NHANES Number examined Age, yr 51.2 (50.7, 51.6) 47.4 (46.9, 48.0)* 51.4 (50.9, 51.8) 49.3 (48.7, 49.8)* BMI, kg m (23.2,23.5) 28.5 (28.3, 28.7)* 23.4 (23.3, 23.5) 28.0 (27.8, 28.3)* Current smoker, 55.4 (53.8, 56.9) 25.4 (23.7, 27.1)* 3.8 (3.2, 4.3) 20.5 (19.0, 22.0)* Diabetes, 8.9 (8.0, 9.8) 10.2 (9.2, 11.3) 6.7 (6.0, 7.44) 7.0 (6.1, 7.8) Dyslipidemia, 37.3 (35.7, 38.8) 41.7 (39.8, 43.6)* 31.3 (30.0, 32.7) 30.5 (28.8, 32.2) Hypertension, 33.1 (31.6, 34.5) 33.1 (31.9, 34.2) 28.4 (27.1, 29.7) 34.4 (33.2, 35.5)* Metabolic syndrome, 38.1 (36.6, 39.6) 49.4 (47.5, 51.3)* 36.9 (35.5, 38.3) 38.8 (37.0, 40.6) Hyperuricemia, 21.1 (19.8, 22.3) 23.4 (22.3, 24.5)** 11.9 (10.9, 12.8) 16.2 (15.3, 17.1)* Mortality study c Data sources CHNS ; Followed to 2009 NHANES III ; Followed to 2006 CHNS ; Followed to 2009 NHANES III ; Followed to 2006 Number examined Age, yr 39.4 (38.9, 39.8) 43.7 (43.1, 44.4)* 39.8 (39.4, 40.3) 46.3 (45.6, 47.0)* BMI, kg m (21.4, 21.5) 25.9 (25.7, 26.0)* 21.8 (21.7, 21.9) 24.5 (24.4, 24.7)* Current smoker, 64.0 (62.6,65.4) 32.2 (30.0, 34.4)* 4.2 (3.6, 4.8) 27.6 (25.7, 29.5)* Body composition study d Data source Zhejiang University Columbia University Zhejiang University Columbia University Number examined Age, yr 50.5 (49.1, 51.9) 44.3 (40.7, 48.0)* 49.6 (48.5, 50.7) 45.8 (42.7, 48.9)*** Weight, kg 66.4 (65.4, 67.5) 83.5 (80.8, 86.3)* 57.3 (56.7, 58.0) 67.5 (64.7, 70.2)* Height, cm (166.8, 168.0) (176.6, 178.9)* (156.0, 156.9) (161.8, 164.5)* BMI, kg m (23.3, 24.0) 26.3 (25.4, 27.2)* 23.4 (23.2, 23.7) 25.4 (24.4, 26.4)* Percent body fat, 20.7 (19.9, 21.4) 21.7 (19.7, 23.7) 31.6 (31.2, 32.1) 32.4 (30.4, 34.4) Percent trunk fat, 13.0 (12.5, 13.5) 11.5 (10.4, 12.7)*** 17.8 (17.5, 18.1) 14.7 (13.7, 15.8)* Trunk to leg fat ratio 2.57 (2.50, 2.64) 1.63 (1.52, 1.74)* 1.98 (1.93, 2.02) 1.08 (1.03, 1.14)* a Data are given as mean (95 confidence interval) unless indicated otherwise. Sampling weights are used to provide population-representative estimates in NHANES. b Analyses included 8,322 Chinese from CHNS 2009 and 16,244 whites from NHANES , after excluding pregnant women and subjects <20 years old. c Analyses included 9,379 Chinese from CHNS followed to 2009 and 6,333 whites from NHANES III followed to 2006, after excluding subjects <18 years of age, with a BMI <15 or 35 kg m 22, died within 3 years, and women who were pregnant at the time of the survey. d Analyses included Chinese from Zhejiang University, China, and 190 whites from Columbia University, U.S., after excluding subjects with missing data on body composition and subjects with positive HIV tests. *P < 0.05; **P < 0.01; ***P < versus Chinese by Wald test or t test, as appropriate. compared by including the two curves in the same figure. A sensitivity analysis restricting to never smokers was also conducted. Body composition study. Polynomial regressions were used to investigate the ethnic difference in the associations of BMI with BF and TF (18,19). To identify the Chinese BMI cutoffs corresponding to whites for having the same fat and fat distribution, sex-specific BMI prediction formulas were firstly developed in Chinese people, with both the linear and quadratic forms of age, height, BF, and TLFR as potential predictors. TLFR was included because fat distribution differs between Chinese and U.S. whites, and because difference in fat distribution can in part account for the higher comorbidities observed in Chinese compared with that in whites for a given BMI (5). Age and height were forced into the equations if their quadratic coefficients were insignificant. These Chinese-based formulas were then applied in the white population to derive a predicted BMI. The relationships between measured and predicted BMIs were analyzed by linear regression models to derive the fat and fat distribution adjusted BMI cutoffs for Chinese. Analyses were carried out using Stata version 11.0 (Stata Corporation, College Station, TX), at two tailed alpha level of Results Chinese-white differences in obesity-related comorbidities Although Chinese had a mean of 5 kgm 22 lower BMI than whites, no significant ethnic differences in prevalence were found for diabetes 686 VOLUME 23 NUMBER 3 MARCH

4 Figure 1 Age- and sex-standardized prevalence of comorbidities in mainland Chinese and U.S. whites, by BMI categories. Analyses include 6,885 Chinese from CHNS 2009 and 12,013 whites from NHANES , after excluding pregnant women, subjects <20 years old, and subjects with a BMI < 20 or 32.5 kg m 22. Age- and sex-specific prevalence are firstly calculated from each study population and then standardized to the 2010 Chinese population, with age categories of 20-44, 45-64, and 65 years. Survey commands of Stata are used to incorporate the survey design in NHANES. and hypertension in men or for diabetes, dyslipidemia, and metabolic syndrome in women (Table 1). Within each BMI category, Chinese had comparable BMI but relatively higher prevalence of comorbidities than whites (Supporting Information Table S1). Further standardizing age and sex revealed similar ethnic differences. In most cases, Chinese in the 2.5 kg m 22 lower BMI category had equivalent or even higher prevalence of comorbidities in comparison with their white counterparts in the higher BMI category (Figure 1). Further standardizing whites to the Chinese BMI distribution showed that Chinese experienced higher odds of comorbidities 43 for diabetes, 30 for dyslipidemia, 28 for hypertension, 38 for metabolic syndrome, and 48 for hyperuricemia relative to whites for a given BMI. VOLUME 23 NUMBER 3 MARCH

5 Chinese BMI Cutoffs He et al. TABLE 2 Sensitivity, specificity, and distance in the receiver operating characteristics () curve for BMI cutoffs in Chinese and U.S. whites a Diabetes Dyslipidemia Hypertension Metabolic syndrome Hyperuricemia BMI kg m 22 Percentile Chinese White a Analyses included 8,322 Chinese from CHNS 2009 and 16,244 whites from NHANES , after excluding pregnant women and subjects <20 years old. sensitivity; spec, specificity; dist, distance in curve. Sampling weights are used to provide population-representative estimates in NHANES. The bold numbers indicate the BMI values with the minimal distance in curve, calculated as the square root of [(1-sensitivity) 2 1(1-specificity) 2 ]. Supporting Information Table S2 presents the results from the logistic regression analyses. Chinese people had significantly higher rates of comorbidities than white people for a given BMI after adjusting for age, sex, and smoking. Interaction analyses showed that Chinese experienced 6 greater odds for diabetes, 13 for dyslipidemia, 11 for hypertension, 10 for metabolic syndrome, and 4 greater for hyperuricemia, respectively, relative to whites for each 1 kg m 22 increase in BMI. Table 2 shows the population percentile of each BMI level and the sensitivity, specificity, and distance on the curve for the detection of comorbidities. Although both corresponding to the BMI percentile of in the population, the shortest distance on the curve was kg m 22 in Chinese versus kg m 22 in whites. Chinese-white differences in BMI-mortality A total of 866 deaths occurred among 9,379 Chinese during a mean of 13.9 years of follow-up, and 1,935 deaths occurred among 6,227 whites during a mean of 13.1 years of follow-up. Chinese people were younger and had lower levels of BMI than white people (Table 1). More Chinese men but less Chinese women were current smokers compared with their white counterparts. Both linear and quadratic forms of BMI were significantly related to mortality in the multivariable Cox regression analyses, suggesting that the risk of death increased with both lower and higher BMI values. Figure 2 compares the Chinese-white difference in the BMI-mortality association: the U-shaped curve shifted 1-2 kg m 22 to the left in Chinese compared to whites, with the mortality nadir of 24.8 kg m 22 (95 confidence interval 22.8 to 26.9 kg m 22 ) in Chinese compared with 26.1 kg m 22 (95 confidence interval 25.2 to 27.1 kg m 22 ) in whites. Consistent results were found when we restricted our analyses to never smokers (mortality nadir, Chinese vs. whites: 24.9 kg m 22 vs kg m 22 ). Chinese-white differences in body composition Compared with Chinese people, white men and women were younger, heavier, taller, and had higher levels of BMI and BF but lower levels of TF and TLFR (Table 1). Corresponding to the white BMIs of 25and30kgm 22, lower cutoffs of 23.0 and 27.9 kg m 22 were found in Chinese men for having the same levels of BF. These lower cutoffs were further reduced to 21.5 and 25.2 kg m 22 when Chinese and white BMIs were linked as having the same TF (Supporting Information Figure S1). In women, a similar pattern, which decreased from 24.4 to 21.0 kg m 22 for overweight, and from 32.5 to 24.7 kg m 22 for obesity, was also found when BF was replaced by TF (Supporting Information Figure S2). 688 VOLUME 23 NUMBER 3 MARCH

6 found to be associated with the lowest risk of mortality in Chinese people, which was consistent with the results in previous studies and close to the overweight BMI cutoffs recommended by WHO. However, this did not mean Chinese and whites should use the same BMI cutoffs because higher nadir in the BMI-mortality curve was found in whites compared with that in Chinese (whites vs. Chinese: 26.1 vs kg m 22 ). Despite that, the ranges of the nadirs identified in the BMI-mortality curve were relatively wide, most probably due to the relatively small sample size, but possibly also due to chance or measurement error. Furthermore, the elevated mortality risk at lower BMI levels might partially be due to reverse causality (21). After excluding smokers and subjects with baseline diseases, the BMI-mortality association was more likely to be J-shaped (22,23). In that case, lower BMIs to define overweight and obesity would be more appropriate. Figure 2 Ethnic difference in BMI-mortality association between mainland Chinese and U.S. whites. Analyses include 9,379 Chinese from CHNS followed to 2009 and 6,333 whites from NHANES III followed to 2006, after excluding pregnant women, subjects with a BMI <15 or 35 kg m 22,subjectsaged<18 years old, or subjects who died during the first 3 years. The curvilinear relationship between BMI and mortality is fitted by a linear-quadratic model by including both BMI and BMI 2 in the Cox model, with adjustment for age, sex, and smoking (never, former, current). Survey commands of Stata are used to incorporate the survey design in NHANES. The main purpose of classifying overweight and obesity was to identify individuals at increased risk of comorbidities (24). Higher prevalence of hypertension, diabetes, and hyperuricemia had been reported in Taiwanese compared to U.S. whites for most BMI values using data from NHANES and the Nutrition and Health Survey in Taiwan ( ) (25). This was consistent with the fact that China had a much lower prevalence of overweight 21.8 in China versus 68.0 in the United States (26,27), according to the WHO Supporting Information Table S3 presents the BMI prediction formulas, which were first derived from Chinese and then applied in the white population to derive the predicted BMIs. Figure 3 shows the relationships between predicted BMI and measured BMI: corresponding to the white BMIs of 25 and 30 kg m 22, lower cutoffs of 22.5 and 25.9 kg m 22 in men, and lower cutoffs of 22.8 and 26.6 kg m 22 in women, were found in Chinese after age, height, BF, and TLFR were taken into account. Discussion Our results support the use of lower BMI cutoffs in Chinese than those in whites because: (1) compared with whites, Chinese experienced higher odds of comorbidities for a given BMI after standardizing for age and sex; (2) Chinese were more susceptible to metabolic disorders than whites for every one-unit increase in BMI; (3) lower BMI cutoffs in Chinese relative to whites have been found to be appropriate for the screening of obesity-related comorbidities; (4) the U-shaped BMI-mortality curve shifted 1-2 kg m 22 to the left in Chinese compared to whites; and (5) lower BMI cutoffs were found in Chinese corresponding to whites for having the same fat and fat distribution. One argument against the use of ethnic-specific BMI cutoffs is that the BMI-mortality association showed no evidence for a lower cutpoint for obesity in Asians compared to Caucasians (20). In 2006, Gu et al. found that a BMI between 24.0 and 24.9 kg m 22 in men and between 25.0 and 26.0 kg m 22 in women was associated with the lowest risk of mortality in Chinese (3). These results are similar to those from another two studies in 2011 based on data from Taiwan and Asian populations (2,4), both of which concluded that Chinese and whites have similar BMI-mortality associations. However, these studies might be misleading because of the lack of a white comparison group. In the current study, a BMI of 24.8 kg m 22 was Figure 3 Relationship between predicted BMI (derived from Chinese formulas) and measured BMI in white men and women. Predicted BMIs are calculated using the equations presented in Supporting Information Table S2. Corresponding to the white BMIs of 25 and 30 kg m 22, lower values of 22.5 and 25.9 kg m 22 in men, and 22.8 and 26.6 kg m 22 in women, were found in Chinese after age, height, percent body fat, and trunk to legs fat ratio were taken into account. VOLUME 23 NUMBER 3 MARCH

7 Chinese BMI Cutoffs He et al. definition but experienced a higher prevalence of diabetes of 11.6 versus 9.6 in the United States (28,29). These findings, together with ours and many others (6,30,31), consistently support Chinese to use lower BMI cutoffs to define overweight and obesity, so that we won t miss the large population who are at increased risk of comorbidities, but will be classified as normal according to the WHO definition. Asians have been repeatedly reported to accumulate more body fat than whites for a given BMI (8,32,33). This observation was consistent with our results in men. However, in women, corresponding to the white BMIsof25and30kgm 22 for having the same BF, lower BMI value of 24.4 kg m 22 but higher BMI value of 32.5 kg m 22 were found in Chinese people, suggesting that the ethnic difference in the association of BMI with BF may vary from normal-weight to obese women. Similar results were obtained in a previous study, which showed that at lower levels of BMI, post-menopausal Asian women tended to have higher BF compared to whites, while at BMI > 30 kg m 22, Asians tended to have less BF than white women (34). Despite these facts, this result should be interpreted with caution because of the limited number of data points above 30 kg m 22 in our study, especially for Chinese women. Although still controversial (20,24), fat and fat distribution have been repeatedly used as a basis for the determination of the appropriate BMI cutoffs (7,35). Previous studies have found that, at any given BF, the BMI for Chinese were 2-5 units lower than in whites (35,36). However, abdominal fat distribution, which can vary dramatically within a narrow range of body fat (24), and which may account for more ethnic differences in metabolic risk factors than total fatness per se (5), was not considered in previous ethnic comparison studies (35,36). Furthermore, previous conclusions were often derived from Asian immigrants living in western countries or whites living in China (8,32) which may introduce bias because the relationship between BMI and body fat can be affected by population migration (37,38). Certain limitations should be noted to interpret the results. First, our sample size for the body composition study was relatively small. However, the quality of the data has been evaluated in our previous study (11). Second, different from the national-representative sample included in NHANES, the CHNS only included 9 out of 32 provinces in China in its sampling frame. However, CHNS is one of the few longitudinal surveys that include information on various obesityrelated factors. Third, <1 of Chinese had a BMI >35 kg m 22 around 1990 in the BMI-mortality study. This limited us from investigating the ethnic difference in the higher BMI range. Fourthly, our study was limited by the use of cross-sectional data in comorbidity studies. Fifth, the difference identified in the current study may reflect not only ethnic difference but also difference in country settings. Therefore our results cannot necessarily be generalized to Chinese immigrants living outside of China. Finally, the current research was not designed for ethnic comparison and the data on lifestyles were collected based on different questionnaires in Chinese and U.S. whites. Thus, we were unable to investigate whether the ethnic difference in the association of BMI with obesity-related outcomes can be modified by environmental factors such as diet and physical activity. Conclusion In conclusion, our results highlight the heterogeneity in the association of BMI with comorbidities, mortality, and body composition between mainland Chinese and U.S. whites, providing a rational basis for the use of lower BMI cutoffs in China. Cautions should be taken because increasing the BMI cutoffs from 24 to 25 kg m 22 will result in a decrease in the prevalence of overweight and obesity by 8.1 in just one night, meaning that about 65 million overweight and obese Chinese will be re-classified as normal weight (27). Such a change will dramatically affect the obesity policy in China, which is now in its critical time to prevent and control the emerging obesity epidemic. Our findings also underscore the need for further studies to investigate whether ethnic-specific BMI cutoffs should be used in different populations. O VC 2015 The Society References 1. World Health Organization, International Association for the study of, International TaskForce. The Asia-Pacific perspective: redefining obesity and its treatment. Melbourne: Health Communications; Lin WY, Tsai SL, Albu JB, et al. Body mass index and all-cause mortality in a large Chinese cohort. CMAJ 2011;183:E329-E Gu D, He J, Duan X, et al. 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