Obesity, an independent risk factor for CVD, is closely associated with elevated risk of diabetes, insulin resistance and other metabolic disorders( Reference Zalesin, Franklin and Miller 1 , Reference Nordmann, Nordmann and Briel 2 ). The WHO defines obesity as an excessive accumulation of fat to an extent that may impair health( 3 ). Assessment of overweight and obesity includes examination of the distribution of body mass components and risk factors for diseases such as type 2 diabetes, dyslipidaemia, impaired glucose tolerance and CVD. Consequently, it is a major health concern to identify obesity in order to estimate the risk of associated disorders. However, obese people may vary in their body fat distribution and disease risk. The accumulation of visceral fat, as opposed to subcutaneous fat, increases the risk of metabolic diseases and CVD( Reference Fox, Massaro and Hoffmann 4 ). Therefore, it is currently accepted that the distribution of body fat, rather than the total amount of adipose tissue, is a crucial determinant of metabolic abnormalities( Reference Walton, Lees and Crook 5 ). MRI and computed tomography have been recognized as the gold standard for measuring the volume of visceral fat, but these two methods are not suitable for large-scale epidemiological studies due to their inconvenience and high cost( 6 ). Therefore, body fat is frequently estimated or reflected by a series of anthropometric indices in most studies.
Among different anthropometric measures, BMI is one of the most commonly used indices. While BMI is a widely accepted and an early applicable measure of obesity, its use has limitations. BMI depends only on height and weight, and does not distinguish between adipose tissue and muscle mass( Reference Nevill, Stewart and Olds 7 ). As one study reported, at the same BMI value, MRI-measured body fat content varied from 7·8 to 38·3 % in men and from 29·9 to 44·2 % in women( Reference Thomas, Parkinson and Frost 8 ). As well, at the same BMI value, individuals from different ethnic groups may exhibit very different body fat distributions. Several studies have reported that people from Asia have greater total body and abdominal fat distribution or a greater incidence of CVD and more metabolic risk factors than people from Europe for the same BMI or waist circumference (WC) value( Reference Deurenberg, Deurenberg-Yap and Guricci 9 , Reference Lear, Toma and Birmingham 10 ). WC, an essential diagnostic component of the metabolic syndrome and a typical anthropometric parameter reflecting central obesity, is strongly related to CVD and a number of studies have demonstrated that WC predicts mortality risk better than BMI( Reference Brenner, Tepylo and Eny 11 – Reference Petursson, Sigurdsson and Bengtsson 15 ). However, a recent systematic review demonstrated that waist-to-height ratio (WHtR) is a significantly more favourable screening tool for adult metabolic risk factors than are BMI and WC( Reference Ashwell, Gunn and Gibson 16 ), and reported that compared with BMI, WC improved the discrimination of adverse cardiovascular risk outcomes by 3 % and WHtR improved discrimination by 4–5 %. As well, WHtR is able to evaluate cardiovascular risk factors and to effectively predict the incidence of metabolic syndrome in non-obese people( Reference Li, Chen and Chang 17 – Reference Zhu, Shen and Ye 20 ). However, the problem is that none of these indices alone is able to distinguish subcutaneous fat from the visceral fat which is more strongly associated with metabolic abnormalities. Some studies still suggest that these indices provide limited information on fat distribution( Reference Donini, Poggiogalle and Del Balzo 21 – Reference Muller, Lagerpusch and Enderle 23 ).
Recently, a body shape index (ABSI), a new body index proposed by Krakauer and Krakauer( Reference Krakauer and Krakauer 24 ), has been reported to be significantly associated with abdominal adipose tissue and to be more associated with premature death than either WC or BMI. In some cohort studies, ABSI showed a positive association with disease risk and mortality hazard( Reference He and Chen 25 , Reference Krakauer and Krakauer 26 ). However, some studies reported that ABSI seemed to be a weaker index for identifying hypertension, diabetes, CVD risk and metabolic syndrome than BMI( Reference Chang, Guo and Chen 27 – Reference Haghighatdoost, Sarrafzadegan and Mohammadifard 29 ). Therefore, this controversy remains to be further studied. As far as we know, the efficacy of ABSI for identifying cardiometabolic risk factors in non-obese Chinese has not been specifically studied.
Body roundness index (BRI), another new index proposed by Thomas et al. and based on WC and height, is a predictor that could improve predictions of body fat percentage and visceral adiposity tissue compared with the traditional metrics of BMI, WC or hip circumference( Reference Thomas, Bredlau and Bosy-Westphal 30 ). Several recent studies have shown that BRI could be used as an adipose indicator for determining the presence of eccentric left ventricular hypertrophy, hyperuricaemia, CVD and diabetes( Reference Chang, Guo and Chen 27 , Reference Zhang, Chang and Guo 31 – Reference Chang, Guo and Li 33 ). However, to date, little evidence has compared BRI with WHtR in the assessment of cardiometabolic risk factors in non-obese Chinese adults.
Therefore, it is necessary to assess whether the new indices better identify cardiometabolic risk factors than the traditional ones. The objective of the present study was to compare the ability of ABSI and BRI with that of WHtR to identify cardiometabolic risk factors in Chinese adults with normal BMI and WC.
Methods
Participants
From July to December 2015, a total of 1596 non-obese people aged 20–60 years (with normal BMI (in the range of 18·5 to 23·9 kg/m2) and WC (male<90 cm, female<80 cm)) who voluntarily visited the Medical Examination Center of Peking Union Medical College Hospital, China Academic Medical Science and Peking Union Medical College (Beijing, China) for a health check-up were recruited for the present study. The mean age of the study participants was 44·4 (sd 8·1) years. All participants were of Han ethnicity and dwelled in the communities of Beijing, China. The exclusion criteria were as follows: (i) evidence of liver or renal insufficiency or malignancy; (ii) a medication history of corticosteroids or hormone therapy in the previous 6 months; (iii) those on a weight-loss programme or who had lost ≥5 % of their body weight in the previous 12 months; (iv) current hyperthyroidism or hypothyroidism or oedema; (v) skeletal deformities or amputations or dependence on wheelchairs or other ambulatory assistive devices; (vi) men whose alcohol consumption more than 14 ethanol units (1 ethanol unit=14 g ethanol) per week and women whose alcohol consumption was more than 10 ethanol units per week; and (vii) women who were pregnant.
The study was approved by the Ethics Committee of Peking Union Medical College Hospital, China Academic Medical Science and Peking Union Medical College. All participants provided written informed consent before participating in the study.
Procedure
Trained physicians administered a standard questionnaire to collect information on age, smoking status (yes/no), alcohol consumption, weight status, medical history and medication use. Routine physical examinations were then performed for all participants. Two blood pressure (BP) recordings (rounded to the nearest 2 mmHg) were obtained from the right arm of the participants while seated after 30 min of rest. The final measurement was the average of these two recordings.
Anthropometric measurements
Participants were requested to wear light clothing and take off their shoes for the measurement of their anthropometric characteristics, which was performed by well-trained examiners. Height was measured (rounded to the nearest 0·1 cm) using a portable stadiometer. Body weight was measured (rounded to the nearest 0·1 kg) using a calibrated scale with the participant in an upright position. BMI was calculated by dividing weight by the square of height (kg/m2). WC was measured (rounded to the nearest 0·1 cm) at the end of normal expiration at the midpoint between the lower border of the rib cage and the iliac crest. WHtR was calculated by dividing waist circumference by height. ABSI and BRI were calculated using the corresponding formula as follows( Reference Krakauer and Krakauer 24 , Reference Zhang, Chang and Guo 31 ):
$${\rm ABSI}\,{\equals}\,{{{\rm WC}} \over {{\rm BMI}^{{2/3}} {\rm height}^{{1/2}} }}$$
and
$${\rm BRI}\,{\equals}\,364{\!} \cdot {\!}2{\minus}365{\!}\cdot{\!}5{\times}\sqrt {1{\minus}\left( {{{({\rm WC}/2\pi )^{2} } \over {(0 {\!}\cdot {\!}5{\times}{\rm height})^{2} }}} \right)} .$$
Biochemical measurements
Blood samples were collected from the peripheral vein of all participants in the morning after a fasting period of 10–12 h. After collection, the samples were immediately centrifuged at 4°C. The plasma was assayed for the lipid profile (including total cholesterol, TAG, LDL cholesterol and HDL cholesterol (HDL-C)), fasting blood glucose (FBG) and high-sensitivity C-reactive protein (hs-CRP) using an automated analyser (Olympus AU5800, Japan).
Definition of cardiometabolic risk factors
In present study, the cardiometabolic risk factors included high BP, dyslipidaemia and dysglycaemia. Both pre-hypertension and hypertension were considered as high BP; pre-hypertension was defined as systolic blood pressure (SBP) of 120–139 mmHg or diastolic blood pressure (DBP) of 80–89 mmHg( Reference Chobanian, Bakris and Black 34 ); and hypertension was defined as SBP≥140 mmHg or DBP≥90 mmHg or currently receiving antihypertensive medication. Dysglycaemia included pre-diabetes and diabetes. The definition of pre-diabetes was FBG greater than 5·6 mmol/l and less than 7·0 mmol/l, and no history of diabetes or use of antidiabetes medication; and diabetes was defined as FBG≥7·0 mmol/l or a history of type 2 diabetes previously( Reference Donahua, Stranges and Reiman 35 , Reference Genuth, Alberti and Bennett 36 ). Dyslipidaemia included high TAG (≥1·71 mmol/l) and low HDL-C (≤1·03 mmol/l for men or ≤1·30 mmol/l for women)( Reference Grundy, Cleeman and Daniels 37 ).
Statistical analyses
Statistical analyses were performed using the statistical software package SPSS version 16.0. Data are expressed as mean and standard deviation for continuous variables and as frequency and percentage for categorical variables. The independent-samples t test was used to compare basic characteristics between males and females. The data of males and females were then analysed separately. Partial correlations were applied to assess the correlations between the body indices and metabolic variables including SBP, DBP, TAG, HDL-C and FBG with controlling for age and hs-CRP. Participants were divided into four groups according to the number of metabolic risk factors (0, 1, 2, ≥3) and univariate analysis was then applied to compare the mean value of body indices in the four groups among both genders with controlling for smoking and drinking status, age and hs-CRP. Receiver-operating characteristic (ROC) curves and areas under the curve (AUC) were employed to evaluate the ability of the three body indices to identify the risk factors and to determine the optimal cut-off values of anthropometric indices; the values of the indices that maximized the Youden index (sensitivity + specificity – 1) were defined as optimal. A comparison of the diagnostic abilities between WHtR and the two new body indices was performed using the AUC, and the significance of the difference between two AUC values was assessed by the method described by Hanley and McNeil( Reference Hanley and McNeil 38 ).
Results
The basic characteristics of the study population are shown in Table 1. Compared with females, males exhibited a significantly higher age, BMI, WC, WHtR, BRI, ABSI, SBP, DBP, total cholesterol, TAG, LDL cholesterol, FBG, hs-CRP and current smoking and smoking rate, but lower HDL-C; in addition, a significantly higher prevalence was observed for high BP, high TAG and dysglycaemia among males. The prevalence of low HDL-C was significantly lower in males than in females.
Characteristics of the study participants according to gender: non-obese adults (n 1596) attending a voluntary health check-up in Beijing, China, July–December 2015
WC, waist circumference; WtHR, waist-to-height ratio; BRI body roundness index; ABSI, a body shape index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; HDL-C, HDL cholesterol; LDL-C, LDL cholesterol; FBG, fasting blood glucose; hs-CRP, high-sensitivity C-reactive protein; BP, blood pressure.
As shown in Table 2, in males, after controlling for age and hs-CRP, partial correlation analysis showed that both BRI and WHtR were positively associated with SBP, DBP and TAG, and were not correlated with FBG. Conversely, BRI and WHtR were negatively correlated with HDL-C; ABSI did not correlate with any metabolic variable (including SBP, DBP, TAG, HDL-C and FBG). In females, after age and hs-CRP were controlled, both BRI and WHtR were positively associated with SBP and TAG, and were not correlated with DBP and FBG. However, they were negatively correlated with HDL-C. Interestingly, ABSI was negatively associated with SBP and TAG, but positively with HDL-C.
Partial correlations between body indices and metabolic components according to gender among non-obese adults (n 1596) attending a voluntary health check-up in Beijing, China, July–December 2015
WtHR, waist-to-height ratio; BRI, body roundness index; ABSI, a body shape index; SBP, systolic blood pressure; DBP, diastolic blood pressure; HDL-C, HDL cholesterol; FBG, fasting blood glucose.
† Controlling for age and high-sensitivity C-reactive protein.
As shown in Table 3, in males, the mean value of WHtR and BRI increased significantly (P for trend <0·001) with the increase in the number of risk factors after controlling for smoking and drinking status, age and hs-CRP, but ABSI did not exhibit a progressive increase in parallel with increased number of risk factors. In females, the mean values of the three indices increased significantly with the increase in the number of risk factors after controlling for smoking and drinking status, age and hs-CRP.
Mean value of each body index, and its standard deviation, according to the number of cardiometabolic risk factors and gender among non-obese adults (n 1596) attending a voluntary health check-up in Beijing, China, July–December 2015
WHtR, waist-to-height ratio; BRI, body roundness index; ABSI, a body shape index.
† Adjusted for smoking and drinking status, age and high-sensitivity C-reactive protein.
The AUC values for discriminating high BP, dysglycaemia, high TAG and low HDL-C by WHtR, BRI and ABSI for males and females are shown in Table 4. In males, with regard to dysglycaemia, high TAG and low HDL-C, the AUC value for BRI was slightly higher than that for WHtR (0·556 v. 0·550; 0·628 v. 0·625; 0·600 v. 0·598, respectively), but the AUC value for BRI regarding high BP was slightly lower than that for WHtR (0·587 v. 0·589). In females, the AUC value for WHtR was slightly larger regarding high BP, dysglycaemia and high TAG than for BRI (0·619 v. 0·618; 0·619 v. 0·614; 0·639 v. 0·638, respectively); the AUC value for WHtR was equal to that for BRI in identifying low HDL-C.
Area under the receiver-operating characteristic curve (AUC), and its 95 % confidence interval, for the identification of cardiometabolic risk factors by each body index according to gender among non-obese adults (n 1596) attending a voluntary health check-up in Beijing, China, July–December 2015
BP, blood pressure; HDL-C, HDL cholesterol; WHtR, waist-to-height ratio; BRI, body roundness index; ABSI, a body shape index.
*P<0·05, ***P<0·001.
According to the ROC curve analysis, ABSI obviously exhibited the lowest AUC value for identifying each cardiometabolic risk factor among the three indices and could hardly be used to identify any risk factor among males and females.
Moreover, according to the method described by Hanley and McNeil( Reference Hanley and McNeil 38 ), there was no significant difference in the AUC value between BRI and WHtR for identifying any of the risk factors in both genders (in males (WHtR v. BRI): high BP, z=0·0673, P=0·946; dysglycaemia, z=0·170, P=0·865; high TAG, z=0·101, P=0·920; low HDL-C, z=0·589, P=0·953; and in females (WHtR v. BRI): high BP, z=0·0321, P=0·974; dysglycaemia, z=0·114, P=0·909; high TAG, z=1·928, P=0·054; low HDL-C, z=1·000, P=0·500).
The AUC values for discriminating the cardiometabolic risk factors (≥1, ≥2 or ≥3) by WHtR, BRI and ABSI for males and females are shown in Table 5. In males, generally, the AUC value for BRI was slightly higher than that for WHtR. Conversely, the AUC value for BRI was slightly lower than that for WHtR in females. The AUC value was lower for ABSI than for WHtR or BRI in both genders.
Area under the receiver-operating characteristic curve (AUC), and its 95 % confidence interval, for each body index according to the number of cardiometabolic risk factors and gender among non-obese adults (n 1596) attending a voluntary health check-up in Beijing, China, July–December 2015
WHtR, waist-to-height ratio; BRI, body roundness index; ABSI, a body shape index.
*P<0·05, ***P<0·001.
The corresponding sensitivity and specificity of the cut-off values of the indices for the identification of cardiometabolic risk factors are presented in Table 6. The optimal cut-off of WHtR for detecting at least one cardiometabolic risk factor was 0·47 in males and 0·46 in females; and that of BRI was 2·8 in males and 2·4 in females.
Sensitivity (Sens) and specificity (Spec) of the cut-off values of the body indices for the identification of cardiometabolic risk factors according to gender among non-obese adults (n 1596) attending a voluntary health check-up in Beijing, China, July–December 2015
WHtR, waist-to-height ratio; BRI, body roundness index; ABSI, a body shape index; BP, blood pressure; HDL-C, HDL cholesterol.
† Data are not shown due to index’s inability to significantly identify the cardiometabolic risk factor.
Discussion
To the best of our knowledge, the present study is the first cross-sectional one specifically comparing the ability of ABSI and BRI with that of WHtR to identify cardiometabolic risk factors in Chinese adults with normal BMI and WC. It is well known that both pre-diabetes and pre-hypertension contribute to the development of CVD( Reference Marini, Succurro and Castaldo 39 , Reference Yu, Huang and Hu 40 ). Thus, these conditions, together with diabetes, hypertension and dyslipidaemia (including high TAG and low HDL-C) were considered as the cardiometabolic risk factors in the present study.
WHtR, an anthropometric index based on WC and height, has recently been demonstrated to be a significantly more favourable screening tool for adult metabolic risk factors than BMI or WC( Reference Ashwell, Gunn and Gibson 16 ). As well, WHtR was able to effectively predict the presence of metabolic syndrome in Chinese postmenopausal women and in non-obese Asians( Reference Hsieh and Muto 18 – Reference Zhu, Shen and Ye 20 , Reference Liu, Ma and Lou 41 ). In a Taiwanese population with normal BMI or WC, those with an elevated WHtR might have greater levels of various cardiometabolic risk factors than those with low WHtR( Reference Li, Chen and Chang 17 ). However, these studies did not compare WHtR with either ABSI or BRI. Recently, ABSI and BRI have been reported to be significantly associated with abdominal adipose tissue and to improve predictions of body fat percentage and visceral adiposity tissue compared with the traditional metrics of BMI and WC( Reference Krakauer and Krakauer 24 , Reference Thomas, Bredlau and Bosy-Westphal 30 ). Until now, limited studies have compared the ability of BRI or ABSI with that of WHtR for predicting cardiometabolic risk factors in non-obese people. Since the characteristics of metabolically healthy obese individuals and of metabolically obese but normal-weight individuals have been described( Reference Stefan, Kantartzis and Machann 42 , Reference Ruderman, Schneider and Berchtold 43 ), we are required to re-evaluate the value of BMI and WC in detecting cardiometabolic risk among non-obese individuals( Reference Zhu, Shen and Ye 20 ). Therefore, we conducted the current study to assess the ability of ABSI and BRI to identify CVD risk factors and determined whether they are superior to WHtR among individuals with normal BMI and WC.
The ABSI, a new body index independent of height, weight and BMI, has been reported to be more predictive for premature mortality than BMI or WC( Reference Krakauer and Krakauer 24 ). One latest population-based study reported that ABSI showed a stronger association with total, cardiovascular and cancer mortality than BMI, WC and WHtR( Reference Dhana, Kavousi and Ikram 44 ). However, Maessen et al. found that ABSI was not a suitable measurement to identify either CVD or CVD risk factors( Reference Maessen, Eijsvogels and Verheggen 32 ). In addition, two Chinese studies reported that compared with BMI and WC, ABSI was not a better predictor for diabetes( Reference He and Chen 25 , Reference Chang, Guo and Chen 27 ). Similar results were also reported in one retrospective cohort study conducted in Japanese adults. In the present study, we found that ABSI did not correlate with any metabolic variable (including SBP, DBP, TAG, HDL-C or FBG) in males and was negatively associated with SBP and TAG, but positively with HDL-C in females; the AUC value for ABSI was obviously lower than that for WHtR in identifying hypertension, dysglycaemia and dyslipidaemia among both genders, suggesting that ABSI could hardly be used to identify any cardiometabolic risk factor among non-obese Chinese adults. As suggested by two Chinese studies( Reference He and Chen 25 , Reference Chang, Guo and Chen 27 ), these discrepant results might be due to ethnic differences or different characteristics of study samples.
The BRI, based on WC and height, is another new obesity index proposed by Thomas et al. to assess obesity( Reference Thomas, Bredlau and Bosy-Westphal 30 ). Thomas et al. showed that BRI can reflect both visceral adipose tissue and body fat percentage and can improve predictions of body fat compared with the traditional indices like BMI and WC. As far as we know, only four studies have applied the BRI to identify the presence of disease. Some studies found that BRI was not superior to the traditional obesity indices (including BMI and WC) for determining the presence of diabetes and CVD as well as CVD risk factors( Reference Chang, Guo and Chen 27 , Reference Maessen, Eijsvogels and Verheggen 32 ). However, one study reported that BRI had a superior predictive ability for identifying hyperuricaemia than BMI and similar capabilities to those of WC and WHtR in women( Reference Zhang, Chang and Guo 31 ). In addition, BRI was found to be a superior measurement compared with BMI, WC and WHtR for determining the presence of left ventricular hypertrophy( Reference Chang, Guo and Li 33 ). However, Santos et al. reported that BRI is limited in predicting percentage of fat mass in athletes, particularly when compared with commonly and readily available field methods like bio-impedance analysis or skinfold prediction models( Reference Santos, Silva and Matias 45 ). The results of our present study demonstrated that while BRI was able to identify cardiometabolic risk factors in non-obese individuals, it did not exhibit a significantly better predictive ability compared with WHtR. The optimal cut-off of BRI for detecting at least one cardiometabolic risk factor was 2·8 in males and 2·4 in females, and that of WHtR for detecting at least one cardiometabolic risk factor was 0·47 in males and 0·46 in females. Of interest, the cut-off value of WHtR in males was closely consistent with one previous Chinese study, while it was not in females( Reference Zhu, Shen and Ye 20 ).
The present study has several limitations that should be mentioned. First, ABSI is independent of height, weight and BMI, and was proposed to predict mortality hazard in a follow-up study; we employed it to identify cardiometabolic risk factors in a cross-sectional study among non-obese people, which may to some extent affect its predictive power. Second, all participants of the present study were of Chinese ethnicity, residents of Beijing and were recruited from a single hospital; therefore, it is unclear whether our results are applicable to other ethnic groups, which requires further investigation. Third, our study participants were people who underwent health check-ups, and some of them might have had diabetes before the check-ups. These people were more concerned about their health status and were more willing to receive a health check-up, which might lead to an increased number with diabetes being included in the study population. Therefore, the high prevalence of diabetes in males (9·8 %) of our study population did not represent the overall prevalence in non-obese men, which may explain the high prevalence of diabetes in males in the study. Finally, we could not provide any mechanistic explanation for our results. As well, we did not examine 2 h postprandial glucose, which may lead to the under-diagnosis of some diabetic subjects.
Conclusions
Despite these limitations, our results showed that WHtR was a simple and effective index in assessment of cardiometabolic risk factors among non-obese adults. When evaluating cardiometabolic risk factors among non-obese adults, BRI could be used as an alternative body index to WHtR, while ABSI could not.
Acknowledgements
Acknowledgements: The authors thank all of the participants and gratefully acknowledge the Medical Examination Center, Peking Union Medical College Hospital, China Academic Medical Science and Peking Union Medical College. Funding: This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. Conflict of interest: The authors report no financial disclosures and no conflicts of interest. The authors alone are responsible for the content and writing of the paper. Authorship: P.J.L. and F.M. designed the study and participated in data management. P.J.L. reviewed and revised the manuscript critically. H.P.L. was responsible for quality assurance and control. Y.N.Z. collected the data. All authors read and approved the final manuscript. Ethics of human subject participation: This study was approved by the Ethics Committee of Peking Union Medical College Hospital, China Academic Medical Science and Peking Union Medical College. All participants provided written informed consent before participating in the study.
