A body shape index and body roundness index: two new body indices to identify diabetes mellitus among rural populations in northeast China

Krakauer et al. demonstrated that the ABSI was independent of height, weight, and BMI, and also found that the ABSI was more predictive for premature mortality than either BMI or WC [18]. As indicated in another study by Krakauer, ABSI was a readily computed dynamic gender and age sensitive indicator of mortality risk across BMI categories and had potential uses for making clinical decisions and for correlation with lifestyle and with other risk factors and health outcomes [26]. However, Maessen MF et al. found that ABSI was not a suitable measurement to identify either CVD or CVD risk factors [23]. He et al. in one perspective study conducted in China found that ABSI, BMI, and WC had similar predictive abilities for new onset DM after analyzing 15 years of follow-up data, suggesting that the ABSI was not better than WC or BMI for predicting DM [20]. Similarly, Fujita M et al. in one retrospective cohort study found that compared with BMI or WC, ABSI was not a better predictor of diabetes, hypertension, and dyslipidemia in Japanese adults [27]. Consistent with the studies from Western and Asian countries [20, 22, 23, 27], our study also found that after adjusting for age, race, family income, education, smoking, and alcohol status, ABSI showed the weakest predictive power for DM compared to other anthropometric indices. The ABSI was initially developed to predict mortality hazard in a follow-up study, and we applied it to predict DM in a cross-sectional study, which may be one reason why the ABSI failed to show superior predictive power. Furthermore, as suggested by other studies [20, 23, 27], we believe the main reason for these discrepant results was ethnic differences and (or) subject characteristics. During the face-to-face interviews conducted in our study, we found that people residing in rural areas had retained their traditional lifestyles. Both men and women of all ages reported engaging in agricultural activities which ranged from sowing in the spring to harvesting in autumn, and gathering firewood in the winter. The intensity of these activities was great enough that individuals tended to have better physical health compared to other less active individuals who had the same BMI. As suggested that ABSI could discern between fat and lean mass [18], the values of ABSI in our study were smaller than those in Krakauer’s study (0.0767 ± 0.0052 vs. 0.0816 ± 0.0053). In our study, some participants in the total group classified as obese by their BMI, could be classified as normal if evaluated by their ABSI. In other words, there were 875 obese people (345 in men and 530 in women, respectively) evaluated by their BMI, accounting for 7.7 % of the total people. This obesity prevalence would be relatively lower if the criterion was based on ABSI. That might be one reason why ABSI showed the lowest reliability for predicting DM, which is closely associated with obesity [2, 3]. Furthermore, as reported in Krakauer’s study, ABSI was strongly correlated with age and sex [18], which was further confirmed by subsequent studies. Xu et al. [28] suggested that the appropriate scaling exponents for standardizing waist circumference for BMI and height in Chinese adolescents were 0.45 and 0.55, respectively. Another study conducted in middle-age and older Indonesian population found that the regression coefficients for men were roughly similar to those reported in Krakauer’s study, but regression coefficients for women were different [29]; suggesting that the appropriate scaling exponents should be modified for gender. Finally, the equations used to calculate the ABSI for men and women were modified as follows: \( \mathrm{ABSI} = \kern0.5em \frac{\mathrm{WC}}{{\mathrm{BMI}}^{2/3}{\mathrm{Height}}^{1/2}} \) for men and \( \mathrm{ABSI} = \kern0.5em \frac{\mathrm{WC}}{{\mathrm{BMI}}^{3/5}{\mathrm{Height}}^{1/5}} \) for women.

Although the precise reasons for the discrepant results were unascertained, ABSI was defined to be independent of BMI and thus could be an important complement to it when identifying subjects at risk of some diseases.

Thomas et al. [19] first developed the BRI to predict body fat, the percentage of visceral adipose tissue, and establish an initial impression of an individual’s physical health. Up to now, only one study has used the BRI to predict disease, and showed that the BRI was capable of identifying both CVD and CVD risk factors. Additionally, the adjusted OR of the BRI was higher than those of the BMI and WC, although the differences were not statistically significant [23]. However, inconsistent with the above study [23], our results demonstrated that while BRI was able to identify DM, it did not show superior predictive power when compared to WC or the WHtR. Furthermore, the values for height, WC, BMI, ABSI and BRI were all lower in our study compared to those in the previous study [23]. Thomas et al. [19] demonstrated that values for BRI can reflect both visceral adipose tissue (VAT) and % body fat, and thus the smaller values for BRI in our study may indicate that the participants had lower amounts of subcutaneous adipose tissue. This is consistent with the above-stated conclusion that smaller values for ABSI indicated a higher deposition of muscular tissue and a lower deposition of visceral adipose tissue in a rural population. Furthermore, as Maessen MF et al. [23] demonstrated in their study that the Spearman rank test revealed a perfect nonlinear relation between BRI and WHtR (r = 1; P = 0.00). Our data analysis showed an exactly coincident results (r = 1; P = 0.00) using the spearman rank test, which indicated that both body indices are closely related. From its definition, BRI is a one-to-one nonlinear transformation of the WHtR. Therefore, ranks for BRI in the population are the same as for WHtR, and the Spearman rank correlation between BRI and WHtR is exactly 1. The reason for the different numbers and odds ratios of each quartile in Table 3 for BRI as compared with WHtR appears to be simply that we rounded the inter-quartile thresholds to two decimal places before applying them to each index. Furthermore, in spite of some shortcomings as demonstrated by Thomas et al. [19], the advantage of the BRI over the WHtR is that it also can be used to estimate the amount of body fat percentage and gives therefore a better impression of physical health status. In conclusion, the BRI showed a superior predictive capacity compared to the ABSI and BMI, and demonstrated potential for improving the detection and evaluation of DM.

This study has some limitations that should be mentioned. First, the ABSI was initially developed to predict mortality hazard in a follow-up study, and we applied it to predict DM in a cross-sectional study, which may be the main reason why the ABSI failed to show superior predictive power. The longitudinal relationship between the two new anthropometric indices and DM should be examined in future studies. Second, our study was conducted with rural populations residing in northeast China, and the unique lifestyle of those populations may have influenced the body shape and metabolic indices of the participants. As previously mentioned, the two new anthropometric indices were first developed in Western countries (both in America), and should be modified as suggested by other studies [27, 28] to make them suitable for use with Chinese populations having different characteristics.