WC is a well-known predictor of abdominal VAT and SAT.19
In 2001, the National Cholesterol Education Program – Adult Treatment Panel III (NCEP-ATP III) included WC as a risk factor for the metabolic syndrome.20
WC could possibly be a better predictor of risk of obesity for Asians as Asians have a tendency to have a higher percentage body fat and visceral fat than Caucasians and African Americans within the same BMI.21,22
However, the correlations between WC and VAT can range from 0.4 to 0.9, depending on sex, age, and severity of obesity. In older women, WC was shown to have a stronger correlation with total body fat (γ 2
= 0.69) than VAT (γ 2
But in pre-menopausal obese women, it has been reported that WC is strongly correlated with VAT by MRI (γ = 0.75).24
In the current study, participants were limited to pre-menopausal female.
Dual energy x-ray absorptiometry is a good method for body composition measurements as it differentiates three basic body components: bone mineral content, body fat, and lean tissue. However DXA does not distinguish intra-abdominal fat from subcutaneous fat. Because AF contains anterior and posterior subcutaneous fat, AF by DXA is a better predictor of total AF than intra-abdominal fat alone. In obese women, it has been reported that DXA was well correlated with VAT by MRI (γ = 0.74).24
Other studies carried out on obese women reported a similar correlation (γ range = 0.72 to 0.99) between AF measured by DXA and VAT measured by CT.9,11,12
To out knowledge, no study has previously compared differences before and after a weight-loss program. The present study indicates that a DXA ROI and DXA trunk fat have high correlations with WC, BMI, and WHtR before and after weight-loss in obese pre-menopausal Korean women. Highly correlated with DXA ROI were WC > BMI > WHtR at baseline (γ = 0.91, 0.87, 0.82, respectively and p < 0.01) and BMI > WC > WHtR at follow-up (γ = 0.88, 0.84, 0.82, respectively and p < 0.01).
The change in a DXA ROI (ΔROI) revealed the highest correlation with BMI (γ = 0.63, p < 0.01), followed by WC and WHtR (γ = 0.39, 0.37, respectively, p < 0.05). The change in conventional DXA trunk fat (Δtrunk fat) failed to show a significant correlation with ΔWC even after adjustment for age and total body fat, but was correlated with ΔBMI (γ = 0.44, p < 0.01)
We hypothesized that WC would be a good measure for the assessment of AF in Asian obese women in both cross-sectional and longitudinal studies where the latter reports on change in AF. However, the change in conventional DXA trunk fat (Δtrunk fat) failed to show a significant correlation with ΔWC even after adjustment for age and total body fat. When we adjusted DXA ROI instead, the correlation with ΔWC turned out to be significant. The change in a DXA ROI (ΔROI) revealed an association with WC and WHtR (γ = 0.39, 0.37, respectively, p
< 0.05). WC was a better surrogate than WHR, which was only useful at baseline but not after weight loss in this study and in a previous study.25
The usefulness of ΔWC from DXA ROI in this study corresponds well with the result: ΔWC was associated with ΔVAT by MRI (γ = 0.80, p
< 0.01; γ = 0.41, p
< 0.05, respectively) reported in a previous study.26
We assume that the DXA ROI is more abdomen-specific and therefore a more valid index of abdominal adiposity than DXA trunk fat, however, more research is needed in this area.
Moreover, it would appear that BMI is a good surrogate for AF after weight loss. It showed the highest correlation with DXA trunk fat and DXA ROI at follow-up and with changes. ΔBMI was suggested as one of the best index of ΔVAT by MRI (γ = 0.85, p
< 0.01) in obese-women27
where ΔBMI was better than ΔDXA ROI (lumbar vertebra 2 - 4, γ = 0.73, p
< 0.01) for ΔVAT by MRI26
. However, it should be noted that DXA estimates fat not adipose tissue which is similar but not identical.27
This study suggests that WC could be a useful predictor of AF both cross-sectionally and with weight loss induced changes. BMI could also be useful just after weight-loss. Future studies with a larger sample size and advanced imaging technique, such as CT or MRI, that can discriminate these data are necessary to further our understanding.