Lipids in health and disease · 2022
Equations for predicting DXA-measured visceral adipose tissue mass based on BMI or weight in adults.
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Song Xuan, Wu Hongxia, Zhang Wenhua, Wang Bei, Sun Hongjun
Аннотация
BACKGROUND: Obesity, especially presenting with excessive amounts of visceral adipose tissue (VAT), is strongly associated with insulin resistance (IR), atherosclerosis, metabolic syndrome, and cardiovascular diseases (CVDs).
AIMS: To construct a predication equation for estimating VAT mass using anthropometric parameters and validate the models with a validation group.
METHODS: Five hundred fifteen subjects (366 for the derivation group and 149 for the validation group) were enrolled in the study. The anthropometric parameters, blood lipid profile, and VAT mass were accessed from medical records. Stepwise regression was applied to develop prediction models based on the dual X-ray absorptiometry (DXA)-measured VAT mass in the derivation group. Bland-Altman plots and correlation analysis were performed to validate the agreements in the validation group. The performance of the prediction equations was evaluated with the Hosmer-Lemeshow test and area under the curve (AUC).
RESULTS: Model 1, which included age, sex, body mass index (BMI), triglyceride (TG), high-density lipoprotein (HDL), and the grade of hepatic steatosis, had a variance of 70%, and model 2, which included age, sex, weight, height, TG, HDL, and the grade of hepatic steatosis, had a variance of 74%. The VAT mass measured by DXA was correlated with age, sex, height, weight, BMI, TG, HDL, and grade of hepatic steatosis. In the validation group, the VAT mass calculated by the prediction equations was strongly correlated with the DXA-VAT mass (r = 0.870, r = 0.875, respectively). The AUC, sensitivity, and specificity of the two prediction equations were not significantly different (both P = 0.933).
CONCLUSION: The study suggests that prediction equations including age, sex, height, BMI, weight, TG, HDL, and the grade of hepatic steatosis could be useful tools for predicting VAT mass when DXA is not available.
AIMS: To construct a predication equation for estimating VAT mass using anthropometric parameters and validate the models with a validation group.
METHODS: Five hundred fifteen subjects (366 for the derivation group and 149 for the validation group) were enrolled in the study. The anthropometric parameters, blood lipid profile, and VAT mass were accessed from medical records. Stepwise regression was applied to develop prediction models based on the dual X-ray absorptiometry (DXA)-measured VAT mass in the derivation group. Bland-Altman plots and correlation analysis were performed to validate the agreements in the validation group. The performance of the prediction equations was evaluated with the Hosmer-Lemeshow test and area under the curve (AUC).
RESULTS: Model 1, which included age, sex, body mass index (BMI), triglyceride (TG), high-density lipoprotein (HDL), and the grade of hepatic steatosis, had a variance of 70%, and model 2, which included age, sex, weight, height, TG, HDL, and the grade of hepatic steatosis, had a variance of 74%. The VAT mass measured by DXA was correlated with age, sex, height, weight, BMI, TG, HDL, and grade of hepatic steatosis. In the validation group, the VAT mass calculated by the prediction equations was strongly correlated with the DXA-VAT mass (r = 0.870, r = 0.875, respectively). The AUC, sensitivity, and specificity of the two prediction equations were not significantly different (both P = 0.933).
CONCLUSION: The study suggests that prediction equations including age, sex, height, BMI, weight, TG, HDL, and the grade of hepatic steatosis could be useful tools for predicting VAT mass when DXA is not available.