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Visceral abdominal adiposity tissue volume quantification using noninvasive MRI in prediction of type 2 diabetes / 中国医学影像技术
Article en Zh | WPRIM | ID: wpr-664765
Biblioteca responsable: WPRO
ABSTRACT
Objective To investigate the feasibility of utilizing visceral abdominal adiposity tissue (VAT) volume quantification using MRI to predict type 2 diabetes mellitus (T2DM).Methods Forty-eight subjects including 15 T2DM (T2DM group),17 impaired glucose tolerance (IGT,IGT group) and 16 normal glucose tolerance (NGT,NGT group) were enrolled in this study.All subjects underwent upper abdominal iterative decomposition of water and fat with echo asymmetry and least square estimation-image quantification (IDEAL-IQ) MRI scanning.VAT volume of the second and third lumber vertebral body ranges (VATV L2,VATV L3),sum of VATV L2 and L3 (total VATV),hepatic and pancreatic fat were measured in fat fraction mapping of T1WI IDAEL-IQ sequence on post-processing workstation.The accuracy of predicting T2DM using VAT was evaluated by Logistic regression equation via ROC curve.Results The mean of VATV L2,VATV L3 and total VATV in T2DM group were significantly higher than those of IGT group and NGT group (P<0.05),while there were no significant difference of these metrics between IGT group and NGT group (P>0.05).Taking 460.34 ml as the cut-off value for VATV L2 to predict T2DM,sensitivity was 73.33%,specificity was 75.76% and accuracy was 75.00%,respectively.Taking 429.46 ml as the cut-off value for VATV L3 to predict T2DM,sensitivity was 86.67%,specificity was 72.73% and accuracy was 77.08%,respectively.Taking 887.83 ml as the cut-off value for total VATV to predict T2DM,the sensitivity,specificity and accuracy were 86.67%,72.73% and 77.08%,respectively.Only VATV L3 was enrolled by Logistic regression equation (P=0.01,OR=1.01),and the sensitivity,specificity and total accuracy of prediction for T2DM were 80.00 %,88.20 %,and 84.40 %,respectively.Conelnsion It is feasible to utilize VAT volume quantification with MRI to predict T2DM.VATV L3 is a better predictor.
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Texto completo: 1 Base de datos: WPRIM Tipo de estudio: Prognostic_studies Idioma: Zh Revista: Chinese Journal of Medical Imaging Technology Año: 2017 Tipo del documento: Article
Texto completo: 1 Base de datos: WPRIM Tipo de estudio: Prognostic_studies Idioma: Zh Revista: Chinese Journal of Medical Imaging Technology Año: 2017 Tipo del documento: Article