Ultrasonographic visceral fat thickness in the first trimester can predict metabolic syndrome and gestational diabetes mellitus

The aim of this study is to evaluate whether ultrasonographic visceral fat thickness measurement in the early gestational period is useful for predicting the development of gestational diabetes mellitus (GDM) and metabolic syndrome (MS). The visceral fat thickness and subcutaneous fat thickness were measured via ultrasound at the first prenatal visit. The correlation between visceral and subcutaneous fat thickness and MS parameters, such as dyslipidemia, hypertension, and insulin resistance, was assessed. We also compared the use of visceral fat thickness measurement with body mass index (BMI) and waist circumference (WC) measurements for predicting the development of GDM. The subcutaneous fat thickness was found to be similar in the normal glucose metabolism and GDM groups at the first visit, whereas the visceral fat thickness was found to be considerably higher in the GDM groups (p=0.04). The visceral fat thickness in the early stage of the gestation was correlated with hyperglycemia, dyslipidemia, high diastolic blood pressure, and insulin resistance. In contrast to subcutaneous fat thickness, BMI, and WC, only the visceral fat thickness was correlated with insulin resistance. The subcutaneous and visceral fat thicknesses at the first visit were significantly higher in the MS group (p=0.02). There was a good correlation between visceral and subcutaneous fat thicknesses (r=0.492, p<0.001); however, there were poor correlations between visceral fat thickness and BMI and WC (r=0.338, p=0.01; r=0.312, p=0.02). The visceral fat thickness seemed to be a more sensitive predictor of GDM than WC and BMI. The optimal cutoff points for predicting GDM were visceral fat thickness 19.5 mm [area under curve (AUC)=0.66, p=0.043], WC 103.5 cm (AUC=0.64, p=0.079), and BMI 34.5 (AUC=0.64, p=0.069). Ultrasonographic visceral fat thickness measurement in the early period of gestation may be an easy, safe, and cost-effective scan test for predicting the development of metabolic diseases and GDM.