title: Ultrasound, anthropometry and bioimpedance: a comparison in predicting fat deposition in non-alcoholic fatty liver disease.
abstract: OBJECTIVE
  The aim of our study was the evaluation of anthropometric measurements [waist circumference and sagittal abdominal diameter (SAD)] and abdominal bioelectrical impedance analysis (BIA) (ViScan, TANITA) in comparison to several abdominal ultrasonographic (US) measurements to estimate visceral fat deposition and liver steatosis in a population of 105 subjects.
  
  
  METHODS
  All 105 patients underwent a complete anthropometric evaluation, blood sample for the determination of total cholesterol, HDL cholesterol, triglycerides, glucose, insulin, high-sensitivity C-reactive protein, BIA and US measurements (peritoneal, pre-peritoneal, peri-renal, para-renal and peri-hepatic fat thickness).
  
  
  RESULTS
  All the ultrasonographic markers considered in our study are related to the presence of non-alcoholic fatty liver disease (NAFLD), and so is true for SAD. Comparing ROC curves, peritoneal fat tissue thickness, SAD and ViScan visceral index are significantly better than waist circumference in predicting the presence of NAFLD (AUC 0.79 ± 0.04; 0.81 ± 0.05; 0.82 ± 0.04 vs 0.76 ± 0.05, respectively).
  
  
  CONCLUSIONS
  According to our data, various methods may be useful in evaluating NAFLD, but only ViScan visceral index, US peritoneal fat thickness and SAD are better than waist circumference. Among them, SAD is the most promising, due to its small cost and time consumption.
authors.affiliation: Division of Metabolic Diseases, Department of Medicine, University Hospital of Padova-Italy, Via Giustiniani 2, 35128, Padova, Italy, nvitturi@gmail.com.
authors.forename: Nicola
authors.initials: N
authors.lastname: Vitturi
authors.forename: Marta
authors.initials: M
authors.lastname: Soattin
authors.forename: Fabio
authors.initials: F
authors.lastname: De Stefano
authors.forename: Daniela
authors.initials: D
authors.lastname: Vianello
authors.forename: Alberto
authors.initials: A
authors.lastname: Zambon
authors.forename: Mario
authors.initials: M
authors.lastname: Plebani
authors.forename: Luca
authors.initials: L
authors.lastname: Busetto
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000886:Anthropometry; D001786:Blood Glucose / Q000378:metabolism; D001823:Body Composition; D050218:Body Fat Distribution; D002097:C-Reactive Protein / Q000378:metabolism; D002784:Cholesterol / Q000378:metabolism; D008076:Cholesterol, HDL / Q000378:metabolism; D015331:Cohort Studies; D017097:Electric Impedance; D005260:Female; D006801:Humans; D007328:Insulin / Q000378:metabolism; D050152:Intra-Abdominal Fat / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D065626:Non-alcoholic Fatty Liver Disease / Q000175:diagnosis / Q000000981:diagnostic imaging* / Q000378:metabolism; D010537:Peritoneum / Q000000981:diagnostic imaging*; D012372:ROC Curve; D064346:Sagittal Abdominal Diameter; D014280:Triglycerides / Q000378:metabolism; D014463:Ultrasonography; D055105:Waist Circumference; D055815:Young Adult
chemical_list: D001786:Blood Glucose; D008076:Cholesterol, HDL; D007328:Insulin; D014280:Triglycerides; D002097:C-Reactive Protein; D002784:Cholesterol