title: Single non-invasive model to diagnose non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH).
abstract: OBJECTIVE
  Non-alcoholic steatohepatitis (NASH) is the progressive form of non-alcoholic fatty liver disease (NAFLD). A liver biopsy is considered the "gold standard" for diagnosing/staging NASH. Identification of NAFLD/NASH using non-invasive tools is important for intervention. The study aims were to: develop/validate the predictive performance of a non-invasive model (index of NASH [ION]); assess the performance of a recognized non-invasive model (fatty liver index [FLI]) compared with ION for NAFLD diagnosis; determine which non-invasive model (FLI, ION, or NAFLD fibrosis score [NFS]) performed best in predicting age-adjusted mortality.
  
  
  METHODS
  From the National Health and Nutrition Examination Survey III database, anthropometric, clinical, ultrasound, laboratory, and mortality data were obtained (n = 4458; n = 861 [19.3%] NAFLD by ultrasound) and used to develop the ION model, and then to compare the ION and FLI models for NAFLD diagnosis. For validation and diagnosis of NASH, liver biopsy data were used (n = 152). Age-adjusted Cox proportional hazard modeling estimated the association among the three non-invasive tests (FLI, ION, and NFS) and mortality.
  
  
  RESULTS
  FLI's threshold score > 60 and ION's threshold score > 22 had similar specificity (FLI = 80% vs ION = 82%) for NAFLD diagnosis; FLI < 30 (80% sensitivity) and ION < 11 (81% sensitivity) excluded NAFLD. An ION score > 50 predicted histological NASH (92% specificity); the FLI model did not predict NASH or mortality. The ION model was best in predicting cardiovascular/diabetes-related mortality; NFS predicted overall or diabetes-related mortality.
  
  
  CONCLUSIONS
  The ION model was superior in predicting NASH and mortality compared with the FLI model. Studies are needed to validate ION.
authors.affiliation: Betty and Guy Beatty Center for Integrated Research, Inova Health System, Falls Church, Virginia, USA.
authors.forename: Munkhzul
authors.initials: M
authors.lastname: Otgonsuren
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Estep
authors.forename: Nayeem
authors.initials: N
authors.lastname: Hossain
authors.forename: Elena
authors.initials: E
authors.lastname: Younossi
authors.forename: Spencer
authors.initials: S
authors.lastname: Frost
authors.forename: Linda
authors.initials: L
authors.lastname: Henry
authors.forename: Sharon
authors.initials: S
authors.lastname: Hunt
authors.forename: Yun
authors.initials: Y
authors.lastname: Fang
authors.forename: Zachary
authors.initials: Z
authors.lastname: Goodman
authors.forename: Zobair M
authors.initials: ZM
authors.lastname: Younossi
mesh_terms: D000328:Adult; D001706:Biopsy; D002318:Cardiovascular Diseases / Q000401:mortality; D003920:Diabetes Mellitus / Q000401:mortality; D003937:Diagnosis, Differential; D003938:Diagnostic Techniques, Digestive System; D005260:Female; D006801:Humans; D008099:Liver / Q000473:pathology; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D065626:Non-alcoholic Fatty Liver Disease / Q000175:diagnosis* / Q000401:mortality; D016016:Proportional Hazards Models
keywords: clinical; epidemiology; fatty liver disease; identification; model; non invasive