title: Computer-aided non-contrast CT-based quantification of pericardial and thoracic fat and their associations with coronary calcium and Metabolic Syndrome.
abstract: BACKGROUND
  Pericardial fat is emerging as an important parameter for cardiovascular risk stratification. We extended previously developed quantitation of thoracic fat volume (TFV) from non-contrast coronary calcium (CC) CT scans to also quantify pericardial fat volume (PFV) and investigated the associations of PFV and TFV with CC and the Metabolic Syndrome (METS).
  
  
  METHODS
  TFV is quantified automatically from user-defined range of CT slices covering the heart. Pericardial fat contours are generated by spline interpolation between 5-7 control points, placed manually on the pericardium within this cardiac range. Contiguous fat voxels within the pericardium are identified as pericardial fat. PFV and TFV were measured from non-contrast CT for 201 patients. In 105 patients, abdominal visceral fat area (VFA) was measured from an additional single-slice CT. In 26 patients, images were quantified by two readers to establish inter-observer variability. TFV and PFV were examined in relation to Body Mass Index (BMI), waist circumference and VFA, standard coronary risk factors (RF), CC (Agatston score >0) and METS.
  
  
  RESULTS
  PFV and TFV showed excellent correlation with VFA (R=0.79, R=0.89, p<0.0001), and moderate correlation with BMI (R=0.49, R=0.48, p<0.0001). In 26 scans, the inter-observer variability was greater for PFV (8.0+/-5.3%) than for TFV (4.4+/-3.9%, p=0.001). PFV and TFV, but not RF, were associated with CC [PFV: p=0.04, Odds Ratio 3.1; TFV: p<0.001, OR 7.9]. PFV and TFV were also associated with METS [PFV: p<0.001, OR 6.1; TFV p<0.001, OR 5.7], unlike CC [OR=1.0 p=NS] or RF. PFV correlated with low-HDL and high-glucose; TFV correlated with low-HDL, low-adiponectin, and high glucose and triglyceride levels.
  
  
  CONCLUSIONS
  PFV and TFV can be obtained easily and reproducibly from routine CC scoring scans, and may be important for risk stratification and monitoring.
authors.affiliation: Departments of Imaging and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA. deyd@cshs.org
authors.forename: Damini
authors.initials: D
authors.lastname: Dey
authors.forename: Nathan D
authors.initials: ND
authors.lastname: Wong
authors.forename: Balaji
authors.initials: B
authors.lastname: Tamarappoo
authors.forename: Ryo
authors.initials: R
authors.lastname: Nakazato
authors.forename: Heidi
authors.initials: H
authors.lastname: Gransar
authors.forename: Victor Y
authors.initials: VY
authors.lastname: Cheng
authors.forename: Amit
authors.initials: A
authors.lastname: Ramesh
authors.forename: Ioannis
authors.initials: I
authors.lastname: Kakadiaris
authors.forename: Guido
authors.initials: G
authors.lastname: Germano
authors.forename: Piotr J
authors.initials: PJ
authors.lastname: Slomka
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Berman
mesh_terms: D000273:Adipose Tissue / Q000000981:diagnostic imaging*; D015992:Body Mass Index; D002118:Calcium / Q000032:analysis / Q000378:metabolism*; D003331:Coronary Vessels / Q000378:metabolism*; D005260:Female; D006801:Humans; D008297:Male; D024821:Metabolic Syndrome / Q000000981:diagnostic imaging* / Q000378:metabolism*; D008875:Middle Aged; D010496:Pericardium / Q000000981:diagnostic imaging*; D013902:Radiography, Thoracic / Q000379:methods; D014057:Tomography, X-Ray Computed / Q000379:methods; D055105:Waist Circumference
chemical_list: D002118:Calcium