TY - JOUR
T1 - Computer-aided non-contrast CT-based quantification of pericardial and thoracic fat and their associations with coronary calcium and metabolic syndrome
AU - Dey, Damini
AU - Wong, Nathan D.
AU - Tamarappoo, Balaji
AU - Nakazato, Ryo
AU - Gransar, Heidi
AU - Cheng, Victor Y.
AU - Ramesh, Amit
AU - Kakadiaris, Ioannis
AU - Germano, Guido
AU - Slomka, Piotr J.
AU - Berman, Daniel S.
N1 - Funding Information:
The authors would like to thank Romalisa Miranda-Peats, Abhishek Shah, Olga Guzovsky for their assistance with patient data analysis. This study was supported by NIH grant number R21EB006829-01A2 (PI: Damini Dey) from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), and also in part by a grant from the Glazer Foundation ( IRB 6318 ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIBIB or the NIH.
PY - 2010/3
Y1 - 2010/3
N2 - Introduction: 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.
AB - Introduction: 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.
KW - Coronary calcium
KW - Metabolic syndrome
KW - Non-contrast CT
KW - Pericardial fat
KW - Thoracic fat
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U2 - 10.1016/j.atherosclerosis.2009.08.032
DO - 10.1016/j.atherosclerosis.2009.08.032
M3 - Article
C2 - 19748623
AN - SCOPUS:77049125938
SN - 0021-9150
VL - 209
SP - 136
EP - 141
JO - Atherosclerosis
JF - Atherosclerosis
IS - 1
ER -