Increased pericardial fat volume measured from noncontrast CT predicts myocardial ischemia by SPECT.

OBJECTIVES We evaluated the association between pericardial fat and myocardial ischemia for risk stratification. BACKGROUND Pericardial fat volume (PFV) and thoracic fat volume (TFV) measured from noncontrast computed tomography (CT) performed for calculating coronary calcium score (CCS) are associated with increased CCS and risk for major adverse cardiovascular events. METHODS From a cohort of 1,777 consecutive patients without previously known coronary artery disease (CAD) with noncontrast CT performed within 6 months of single photon emission computed tomography (SPECT), we compared 73 patients with ischemia by SPECT (cases) with 146 patients with normal SPECT (controls) matched by age, gender, CCS category, and symptoms and risk factors for CAD. TFV was automatically measured. Pericardial contours were manually defined within which fat voxels were automatically identified to compute PFV. Computer-assisted visual interpretation of SPECT was performed using standard 17-segment and 5-point score model; perfusion defect was quantified as summed stress score (SSS) and summed rest score (SRS). Ischemia was defined by: SSS - SRS ≥4. Independent relationships of PFV and TFV to ischemia were examined. RESULTS Cases had higher mean PFV (99.1 ± 42.9 cm(3) vs. 80.1 ± 31.8 cm(3), p = 0.0003) and TFV (196.1 ± 82.7 cm(3) vs. 160.8 ± 72.1 cm(3), p = 0.001) and higher frequencies of PFV >125 cm(3) (22% vs. 8%, p = 0.004) and TFV >200 cm(3) (40% vs. 19%, p = 0.001) than controls. After adjustment for CCS, PFV and TFV remained the strongest predictors of ischemia (odds ratio [OR]: 2.91, 95% confidence interval [CI]: 1.53 to 5.52, p = 0.001 for each doubling of PFV; OR: 2.64, 95% CI: 1.48 to 4.72, p = 0.001 for TFV). Receiver operating characteristic analysis showed that prediction of ischemia, as indicated by receiver-operator characteristic area under the curve, improved significantly when PFV or TFV was added to CCS (0.75 vs. 0.68, p = 0.04 for both). CONCLUSIONS Pericardial fat was significantly associated with myocardial ischemia in patients without known CAD and may help improve risk assessment.

[1]  R. D'Agostino.  Propensity Scores in Cardiovascular Research , 2007, Circulation.

[2]  Carl D Langefeld,et al.  Pericardial and Visceral Adipose Tissues Measured Volumetrically With Computed Tomography Are Highly Associated in Type 2 Diabetic Families , 2005, Investigative radiology.

[3]  Maximilian Reiser,et al.  Pericardial Adipose Tissue Determined by Dual Source CT Is a Risk Factor for Coronary Atherosclerosis , 2009, Arteriosclerosis, thrombosis, and vascular biology.

[4]  S. Yamashita,et al.  Abdominal fat: standardized technique for measurement at CT. , 1999, Radiology.

[5]  Moyses Szklo,et al.  Coronary calcium as a predictor of coronary events in four racial or ethnic groups. , 2008, The New England journal of medicine.

[6]  B. Sianesi,et al.  PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing , 2003 .

[7]  D. Dey,et al.  Automated Quantitation of Pericardiac Fat From Noncontrast CT , 2008, Investigative radiology.

[8]  D. Berman,et al.  Use of coronary calcium scanning for predicting inducible myocardial ischemia: Influence of patients’ clinical presentation , 2007, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[9]  R. Detrano,et al.  Quantification of coronary artery calcium using ultrafast computed tomography. , 1990, Journal of the American College of Cardiology.

[10]  G Germano,et al.  Automatic reorientation of three-dimensional, transaxial myocardial perfusion SPECT images. , 1995, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[11]  Damini Dey,et al.  Pericardial fat burden on ECG-gated noncontrast CT in asymptomatic patients who subsequently experience adverse cardiovascular events. , 2010, JACC. Cardiovascular imaging.

[12]  R. Luben,et al.  Apolipoprotein A-II Is Inversely Associated With Risk of Future Coronary Artery Disease , 2007, Circulation.

[13]  Udo Hoffmann,et al.  Pericardial Fat, Visceral Abdominal Fat, Cardiovascular Disease Risk Factors, and Vascular Calcification in a Community-Based Sample: The Framingham Heart Study , 2008, Circulation.

[14]  J. Shirani,et al.  Clinical significance of epicardial fat measured using cardiac multislice computed tomography. , 2008, The American journal of cardiology.

[15]  Udo Hoffmann,et al.  Abdominal Visceral and Subcutaneous Adipose Tissue Compartments: Association With Metabolic Risk Factors in the Framingham Heart Study , 2007, Circulation.

[16]  Guido Germano,et al.  Combined supine and prone quantitative myocardial perfusion SPECT: method development and clinical validation in patients with no known coronary artery disease. , 2006, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[17]  E. Rimm,et al.  Cardiac Sodium Channel Gene Variants and Sudden Cardiac Death in Women , 2008, Circulation.

[18]  Udo Hoffmann,et al.  Association of pericardial fat, intrathoracic fat, and visceral abdominal fat with cardiovascular disease burden: the Framingham Heart Study. , 2008, European heart journal.

[19]  Leif E. Peterson,et al.  The coronary artery calcium score and stress myocardial perfusion imaging provide independent and complementary prediction of cardiac risk. , 2009, Journal of the American College of Cardiology.

[20]  I. Kakadiaris,et al.  Computer-aided non-contrast CT-based quantification of pericardial and thoracic fat and their associations with coronary calcium and Metabolic Syndrome. , 2010, Atherosclerosis.

[21]  E. Catmull,et al.  A CLASS OF LOCAL INTERPOLATING SPLINES , 1974 .

[22]  L. Sjöström,et al.  Total and visceral adipose-tissue volumes derived from measurements with computed tomography in adult men and women: predictive equations. , 1988, The American journal of clinical nutrition.

[23]  R. Detrano,et al.  The Association of Pericardial Fat With Calcified Coronary Plaque , 2008, Obesity.

[24]  D. Berman,et al.  Automated quantification of myocardial perfusion SPECT using simplified normal limits , 2004, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[25]  Elliott S Fisher,et al.  Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods. , 2007, JAMA.

[26]  D. Berman,et al.  Metabolic syndrome and diabetes are associated with an increased likelihood of inducible myocardial ischemia among patients with subclinical atherosclerosis. , 2005, Diabetes care.

[27]  A Cederblad,et al.  Determination of total adipose tissue and body fat in women by computed tomography, 40K, and tritium. , 1986, The American journal of physiology.

[28]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[29]  D. Berman,et al.  Comparison of the Short‐Term Survival Benefit Associated With Revascularization Compared With Medical Therapy in Patients With No Prior Coronary Artery Disease Undergoing Stress Myocardial Perfusion Single Photon Emission Computed Tomography , 2003, Circulation.

[30]  Nikolaos Alexopoulos,et al.  Epicardial adipose tissue volume and coronary artery calcium to predict myocardial ischemia on positron emission tomography-computed tomography studies , 2010, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[31]  Guido Germano,et al.  Quantitative assessment of myocardial perfusion abnormality on SPECT myocardial perfusion imaging is more reproducible than expert visual analysis , 2009, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[32]  Daniel S. Berman,et al.  Prognostic validation of a 17-segment score derived from a 20-segment score for myocardial perfusion spect interpretation , 2004, Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology.

[33]  J. Takasu,et al.  Pericardial fat accumulation in men as a risk factor for coronary artery disease. , 2001, Atherosclerosis.

[34]  A. Stillman,et al.  EPICARDIAL ADIPOSE TISSUE AND CORONARY ARTERY PLAQUE CHARACTERISTICS , 2010 .

[35]  R. Seibel,et al.  Association of pericoronary fat volume with atherosclerotic plaque burden in the underlying coronary artery: a segment analysis. , 2010, Atherosclerosis.

[36]  Moyses Szklo,et al.  Association between non-subcutaneous adiposity and calcified coronary plaque: a substudy of the Multi-Ethnic Study of Atherosclerosis. , 2008, The American journal of clinical nutrition.

[37]  S. Yusuf,et al.  Lipid profile, plasma apolipoproteins, and risk of a first myocardial infarction among Asians: an analysis from the INTERHEART Study. , 2009, Journal of the American College of Cardiology.

[38]  J. Murabito,et al.  Visceral and Subcutaneous Adipose Tissue Volumes Are Cross-Sectionally Related to Markers of Inflammation and Oxidative Stress: The Framingham Heart Study , 2007, Circulation.

[39]  D. Berman,et al.  Adenosine myocardial perfusion single-photon emission computed tomography in women compared with men. Impact of diabetes mellitus on incremental prognostic value and effect on patient management. , 2003, Journal of the American College of Cardiology.