Our results showed that PFV and TFV measured by noncontrast CT are strongly associated with ischemia by SPECT; to our knowledge, this is the first demonstration of the additive value of PFV and TFV for prediction of ischemia.
It has been suggested that pericardial fat may simply be a marker of overall metabolic risk rather than serving as an active mediator of CAD (4
); however, recent studies have shown that increased pericardial fat is strongly associated with increased coronary artery calcium, coronary plaque burden, and MACE. These findings and our observations of the strong association between pericardial fat and ischemia suggest that pericardial fat may play a more direct role in causing coronary atherosclerosis (1
Although the exact pathophysiologic mechanism has not been characterized, it has been hypothesized that pericardial fat releases inflammatory signals that promote atherogenesis in coronary arteries (6
). It is thought that close proximity of pericardial fat to the coronary arteries may play a role in promoting atherogenesis (9
). In a recent study, Mahabadi et al. (38
) showed that there is a strong independent association between the local pericardial (pericoronary) fat that immediately surrounds the coronary arteries and the presence of both noncalcified and calcified coronary artery plaques. More recently Janik et al. (39
) showed—in a small cohort of 45 patients—that epicardial fat is associated with ischemia by positron emission tomography and may be a better predictor of ischemia than CCS. The association between increased pericardial fat and ischemia seen in our study may be related to the paracrine effect exerted by pericardial and pericoronary artery fat on coronary atherosclerosis as postulated by Mahabadi et al. (38
We recently showed that PFV exhibits a significant association with MACE after adjustments for traditional risk factors for CAD and the FRS in a case-control study (15
). In a large population-based study, pericardial fat was also found to be strongly associated with a history of adverse cardiovascular events (8
). Our observations further highlight the relationship between pericardial fat and CAD by demonstrating that there is a strong association between PFV and myocardial ischemia.
The relative importance of PFV and TFV in mediating coronary atherosclerosis has been a subject of investigation. Rosito et al. (9
) showed that PFV but not TFV was associated with coronary calcification. Mahabadi et al. (10
) showed that PFV, but not intrathoracic fat (equivalent to the extrapericardial TFV in our study) measured using noncontrast CT, was related to burden of prior cardiovascular disease after adjustment for age, sex, BMI, and waist circumference, suggesting that PFV may be a more specific disease marker. In our analysis, PFV and TFV were dependent variables and increased linearly. Increases in PFV and TFV were both associated with myocardial ischemia. In secondary analysis, the relationship between extra- pericardial fat (TFV – PFV) and ischemia was less significant than the association between PFV and ischemia, indicating that the association of TFV is mainly driven by PFV. Our findings suggest that fat stores that immediately surround the coronary artery may have an impact on myocardial ischemia. These findings are consistent with our previous study demonstrating the preferential association between PFV over TFV and risk of MACE (15
The sensitivity and PPV for both PFV and TFV for detection of myocardial ischemia are low, but the specificity and negative predictive value are higher. The low sensitivity and PPV are expected because this measurement of fat around the coronary arteries is not a direct assessment of the presence of abnormality within the coronary arteries. However, the higher specificity suggests that pericardial fat measurement may potentially help define population subgroups in which further evaluation of myocardial ischemia may be warranted. A CCS ≥400 is traditionally considered to define patients who need further evaluation for ischemia (34
). In patients with metabolic syndrome or diabetes the observed frequency of myocardial ischemia has led to a suggestion that a CCS ≥100 may be a more appropriate threshold (36
). Our findings suggest that increased PFV may also define a patient group in which a lower threshold for ischemia testing may be appropriate. The significant relationship between pericardial fat and myocardial ischemia in our study needs to be further investigated with additional whole cohort studies.
We have previously shown that a greater number of patients who experienced MACE had a PFV >125 cm3
compared with event-free controls (15
). In this study, we report that a similar threshold of PFV may confer increased risk of myocardial ischemia; an increased proportion of patients with ischemia exhibit PFV >125 cm3
compared with nonischemic controls.
In our study, patients (cases and controls) were matched based on standard CCS categories (CCS 0 to 99, CCS 100 to 399, CCS 400 to 999, and CCS ≥1,000). Therefore, after matching, the categories were not significantly different (p = 0.86); however, log-transformed CCS was not matched, and there is a trend toward significant difference (p = 0.06) for log-transformed CCS between cases and controls in univariate analysis. In multivariable analysis, log-transformed CCS was significantly associated with myocardial ischemia. However, because we matched cases and controls based on CCS categories to minimize the confounding effects of CCS, further examination of the relative strengths of CCS and PFV through whole-cohort study is needed. Our current results lead us to hypothesize that increases in the number of future cardiac events in patients with high PFV and TFV may be related to an increased risk of myocardial ischemia. Whether the risk of ischemia in this patient cohort is related to an increase in coronary plaque burden, stenosis severity, or abnormal endothelial reactivity still remains to be characterized.
A case-control study design, although commonly used to assess the epidemiologic importance of potentially novel disease markers (16
), is more sensitive to the effects of confounding factors than a cohort study design. We chose a case-control design with rigorous matching of potential confounding variables because complete quantification of both PFV and TFV in a large population using currently available techniques would be prohibitively time and labor intensive. Our study population consisted of patients without previously known CAD who underwent CCS and MPI by SPECT whether symptomatic or asymptomatic; however, symptoms were matched between cases and controls and were therefore not significantly different (). Furthermore, PFV and TFV emerged as predictors of ischemia in a multivariable analysis. C-reactive protein was not available for most of our patients, and correlation of C-reactive protein PFV or ischemia could not be assessed. Our matching for CCS categories minimized the importance of CCS in multivariable analysis. Although our results suggest additive utility of pericardial fat to traditional risk-stratifying factors, including CCS, for prediction of ischemia, confirmation through longitudinal whole-cohort evaluation is needed.