Methods

A total of 104 consecutive patients with FH without known CAD exhibiting a single mutation in the LDL receptor or proprotein convertase subtilisin/kexin type 9 (PCSK9) gene who underwent 64-detector row CCTA from January 2008 to December 2012 because of any clinical indications, including chest symptom, signs of cardiac diseases, peripheral artery disease, cerebrovascular disease, or multiple coronary risk factors were retrospectively analyzed. Of the 104 patients with heterozygous FH, 3 subjects with poor images (2.9%) were excluded; thus, 101 subjects with heterozygous FH whose ages range from 22 to 84 years remained in this analysis (men = 52, mean age 56 ± 16 years, mean LDL-C 264 ± 58 mg/dl). The characteristics of the study subjects are listed in Table 1 and Supplementary Table 1 . Median follow-up period was 941 days. We defined major adverse cardiac events (MACE) as cardiac death, ST-elevated myocardial infarction, non–ST-elevated myocardial infarction, unstable angina pectoris, staged percutaneous coronary intervention, or coronary artery bypass grafting.

Hypertension was defined as systolic blood pressure of at least 140 mm Hg, diastolic blood pressure of at least 90 mm Hg, or use of antihypertensive medication. Presence of diabetes was defined as previously described by Japan Diabetes Society or the use of diabetes medication. Body mass index (BMI) was defined as body weight in kilograms divided by the square of height measured in meters. Serum concentrations of total cholesterol, triglyceride, and high-density lipoprotein cholesterol (HDL-C) were determined enzymatically. LDL-C concentrations were calculated using the Friedewald formula. Genomic DNA was isolated from peripheral white blood cells according to standard procedures and was used for polymerase chain reaction. Genotypes of all the participants in this study were determined as previously described. The institutional review board approved the study protocol. All patients gave written informed consent.

CCTA was performed with a dual-source 64-slice (Somatom Definition Flash; Siemens Medical System, Erlangen, Germany). A non–contrast-enhanced scan was performed to assess coronary calcium and defined the anatomic range for subsequent CCTA. This scan was automatically triggered and performed using the following scan parameters: collimation 0.6 mm; gantry rotation time 280 ms; tube voltage 120 kV; and tube current 100 mA. For the contrast-enhanced scan, collimation was 0.6 mm and gantry rotation time was 280 ms. The tube voltage and current were 120 kV and 340 mA, respectively. Fifty to eighty milliliters of nonionic iodinated contrast (370 mg iodine/ml, Iopamidol-370; Bayer Healthcare Pharmaceuticals, Osaka, Japan) was injected using a dual-flow injector (Dual Shot GX; Nemoto Kyorindo, Tokyo, Japan) through an antecubital vein. The iodine load was based on body weight. Image acquisition was manually triggered on arrival of contrast in the left main coronary trunk. Patients with a heart rate >100 beats/min and with no contraindications to β blockers received intravenous β-blocker therapy (landiolol hydrochloride 0.125 mg/kg) just before the computed tomographic (CT) scan. Multiple phases were used to assess the images of different arteries. In addition, we constructed 3-dimensional rotation images to assess the diagonal and other small branches.

Two experienced radiologists, blinded with regard to the clinical status, evaluated all CCTA scans separately. Despite our efforts, the segments that were uninterpretable were scored as the same as most proximal segment which was interpretable (72 segments of a total of 1,717 segments: 4.2%). Discrepancies in evaluation were resolved during a consensus reading. Angiographic analysis by coronary computed tomography was performed according to a 17-segment American Heart Association classification. Coronary plaque burden was assessed by assigning a score (0 to 5) to each of 17 coronary artery segments according to the Society of Cardiovascular Computed Tomography (SCCT) guidelines (0, Normal: absence of plaque and no luminal stenosis; 1, Minimal: plaque with <25% stenosis; 2, Mild: 25% to 49% stenosis; 3, Moderate: 50% to 69% stenosis; 4, Severe: 70% to 99% stenosis; 5, Occluded). Those scores were summed and natural log-transformed, because of its skewed distribution.

Categorical variables were expressed as percentages. Fisher’s exact test or chi-square test was used as appropriate. Continuous variables with a normal distribution were shown as mean (±SD) and were compared using unpaired Student t tests. The plaque burden score cut-off value was determined on the basis of receiver operating characteristic (ROC) curve analysis. The cumulative fraction of events was estimated as 1 minus the Kaplan–Meier estimate of survival free of MACE. Differences in the cumulative fraction of events between subgroups were assessed by the log-rank test according to the cutoff. We initially analyzed all available risk factors using a univariate model; then, multivariate Cox regression analysis was performed using only the covariates that were significantly associated with MACE in the univariate analysis. Intraobserver or interobserver variability among readers was assessed using the Bland–Altmann method and coefficient of variation (CV) with 20 randomly selected subjects. All statistical analyses were conducted using R. All p values <0.05 were considered statistically significant.

Results

The clinical characteristics of patients with or without subsequent MACE are shown in Table 1 . The frequencies of the traditional coronary risk factors, such as age, hypertension, diabetes mellitus, smoking habits, and BMI, were significantly greater, whereas HDL-C was significantly less in patients with FH with MACE compared to those without MACE. Interestingly, plaque burden was significantly greater in patients with FH with MACE than those without MACE. The genetic backgrounds of the study participants are provided in Supplementary Table 1 . A nonsense mutation (c.2431A>T) in the LDL receptor gene was common (40%), and the remaining participants carried 26 other different gene mutations, including PCSK9.

Intraobserver and interobserver reproducibility for measurements of plaque burden scores is shown in Figure 1 . Bland–Altman analysis demonstrated good agreements between both within intraobserver with a CV of 9.1% ( Figure 1 ) and within interobserver with a CV of 9.9% ( Figure 1 ) measurements.

Bland–Altman analysis for the measurement of plaque burden score Bland–Altman analysis demonstrated good agreements between the measurements (A) within intraobserver and (B) within interobserver.

To evaluate if coronary plaque burden and traditional risk factors were determinants of the occurrence of MACE, univariate analysis was performed ( Table 2 ). The median of the coronary plaque burden score was 2.78 (raw score 16.1). As a result, age, hypertension, diabetes mellitus, smoking, BMI, and plaque burden score ≥ median were significant predictors for MACE ( Table 2 ). In addition, multivariate analysis showed that the presence of hypertension and a plaque burden score ≥ median were significant independent prognostic factors ( Table 2 ).

On the basis of the ROC curve analysis, the optimal plaque burden score cut-off value for developing MACE was 3.35 (raw score 28.5), the sensitivity and specificity of which were 85.7% and 82.5%, respectively, with a area under the ROC curve of 0.90 ( Figure 2 ). Table 3 compares the clinical profiles of patients with a plaque burden score ≥3.35 and those with <3.35. The frequencies of the traditional coronary risk factors, such as age, hypertension, diabetes mellitus, and smoking habit, were greater in patients with a plaque burden score ≥3.35 than those with a score <3.35. Moreover, BMI and the duration under statin therapy were greater in patients with a plaque burden score ≥3.35 than those with a score <3.35.

ROC curve analysis and survival analysis. (A) ROC curve analysis revealed a plaque burden score of 3.35 as the optimal cutoff for predicting MACE, the sensitivity and specificity of which were 85.7% and 82.5%, respectively, with a area under the ROC curve of 0.90. (B) Cumulative event rates according to the cutoff. Blue dotted line indicates subjects with a plaque burden score ≥3.35. Red solid line indicates subjects with a plaque burden score <3.35.

Table 3

Baseline characteristics divided by the plaque score cut-off value

Variable	Plaque burden score		p value
	≥ 3.35 (n=32)	< 3.35 (n=69)
Age (years)	61.0±13.7	48.0±12.8	< 0.05
Men	15 (47%)	37 (54%)	n.s.
Hypertension	23 (72%)	11 (16%)	< 0.05
Diabetes mellitus	15 (47%)	7 (10%)	< 0.05
Smoker	19 (59%)	17 (25%)	< 0.05
Body mass index (kg/m 2 )	25.7±3.3	23.1±2.9	< 0.05
Total cholesterol (mg/dL)	341±49	346±59	n.s.
Low-density lipoprotein cholesterol (mg/dL)	262±70	264±53	n.s.
High-density lipoprotein cholesterol (mg/dL)	44±12	56±13	< 0.05
Triglyceride (mg/dL)	129±84	138±99	n.s.
Plaque burden score	3.68±0.22	1.86±1.15	< 0.05
Statins	25 (78%)	42 (61%)	n.s.
Statins duration (years)	10.5±9.2	6.2±8.2	< 0.05

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