DOI: 10.1111/eci.12295
ORIGINAL ARTICLE Role of 9p21 and 2q36 variants and arterial stiffness in the prediction of coronary artery disease Konstantinos Vakalis*, Aris Bechlioulis*, Katerina K. Naka*, Anthoula Chatzikyriakidou†, Konstantina Gartzonika‡, Patra Vezyraki§, Georgios Kolios¶, Konstantinos Pappas*, Christos S. Katsouras*, Ioannis Georgiou† and Lampros K. Michalis* * Michaelidion Cardiac Center and Department of Cardiology, Medical School, University of Ioannina, Ioannina, Greece, †Genetics Unit, Department of Obstetrics and Gynaecology, Medical School, University of Ioannina, Ioannina, Greece, ‡Laboratory of Microbiology, Medical School, University of Ioannina, Ioannina, Greece, §Laboratory of Physiology, Medical School, University of Ioannina, Ioannina, Greece, ¶Laboratory of Biochemistry, University Hospital of Ioannina, Ioannina, Greece
ABSTRACT Background Genetic polymorphisms and arterial stiffness indices have been associated with cardiovascular prognosis and the presence and extent of angiographic coronary artery disease (CAD). We aimed to investigate whether arterial stiffness indices and 9p21 and 2q36 variants may improve prediction of CAD presence and extent when added to classical cardiovascular risk factors in patients at high risk for CAD. Materials and methods In this cross-sectional study, we enrolled 183 consecutive patients with suspected stable CAD (age 61 9 years, 134 males) referred for diagnostic coronary angiography. Framingham risk score (FRS) was calculated. Arterial stiffness was assessed by carotid–femoral pulse wave velocity (PWV) and central augmentation index (AIx) using applanation tonometry. Genetic polymorphisms of 9p21 (rs1333049) and 2q36 (rs2943634) loci were also analysed. Results Higher FRS and PWV and the presence of rs2943634 risk allele were independent predictors of CAD (Nagelkerke R2 0252, P < 0001), while higher FRS and the presence of rs1333049 risk allele were independent predictors of multivessel CAD (Nagelkerke R2 0190, P < 0001). Genetic polymorphisms and vascular indices did not improve the predictive accuracy of FRS-based models (P > 01 for all) for CAD presence or extent. Conclusions In these high-risk patients, 9p21 and 2q36 variants and PWV were independently associated with CAD presence and extent, but the addition of both genetic data and arterial stiffness indices to FRS did not improve the prediction of CAD compared with FRS alone. Further studies are needed to clarify the prognostic role of genetic and vascular indices in the prediction of angiographic CAD. Keywords 2q36 polymorphisms, 9p21 polymorphisms, arterial stiffness, framingham risk score, pulse wave velocity, suspected stable coronary artery disease. Eur J Clin Invest 2014; 44 (8): 784–794
Introduction Coronary artery disease (CAD) is the leading cause of mortality in the western world [1]. Atherosclerosis, the most important underlying cause of CAD, has both environmental and genetic determinants. In the last decade, the technological advances in genomic medicine have shed light in the genomic mapping of complex diseases including cardiovascular diseases. Genomewide association studies have shown that 9p21 and 2q36 loci may be related to increased risk for CAD independently of traditional risk factors [2–4]. 9p21 has been suggested to exert direct effects on vascular wall independently of the presence of other established proatherosclerotic factors and accelerate
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atherogenesis [5]. An association with higher risk for abdominal aortic aneurysms [6] and cerebrovascular atherosclerosis [7] also supports this concept. 9p21 risk variants have been independently associated with the presence and extent of angiographic coronary atherosclerosis [5,8–11], but whether these could improve prediction of angiographic CAD when used in addition to classical risk factors has not been studied previously. Rs1333049 polymorphism is probably the most commonly studied polymorphism in 9p21 locus; strong associations with CAD events [2,3] as well as with CAD presence and extent [5,9,11,12] have been reported. On the other hand, rs2943634 polymorphism in 2q36 locus is found in the proximity of IRS-1 gene encoding insulin receptor substrate 1 that has been
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conforms to STREGA recommendations for reporting genetic association studies [26].
associated with an increased risk for diabetes and cardiometabolic risk [13]. The association of rs2943634 polymorphism with CAD risk has not been validated consistently in previous studies [14,15] while its relation with the presence of angiographic CAD has not been clarified [16]. Arterial stiffness, as assessed by carotid–femoral pulse wave velocity (PWV), is currently recognised as a measure of ‘vascular health’ and has been associated with cardiovascular prognosis in various populations [17,18]. Increased PWV has been also related to the presence of angiographic CAD [19–22]. Central augmentation index (AIx) is a more complex measure of arterial stiffness that is influenced by both aortic stiffening and the magnitude of wave reflection from peripheral circulation. AIx has been also associated with cardiovascular prognosis [23,24] and the presence of angiographic CAD [20,25]. The incremental predictive value of arterial stiffness indices PWV and AIx in addition to established cardiovascular risk factors for angiographic CAD presence and extent has not been previously studied. The aim of our study was to investigate whether the prediction of angiographic CAD presence and extent based on classical cardiovascular risk factors could be improved by including the assessment of arterial stiffness indices (PWV and AIx) and genetic variants (namely rs1333049 at 9p21 locus and rs2943634 at 2q36 locus), in patients with suspected stable CAD undergoing diagnostic coronary angiography.
Smokers were defined as those who were smoking at the time of enrolment or those who had stopped for less than 12 months. Hypertension was defined as systolic blood pressure (SBP) > 140 mmHg and/or diastolic blood pressure (DBP) > 90 mmHg or administration of antihypertensive medications. Hypercholesterolaemia was defined as low-density lipoprotein cholesterol (LDL-c) > 115 mg/dL (298 mM) or administration of anticholesterolaemic medications. Diabetes was defined as a fasting blood glucose concentration ≥ 126 mg/dL (699 mM) or administration of antihyperglycaemic medications. Creatinine clearance was estimated using the Modification in Diet in Renal Disease (MDRD) formula [27]. Framingham risk score (FRS), a multivariate risk function predicting 10-year risk of developing coronary events [28], was calculated using the following risk factors: age, gender, smoking, blood pressure, total and highdensity lipoprotein (HDL) cholesterol and diabetes. FRS has also been reported to predict the presence of angiographic CAD [19,29,30]. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m2). The minimum waist circumference between the pelvic brim and the costal margin was measured.
Materials and methods
Coronary angiography
Population This prospective cross-sectional study enrolled 183 consecutive subjects with suspected stable CAD referred for diagnostic coronary angiography at the Department of Cardiology, University Hospital of Ioannina during a 6-month period (March 2006–September 2006). Subjects were referred due to symptoms, clinical signs or a positive noninvasive stress test indicating a high risk for stable CAD. Patients with acute coronary syndrome, any history of previously established CAD, cerebrovascular and symptomatic peripheral vascular disease, valvular heart disease, prosthetic valves, congenital heart disease, hypertrophic obstructive cardiomyopathy, as well as those on haemodialysis were excluded from the study. Cardiovascular risk factors and medications were recorded in detail, and all patients underwent coronary angiography. Blood samples were drawn from all patients early in the morning after an overnight fast and just before coronary angiography. The study protocol was approved by the Ethics Committee of the University Hospital of Ioannina, Greece. The study complied with the Declaration of Helsinki, and all participants provided written informed consent. Reporting of the study
Cardiovascular risk factor assessment
Coronary angiography was performed according to the standard Judkins technique. Significant CAD was defined as ≥ 50% stenosis in the internal diameter of at least one coronary artery (≥ 30% for the left main coronary artery). Multivessel CAD was defined as the presence of significant CAD in at least two coronary vessels or left main coronary artery. All coronary angiograms were visually assessed by two experienced angiographers, and a consensus was reached. Reviewers were blinded to the results of vascular indices and genetic polymorphisms analysis.
Assessment of arterial stiffness All measurements were taken in the morning before the catheterisation. The assessment of arterial stiffness was performed noninvasively with the commercially available Sphygmocor system (Version 7.01, At Cor Medical, Sydney, NSW, Australia) using applanation tonometry by a single operator who was blinded to the results of coronary angiography and other findings. The methodology used has been described previously in detail [19]. Carotid–femoral pulse wave velocity (PWV) and central augmentation index (AIx) and pulse pressure were measured. Pressure waveforms were recorded from the carotid and femoral arteries, and wave transit time (t) was calculated
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by the system software, using the R wave on the simultaneously recorded electrocardiogram as reference frame. The distance travelled by the pulse wave was measured over the body surface as the distance between the two recording sites, and the distance from the suprasternal notch to the carotid was subtracted; measurements were made using a tape measure according to the most recent recommendations [31]. PWV was calculated as distance/transit time and was assessed by measuring carotid–femoral arteries. Central aortic pressure waveforms were generated from the right radial pressure waveform using a previously validated transfer function and were analysed to calculate the central pulse pressure and augmentation index corrected for heart rate (AIx@75). In studies performed on two separate days (8–12 days apart) in 12 subjects by a single operator, the within-subject coefficient of variation of PWV and AIx@75 were 56% and 120%, respectively.
Laboratory measurements Fasting plasma glucose, serum lipids and creatinine were measured using standard methodology. Serum hsCRP was measured using rate turbidimetry (IMMAGE Immunochemistry Systems and Calibrator 5 Plus, Beckman Coulter Inc, Fullerton, CA, USA) (assay sensitivity 002 mg/dL). Genomic DNA was extracted from peripheral blood lymphocytes according to the standard salt extraction procedure. Polymorphisms rs1333049 and rs2943634 were amplified using the following primer pairs: rs1333049F: 50 -TTC CAA CTT GTG TAT GAC-30 , rs1333049R: 50 -ACA TTT CCT TCA CTA CTG-30 , rs2943634F: 50 -CCC TAG AAT ACT GTT GTA ATC-30 and rs2943634R: 50 -CAC TTG AAA ATT GTA GTT GCT C-30 . Polymerase chain reaction single-strand conformation polymorphism (PCR-SSCP) analysis was used as the screening method for the genetic variants. Five microlitres of the PCR product were mixed with 5 mL of denaturing solution containing 95% deionised formamide, 005% bromophenol blue, 005% xylene cyanol and 20 mM ethylenediaminetetraacetic acid (EDTA). The mixture was heated at 95 °C for 5 min, then it was chilled on ice and subsequently loaded onto 10% nondenaturing polyacrylamide gel with 5% glycerol in case of variant rs2943634 and without glycerol in case of rs1333049. Gels were allowed to run at 4 °C for 18–20 h at 7 V/cm. SSCP patterns were detected by silver staining and were determined using the ABI 3700 DNA Automated Sequencer. Genetic analysis for determination of various genotypes was performed in 180 of 183 participants.
Statistical analysis Kolmogorov–Smirnov Z-test was used to identify continuous variables that were not normally distributed. Normally distributed continuous data are presented as mean SD, while
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not normally distributed continuous data are presented as median (min, max). Parameters between various groups (CAD vs. no CAD, multivessel CAD vs. no multivessel CAD, various genotype groups) were compared using v2 test for categorical parameters, while Students’ t-test, Mann–Whitney U-test, Oneway ANOVA or Kruskal–Wallis H test were used for continuous variables. Logistic regression analysis was used to create models for the prediction of significant CAD or multivessel CAD. Four separate regression models were constructed: Model 1 – FRS only, Model 2 – Model 1 + Vascular indices, Model 3 – Model 1 + genetic data and Model 4 – Model 1 + genetic data+vascular indices. The area under the curve (AUC) of regression models was calculated, and their predictive accuracy was compared using the methodology described by Hannley and McNeil (c-statistic) [32]. Regarding the genetic data, the allele frequency was calculated, and departure from Hardy–Weinberg equilibrium was assessed using Fischer’s exact test. The relation of various genotypes with CAD or multivessel CAD was estimated by univariate logistic regression analysis, and dominant, recessive and additive models for genotypes were implemented. The results were adjusted for the risk of false positive findings using the false discovery rate (FDR) [33]. Based on the univariate analysis, only the dominant model for genotype analysis was used for both polymorphisms in the prediction models. P values were always two-sided, and a value of P < 005 was considered significant. The SPSS statistical software package (version 15.0 for Windows, SPSS Inc. Chicago, IL, USA) was used.
Results Demographic, anthropometric, clinical and biochemical parameters and genotype frequencies of both polymorphisms studied, in patients with significant angiographic CAD compared with those without CAD and in patients with multivessel CAD vs. single vessel disease or no CAD, are shown in Table 1. FRS was higher in the presence of significant CAD or multivessel CAD (P < 0001 for both). PWV and central pulse pressure were significantly higher in patients with significant angiographic CAD compared with patients with no CAD (P < 005 for both indices) but did not differ significantly between patients with multivessel CAD vs. patients with single vessel disease or no CAD (Fig. 1). AIx did not differ between various subgroups (Fig. 1). Both polymorphisms were in Hardy–Weinberg equilibrium (P > 005 for both). The univariate associations of genotypes (including dominant, recessive and additive model analysis for risk alleles) of studied polymorphisms with the presence of CAD and multivessel CAD are shown in Table 2. The presence of the risk allele of the polymorphism rs2943634 was associated with higher prevalence of CAD (OR 266, P = 0045, FDR
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Table 1 Baseline characteristics in patients with significant CAD and multivessel CAD Significant CAD
Age, years
Multivessel CAD
Yes, n = 84
No, n = 99
P
63 9
59 10
0022
Yes, n = 56
No, n = 127
P
63 8
60 10
0038
Smoking, n (%)
33 (39)
32 (32)
0327
23 (41)
42 (33)
0297
Male gender, n (%)
69 (82)
65 (66)
0012
48 (86)
86 (68)
0011
Family history CAD, n (%)
26 (31)
32 (32)
0843
20 (36)
38 (30)
0438
Hypertension, n (%)
74 (88)
69 (70)
0003
51 (91)
92 (52)
0005
Hypercholesterolaemia, n (%)
70 (83)
87 (88)
0380
44 (79)
113 (89)
0063
Diabetes mellitus, n (%)
41 (49)
33 (33)
0034
33 (59)
41 (32)
0001
Body mass index, kg/m
279 36
289 40
0069
278 36
288 39
0124
Waist circumference, cm
103 9
105 11
0142
102 9
105 11
0111
2
Glucose, mg/dL
116 (71, 348)
100 (68, 249)
0002
120 (83, 348)
101 (68, 249)
< 0001
eGFR, mL/min/173 m
768 141
760 134
0678
781 145
756 134
0248
Total cholesterol, mg/dL
201 44
215 47
0033
197 42
214 47
0025
2
HDL-c, mg/dL
42 (19, 75)
49 (28, 87)
< 0001
40 (19, 75)
46 (25, 87)
Triglycerides, mg/dL
147 61
129 64
0057
154 63
130 62
LDL-c, mg/dL
129 39
139 44
0141
125 36
139 44
Hs-CRP, mg/L
23 (02, 297)
24 (02, 245)
0698
24 (02, 297)
< 0001 0020 0038
23 (02, 245)
0516
Systolic blood pressure, mmHg
146 17
140 17
0031
146 17
141 17
0077
Diastolic blood pressure, mmHg
82 9
82 10
0975
82 9
82 10
0850
Framingham risk score, %
25 (7, 82)
16 (3, 70)
Homozygous risk allele
25 (31)
Heterozygous risk allele/wild type Homozygous wild type
< 0001
25 (8, 82)
16 (3, 70)
29 (29)
16 (30)
38 (30)
43 (52)
42 (43)
31 (57)
54 (43)
14 (17)
27 (28)
7 (13)
34 (27)
Homozygous risk allele
33 (40)
38 (39)
22 (41)
49 (39)
Heterozygous risk allele/wild type
43 (53)
43 (44)
27 (50)
59 (47)
6 (7)
17 (17)
5 (9)
18 (14)
< 0001
rs1333049 polymorphism, n (%)
0220
0084
rs2943634 polymorphism, n (%)
Homozygous wild type
0121
0651
Continuous data are shown as mean SD (normal distribution) or median (min, max) (not normal distribution). Bold–italic represent P-values ≤ 0.05. Abbreviations: CAD, coronary artery disease; eGFR, estimated glomerular filtration rate; HDL-c, high-density lipoprotein cholesterol; Hs-CRP, high-sensitivity C-reactive protein; LDL-c, low-density lipoprotein cholesterol. Conversion factors to SI units are the following: 00555 for glucose, 00259 for cholesterol and 00113 for triglycerides.
corrected P value=009), while the presence of the risk allele of the polymorphism rs1333049 was associated with higher prevalence of multivessel CAD (OR 248, P = 004, FDR corrected P value=008). No significant differences in metabolic and vascular parameters were observed among the various studied genotypes (Table 3).
In multivariate analysis, in fully adjusted models, higher FRS and PWV and the presence of risk allele of polymorphism rs2943634 were independent predictors of significant CAD (Nagelkerke R2 0252, P < 0001) (Table 4). Accordingly, in fully adjusted models, higher FRS and the presence of risk allele of polymorphism rs1333049 were independent predictors of
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Figure 1 Arterial stiffness indices according to the presence of coronary artery disease (CAD) and multivessel disease (MVD).
multivessel CAD (Nagelkerke R2 0190, P < 0001) (Table 4). The addition of genetic polymorphisms and vascular indices (alone or in combination) in FRS-based models did not improve significantly the predictive accuracy of these models (P > 01 for all) (Table 5).
Discussion The present study showed that in high-risk patients with probable stable CAD, polymorphisms of the loci 2q36 (rs2943634) and 9p21 (rs1333049) were independently associated with the presence and extent of CAD, respectively; the presence of the risk alleles was related to a 25–30 times greater risk. 9p21 genetic variants have been previously reported to be associated with increased prevalence of significant angiographic CAD [8,9,11] as
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well as greater CAD extent [5,9,11,12] independently of classical risk factors. In accordance with previous studies, 9p21 variants were not associated with risk factors [2,3,14,34]; only a minor association of rs1333049 risk allele with higher triglycerides (P = 0049) was currently shown. Although the 2q36 locus has been previously related to increased risk for CAD events independently of traditional risk factors [3,4], its association with the presence and extent of angiographic CAD has been little studied. In contrast to a previous study that showed no clear association of the rs2943634 polymorphism with the presence of angiographic CAD in a population similar to ours [16], we showed that the risk allele was related with higher prevalence of significant CAD. No association of rs2943634 polymorphism with HDL-c was observed in our study unlike previous reports [14,15].
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Table 2 Univariate associations of studied polymorphisms with CAD and multivessel CAD using various genotype model analysis (additive, dominant and recessive) for risk alleles SNP rs1333049
Outcome CAD
Genotype model WW* vs. WR vs. RR
OR (95% CI) †
197 (091, 428) ‡
Multivessel CAD
CAD
0235
WW* vs. WR/RR
185 (089, 382)
0098
WW/WR* vs. RR
104 (055, 198)
0896
WW* vs. WR vs. RR
†
279 (111, 704)
0030
205 (075, 557)
0161
WW* vs. WR/RR
248 (103, 602)
0040
WW/WR* vs. RR
098 (049, 196)
0975
WW* vs. WR vs. RR
†
283 (103, 788) ‡
Multivessel CAD
0085
166 (072, 385)
‡
rs2943634
P value
0041
246 (087, 697)
0090
WW* vs. WR/RR
266 (102, 709)
0045
WW/WR* vs. RR
106 (058, 194)
0841
WW* vs. WR vs. RR
†
165 (055, 490)
0370
162‡ (053, 491)
0397
WW* vs. WR/RR
163 (057, 465)
0358
WW/WR* vs. RR
108 (056, 207)
0816
*reference genotype. † WR vs. WW. ‡ RR vs. WW. Abbreviations: CAD, coronary artery disease; SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval; W, wild-type allele; and R, risk allele.
Despite the independent association of these polymorphisms with the presence and extent of CAD shown in our study, similar to other reports, their value in addition to risk factors in the prediction of risk for angiographic CAD has not been previously studied. In the current study, we showed that adding these polymorphisms in multivariate regression models did not significantly improve the accuracy of risk factors, as expressed by FRS, in predicting angiographic CAD. It may be possible that genetic variants may contribute the most in the prediction of CAD in subjects at low/intermediate risk, as suggested by some studies. 9p21 variants have been previously shown to improve the accuracy and reclassification over FRS in prediction of cardiovascular events, specifically in intermediate CAD risk patients [8]. Furthermore, the association of 9p21 variants (rs1333049 polymorphism) with CAD presence and severity has been reported to be most evident in younger, nondiabetic patients [5,10]. On the other hand, our population was predominantly of high risk (> 50% of our patients had FRS > 20% and ca. 40% were diabetics) and had a high prevalence of risk alleles (> 75% of our population), in contrast to their prevalence reported in the general population, that is about 50% [3]. To our
knowledge, there are no data regarding risk reclassification using 2q36 variants. A potential association of 9p21 locus with indices of arterial stiffness was explored in our population as it has been previously suggested that 9p21 may exert direct effects on vascular wall, independently of the presence of other established proatherosclerotic factors, promoting thus atherogenesis and atherosclerosis progression [5,35]. Few studies have also addressed the relationship of 9p21 locus polymorphisms with arterial stiffness; although two small studies have reported a significant association of 9p21 variants with PWV [36,37], two larger studies have not yielded any significant association of PWV with 9p21 polymorphisms [38,39], in accordance with our results. No clear association between 9p21 variants and early markers of subclinical atherosclerosis such as endotheliumdependent vasodilation and carotid intima-media thickness [40–42] has been shown, while the association of central pressures and AIx with 9p21 variants has not been studied previously. Other pathophysiological processes besides arterial stiffening may mediate the suggested direct vascular effects of 9p21 variants. Regarding the association of 2q36 variants with
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Table 3 Characteristics of patients according to the genotypes of rs1333049 and rs2943634 polymorphisms rs1333049 polymorphism RR, n = 54
WR, n = 85
WW, n = 41
WR/RR, n = 139
P*
P†
Family history CAD, n (%)
20 (37)
24 (28)
14 (34)
44 (32)
0532
0764
Hypertension, n (%)
40 (74)
70 (82)
30 (73)
110 (79)
0375
0419
Hypercholesterolaemia, n (%)
48 (89)
70 (82)
37 (90)
118 (85)
0380
0384
Diabetes mellitus, n (%)
22 (41)
36 (42)
13 (32)
58 (42)
0505
0249
Body mass index, kg/m
289 43
287 38
277 33
288 40
0269
0107
Waist circumference, cm
104 10
104 11
104 11
104 10
0918
0854
0758
0626
2
Glucose, mg/dL
111 (82, 225)
105 (68, 306)
103 (76, 234)
108 (68, 306)
eGFR, mL/min/173 m
767 144
772 146
748 112
770 140
0658
0371
Total cholesterol, mg/dL
214 47
203 42
213 52
207 45
0274
0466
0294
0169
2
HDL-c, mg/dL
46 (25, 82)
43 (19, 83)
46 (29, 87)
44 (19, 83)
Triglycerides, mg/dL
140 67
143 63
120 59
142 64
0139
0049
LDL-c, mg/dL
139 42
129 36
140 51
133 39
0246
0367
0430
0677
0761
0459
Hs-CRP, mg/L
24 (02, 245)
Systolic blood pressure, mmHg
142 17
22 (02, 297) 142 17
24 (03, 280) 144 17
23 (02, 297) 142 17
Diastolic blood pressure, mmHg
82 11
82 10
82 8
82 10
0954
0792
Pulse wave velocity, m/s
97 24
96 23
99 26
96 23
0751
0496
Augmentation index, %
255 86
235 83
232 97
243 84
0331
0487
48 12
46 10
49 10
47 11
0392
0232
0854
0575
P*
P†
Central pulse pressure, mmHg Framingham risk score, %
20 (4, 70)
20 (4, 70)
20 (3, 82)
20 (4, 70)
rs2943634 polymorphism RR, n = 71
WR, n = 86
WW, n = 23
WR/RR, n = 157
Family history CAD, n (%)
18 (25)
33 (38)
7 (30)
51 (32)
0217
0844
Hypertension, n (%)
58 (82)
65 (76)
17 (74)
123 (78)
0586
0633
Hypercholesterolaemia, n (%)
61 (86)
73 (85)
21 (91)
134 (85)
0730
0746
Diabetes mellitus, n (%)
24 (34)
36 (42)
11 (48)
60 (38)
0400
0379
Body mass index, kg/m
282 35
291 42
276 36
287 39
0167
0220
Waist circumference, cm
103 11
105 10
103 10
104 11
0545
0798
0556
0916
2
Glucose, mg/dL
105 (68, 306)
111 (82, 253)
101 (83, 234)
106 (68, 306)
eGFR, mL/min/173 m
754 138
773 130
765 168
765 134
0686
0984
Total cholesterol, mg/dL
200 48
215 42
212 55
208 45
0114
0738
0714
0670
2
HDL-c, mg/dL
43 (19, 87)
46 (27, 82)
47 (25, 72)
44 (19, 87)
Triglycerides, mg/dL
137 59
141 68
124 66
139 63
0512
0285
LDL-c, mg/dL
126 44
139 36
141 52
133 40
0103
0401
0187
0092
0479
0877
Hs-CRP, mg/L Systolic blood pressure, mmHg
790
23 (02, 297) 144 17
22 (03, 245) 141 17
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34 (05, 280) 142 15
23 (02, 297) 142 17
9P21 AND 2Q36 IN CORONARY ARTERY DISEASE
Table 3 Continued rs2943634 polymorphism P*
P†
82 10
0736
0881
102 23
96 24
0340
0263
232 91
229 81
242 88
0252
0526
46 11
46 9
47 11
0243
0663
0903
0678
RR, n = 71
WR, n = 86
Diastolic blood pressure, mmHg
83 9
82 11
82 9
Pulse wave velocity, m/s
94 19
97 27
Augmentation index, %
254 84 49 12
Central pulse pressure, mmHg Framingham risk score, %
20 (3, 56)
WW, n = 23
20 (5, 82)
18 (3, 70)
WR/RR, n = 157
20 (3, 82)
Continuous data are shown as mean SD (normal distribution) or median (min, max) (not normal distribution). * Pfor comparisons among the three individual genotypes. † Pfor the comparison between the presence of risk allele and homozygous wild type. Abbreviations: CAD, coronary artery disease; eGFR, estimated glomerular filtration rate; HDL-c, high-density lipoprotein cholesterol; Hs-CRP, high-sensitivity Creactive protein; LDL-c, low-density lipoprotein cholesterol; R, risk allele; and W, wild-type allele. Conversion factors to SI units are the following: 00555 for glucose, 00259 for cholesterol and 00113 for triglycerides.
Table 4 Independent predictors of significant and multivessel CAD in each model OR
95% CI
P
Nagelkerke R2
0197
Presence of significant angiographic CAD FRS
FRS*, %
3811
2212, 6567
< 0001
FRS + Vascular indices
FRS*, %
3518
2003, 6179
< 0001
PWV, m/s
1157
1004, 1333
0044
FRS*, %
3956
2257, 6934
< 0001
rs2943634 risk allele
2882
1000, 8301
0050
FRS*, %
3568
2002, 6358
< 0001
PWV, m/s
1184
1019, 1376
0027
rs2943634 risk allele
3297
1135, 9583
0028
FRS
FRS*, %
3504
1970, 6232
< 0001
0161
FRS + Vascular indices
FRS*, %
3504
1970, 6232
< 0001
0161
FRS + Genes
FRS*, %
3551
1964, 6418
< 0001
rs1333049 risk allele
2538
1000, 6447
0050
FRS*, %
3551
1964, 6418
< 0001
rs1333049 risk allele
2538
1000, 6447
0050
FRS + Genes
FRS + Genes + Vascular indices
0221
0227
0252
Presence of angiographic multivessel CAD
FRS + Genes + Vascular indices
0190
0190
*Natural logarithm-transformed values due to not normal distribution. Abbreviations: CAD, coronary artery disease; CI, confidence interval; FRS, Framingham risk score; OR, odds ratio; and PWV, pulse wave velocity.
subclinical atherosclerosis indices, no association of rs2943634 polymorphism with carotid intima-media thickness, aortic calcium and ankle-brachial index was found [13,42], while its relation to arterial stiffness indices has not been previously studied. Based on the lack of association between genetic variants and arterial stiffness indices, we set to investigate whether the
addition of both genetic data and vascular indices could significantly enhance prediction of CAD. Initially, we investigated the role of vascular indices alone in the prediction of CAD. We showed that increasing PWV was an independent predictor of the presence of angiographic CAD in our population similar to previous reports [19–22,43]. No association of PWV with CAD extent was found in contrast with previous smaller studies
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Table 5 Accuracy of models predicting the presence and extent of CAD using FRS, vascular indices and studied polymorphisms AUC
Standard error
P*
P†
Presence of significant angiographic CAD FRS
0727
0038
< 0001
–
FRS + Vascular indices
0731
0037
< 0001
0162
FRS + Genes
0747
0037
< 0001
0138
FRS + Genes + Vascular indices
0756
0036
< 0001
0107
Presence of angiographic multivessel CAD FRS
0718
0040
< 0001
–
FRS + Vascular indices
0718
0040
< 0001
10
FRS + Genes
0739
0039
< 0001
0150
FRS + Genes + Vascular indices
0739
0039
< 0001
0150
*P values for AUC in each model. † P values for comparison vs. the model with FRS only. Bold–italic represent P-values ≤ 0.05. Abbreviations: AUC, area under curve; CAD, coronary artery disease; and FRS, Framingham risk score.
[20,22]. No association of other vascular indices with angiographic CAD was observed; conflicting results have been reported using AIx [21,25,44]. Adding PWV to a regression model with classical risk factors (i.e. FRS) did not significantly improve the predictive accuracy of FRS for the presence or extent of CAD. To our knowledge, only one previous study in patients with a history of stroke reported an improvement in the prediction of CAD presence with the use of PWV beyond FRS [43]. Finally, when fully adjusted regression models (i.e. including FRS, genetic variants and arterial stiffness indices) were used, prediction of the CAD presence/extent was not improved compared with FRS alone. This has not been studied before; only a modest improvement in prediction of coronary events was shown in intermediate-risk patients when adding 9p21 polymorphisms on carotid intima-media thickness and classical risk factors [45].
Limitations The main limitation of our study is the small sample size; further larger studies are needed to confirm our findings. This was an observational study in a group of patients with suspected stable CAD, and thus, conclusions are probably limited to this population. Subjects undergoing invasive coronary angiography are usually symptomatic or at high risk and may not be representative of the general population. Variations in referral
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patterns for angiography could also be a source of selection bias. The use of coronary angiography compared with other modalities (i.e. intravascular ultrasound) to assess coronary atherosclerosis is limited by the fact that substantial disease burden in arterial walls can be missed. Our study enrolled only white Caucasians, and thus, our findings may not be applicable to populations of other descent. Other measures of subclinical atherosclerosis such as endothelium-dependent vasodilation or carotid intima-media thickness were not currently measured. In conclusion, rs2943634 and rs1333049 polymorphisms and carotid–femoral PWV were associated with presence and extent of CAD independent of risk factor burden as assessed by FRS in high-risk patients with suspected stable CAD undergoing diagnostic coronary angiography. In this high-risk population, the addition of genetic data and arterial stiffness indices, alone or in combination, to FRS did not improve the prediction of CAD presence or extent compared with FRS alone. Further studies are needed to confirm our findings and clarify the role of genetic data and noninvasive arterial stiffness indices in the prediction of coronary atherosclerosis.
Sources of funding The study was partly funded by a grant from the Research Committee of University of Ioannina (project code 80010). Acknowledgements We thank Clinical and Molecular Epidemiology Dr Evagelia Ntzani, Assistant Professor in the Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece for valuable help and statistical advice. Conflict of interest The authors declare that they have no conflict of interest. Contributions KV, KKN, CSK and LKM were involved in study conception and design. KV, KKN, KP, CSK and LKM participated in enrolment of patients and acquisition of data. AC, KG, PV, GK and IG participated in the analysis of blood samples for the determination of metabolic parameters, hsCRP and genetic polymorphisms. AB and KKN performed statistical analysis, while AB, KKN and LKM assisted in the interpretation of the study findings. All authors were involved in drafting the article or revising it and approved the final version of the manuscript. Address Michaelidion Cardiac Center and Department of Cardiology, University of Ioannina, Ioannina, Greece (K. Vakalis, A. Bechlioulis, K. K. Naka, K. Pappas, C. S. Katsouras, L. K. Michalis); Genetics Unit, Department of Obstetrics and Gynaecology,
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University of Ioannina, Ioannina, Greece (A. Chatzikyriakidou, I. Georgiou); Laboratory of Microbiology, Medical School, University of Ioannina, Ioannina, Greece (K. Gartzonika); Laboratory of Physiology, Medical School, University of Ioannina, Ioannina, Greece (P. Vezyraki); Laboratory of Biochemistry, University Hospital of Ioannina, Ioannina, Greece (G. Kolios). Correspondence to: Professor Lampros K. Michalis, MD, Department of Cardiology and Michaelidion Cardiac Centre, University of Ioannina, Ioannina 45 110, Greece. Tel.: (+30) 26510 07710; fax: (+30) 26510 07865; e-mail: [email protected] Received 12 February 2014; accepted 13 June 2014 References 1 Roger VL, Go AS, Lloyd-Jones DM, Benjamin EJ, Berry JD, Borden WB et al. Heart disease and stroke statistics–2012 update: a report from the American Heart Association. Circulation 2012;125: e2–220. 2 McPherson R, Pertsemlidis A, Kavaslar N, Stewart A, Roberts R, Cox DR et al. A common allele on chromosome 9 associated with coronary heart disease. Science 2007;316:1488–91. 3 Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, Mayer B et al. Genomewide association analysis of coronary artery disease. N Engl J Med 2007;357:443–53. 4 Franceschini N, Carty C, Buzkova P, Reiner AP, Garrett T, Lin Y et al. Association of genetic variants and incident coronary heart disease in multiethnic cohorts: the PAGE study. Circ Cardiovasc Genet 2011;4:661–72. 5 Dandona S, Stewart AF, Chen L, Williams K, So D, O’Brien E et al. Gene dosage of the common variant 9p21 predicts severity of coronary artery disease. J Am Coll Cardiol 2010;56:479–86. 6 Helgadottir A, Thorleifsson G, Magnusson KP, Gretarsdottir S, Steinthorsdottir V, Manolescu A et al. The same sequence variant on 9p21 associates with myocardial infarction, abdominal aortic aneurysm and intracranial aneurysm. Nat Genet 2008;40:217–24. 7 Matarin M, Brown WM, Singleton A, Hardy JA, Meschia JF. Whole genome analyses suggest ischemic stroke and heart disease share an association with polymorphisms on chromosome 9p21. Stroke 2008;39:1586–9. 8 Anderson JL, Horne BD, Kolek MJ, Muhlestein JB, Mower CP, Park JJ et al. Genetic variation at the 9p21 locus predicts angiographic coronary artery disease prevalence but not extent and has clinical utility. Am Heart J 2008;156:1155–62 e2. 9 Patel RS, Su S, Neeland IJ, Ahuja A, Veledar E, Zhao J et al. The chromosome 9p21 risk locus is associated with angiographic severity and progression of coronary artery disease. Eur Heart J 2010;31:3017–23. 10 Chan K, Motterle A, Laxton RC, Ye S. Common variant on chromosome 9p21 predicts severity of coronary artery disease. J Am Coll Cardiol 2011;57:1497–8; author reply 8–9. 11 Chan K, Patel RS, Newcombe P, Nelson CP, Qasim A, Epstein SE et al. Association between the chromosome 9p21 locus and angiographic coronary artery disease burden: a collaborative metaanalysis. J Am Coll Cardiol 2013;61:957–70. 12 Chen SN, Ballantyne CM, Gotto AM Jr, Marian AJ. The 9p21 susceptibility locus for coronary artery disease and the severity of coronary atherosclerosis. BMC Cardiovasc Disord 2009;9:3.
13 Lim S, Hong J, Liu CT, Hivert MF, White CC, Murabito JM et al. Common variants in and near IRS1 and subclinical cardiovascular disease in the Framingham Heart Study. Atherosclerosis 2013;229:149–54. 14 Karvanen J, Silander K, Kee F, Tiret L, Salomaa V, Kuulasmaa K et al. The impact of newly identified loci on coronary heart disease, stroke and total mortality in the MORGAM prospective cohorts. Genet Epidemiol 2009;33:237–46. 15 Arregui M, Fisher E, Knuppel S, Buijsse B, di Giuseppe R, Fritsche A et al. Significant associations of the rs2943634 (2q36.3) genetic polymorphism with adiponectin, high density lipoprotein cholesterol and ischemic stroke. Gene 2012;494:190–5. 16 Muendlein A, Saely CH, Rhomberg S, Sonderegger G, Loacker S, Rein P et al. Evaluation of the association of genetic variants on the chromosomal loci 9p21.3, 6q25.1, and 2q36.3 with angiographically characterized coronary artery disease. Atherosclerosis 2009;205:174– 80. 17 Laurent S, Boutouyrie P, Asmar R, Gautier I, Laloux B, Guize L et al. Aortic stiffness is an independent predictor of all-cause and cardiovascular mortality in hypertensive patients. Hypertension 2001;37:1236–41. 18 Vlachopoulos C, Aznaouridis K, Stefanadis C. Prediction of cardiovascular events and all-cause mortality with arterial stiffness: a systematic review and meta-analysis. J Am Coll Cardiol 2010;55:1318–27. 19 Bechlioulis A, Vakalis K, Naka KK, Bourantas CV, Papamichael ND, Kotsia A et al. Increased aortic pulse wave velocity is associated with the presence of angiographic coronary artery disease in overweight and obese patients. Am J Hypertens 2012;26:265–70. 20 Covic A, Haydar AA, Bhamra-Ariza P, Gusbeth-Tatomir P, Goldsmith DJ. Aortic pulse wave velocity and arterial wave reflections predict the extent and severity of coronary artery disease in chronic kidney disease patients. J Nephrol 2005;18:388–96. 21 Hope SA, Antonis P, Adam D, Cameron JD, Meredith IT. Arterial pulse wave velocity but not augmentation index is associated with coronary artery disease extent and severity: implications for arterial transfer function applicability. J Hypertens 2007;25:2105–9. 22 Alarhabi AY, Mohamed MS, Ibrahim S, Hun TM, Musa KI, Yusof Z. Pulse wave velocity as a marker of severity of coronary artery disease. J Clin Hypertens (Greenwich) 2009;11:17–21. 23 Vlachopoulos C, Aznaouridis K, O’Rourke MF, Safar ME, Baou K, Stefanadis C. Prediction of cardiovascular events and all-cause mortality with central haemodynamics: a systematic review and meta-analysis. Eur Heart J 2010;31:1865–71. 24 Weber T, O’Rourke MF, Lassnig E, Porodko M, Ammer M, Rammer M et al. Pulse waveform characteristics predict cardiovascular events and mortality in patients undergoing coronary angiography. J Hypertens 2010;28:797–805. 25 Weber T, Auer J, O’Rourke MF, Kvas E, Lassnig E, Berent R et al. Arterial stiffness, wave reflections, and the risk of coronary artery disease. Circulation 2004;109:184–9. 26 Simera I, Moher D, Hoey J, Schulz KF, Altman DG. A catalogue of reporting guidelines for health research. Eur J Clin Invest 2010;40:35– 53. 27 Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 1999;130:461–70. 28 Anderson KM, Wilson PW, Odell PM, Kannel WB. An updated coronary risk profile. A statement for health professionals. Circulation 1991;83:356–62.
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29 Lee BC, Lee WJ, Hsu HC, Chien KL, Shih TT, Chen MF. Using clinical cardiovascular risk scores to predict coronary artery plaque severity and stenosis detected by CT coronary angiography in asymptomatic Chinese subjects. Int J Cardiovasc Imaging 2011;27:669–78. 30 Versteylen MO, Joosen IA, Shaw LJ, Narula J, Hofstra L. Comparison of Framingham, PROCAM, SCORE, and Diamond Forrester to predict coronary atherosclerosis and cardiovascular events. J Nucl Cardiol 2011;18:904–11. 31 Van Bortel LM, Laurent S, Boutouyrie P, Chowienczyk P, Cruickshank JK, De Backer T et al. Expert consensus document on the measurement of aortic stiffness in daily practice using carotidfemoral pulse wave velocity. J Hypertens 2012;30:445–8. 32 Hanley JA, McNeil BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983;148:839–43. 33 Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Methodol 1995;57:289–300. 34 Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T, Jonasdottir A et al. A common variant on chromosome 9p21 affects the risk of myocardial infarction. Science 2007;316:1491–3. 35 Ye S, Willeit J, Kronenberg F, Xu Q, Kiechl S. Association of genetic variation on chromosome 9p21 with susceptibility and progression of atherosclerosis: a population-based, prospective study. J Am Coll Cardiol 2008;52:378–84. 36 Cesana F, Nava S, Menni C, Boffi L, Varrenti M, Meani P et al. Does the 9p region affect arterial stiffness? Results from a cohort of hypertensive individuals. Blood Press 2013;22:302–6. 37 Phababpha S, Kukongviriyapan U, Pakdeechote P, Senggunprai L, Kukongviriyapan V, Settasatian C et al. Association of arterial stiffness with single nucleotide polymorphism rs1333049 and metabolic risk factors. Cardiovasc Diabetol 2013;12:93. 38 Levy D, Larson MG, Benjamin EJ, Newton-Cheh C, Wang TJ, Hwang SJ et al. Framingham Heart Study 100K Project:
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genome-wide associations for blood pressure and arterial stiffness. BMC Med Genet 2007;8(Suppl 1):S3. Mitchell GF, Verwoert GC, Tarasov KV, Isaacs A, Smith AV, Yasmin et al. Common genetic variation in the 30 -BCL11B gene desert is associated with carotid-femoral pulse wave velocity and excess cardiovascular disease risk: the AortaGen Consortium. Circ Cardiovasc Genet 2012;5:81–90. Samani NJ, Raitakari OT, Sipila K, Tobin MD, Schunkert H, Juonala M et al. Coronary artery disease-associated locus on chromosome 9p21 and early markers of atherosclerosis. Arterioscler Thromb Vasc Biol 2008;28:1679–83. Aschauer S, Mittermayer F, Wagner CC, Schmidt WM, Brunner M, Haslacher H et al. Forearm vasodilator reactivity in homozygous carriers of the 9p21.3 rs1333049 G>C polymorphism. Eur J Clin Invest 2010;40:700–5. Cunnington MS, Mayosi BM, Hall DH, Avery PJ, Farrall M, Vickers MA et al. Novel genetic variants linked to coronary artery disease by genome-wide association are not associated with carotid artery intima-media thickness or intermediate risk phenotypes. Atherosclerosis 2009;203:41–4. Calvet D, Touze E, Laurent S, Varenne O, Sablayrolles JL, Boutouyrie P et al. Aortic stiffness measurement improves the prediction of asymptomatic coronary artery disease in stroke/ transient ischemic attack patients. Int J Stroke 2014;9:291–6. Cho SW, Kim BK, Kim JH, Byun YS, Goh CW, Rhee KJ et al. Noninvasively measured aortic wave reflection and pulse pressure amplification are related to the severity of coronary artery disease. J Cardiol 2013;62:131–7. Nambi V, Boerwinkle E, Lawson K, Brautbar A, Chambless L, Franeschini N et al. The 9p21 genetic variant is additive to carotid intima media thickness and plaque in improving coronary heart disease risk prediction in white participants of the Atherosclerosis Risk in Communities (ARIC) Study. Atherosclerosis 2012;222:135–7.
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ORIGINAL ARTICLE Role of 9p21 and 2q36 variants and arterial stiffness in the prediction of coronary artery disease Konstantinos Vakalis*, Aris Bechlioulis*, Katerina K. Naka*, Anthoula Chatzikyriakidou†, Konstantina Gartzonika‡, Patra Vezyraki§, Georgios Kolios¶, Konstantinos Pappas*, Christos S. Katsouras*, Ioannis Georgiou† and Lampros K. Michalis* * Michaelidion Cardiac Center and Department of Cardiology, Medical School, University of Ioannina, Ioannina, Greece, †Genetics Unit, Department of Obstetrics and Gynaecology, Medical School, University of Ioannina, Ioannina, Greece, ‡Laboratory of Microbiology, Medical School, University of Ioannina, Ioannina, Greece, §Laboratory of Physiology, Medical School, University of Ioannina, Ioannina, Greece, ¶Laboratory of Biochemistry, University Hospital of Ioannina, Ioannina, Greece
ABSTRACT Background Genetic polymorphisms and arterial stiffness indices have been associated with cardiovascular prognosis and the presence and extent of angiographic coronary artery disease (CAD). We aimed to investigate whether arterial stiffness indices and 9p21 and 2q36 variants may improve prediction of CAD presence and extent when added to classical cardiovascular risk factors in patients at high risk for CAD. Materials and methods In this cross-sectional study, we enrolled 183 consecutive patients with suspected stable CAD (age 61 9 years, 134 males) referred for diagnostic coronary angiography. Framingham risk score (FRS) was calculated. Arterial stiffness was assessed by carotid–femoral pulse wave velocity (PWV) and central augmentation index (AIx) using applanation tonometry. Genetic polymorphisms of 9p21 (rs1333049) and 2q36 (rs2943634) loci were also analysed. Results Higher FRS and PWV and the presence of rs2943634 risk allele were independent predictors of CAD (Nagelkerke R2 0252, P < 0001), while higher FRS and the presence of rs1333049 risk allele were independent predictors of multivessel CAD (Nagelkerke R2 0190, P < 0001). Genetic polymorphisms and vascular indices did not improve the predictive accuracy of FRS-based models (P > 01 for all) for CAD presence or extent. Conclusions In these high-risk patients, 9p21 and 2q36 variants and PWV were independently associated with CAD presence and extent, but the addition of both genetic data and arterial stiffness indices to FRS did not improve the prediction of CAD compared with FRS alone. Further studies are needed to clarify the prognostic role of genetic and vascular indices in the prediction of angiographic CAD. Keywords 2q36 polymorphisms, 9p21 polymorphisms, arterial stiffness, framingham risk score, pulse wave velocity, suspected stable coronary artery disease. Eur J Clin Invest 2014; 44 (8): 784–794
Introduction Coronary artery disease (CAD) is the leading cause of mortality in the western world [1]. Atherosclerosis, the most important underlying cause of CAD, has both environmental and genetic determinants. In the last decade, the technological advances in genomic medicine have shed light in the genomic mapping of complex diseases including cardiovascular diseases. Genomewide association studies have shown that 9p21 and 2q36 loci may be related to increased risk for CAD independently of traditional risk factors [2–4]. 9p21 has been suggested to exert direct effects on vascular wall independently of the presence of other established proatherosclerotic factors and accelerate
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atherogenesis [5]. An association with higher risk for abdominal aortic aneurysms [6] and cerebrovascular atherosclerosis [7] also supports this concept. 9p21 risk variants have been independently associated with the presence and extent of angiographic coronary atherosclerosis [5,8–11], but whether these could improve prediction of angiographic CAD when used in addition to classical risk factors has not been studied previously. Rs1333049 polymorphism is probably the most commonly studied polymorphism in 9p21 locus; strong associations with CAD events [2,3] as well as with CAD presence and extent [5,9,11,12] have been reported. On the other hand, rs2943634 polymorphism in 2q36 locus is found in the proximity of IRS-1 gene encoding insulin receptor substrate 1 that has been
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conforms to STREGA recommendations for reporting genetic association studies [26].
associated with an increased risk for diabetes and cardiometabolic risk [13]. The association of rs2943634 polymorphism with CAD risk has not been validated consistently in previous studies [14,15] while its relation with the presence of angiographic CAD has not been clarified [16]. Arterial stiffness, as assessed by carotid–femoral pulse wave velocity (PWV), is currently recognised as a measure of ‘vascular health’ and has been associated with cardiovascular prognosis in various populations [17,18]. Increased PWV has been also related to the presence of angiographic CAD [19–22]. Central augmentation index (AIx) is a more complex measure of arterial stiffness that is influenced by both aortic stiffening and the magnitude of wave reflection from peripheral circulation. AIx has been also associated with cardiovascular prognosis [23,24] and the presence of angiographic CAD [20,25]. The incremental predictive value of arterial stiffness indices PWV and AIx in addition to established cardiovascular risk factors for angiographic CAD presence and extent has not been previously studied. The aim of our study was to investigate whether the prediction of angiographic CAD presence and extent based on classical cardiovascular risk factors could be improved by including the assessment of arterial stiffness indices (PWV and AIx) and genetic variants (namely rs1333049 at 9p21 locus and rs2943634 at 2q36 locus), in patients with suspected stable CAD undergoing diagnostic coronary angiography.
Smokers were defined as those who were smoking at the time of enrolment or those who had stopped for less than 12 months. Hypertension was defined as systolic blood pressure (SBP) > 140 mmHg and/or diastolic blood pressure (DBP) > 90 mmHg or administration of antihypertensive medications. Hypercholesterolaemia was defined as low-density lipoprotein cholesterol (LDL-c) > 115 mg/dL (298 mM) or administration of anticholesterolaemic medications. Diabetes was defined as a fasting blood glucose concentration ≥ 126 mg/dL (699 mM) or administration of antihyperglycaemic medications. Creatinine clearance was estimated using the Modification in Diet in Renal Disease (MDRD) formula [27]. Framingham risk score (FRS), a multivariate risk function predicting 10-year risk of developing coronary events [28], was calculated using the following risk factors: age, gender, smoking, blood pressure, total and highdensity lipoprotein (HDL) cholesterol and diabetes. FRS has also been reported to predict the presence of angiographic CAD [19,29,30]. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m2). The minimum waist circumference between the pelvic brim and the costal margin was measured.
Materials and methods
Coronary angiography
Population This prospective cross-sectional study enrolled 183 consecutive subjects with suspected stable CAD referred for diagnostic coronary angiography at the Department of Cardiology, University Hospital of Ioannina during a 6-month period (March 2006–September 2006). Subjects were referred due to symptoms, clinical signs or a positive noninvasive stress test indicating a high risk for stable CAD. Patients with acute coronary syndrome, any history of previously established CAD, cerebrovascular and symptomatic peripheral vascular disease, valvular heart disease, prosthetic valves, congenital heart disease, hypertrophic obstructive cardiomyopathy, as well as those on haemodialysis were excluded from the study. Cardiovascular risk factors and medications were recorded in detail, and all patients underwent coronary angiography. Blood samples were drawn from all patients early in the morning after an overnight fast and just before coronary angiography. The study protocol was approved by the Ethics Committee of the University Hospital of Ioannina, Greece. The study complied with the Declaration of Helsinki, and all participants provided written informed consent. Reporting of the study
Cardiovascular risk factor assessment
Coronary angiography was performed according to the standard Judkins technique. Significant CAD was defined as ≥ 50% stenosis in the internal diameter of at least one coronary artery (≥ 30% for the left main coronary artery). Multivessel CAD was defined as the presence of significant CAD in at least two coronary vessels or left main coronary artery. All coronary angiograms were visually assessed by two experienced angiographers, and a consensus was reached. Reviewers were blinded to the results of vascular indices and genetic polymorphisms analysis.
Assessment of arterial stiffness All measurements were taken in the morning before the catheterisation. The assessment of arterial stiffness was performed noninvasively with the commercially available Sphygmocor system (Version 7.01, At Cor Medical, Sydney, NSW, Australia) using applanation tonometry by a single operator who was blinded to the results of coronary angiography and other findings. The methodology used has been described previously in detail [19]. Carotid–femoral pulse wave velocity (PWV) and central augmentation index (AIx) and pulse pressure were measured. Pressure waveforms were recorded from the carotid and femoral arteries, and wave transit time (t) was calculated
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by the system software, using the R wave on the simultaneously recorded electrocardiogram as reference frame. The distance travelled by the pulse wave was measured over the body surface as the distance between the two recording sites, and the distance from the suprasternal notch to the carotid was subtracted; measurements were made using a tape measure according to the most recent recommendations [31]. PWV was calculated as distance/transit time and was assessed by measuring carotid–femoral arteries. Central aortic pressure waveforms were generated from the right radial pressure waveform using a previously validated transfer function and were analysed to calculate the central pulse pressure and augmentation index corrected for heart rate (AIx@75). In studies performed on two separate days (8–12 days apart) in 12 subjects by a single operator, the within-subject coefficient of variation of PWV and AIx@75 were 56% and 120%, respectively.
Laboratory measurements Fasting plasma glucose, serum lipids and creatinine were measured using standard methodology. Serum hsCRP was measured using rate turbidimetry (IMMAGE Immunochemistry Systems and Calibrator 5 Plus, Beckman Coulter Inc, Fullerton, CA, USA) (assay sensitivity 002 mg/dL). Genomic DNA was extracted from peripheral blood lymphocytes according to the standard salt extraction procedure. Polymorphisms rs1333049 and rs2943634 were amplified using the following primer pairs: rs1333049F: 50 -TTC CAA CTT GTG TAT GAC-30 , rs1333049R: 50 -ACA TTT CCT TCA CTA CTG-30 , rs2943634F: 50 -CCC TAG AAT ACT GTT GTA ATC-30 and rs2943634R: 50 -CAC TTG AAA ATT GTA GTT GCT C-30 . Polymerase chain reaction single-strand conformation polymorphism (PCR-SSCP) analysis was used as the screening method for the genetic variants. Five microlitres of the PCR product were mixed with 5 mL of denaturing solution containing 95% deionised formamide, 005% bromophenol blue, 005% xylene cyanol and 20 mM ethylenediaminetetraacetic acid (EDTA). The mixture was heated at 95 °C for 5 min, then it was chilled on ice and subsequently loaded onto 10% nondenaturing polyacrylamide gel with 5% glycerol in case of variant rs2943634 and without glycerol in case of rs1333049. Gels were allowed to run at 4 °C for 18–20 h at 7 V/cm. SSCP patterns were detected by silver staining and were determined using the ABI 3700 DNA Automated Sequencer. Genetic analysis for determination of various genotypes was performed in 180 of 183 participants.
Statistical analysis Kolmogorov–Smirnov Z-test was used to identify continuous variables that were not normally distributed. Normally distributed continuous data are presented as mean SD, while
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not normally distributed continuous data are presented as median (min, max). Parameters between various groups (CAD vs. no CAD, multivessel CAD vs. no multivessel CAD, various genotype groups) were compared using v2 test for categorical parameters, while Students’ t-test, Mann–Whitney U-test, Oneway ANOVA or Kruskal–Wallis H test were used for continuous variables. Logistic regression analysis was used to create models for the prediction of significant CAD or multivessel CAD. Four separate regression models were constructed: Model 1 – FRS only, Model 2 – Model 1 + Vascular indices, Model 3 – Model 1 + genetic data and Model 4 – Model 1 + genetic data+vascular indices. The area under the curve (AUC) of regression models was calculated, and their predictive accuracy was compared using the methodology described by Hannley and McNeil (c-statistic) [32]. Regarding the genetic data, the allele frequency was calculated, and departure from Hardy–Weinberg equilibrium was assessed using Fischer’s exact test. The relation of various genotypes with CAD or multivessel CAD was estimated by univariate logistic regression analysis, and dominant, recessive and additive models for genotypes were implemented. The results were adjusted for the risk of false positive findings using the false discovery rate (FDR) [33]. Based on the univariate analysis, only the dominant model for genotype analysis was used for both polymorphisms in the prediction models. P values were always two-sided, and a value of P < 005 was considered significant. The SPSS statistical software package (version 15.0 for Windows, SPSS Inc. Chicago, IL, USA) was used.
Results Demographic, anthropometric, clinical and biochemical parameters and genotype frequencies of both polymorphisms studied, in patients with significant angiographic CAD compared with those without CAD and in patients with multivessel CAD vs. single vessel disease or no CAD, are shown in Table 1. FRS was higher in the presence of significant CAD or multivessel CAD (P < 0001 for both). PWV and central pulse pressure were significantly higher in patients with significant angiographic CAD compared with patients with no CAD (P < 005 for both indices) but did not differ significantly between patients with multivessel CAD vs. patients with single vessel disease or no CAD (Fig. 1). AIx did not differ between various subgroups (Fig. 1). Both polymorphisms were in Hardy–Weinberg equilibrium (P > 005 for both). The univariate associations of genotypes (including dominant, recessive and additive model analysis for risk alleles) of studied polymorphisms with the presence of CAD and multivessel CAD are shown in Table 2. The presence of the risk allele of the polymorphism rs2943634 was associated with higher prevalence of CAD (OR 266, P = 0045, FDR
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Table 1 Baseline characteristics in patients with significant CAD and multivessel CAD Significant CAD
Age, years
Multivessel CAD
Yes, n = 84
No, n = 99
P
63 9
59 10
0022
Yes, n = 56
No, n = 127
P
63 8
60 10
0038
Smoking, n (%)
33 (39)
32 (32)
0327
23 (41)
42 (33)
0297
Male gender, n (%)
69 (82)
65 (66)
0012
48 (86)
86 (68)
0011
Family history CAD, n (%)
26 (31)
32 (32)
0843
20 (36)
38 (30)
0438
Hypertension, n (%)
74 (88)
69 (70)
0003
51 (91)
92 (52)
0005
Hypercholesterolaemia, n (%)
70 (83)
87 (88)
0380
44 (79)
113 (89)
0063
Diabetes mellitus, n (%)
41 (49)
33 (33)
0034
33 (59)
41 (32)
0001
Body mass index, kg/m
279 36
289 40
0069
278 36
288 39
0124
Waist circumference, cm
103 9
105 11
0142
102 9
105 11
0111
2
Glucose, mg/dL
116 (71, 348)
100 (68, 249)
0002
120 (83, 348)
101 (68, 249)
< 0001
eGFR, mL/min/173 m
768 141
760 134
0678
781 145
756 134
0248
Total cholesterol, mg/dL
201 44
215 47
0033
197 42
214 47
0025
2
HDL-c, mg/dL
42 (19, 75)
49 (28, 87)
< 0001
40 (19, 75)
46 (25, 87)
Triglycerides, mg/dL
147 61
129 64
0057
154 63
130 62
LDL-c, mg/dL
129 39
139 44
0141
125 36
139 44
Hs-CRP, mg/L
23 (02, 297)
24 (02, 245)
0698
24 (02, 297)
< 0001 0020 0038
23 (02, 245)
0516
Systolic blood pressure, mmHg
146 17
140 17
0031
146 17
141 17
0077
Diastolic blood pressure, mmHg
82 9
82 10
0975
82 9
82 10
0850
Framingham risk score, %
25 (7, 82)
16 (3, 70)
Homozygous risk allele
25 (31)
Heterozygous risk allele/wild type Homozygous wild type
< 0001
25 (8, 82)
16 (3, 70)
29 (29)
16 (30)
38 (30)
43 (52)
42 (43)
31 (57)
54 (43)
14 (17)
27 (28)
7 (13)
34 (27)
Homozygous risk allele
33 (40)
38 (39)
22 (41)
49 (39)
Heterozygous risk allele/wild type
43 (53)
43 (44)
27 (50)
59 (47)
6 (7)
17 (17)
5 (9)
18 (14)
< 0001
rs1333049 polymorphism, n (%)
0220
0084
rs2943634 polymorphism, n (%)
Homozygous wild type
0121
0651
Continuous data are shown as mean SD (normal distribution) or median (min, max) (not normal distribution). Bold–italic represent P-values ≤ 0.05. Abbreviations: CAD, coronary artery disease; eGFR, estimated glomerular filtration rate; HDL-c, high-density lipoprotein cholesterol; Hs-CRP, high-sensitivity C-reactive protein; LDL-c, low-density lipoprotein cholesterol. Conversion factors to SI units are the following: 00555 for glucose, 00259 for cholesterol and 00113 for triglycerides.
corrected P value=009), while the presence of the risk allele of the polymorphism rs1333049 was associated with higher prevalence of multivessel CAD (OR 248, P = 004, FDR corrected P value=008). No significant differences in metabolic and vascular parameters were observed among the various studied genotypes (Table 3).
In multivariate analysis, in fully adjusted models, higher FRS and PWV and the presence of risk allele of polymorphism rs2943634 were independent predictors of significant CAD (Nagelkerke R2 0252, P < 0001) (Table 4). Accordingly, in fully adjusted models, higher FRS and the presence of risk allele of polymorphism rs1333049 were independent predictors of
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Figure 1 Arterial stiffness indices according to the presence of coronary artery disease (CAD) and multivessel disease (MVD).
multivessel CAD (Nagelkerke R2 0190, P < 0001) (Table 4). The addition of genetic polymorphisms and vascular indices (alone or in combination) in FRS-based models did not improve significantly the predictive accuracy of these models (P > 01 for all) (Table 5).
Discussion The present study showed that in high-risk patients with probable stable CAD, polymorphisms of the loci 2q36 (rs2943634) and 9p21 (rs1333049) were independently associated with the presence and extent of CAD, respectively; the presence of the risk alleles was related to a 25–30 times greater risk. 9p21 genetic variants have been previously reported to be associated with increased prevalence of significant angiographic CAD [8,9,11] as
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well as greater CAD extent [5,9,11,12] independently of classical risk factors. In accordance with previous studies, 9p21 variants were not associated with risk factors [2,3,14,34]; only a minor association of rs1333049 risk allele with higher triglycerides (P = 0049) was currently shown. Although the 2q36 locus has been previously related to increased risk for CAD events independently of traditional risk factors [3,4], its association with the presence and extent of angiographic CAD has been little studied. In contrast to a previous study that showed no clear association of the rs2943634 polymorphism with the presence of angiographic CAD in a population similar to ours [16], we showed that the risk allele was related with higher prevalence of significant CAD. No association of rs2943634 polymorphism with HDL-c was observed in our study unlike previous reports [14,15].
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Table 2 Univariate associations of studied polymorphisms with CAD and multivessel CAD using various genotype model analysis (additive, dominant and recessive) for risk alleles SNP rs1333049
Outcome CAD
Genotype model WW* vs. WR vs. RR
OR (95% CI) †
197 (091, 428) ‡
Multivessel CAD
CAD
0235
WW* vs. WR/RR
185 (089, 382)
0098
WW/WR* vs. RR
104 (055, 198)
0896
WW* vs. WR vs. RR
†
279 (111, 704)
0030
205 (075, 557)
0161
WW* vs. WR/RR
248 (103, 602)
0040
WW/WR* vs. RR
098 (049, 196)
0975
WW* vs. WR vs. RR
†
283 (103, 788) ‡
Multivessel CAD
0085
166 (072, 385)
‡
rs2943634
P value
0041
246 (087, 697)
0090
WW* vs. WR/RR
266 (102, 709)
0045
WW/WR* vs. RR
106 (058, 194)
0841
WW* vs. WR vs. RR
†
165 (055, 490)
0370
162‡ (053, 491)
0397
WW* vs. WR/RR
163 (057, 465)
0358
WW/WR* vs. RR
108 (056, 207)
0816
*reference genotype. † WR vs. WW. ‡ RR vs. WW. Abbreviations: CAD, coronary artery disease; SNP, single nucleotide polymorphism; OR, odds ratio; CI, confidence interval; W, wild-type allele; and R, risk allele.
Despite the independent association of these polymorphisms with the presence and extent of CAD shown in our study, similar to other reports, their value in addition to risk factors in the prediction of risk for angiographic CAD has not been previously studied. In the current study, we showed that adding these polymorphisms in multivariate regression models did not significantly improve the accuracy of risk factors, as expressed by FRS, in predicting angiographic CAD. It may be possible that genetic variants may contribute the most in the prediction of CAD in subjects at low/intermediate risk, as suggested by some studies. 9p21 variants have been previously shown to improve the accuracy and reclassification over FRS in prediction of cardiovascular events, specifically in intermediate CAD risk patients [8]. Furthermore, the association of 9p21 variants (rs1333049 polymorphism) with CAD presence and severity has been reported to be most evident in younger, nondiabetic patients [5,10]. On the other hand, our population was predominantly of high risk (> 50% of our patients had FRS > 20% and ca. 40% were diabetics) and had a high prevalence of risk alleles (> 75% of our population), in contrast to their prevalence reported in the general population, that is about 50% [3]. To our
knowledge, there are no data regarding risk reclassification using 2q36 variants. A potential association of 9p21 locus with indices of arterial stiffness was explored in our population as it has been previously suggested that 9p21 may exert direct effects on vascular wall, independently of the presence of other established proatherosclerotic factors, promoting thus atherogenesis and atherosclerosis progression [5,35]. Few studies have also addressed the relationship of 9p21 locus polymorphisms with arterial stiffness; although two small studies have reported a significant association of 9p21 variants with PWV [36,37], two larger studies have not yielded any significant association of PWV with 9p21 polymorphisms [38,39], in accordance with our results. No clear association between 9p21 variants and early markers of subclinical atherosclerosis such as endotheliumdependent vasodilation and carotid intima-media thickness [40–42] has been shown, while the association of central pressures and AIx with 9p21 variants has not been studied previously. Other pathophysiological processes besides arterial stiffening may mediate the suggested direct vascular effects of 9p21 variants. Regarding the association of 2q36 variants with
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Table 3 Characteristics of patients according to the genotypes of rs1333049 and rs2943634 polymorphisms rs1333049 polymorphism RR, n = 54
WR, n = 85
WW, n = 41
WR/RR, n = 139
P*
P†
Family history CAD, n (%)
20 (37)
24 (28)
14 (34)
44 (32)
0532
0764
Hypertension, n (%)
40 (74)
70 (82)
30 (73)
110 (79)
0375
0419
Hypercholesterolaemia, n (%)
48 (89)
70 (82)
37 (90)
118 (85)
0380
0384
Diabetes mellitus, n (%)
22 (41)
36 (42)
13 (32)
58 (42)
0505
0249
Body mass index, kg/m
289 43
287 38
277 33
288 40
0269
0107
Waist circumference, cm
104 10
104 11
104 11
104 10
0918
0854
0758
0626
2
Glucose, mg/dL
111 (82, 225)
105 (68, 306)
103 (76, 234)
108 (68, 306)
eGFR, mL/min/173 m
767 144
772 146
748 112
770 140
0658
0371
Total cholesterol, mg/dL
214 47
203 42
213 52
207 45
0274
0466
0294
0169
2
HDL-c, mg/dL
46 (25, 82)
43 (19, 83)
46 (29, 87)
44 (19, 83)
Triglycerides, mg/dL
140 67
143 63
120 59
142 64
0139
0049
LDL-c, mg/dL
139 42
129 36
140 51
133 39
0246
0367
0430
0677
0761
0459
Hs-CRP, mg/L
24 (02, 245)
Systolic blood pressure, mmHg
142 17
22 (02, 297) 142 17
24 (03, 280) 144 17
23 (02, 297) 142 17
Diastolic blood pressure, mmHg
82 11
82 10
82 8
82 10
0954
0792
Pulse wave velocity, m/s
97 24
96 23
99 26
96 23
0751
0496
Augmentation index, %
255 86
235 83
232 97
243 84
0331
0487
48 12
46 10
49 10
47 11
0392
0232
0854
0575
P*
P†
Central pulse pressure, mmHg Framingham risk score, %
20 (4, 70)
20 (4, 70)
20 (3, 82)
20 (4, 70)
rs2943634 polymorphism RR, n = 71
WR, n = 86
WW, n = 23
WR/RR, n = 157
Family history CAD, n (%)
18 (25)
33 (38)
7 (30)
51 (32)
0217
0844
Hypertension, n (%)
58 (82)
65 (76)
17 (74)
123 (78)
0586
0633
Hypercholesterolaemia, n (%)
61 (86)
73 (85)
21 (91)
134 (85)
0730
0746
Diabetes mellitus, n (%)
24 (34)
36 (42)
11 (48)
60 (38)
0400
0379
Body mass index, kg/m
282 35
291 42
276 36
287 39
0167
0220
Waist circumference, cm
103 11
105 10
103 10
104 11
0545
0798
0556
0916
2
Glucose, mg/dL
105 (68, 306)
111 (82, 253)
101 (83, 234)
106 (68, 306)
eGFR, mL/min/173 m
754 138
773 130
765 168
765 134
0686
0984
Total cholesterol, mg/dL
200 48
215 42
212 55
208 45
0114
0738
0714
0670
2
HDL-c, mg/dL
43 (19, 87)
46 (27, 82)
47 (25, 72)
44 (19, 87)
Triglycerides, mg/dL
137 59
141 68
124 66
139 63
0512
0285
LDL-c, mg/dL
126 44
139 36
141 52
133 40
0103
0401
0187
0092
0479
0877
Hs-CRP, mg/L Systolic blood pressure, mmHg
790
23 (02, 297) 144 17
22 (03, 245) 141 17
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34 (05, 280) 142 15
23 (02, 297) 142 17
9P21 AND 2Q36 IN CORONARY ARTERY DISEASE
Table 3 Continued rs2943634 polymorphism P*
P†
82 10
0736
0881
102 23
96 24
0340
0263
232 91
229 81
242 88
0252
0526
46 11
46 9
47 11
0243
0663
0903
0678
RR, n = 71
WR, n = 86
Diastolic blood pressure, mmHg
83 9
82 11
82 9
Pulse wave velocity, m/s
94 19
97 27
Augmentation index, %
254 84 49 12
Central pulse pressure, mmHg Framingham risk score, %
20 (3, 56)
WW, n = 23
20 (5, 82)
18 (3, 70)
WR/RR, n = 157
20 (3, 82)
Continuous data are shown as mean SD (normal distribution) or median (min, max) (not normal distribution). * Pfor comparisons among the three individual genotypes. † Pfor the comparison between the presence of risk allele and homozygous wild type. Abbreviations: CAD, coronary artery disease; eGFR, estimated glomerular filtration rate; HDL-c, high-density lipoprotein cholesterol; Hs-CRP, high-sensitivity Creactive protein; LDL-c, low-density lipoprotein cholesterol; R, risk allele; and W, wild-type allele. Conversion factors to SI units are the following: 00555 for glucose, 00259 for cholesterol and 00113 for triglycerides.
Table 4 Independent predictors of significant and multivessel CAD in each model OR
95% CI
P
Nagelkerke R2
0197
Presence of significant angiographic CAD FRS
FRS*, %
3811
2212, 6567
< 0001
FRS + Vascular indices
FRS*, %
3518
2003, 6179
< 0001
PWV, m/s
1157
1004, 1333
0044
FRS*, %
3956
2257, 6934
< 0001
rs2943634 risk allele
2882
1000, 8301
0050
FRS*, %
3568
2002, 6358
< 0001
PWV, m/s
1184
1019, 1376
0027
rs2943634 risk allele
3297
1135, 9583
0028
FRS
FRS*, %
3504
1970, 6232
< 0001
0161
FRS + Vascular indices
FRS*, %
3504
1970, 6232
< 0001
0161
FRS + Genes
FRS*, %
3551
1964, 6418
< 0001
rs1333049 risk allele
2538
1000, 6447
0050
FRS*, %
3551
1964, 6418
< 0001
rs1333049 risk allele
2538
1000, 6447
0050
FRS + Genes
FRS + Genes + Vascular indices
0221
0227
0252
Presence of angiographic multivessel CAD
FRS + Genes + Vascular indices
0190
0190
*Natural logarithm-transformed values due to not normal distribution. Abbreviations: CAD, coronary artery disease; CI, confidence interval; FRS, Framingham risk score; OR, odds ratio; and PWV, pulse wave velocity.
subclinical atherosclerosis indices, no association of rs2943634 polymorphism with carotid intima-media thickness, aortic calcium and ankle-brachial index was found [13,42], while its relation to arterial stiffness indices has not been previously studied. Based on the lack of association between genetic variants and arterial stiffness indices, we set to investigate whether the
addition of both genetic data and vascular indices could significantly enhance prediction of CAD. Initially, we investigated the role of vascular indices alone in the prediction of CAD. We showed that increasing PWV was an independent predictor of the presence of angiographic CAD in our population similar to previous reports [19–22,43]. No association of PWV with CAD extent was found in contrast with previous smaller studies
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Table 5 Accuracy of models predicting the presence and extent of CAD using FRS, vascular indices and studied polymorphisms AUC
Standard error
P*
P†
Presence of significant angiographic CAD FRS
0727
0038
< 0001
–
FRS + Vascular indices
0731
0037
< 0001
0162
FRS + Genes
0747
0037
< 0001
0138
FRS + Genes + Vascular indices
0756
0036
< 0001
0107
Presence of angiographic multivessel CAD FRS
0718
0040
< 0001
–
FRS + Vascular indices
0718
0040
< 0001
10
FRS + Genes
0739
0039
< 0001
0150
FRS + Genes + Vascular indices
0739
0039
< 0001
0150
*P values for AUC in each model. † P values for comparison vs. the model with FRS only. Bold–italic represent P-values ≤ 0.05. Abbreviations: AUC, area under curve; CAD, coronary artery disease; and FRS, Framingham risk score.
[20,22]. No association of other vascular indices with angiographic CAD was observed; conflicting results have been reported using AIx [21,25,44]. Adding PWV to a regression model with classical risk factors (i.e. FRS) did not significantly improve the predictive accuracy of FRS for the presence or extent of CAD. To our knowledge, only one previous study in patients with a history of stroke reported an improvement in the prediction of CAD presence with the use of PWV beyond FRS [43]. Finally, when fully adjusted regression models (i.e. including FRS, genetic variants and arterial stiffness indices) were used, prediction of the CAD presence/extent was not improved compared with FRS alone. This has not been studied before; only a modest improvement in prediction of coronary events was shown in intermediate-risk patients when adding 9p21 polymorphisms on carotid intima-media thickness and classical risk factors [45].
Limitations The main limitation of our study is the small sample size; further larger studies are needed to confirm our findings. This was an observational study in a group of patients with suspected stable CAD, and thus, conclusions are probably limited to this population. Subjects undergoing invasive coronary angiography are usually symptomatic or at high risk and may not be representative of the general population. Variations in referral
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patterns for angiography could also be a source of selection bias. The use of coronary angiography compared with other modalities (i.e. intravascular ultrasound) to assess coronary atherosclerosis is limited by the fact that substantial disease burden in arterial walls can be missed. Our study enrolled only white Caucasians, and thus, our findings may not be applicable to populations of other descent. Other measures of subclinical atherosclerosis such as endothelium-dependent vasodilation or carotid intima-media thickness were not currently measured. In conclusion, rs2943634 and rs1333049 polymorphisms and carotid–femoral PWV were associated with presence and extent of CAD independent of risk factor burden as assessed by FRS in high-risk patients with suspected stable CAD undergoing diagnostic coronary angiography. In this high-risk population, the addition of genetic data and arterial stiffness indices, alone or in combination, to FRS did not improve the prediction of CAD presence or extent compared with FRS alone. Further studies are needed to confirm our findings and clarify the role of genetic data and noninvasive arterial stiffness indices in the prediction of coronary atherosclerosis.
Sources of funding The study was partly funded by a grant from the Research Committee of University of Ioannina (project code 80010). Acknowledgements We thank Clinical and Molecular Epidemiology Dr Evagelia Ntzani, Assistant Professor in the Unit, Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece for valuable help and statistical advice. Conflict of interest The authors declare that they have no conflict of interest. Contributions KV, KKN, CSK and LKM were involved in study conception and design. KV, KKN, KP, CSK and LKM participated in enrolment of patients and acquisition of data. AC, KG, PV, GK and IG participated in the analysis of blood samples for the determination of metabolic parameters, hsCRP and genetic polymorphisms. AB and KKN performed statistical analysis, while AB, KKN and LKM assisted in the interpretation of the study findings. All authors were involved in drafting the article or revising it and approved the final version of the manuscript. Address Michaelidion Cardiac Center and Department of Cardiology, University of Ioannina, Ioannina, Greece (K. Vakalis, A. Bechlioulis, K. K. Naka, K. Pappas, C. S. Katsouras, L. K. Michalis); Genetics Unit, Department of Obstetrics and Gynaecology,
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9P21 AND 2Q36 IN CORONARY ARTERY DISEASE
University of Ioannina, Ioannina, Greece (A. Chatzikyriakidou, I. Georgiou); Laboratory of Microbiology, Medical School, University of Ioannina, Ioannina, Greece (K. Gartzonika); Laboratory of Physiology, Medical School, University of Ioannina, Ioannina, Greece (P. Vezyraki); Laboratory of Biochemistry, University Hospital of Ioannina, Ioannina, Greece (G. Kolios). Correspondence to: Professor Lampros K. Michalis, MD, Department of Cardiology and Michaelidion Cardiac Centre, University of Ioannina, Ioannina 45 110, Greece. Tel.: (+30) 26510 07710; fax: (+30) 26510 07865; e-mail: [email protected] Received 12 February 2014; accepted 13 June 2014 References 1 Roger VL, Go AS, Lloyd-Jones DM, Benjamin EJ, Berry JD, Borden WB et al. Heart disease and stroke statistics–2012 update: a report from the American Heart Association. Circulation 2012;125: e2–220. 2 McPherson R, Pertsemlidis A, Kavaslar N, Stewart A, Roberts R, Cox DR et al. A common allele on chromosome 9 associated with coronary heart disease. Science 2007;316:1488–91. 3 Samani NJ, Erdmann J, Hall AS, Hengstenberg C, Mangino M, Mayer B et al. Genomewide association analysis of coronary artery disease. N Engl J Med 2007;357:443–53. 4 Franceschini N, Carty C, Buzkova P, Reiner AP, Garrett T, Lin Y et al. Association of genetic variants and incident coronary heart disease in multiethnic cohorts: the PAGE study. Circ Cardiovasc Genet 2011;4:661–72. 5 Dandona S, Stewart AF, Chen L, Williams K, So D, O’Brien E et al. Gene dosage of the common variant 9p21 predicts severity of coronary artery disease. J Am Coll Cardiol 2010;56:479–86. 6 Helgadottir A, Thorleifsson G, Magnusson KP, Gretarsdottir S, Steinthorsdottir V, Manolescu A et al. The same sequence variant on 9p21 associates with myocardial infarction, abdominal aortic aneurysm and intracranial aneurysm. Nat Genet 2008;40:217–24. 7 Matarin M, Brown WM, Singleton A, Hardy JA, Meschia JF. Whole genome analyses suggest ischemic stroke and heart disease share an association with polymorphisms on chromosome 9p21. Stroke 2008;39:1586–9. 8 Anderson JL, Horne BD, Kolek MJ, Muhlestein JB, Mower CP, Park JJ et al. Genetic variation at the 9p21 locus predicts angiographic coronary artery disease prevalence but not extent and has clinical utility. Am Heart J 2008;156:1155–62 e2. 9 Patel RS, Su S, Neeland IJ, Ahuja A, Veledar E, Zhao J et al. The chromosome 9p21 risk locus is associated with angiographic severity and progression of coronary artery disease. Eur Heart J 2010;31:3017–23. 10 Chan K, Motterle A, Laxton RC, Ye S. Common variant on chromosome 9p21 predicts severity of coronary artery disease. J Am Coll Cardiol 2011;57:1497–8; author reply 8–9. 11 Chan K, Patel RS, Newcombe P, Nelson CP, Qasim A, Epstein SE et al. Association between the chromosome 9p21 locus and angiographic coronary artery disease burden: a collaborative metaanalysis. J Am Coll Cardiol 2013;61:957–70. 12 Chen SN, Ballantyne CM, Gotto AM Jr, Marian AJ. The 9p21 susceptibility locus for coronary artery disease and the severity of coronary atherosclerosis. BMC Cardiovasc Disord 2009;9:3.
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