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Medical Science Monitor: International Medical Journal of Experimental and Clinical Research logoLink to Medical Science Monitor: International Medical Journal of Experimental and Clinical Research
. 2014 Sep 30;20:1758–1767. doi: 10.12659/MSM.892091

Correlation of PCSK9 Gene Polymorphism with Cerebral Ischemic Stroke in Xinjiang Han and Uygur Populations

Dengfeng Han 1,A,B,C,D,E,F,*, Jianhua Ma 1,A,B,C,D,*, Xiaoning Zhang 1,A,D,G,✉, Jian Cai 1,B,C, Jinlan Li 1,B,F, Tuerhong Tuerxun 1,B,F, Chenguang Hao 1,B, Lei Du 1,B, Jing Lei 1,B
PMCID: PMC4189717  PMID: 25266949

Abstract

Background

Cerebral ischemic stroke (CIS) is a major cause of morbidity and mortality. Its main pathological basis is atherosclerosis (AS); in turn, the main risk factor in AS is dyslipidemia. Human proprotein convertase subtilisin/kexin9 (PCSK9) plays a key role in regulating plasma low-density lipoprotein (LDL) cholesterol levels. We sought to assess the association between PCSK9 and CIS in Chinese Han and Uygur populations.

Material/Methods

We selected 408 CIS patients and 348 control subjects and used a single-base terminal extension (SNaPshot) method to detect the genotypes of the 20 single-nucleotide polymorphisms (SNPs) in PCSK9.

Results

Distribution of SNP8 (rs529787) genotypes showed a significant difference between CIS and control participants (P=0.049). However, when analyzing Han and Uygur populations separately, we found that only Han subjects showed distribution of SNP1 (rs1711503), SNP2 (rs2479408), and SNP8 (rs529787) alleles that was significantly different between CIS and control participants (P=0.028, P=0.013, P=0.006, respectively), and distribution of SNP2 (rs2479408) in the dominant model (CC vs. CG + GG) was significantly different between CIS and control participants (P=0.013), even after adjustment for covariates (OR: 75.262, 95% confidence interval [CI]: 7.232–783.278, P<0.001). Distribution of the 2 haplotypes (A-C and G-C) (rs1711503 and rs2479408) was significantly different between CIS and control participants (both, P=0.011).

Conclusions

Both rs1711503 and rs2479408 of PCSK9 genes were associated with CIS in the Han population of China. A-C haplotype may be a genetic marker of CIS risk in this population.

MeSH Keywords: Atherosclerosis, Ethnic Groups, Ischemic Attack, Transient, Proprotein Convertases

Background

Cerebral ischemic stroke (CIS) is a major cause of morbidity and mortality, and is expected to remain so until at least 2030 [1]. CIS and coronary heart disease (CHD) are major manifestations of atherosclerotic processes. High plasma levels of low-density lipoprotein cholesterol (LDL-C) have consistently been shown to be a risk factor for the development of atherosclerosis [2]. Plasma concentrations of LDL-C are determined primarily by the activity of the LDL receptor (LDLR) in the liver. Proprotein convertase subtilisin-like kexin type 9 (PCSK9) was recently discovered to be a major factor in cholesterol homeostasis through enhanced degradation of LDLR [3–6] and possibly in neural development. However, both rare mutations and common variants in the coding regions of PCSK9 can affect LDL cholesterol levels and stroke risk. Recent studies identified several PCSK9 variants influencing circulating LDL-C levels [7,8]. Since the first identification mutation of PCSK9 was implicated in autosomal dominant hypercholesterolemia by Abifadel [9], more than 53 missense variants have been identified. A common SNP, E670G (rs505151) in exon 12 of PCSK9, results in the substitution of glutamate for a glycine residue at position 670 in the protein [10] Carriers of 670 Gln in the general population presented increased plasma TC, LDL-C, and ApoB levels. Another study suggested a key role played by the E670G polymorphism in determining plasma LDL-C levels and the severity of coronary atherosclerosis in the United States [11]. More recently, the presence of the 670G allele was significantly associated with an increased risk of large-vessel atherosclerosis (LVA) stroke [12] and intimal media thickness (IMT) [13]. However, these studies were inconsistent with previous studies [14–16], which were conducted in Caucasian and African populations and failed to find this association. Furthermore, the carriers of 670G showed significantly increased LDL in men but not in women in a European population [17]. In addition, the rs72555377 insertion polymorphism in exon 1 of PCSK9 is associated with lower LDL-C in Caucasian populations [18], while the L11 allele, with insertion of 2 Leucines, is associated with higher LDL-C [11], and rs562556 (Ile474Val) in exon9 of the PCSK9 gene is associated with approximately 7% lower LDL cholesterol levels in carriers in a Japanese population [19].

In our study, we used a single-base terminal extension (SNaPshot) method to detect the genotypes of the 20 single-nucleotide polymorphisms (SNP) in the PCSK9 gene to assess the association between the human PCSK9 gene polymorphism and CIS in members of the Han and Uygur populations of China.

Material and Methods

Ethics approval of the study protocol

Written informed consent was obtained from all participants. All participants explicitly provided permission for DNA analyses as well as collection of relevant clinical data. This study was approved by the Ethics Committee of the First Affiliated Hospital of Xinjiang Medical University, Urumqi, China (NO. 20120510). It was conducted according to the standards of the Declaration of Helsinki.

Subjects

Subjects were from a Han population and a Uygur population who lived in the Xinjiang Uygur Autonomous Region of China. We recruited the CIS group from the First Affiliated Hospital of Xinjiang Medical University Neurology Department between since October 2011 and May 2012, and the control group came from the same hospital in the same period.

In the CIS group, there were 408 CIS patients (158 Uygur, 250 Han), mean age 61.97±11.80 years. Inclusion criteria were: (1) diagnosed in accordance with the standards set at 10 international classifications of diseases (ICD10); (2) confirmed by MRI. Exclusion criteria were: (1) patients with CHD; (2) hemorrhagic cerebrovascular disease confirmed by CT or MRI; (3) refused to participate in trials.

In the Control group there were 348 of healthy controls (149 Uygur, 199 Han), mean age 61.84±11.65 years. Inclusion criteria were: (1) aged >40; (2) no known family history of cerebrovascular disease; (3) the cardiopulmonary physical examination and nervous system examination did not find abnormalities; (4) MRI negative except for cerebrovascular disease. Exclusion criteria: acute or chronic infection, malignant tumor, autoimmune diseases.

Clinical characteristics of the study participants

All patients completed the standard test registration form, and disclosed the following data: (1) General information: age, sex, race. (2) Personal history: smoking history (daily average smoking, smoking an average of ≥1 day or more, time >1 year, defined as smoking), (drinking alcohol an average of ≥3 times per week, more than 50 g each time >1 year, defined as drinking), hypertension, diabetes, hyperlipidemia, transient ischemic attack (TIA), atrial fibrillation (AF), heart valve disease, heart valve replacement, peripheral vascular disease. Hypertension: the Seventh World Health Organization /International Society of Hypertension League Conference defined the new standard for the diagnosis of hypertension; in our study, the diagnosis of hypertension was established if patients were treated with antihypertensive medication or if the mean of 3 measurements of systolic blood pressure (SBP) >140 mm Hg or diastolic blood pressure (DBP) >90 mm Hg, respectively. Diabetes mellitus was diagnosed according to the criteria of the American Diabetes Association [20]. Individuals with daytime random blood glucose ≥11.1 mmol/l or after fasting glucose ≥7.0mmol/l or glucose in line 2 h ≥11.1mmol/l or with a history of diabetes or treatment with insulin were considered diabetic. (3) Medical history prior to admission: treatment with antihypertensive drugs, antiplatelet drugs and anticoagulants, diabetes, lipid drug, anti-seizure medication, birth control pills, hormones. (4) Family history: whether grandparents, parents, siblings, and children had hypertension, diabetes, cerebral hemorrhage, cerebral infarction, myocardial infarction, coronary heart disease, or arrhythmia incidence. (5) Physical examination: height, weight, blood pressure, pulse, temperature. (6) Special tests: electrocardiogram, chest X-ray, heart neck ultrasound, blood routine, blood glucose, blood lipids.

Biochemical analysis

Serum concentrations of total cholesterol (TC), triglyceride (TG), glucose (Glu), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), apolipo protein A1 (ApoA1), apolipo protein B (ApoB), and apolipo lipoprotein a (ApoLpa) were measured using standard methods in the Central Laboratory of First Affiliated Hospital of Xinjiang Medical University.

Blood collection and DNA extraction

Fasting blood samples (5 mL) drawn by venipuncture were taken from all participants early in the morning. The blood samples were drawn into a 5-mL ethylene diamine tetraacetic acid (EDTA) tube and centrifuged at 4000×g for 5 min to separate the plasma content. Genomic DNA was extracted from the peripheral leukocytes using standard phenol-chloroform method. The DNA samples were stored at −80°C until use, then diluted to 50 ng/μL concentration.

SNaPshot Reactions

We selected the genotypes of the 20 SNPs in the PCSK9 gene using the Haploview 4.2 software and the HapMap phrase II database by using minor allele frequency (MAF) ≤0.1 and linkage disequilibrium patterns with r2 ≥0.5 as a cut-off. The position of the 20 SNPs was by order of increasing distance from the gene PCSK9 5′ end (Table 1). We used single-base terminal extension (SNaPshot) method to genotype. SNaPshot reactions were performed as described by the manufacturer (Applied Biosystems, Warrington, UK). Briefly, 4.0-μl of PCR product was incubated at 37°C for 60 min with 2-U shrimp alkaline phosphatase (SAP) and 2-U Exonuclease I (ExoI). Following a 15-min incubation to inactivate the enzymes, 1 ul of digested PCR product was mixed with 5 ul of ready reaction premix, 1 ul of 1.0- UM primer (Table 1), and 3 ul of dH2O. This mixture was placed in the thermal cycler and underwent 25 cycles of 96°C for 10 s, 50°C for 5 s, and 60°C for 30 s. When completed, 0.5-U SAP was added and the reaction mixture was incubated for 60 min. Prior to loading onto the PRISM 310, 10 ul of formamide was added to 1 ul of reaction mixture and samples were heated to 95°C for 5 min.

Table 1.

Genotype and allele distributions of the twenty SNPs in patients with CIS and control subjects.

SNP Chr. 1 postion Function dbSNP allele MAF Total Han Uygur
CIS Control P value CIS Control P value CIS Control P value
1 rs17111503 55503448 5′ near gene Upstream variant 2KB A/G 0.3375 Genotype AA 81 59 0.223 42 22 0.094 39 37 0.757
SNP1 AG 197 158 115 86 82 72
GG 130 131 93 91 37 40
Allele A 359 276 0.088 199 130 0.028* 160 146 0.684
G 457 420 301 268 156 152
2 rs2479408 55504118 5′ near gene Upstream variant 2KB C/G 0.1708 Genotype CC 385 314 0.064 249 192 0.013* 136 122 0.446
SNP2 CG 21 33 1 7 20 36
GG 2 1 0 0 2 1
Allele C 791 661 0.050 499 391 0.013* 292 270 0.423
G 25 35 1 7 24 28
rs2479409 55504650 5′ near gene Upstream variant 2KB A/G 0.4362 Genotype AA 75 58 0.789 34 24 0.864 41 34 0.715
3 SNP3 AG 190 162 110 87 80 75
GG 143 128 106 88 37 40
Allele A 340 278 0.496 178 135 0.599 162 143 0.416
G 476 418 322 263 154 155
4 rs11583680 55505668 Exon 1 Missense (V-A) T/C 0.0905 Genotype CC 328 280 0.237 194 159 0.099 134 121 0.673
SNP4 CT 78 62 56 37 22 25
TT 2 6 0 3 2 3
Allele C 734 622 0.710 444 355 0.850 290 267 0.350
T 82 74 56 43 26 31
5 rs10888896 555509213 Intron 1 Intron variant C/G 0.2374 Genotype CC 326 280 0.797 203 169 0.360 123 111 0.782
SNP5 CG 76 61 45 27 31 34
GG 6 7 2 3 4 4
Allele C 728 621 0.995 451 365 0.436 277 256 0.521
G 88 75 49 33 39 42
6 rs4927193 55509872 Intron 2 Intron variant C/T 0.1377 Genotype CC 2 5 0.347 0 3 0.097 2 2 0.863
SNP6 CT 82 64 59 39 23 25
TT 324 279 191 157 133 122
Allele C 86 74 0.953 59 45 0.818 27 29 0.609
T 730 622 441 353 289 269
7 rs499718 55512549 Intron 3 Intron variant C/T 0.247 Genotype CC 276 240 0.829 154 130 0.564 122 110 0.778
SNP7 CT 116 97 86 64 30 33
TT 16 11 10 5 6 6
Allele C 668 577 0.570 394 324 0.332 274 253 0.520
T 148 119 106 74 42 45
8 rs529787 55513521 Intron 3 Intron variant C/G 0.1166 Genotype CC 384 312 0.049* 249 191 0.006* 135 121 0.452
SNP8 CG 22 35 1 8 21 27
GG 2 1 0 0 2 1
Allele C 790 659 0.039* 499 390 0.006* 291 269 0.426
G 26 37 1 8 25 29
9 rs11206514 55516004 Intron 3 Intron variant A/C 0.4096 Genotype AA 248 212 0.670 152 124 0.899 96 88 0.727
SNP9 AC 141 115 89 67 52 48
CC 19 21 9 8 10 13
Allele A 637 539 0.772 393 315 0.842 244 224 0.551
C 179 157 107 83 72 74
10 rs572512 55517344 Intron 3 Intron variant C/T 0.4596 Genotype CC 54 40 0.711 26 14 0.365 28 26 0.993
SNP10 CT 171 144 102 78 69 66
TT 183 164 122 107 61 57
Allele C 279 224 0.469 154 106 0.171 125 118 0.991
T 537 472 346 292 191 180
11 rs2479413 55518682 Intron 5 Intron variant C/T 0.3191 Genotype CC 225 193 0.090 141 124 0.183 84 69 0.054
SNP11 CT 150 140 95 70 55 70
TT 33 15 14 5 19 10
Allele C 600 526 0.363 377 318 0.109 223 208 0.834
T 216 170 123 80 93 90
12 rs7552841 55518752 Intron 5 Intron variant C/T 0.284 Genotype CC 264 235 0.602 176 143 0.886 88 92 0.502
SNP12 CT 126 96 65 48 61 48
TT 18 17 9 8 9 9
Allele C 654 566 0.564 417 334 0.834 237 232 0.406
T 162 130 83 64 79 66
13 rs557435 55520864 Intron 5 Intron variant A/G 0.1662 Genotype AA 5 1 0.242 1 0 0.668 4 1 0.260
SNP13 AG 56 56 32 25 24 31
GG 347 291 217 174 130 117
Allele A 66 58 0.862 34 25 0.755 32 33 0.703
G 750 638 466 373 284 265
14 rs693668 55521109 Intron 5 Intron variant A/G 0.3912 Genotype AA 212 189 0.772 131 116 0.441 81 73 0.908
SNP14 AG 164 135 100 71 64 64
GG 32 24 19 12 13 12
Allele A 588 513 0.472 362 303 0.205 226 210 0.774
G 228 183 138 95 90 88
15 R434W 5552339? Exon 8 Missense (R-W) C/T / Genotype CC 408 348 250 199 158 149
SNP15 CT
TT
Allele C 816 696 500 199 316 298
T
16 rs540796 55524197 Exon 9 Synonymous codon (V-V) G/A 0.1354 Genotype AA 1 2 0.585 0 0 0.716 1 2 0.340
SNP16 AG 29 20 5 5 24 25
GG 378 326 245 194 133 132
Allele A 31 24 0.716 5 5 0.714 26 19 0.378
G 785 672 495 393 290 279
17 rs149311926 55525315 Exon 10 Missense (E-Q) G/C 0.0005 Genotype CC 408 348 250 199 158 149
SNP17 CG
GG
Allele C 816 696 500 398 316 298
G
18 rs483462 55525400 Intron 10 Intron variant A/G 0.3223 Genotype AA 279 234 0.939 170 135 0.907 109 99 0.837
SNP18 AG 116 102 73 57 43 45
GG 13 12 7 7 6 5
Allele A 674 570 0.721 413 327 0.863 261 243 0.734
G 142 126 87 71 55 55
19 rs10465832 55528807 Intron 11 Intron variant C/G 0.1483 Genotype CC 2 3 0.778 1 2 0.654 1 1 0.419
SNP19 CG 75 67 54 39 21 28
GG 331 278 195 158 136 120
Allele C 79 73 0.603 56 43 0.850 23 30 0.218
G 737 623 444 355 293 268
20 rs505151 55529187 Exon 12 Missense (E-G) A/G 0.0983 Genotype AA 365 310 0.878 219 179 0.537 146 131 0.399
SNP20 AG 41 37 30 20 11 17
GG 2 1 1 0 1 1
Allele A 771 657 0.940 468 378 0.380 303 279 0.207
G 45 39 32 20 13 19
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Statistical analysis

All continuous variables (e.g., age, BMI, pulse, and cholesterol levels) are presented as means ± standard deviation (S.D.). The difference between the CIS and control groups was analyzed using an independent-sample T-test. The differences in the frequencies of sex, hypertension, diabetes mellitus, smoking, drinking, and genotypes were analyzed using chi-square test or Fisher’s exact test, as appropriate. Hardy-Weinberg equilibrium was assessed by chi-square analysis. Logistic regression analyses with effect ratios (odds ratio [OR] and 95% CI) were used to assess the contribution of the major risk factors. All statistical analyses were performed using SPSS 17.0 for Windows (SPSS Institute, Chicago, USA). Haplotypes were estimated using the SHEsis platform [21,22]. P-values of less than 0.05 were considered to be statistically significant.

Results

Table 2 showed the clinical characteristics of the CIS patients (n=408) and control participants (n=348). For all Han and Uygur subjects, there were no significant differences in age and sex between CIS patients and control subjects, indicating the study was an age- and sex-matched case-control study. We observed several differences between the groups of patients. As expected, several common risk factors for CIS were significantly different between the 2 subgroups: Glu, low HDL-C, high LDL-C, EH, and DM. Other CIS risk factors, such as high TC, TG levels, and cigarette smoking and drinking, were not significantly different.

Table 2.

Characteristics of study participants.

Total Han Uygur
Stroke patients Control subjects p Value Stroke patients Control subjects p Value Stroke patients Control subjects p Value
Number (n) 408 348 250 199 158 149
Sex(M/W) 242/166 183/165 0.063 144/106 102/97 0.183 98/60 81/68 0.203
Age (years) 61.97±11.80 61.84±11.65 0.885 63.56±11.37 62.35±11.79 0.269 59.44±12.01 61.17±11.45 0.198
BMI (kg/m2) 24.67±3.36 24.51±2.93 0.508 24.30±3.30 24.20±3.13 0.728 25.23±3.37 24.93±2.60 0.386
Glu (mmol/L) 6.90±3.30 5.45±2.68 <0.001* 6.86±3.12 5.24±1.44 <0.001* 6.98±3.55 5.74±3.73 <0.003*
TG (mmol/L) 1.90±1.12 2.04±1.30 0.122 1.81±1.08 1.90±1.21 0.406 2.03±1.17 2.22±1.40 0.221
TC (mmol/L) 4.38±0.96 4.27±1.24 0.182 4.35±0.95 4.43±1.24 0.444 4.42±0.98 4.06±1.22 0.004*
HDL (mmol/L) 1.05±0.35 1.36±0.90 <0.001* 1.07±0.26 1.35±0.84 <0.001* 1.02±0.44 1.37±0.98 <0.001*
LDL (mmol/L) 2.76±0.88 2.52±0.78 <0.001* 2.68±0.86 2.51±0.78 0.038* 2.87±0.89 2.52±0.79 <0.001*
ApoA1 (mmol/L) 1.25±0.27 1.22±0.35 0.216 1.27±0.22 1.24±0.30 0.310 1.21±0.32 1.18±0.40 0.538
ApoB (mmol/L) 0.89±0.76 0.89±0.61 0.909 0.90±0.75 0.90±0.79 0.986 0.87±0.24 0.88±0.23 0.612
ApL(a) (mmol/L) 195.27±146.14 172.94±113.84 0.019* 199.72±146.08 192.68±136.62 0.602 188.20±146.42 146.57±64.73 0.001*
EH (Y/N) 284/118 98/246 <0.001* 175/72 54/143 <0.001* 129/46 44/103 <0.001*
DM (Y/N) 125/269 65/272 <0.001* 78/164 31/168 <0.001* 47/105 34/104 0.242
Smoke (Y/N) 117/279 86/250 0.234 79/169 51/144 0.208 38/110 35/106 0.893
Drinking(Y/N) 0.204 44/290 0.161 43/204 26/167 0.292 23/120 18/123 0.500
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BMI – body mass index; BUN – blood urea nitrogen; Glu – glucose; TG – triglyceride; TC – total cholesterol; HDL – high density lipoprotein; LDL – low density lipoprotein; EH – essential hypertension; DM – diabetes mellitus. Continuous variable were expressed as mean ± standard deviation. P value of continuous variables was calculated by independent T-T test. The P value of categorical variable was calculated by Fisher’s exact test.

*

P<0.05.

Table 1 shows the basic information and the distribution of genotypes and alleles of the 20 SNPs for the PCSK9 gene. The position of the 20 SNPs was by order of increasing distance from the gene PCSK9 5′ end. We observed that the distribution of genotypes and alleles of 3 SNPs (SNP1, SNP2, and SNP8) were significantly different between CIS group and control participants. All SNPs were consistent with Hardy-Weinberg expectations (data not shown).

The 3 SNPs among the 3 groups (Total, Han, and Uyghur) were examined by Hardy-Weinberg equilibrium test and no significant differences were found in these 3 groups (data not shown).

In the study, we confirmed the distribution of genotypes and alleles of the 3 SNPs (SNP1, SNP2, and SNP8) for the PCSK9 gene. For SNP1 (rs17111503), the distribution of alleles showed a significant difference between CIS and control participants (P=0.028) in the Han group, but not in the total group and Uygur group. For SNP2 (rs2479408), the distribution of alleles, the dominant model (CC vs. CG + GG), and the additive model (CG vs. CC + GG) showed a significant difference between CIS and control participants in total and Han groups, but not in the Uygur group. C allele of rs2479408 was significantly higher in CIS patients than in control participants (total: 96.94% vs. 94.97%; Han: 99.80% vs. 98.24%). For SNP3 (rs529787), the distribution of alleles, the dominant model (CC vs. CG + GG) and the additive model (CG vs. CC + GG) showed a significant difference between CIS and control participants in the total and Han groups, but not in the Uygur group. C allele of rs529787 was significantly higher in CIS patients than in control participants (Total: 96.81% vs. 94.68%; Han: 99.80% vs. 97.99%) (data no shown).

Table 3 and Figure 1 show patterns of linkage disequilibrium in the PCSK9 gene, with their |D′| and r2 values. |D′| values from 0.7 to 1 indicate strong LD between a pair of SNPs. |D′| values from 0.25 to 0.7 indicate moderate LD and |D′| values of 0–0.25 indicate low LD. In the study, 3 strong LD patterns were observed between SNP1 and SNP2 (|D′|=0.999), SNP2 and SNP8 (|D′|=0.983), and SNP1 and SNP8 (|D′|=0.999). We consider that all 3 SNPs were located in 1 haplotype block. The r2 value of SNP2–SNP8 >0.5 means the SNP2 and SNP8 can replace each other [11] and they cannot construct haplotypes simultaneously. Therefore, given that the position of SNP1 and SNP2 are both in 2KB upstream of PCSK9 gene and the position of SNP8 is in intron3, we constructed the haplotypes using SNP1 and SNP2.

Table 3.

Pairwise linkage disequilibrium (| D’| above diagonal and r2 below diagonal) for the three SNPs.

Total Han Uygur
|D′| |D′| |D′|
SNP SNP1 SNP2 SNP8 SNP SNP1 SNP2 SNP8 SNP SNP1 SNP2 SNP8
r2 SNP1 0.999 0.999 SNP1 1.000 0.988 SNP1 0.999 0.999
SNP2 0.057 0.983 SNP2 0.016 1.000 SNP2 0.093 0.979
SNP8 0.060 0.918 SNP3 0.017 0.888 SNP8 0.097 0.919
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Figure 1.

Figure 1

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Pairwise estimates of linkage disequilibrium (LD) between each PCSK9 polymorphism were plotted using SHEsis platform. Each polymorphism is numbered according to its position in the PCSK9 gene as presented (left shows |D′| and right shows r2).

Table 4 shows the distribution of haplotypes in CIS patient and control participants. There were 4 haplotypes established in all subjects. The overall distribution of the haplotypes were significantly different between the CIS patients and the control subjects (all P<0.05). The most frequent haplotype in this study was A-C haplotype. For Han, the frequency of A-C was significantly higher in the CIS patients than in the control subjects (P=0.0011). In addition, the frequency of the G-C haplotype was lower in the CIS patients than in the control subjects (P=0.0011).

Table 4.

Haplotype analysis of the two SNPs (rs17111503 and rs2479408).

Haplotype Case (freq) Control (freq) Odds Ratio [95% CI] P
Total AC 334.02 (0.409) 241.01 (0.346) 1.308 [1.061–1.613] 0.012*
AG 24.98 (0.031) 34.99 (0.050) 0.597 [0.353–1.007] 0.051
GC 456.98 (0.560) 419.99 (0.603) 0.837 [0.681–1.027] 0.088
GG 0.02 (0.000) 0.01 (0.000)
Han AC 198.05 (0.396) 123.00 (0.309) 1.434 [1.085–1.895] 0.011*
AG 0.95 (0.002) 7.00 (0.018)
GC 300.95 (0.602) 268.00 (0.673) 0.697 [0.528–0.922] 0.011*
GG 0.05 (0.000) 0.00 (0.000)
Uygur AC 136.01 (0.430) 118.01 (0.396) 1.153 [0.836–1.590] 0.387
AG 23.99 (0.076) 27.99 (0.094) 0.792 [0.448–1.401] 0.423
GC 155.99 (0.494) 151.99 (0.510) 0.936 [0.682–1.285] 0.685
GG 0.01 (0.000) 0.01 (0.000)
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All those frequency<0.03 will be ignored in analysis.

Table 5 showed that multiple logistic regression analyses were performed with age, sex, BMI, HDL-C, LDL-C, TC, TG, ApoA1 ApoB, ApoLpa, EH, DM, and smoking and drinking, because these variables were the major confounding factors for CIS. The significant difference of the dominant model (CC vs. CG + GG) of rs2479408 was retained after adjustment for covariates in the Han, but not in the Uygur group (OR: 75.262, 95% confidence interval [CI]: 7.232–783.278, P<0.001).

Table 5.

Multiple logistic regression analysis for stoke patients and control subjects.

Total Han Uygur
OR 95% CI P OR 95% CI P OR 95% CI P
Lower Upper Lower Upper Lower Upper
rs2479408 (CC/CG+GG) 10.544 3.336 33.328 0.000* 75.262 7.232 783.278 0.000* 2.229 0.449 11.060 0.327
sex 10.544 3.336 33.328 0.613 1.147 0.651 2.019 0.635 1.045 0.558 1.956 0.891
age 1.001 0.986 1.016 0.901 0.997 0.976 1.018 0.762 1.017 0.991 1.043 0.196
BMI 0.981 0.924 1.041 0.522 0.983 0.905 1.068 0.686 0.987 0.897 1.086 0.789
TG 1.109 0.953 1.291 0.181 1.228 0.981 1.537 0.073 1.118 0.882 1.418 0.356
TC 1.031 0.851 1.250 0.756 1.239 0.933 1.646 0.139 0.715 0.508 1.008 0.055
HDL-C 1.783 1.288 2.468 0.000* 2.568 1.413 4.666 0.002* 1.297 0.854 1.970 0.223
LDL-C 0.685 0.528 0.889 0.004* 0.660 0.453 0.961 0.030* 0.752 0.483 1.169 0.205
APOA1 0.990 0.556 1.762 0.974 0.744 0.269 2.061 0.570 1.348 0.617 2.945 0.453
APOB 1.103 0.889 1.370 0.373 1.114 0.873 1.421 0.388 1.292 0.380 4.392 0.681
APL (a) 0.999 0.997 1.000 0.061 0.999 0.997 1.001 0.225 0.996 0.993 1.000 0.031
EH 5.308 3.700 7.615 0.000* 6.366 3.877 10.453 0.000* 5.112 2.836 9.215 0.000
DM 2.407 1.546 3.746 0.000* 4.746 2.403 9.376 0.000* 1.379 0.717 2.655 0.336
Smoking 1.137 0.656 1.972 0.647 1.133 0.542 2.370 0.739 0.956 0.376 2.433 0.925
Drinking 8.645 3.174 23.549 0.000* 52.408 5.808 472.912 0.000* 1.883 0.495 7.165 0.353
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Discussion

PCSK9, also known as neural apoptosis-regulated convertase 1 (NARC1), is the ninth member of the proprotein convertase (PC) family [23]. The human PCSK9 gene is located on chromosome 1p32.3; it encompasses 12 exons and encodes a 692 amino acid glycoprotein. PCSK9 is synthesized as an inactive zymogen, pro-PCSK9 (73 kDa) and contains a signal peptide, a prodomain (residues 31–152) and a catalytic domain (residues 153–451) followed by a C-terminal domain (residues 452–692) [24]. PCSK9 acts as a serine protease and molecular chaperone that reduces both hepatic and extrahepatic low-density lipoprotein receptor levels through an endosomal/lysosomal pathway and increases plasma LDL cholesterol [4,25]. PCSK9 may also regulate apolipoprotein B-containing lipoprotein production and apoB secretion [26,27].

Recent advances revealed a large number of genetic variants of PCSK9 that may modulate plasma cholesterol levels either positively or negatively. “Gain of function” missense mutations in PCSK9 were associated with autosomal-dominant hypercholesterolemia (ADH), a rare form of familial hypercholesterolemia (FH) in which neither the LDLR nor the ligand binding domain of apolipoprotein (apo) B100 are mutated [28,29]. “Loss of function” nonsense mutations in PCSK9 were associated with low plasma LDL-C levels and a reduced incidence of cardiovascular disease [30,31]. Later, many in vitro and in vivo overexpression and knockout/knockdown studies confirmed that PCSK9 targets the LDLR for degradation [32–34]. Studies have confirmed that both rare mutations and common variants in the coding regions of PCSK9 affect LDL cholesterol levels and stroke risk. In this study, we selected 20 SNPs of PCSK9 and used case-control analyses to assess the association between the human PCSK9 gene polymorphism and CIS in the Han and Uygur populations.

Our findings showed the distribution of SNP8 (rs529787) genotypes were significantly different between CIS and control participants (P=0.049). However, when analyzing Han and Uygur groups separately, we found that only in the Han population was the distribution of SNP1 (rs1711503), SNP2 (rs2479408), and SNP8 (rs529787) alleles significantly different between CIS and control participants (P=0.028, P=0.013, P=0.006, respectively). For SNP1 (rs17111503), the frequency of A allele was higher in CIS than in control participants (P=0.028, 39.80% vs. 32.66%) in the Han group, indicating that the risk of CIS was increased with the A allele of rs17111503. For SNP2 (rs2479408), the distribution of alleles, the dominant model (CC vs. CG + GG), and the additive model (CG vs. CC + GG) showed a significant difference between CIS and control participants in total and Han groups, but not in the Uygur group. C allele of rs2479408 was significantly higher in CIS patients than in control participants (total: 96.94% vs. 94.97%; Han: 99.80% vs. 98.24%). Moreover, the significant difference of the dominant model (CC vs. CG + GG) of rs2479408 was retained after adjustment for covariates: age, sex, BMI, HDL-C, LDL-C, TC, TG, ApoA1 ApoB, ApoLpa, EH, DM, and smoking and drinking in the Han group (OR: 75.262, 95% confidence interval [CI]: 7.232–783.278, P<0.001), indicating that the risk of CIS was increased with the C allele of rs2479408. For SNP3 (rs529787), the distribution of alleles, the dominant model (CC vs. CG + GG), and the additive model (CG vs. CC + GG) showed a significant difference between CIS and control participants in total and Han groups, but not in Uygurs. C allele of rs529787 was significantly higher in CIS patients than in control participants (total: 96.81% vs. 94.68%; Han: 99.80% vs. 97.99%). When we constructed the haplotypes using SNP1 and SNP2, we found that the most frequent haplotype in this study was A-C haplotype. For Han, the frequency of A-C was significantly higher in the CIS patients than in the control subjects (P=0.0011), but the frequency of the G-C haplotype was lower in the CIS patients than in the control subjects (P=0.0011). This fully showed that A allele of rs17111503 and C allele of rs2479408 may be the risk factor of CIS, and G allele of rs17111503 and G allele of rs2479408 may be the protective factor of CIS.

SNP20 (rs505151) was observed in the exon12 of the PCSK9 gene and the polymorphisms caused the substitution of glutamate for a glycine residue at position 670 in the protein. The studies about the association between rs505151 of PCSK9 gene polymorphisms (E670G) and the cardiovascular risk have provided inconsistent results, as the introduction of description. Our study was consistent with previous studies [14–16] showing no significant association between the polymorphism of PCSK9 (rs505151) and CIS. By comparison, we found the age of our control subjects was higher than the other studies [11,12] and the study by Afef Slimani [35]. In our study, there were no significant difference in age between CIS patients (age: 63.56±11.37) and control subjects (age: 62.35±11.79) (P=0.269), but in the study by Afef Slimani, there were significant difference in age between CIS patients (age: 66/54.5–76.50) and control subjects (age: 49/45–55) (P<0.0001). Age is a risk factor for stroke, and this may be why our conclusions were not consistent with their conclusions. In addition, there may be differences in populations and geographical factors that explain some differences.

Conclusions

We found that both rs1711503 and 2479408 of PCSK9 were associated with CIS in the Han population of China. A-C haplotype may be a risk genetic marker of CIS in Han in China. A allele of rs17111503 and C allele of rs2479408 may be the risk marker of CIS. Studies with statistically significant numbers of clinical samples are needed for further research in China.

Footnotes

Source of support: This work was supported by grants from the National Natural Science Foundation of China (81160145, 81260180), the proprotein convertase Hay bacteriolysin 9 gene polymorphism studies in patients with ischemic stroke with carotid atherosclerosis in Xinjiang Han and Uyghur populations (81160145), and the apoplexy folic acid metabolism pathway gene of rare and common variants of high-throughput sequencing studies in patients with ischemic stroke with carotid atherosclerosis in Xinjiang Han and Uyghur populations (81260180)

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