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CD36 Gene Polymorphisms Are Associated with Intracerebral Hemorrhge Susceptibility in a Han Chinese Population.

1. Introduction

Intracerebral hemorrhage (ICH) is a crucial classification of stroke and has a high rate of mortality and morbidity [1, 2]. The pathogenesis of ICH is complex and influenced by environmental factors and genetic factors. However, its pathogenesis is not yet clear. Hypertension is the most common contributing factor to primary intracerebral hemorrhage. Factors including smoking, drinking, diabetes, and male gender increase the riskfurther [3-5]. In addition, many studies have shown an inverse association between the level of serum cholesterol and ICH [6]. More importantly, the role of genetics in the pathogenesis of ICH has received wide attention [7, 8]. Epidemiological studies have demonstrated that a history of a first-degree relative with ICH is an independent risk factor for lobar and nonlobar ICH [4]. It has also been reported that a family history of any stroke was a significant risk factor for patients with ICH who were <70 years compared with those who were >70 years [9]. Taken together, the observed differences based on family history indicate that genetic factors play an important role in the incidence of ICH. CD36 is a type B scavenger receptor, now officially designated as SR-B2, and CD36 gene located in the 7q11.2 chromosome with 17 exons and 16 introns and is expressed on the surface of various cells: platelets, microvascular endothelial cells, monocytes/macrophages, dendritic cells, adipocytes, striated muscle cells, and hematopoietic cells [10, 11]. As a result of its expression in multiple cell types, it can be involved in a variety of biological processes, including transport of oxidized LDL (oxLDL) and fatty acids by macrophages and monocytes, and it participates in processes of inflammation, phagocytosis, and endocytosis [12, 13]. In addition, a wide variety of studies have investigated the important roles CD36 plays in many disorders, such as coronary heart disease, hypertension, Alzheimer's Disease, insulin resistance, and metabolic syndrome [14-17]. However, the relationship between CD36 gene polymorphisms and the risk of ICH has not yet been studied. Thus, to clarify the association of CD36 with ICH, we conducted this case-control study to find any SNPs of the CD36 gene associated with the risk of ICH.

2. Material and Methods

2.1. Ethics Statement. The study was approved by the local Ethics Committee of Xinqiao Hospital, Third Military Medical University (Chongqing, China), and written consent forms for genetic screening were obtained for all participants from the participant or from their legal representatives.

2.2. Study Population. In this study, a total of 292 patients with ICH were consecutively recruited from Chongqing Xinqiao Hospital from October 2014 to November 2016. All patients were diagnosed with ICH based on results of brain computed tomography (CT) scan and/or magnetic resonance imaging (MRI). The subjects were not eligible if the ICH was caused by trauma, neoplasms, anticoagulant therapy, coagulation disorders, aneurysms, or vascular malformations or if the patient declined to participate in this study. The 298 participants in the control group were randomly selected from the health examination center of Chongqing Xinqiao Hospital during the same period. The inclusion criterion for the controls was the absence of symptoms or medical history of stroke. The baseline characteristics and vascular risk factors were recorded, including age, gender, height, weight, body mass index (BMI), hypertension, coronary heart disease, diabetes mellitus, smoking and drinking habits, total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), and family history of stroke, which are listed in Table 1. Smokers or drinkers were defined as participants who smoked >100 cigarettes or drank [greater than or equal to] 12 times a year. BMI = weight/[height.sup.2] (kg/[m.sup.2]).

2.3. Polymorphism Selection and Genotyping. SNPs were selected based on their functional relevance and minor allele frequency from Beijing (CHB) genotype data in the HapMap database (release #28, August 2010, https://www.hapmap .org/). The inclusion criteria were as follows: (1) all eligible SNP minor allele frequencies > 0.05 in the HapMap database,

(2) SNP mutations led to amino acid changes according to the dbSNP, and (3) SNPs were located in one haplotype block and were in complete linkage disequilibrium (LD) (determined with the criterion of [r.sup.2] > 0.8). Eleven common SNPs spanning the CD36 gene were included. Detailed information of each SNP is shown in Table 2.

Venous blood were collected in EDTA-coated vials after at least a 12-hour overnight fasting period. And the ICH patients' venous blood were collected within 24 h after ICH occurred. Genomic DNA was extracted using the Wizard Genomic DNA Purification Kit (Promega, USA) following the manufacturer's instructions. Improved Multiple Ligase Detection Reaction (iMLDR) was applied for genotyping [18]. Data analysis was carried out using GeneMapper Software version 4.0. Genotyping was carried out blind to group status.

2.4. Statistical Analysis. Statistical analyses were performed using SPSS 17.0 software (SPSS, Inc., Chicago, IL, USA). The presence of the Hardy-Weinberg equilibrium was determined using the chi-square test. The categorical variables were tested using the chi-squared ([chi square]) test. Categorical variables are expressed as proportions (%) and continuous variables as the mean [+ or -] standard deviation. For continuous variables, all comparisons between ICH group and the control group were made using independent t-test and among genotype groups by one-way ANOVA followed by LSD test and multiple comparisons. The genotype and allele distributions in ICH patients and control subjects were determined using the chi-squared ([chi square]) test. Multivariate logistic regression was used to analyze the genotype frequency of the subjects specified by different genetic models (additive, dominant and recessive comparison) and to calculate the P value, odds ratios (ORs), and 95% confidence intervals (CIs) after adjustment for covariates. Statistically significance was set at P < 0.05.

3. Results

A total of 292 ICH patients and 298 control subjects were included in this study. The clinical and laboratory parameters of all subjects in this study are presented in Table 1. The average age and sex distribution were similar between the ICH group and the control group (P > 0.05). As previous studies found, the prevalence of hypertension, drinking history, and family history in the ICH group were significantly higher than in the control group, and there were significant differences between the two groups (P < 0.05). BMI values and TC, TG, and LDL-C levels were also slightly raised in ICH patients. There were no significant differences between the two groups in gender, HDL, smoking history, and prevalence of coronary heart disease and diabetes mellitus.

Basic information of the eleven selected SNPs is shown in Table 2. The genotype and allelic frequencies of the SNPs in the ICH group and control group are shown in Table 3. All of the genotypes of eleven SNPs were in agreement with the HWE for the control group (P > 0.05), which indicates that the data remain constant in the population (data not shown).

Logistic regression analysis was performed after adjusting for age, gender, body mass index, hypertension, coronary heart disease, diabetes mellitus, smoking and drinking habits, TC, TG, HDL, LDL, and family history of stroke. A significant association between CD36 and ICH was seen in rs1194182, showing that genotype GG was a risk factor for ICH compared with genotype GC-CC (dominant model, OR = 0.674, 95% CI 0.457-0.992, P = 0.046). However, allelic frequencies of rs1194182 in the ICH group and control group have no significant difference. In addition, no associations between the other ten SNPs and the risk of ICH were observed in this study.

All the participants were divided into subgroups to examine whether there were associations between CD36 and ICH in gender or hypertension, ICH location, which are listed in Table 4. After logistic regression analysis for rs1194182, the genotype GG distribution (dominant model, OR = 0.578,95% CI 0.341-0.98, P = 0.042) and the allele G frequencies (OR = 1.533, 95% CI 1.095-2.145, P = 0.012) were significantly different between ICH patients and controls in the hypertension group. And among nonlobar ICH group, we found a significant difference in the genotype of GG distribution compared to controls (dominant model, OR = 0.645, 95% CI 0.461-0.997, P = 0.043). No significant association could be found in the normotensive group, nonlobar ICH group, the male group, and the female group. CD36 is involved in a variety of roles in lipid metabolism. We then analyzed the rs1194182 genotype distributions among different subgroups, including BMI, TC, TG, HDL, and LDL. However, no significant differences were found in different lipid groups (Table 5).

4. Discussion

In our study, we identified eleven SNPs of CD36 and found that SNP rs1194182 has significant differences in this case-controlled study among a Chinese Han population after adjusting for age, gender, body mass index, hypertension, coronary heart disease, diabetes mellitus, smoking and drinking habits, TC, TG, HDL, LDL, and family history of stroke. Subgroup analysis conducted for the SNP rs1194182 in CD36 showed that GG genotype frequencies were significantly different between ICH patients and controls in hypertension group via a dominant model, especially in the hypertension group.

ICH is an important subtype of stroke and is etiologically diverse. The pathogenesis of ICH involves many factors. As found in our study, CD36 SNP rs1194182 may increase the risk of ICH. However, the mechanism by which CD36 increases the risk of ICH is unknown. It is known that hypertension plays an important role in the pathogenesis of ICH [19], and many studies have found that CD36 is closely related to the development of hypertension [14, 20, 21]. In a microarray analysis study of the differential gene expression between hypertensives and normotensives, 31 genes were upregulated and 18 genes were downregulated, including the CD36 gene with 4.8-fold changes and significant differences between hypertensive and normotensive groups [22]. CD36 deficient individuals were found to have increased blood pressure levels [23]. The +273A/G polymorphism in CD36 was associated with essential hypertension especially in males [14]. Thus, CD36 may contribute to the development of ICH by the following mechanisms. One possibility is through regulating the function of endothelin-1 and altering the properties of vascular smooth muscle, which lead to the development of hypertension and atherosclerosis [24]. Pravenec et al. found that deficiency in renal expression of CD36 could increase blood pressure [21, 25]. In our study, the results showed that rs1194182 polymorphism in the CD36 was significantly associated with ICH in the hypertension group, which indicated that significant correlation existed between rs1194182 polymorphism and hypertension on ICH. Moreover, previous study found that ICH occurring in different location may have different pathophysiology, among all cases of lobar ICH, which mainly caused by an apolipoprotein E4 or E2 allele. But about half of all cases of nonlobar ICH are attributable to hypertension [4]. Thus, we assume that CD36 gives rise to ICH through blood pressure pathways. In addition, SNP rs1194182 in the CD36 gene could be a molecular marker for ICH particularly in hypertensive patients.

CD36 is involved in a variety of lipid metabolism pathways. CD36 has the ability to facilitate the uptake of long-chain fatty acids in muscle and adipose tissues, which contributes to the regulation of lipid metabolism and insulin resistance [26, 27]. CD36 is also expressed on macrophages as a receptor for oxidized low-density lipoproteins and plays an important role in the development of atherosclerosis [28]. Previous Japanese studies have reported an association between rare CD36 variants and high blood levels of free fatty acid and triglycerides [29, 30]. CD36 sequence variants were also associated with HDL-C levels [31]. A metaanalysis found that the CD36 gene was significantly linked to triglycerides and triglycerides/HDL-C ratio but not linked to LDL or total cholesterol [32]. In addition, many studies have demonstrated that the polymorphism of CD36 influences the serum lipid levels in the patients of atherosclerosis, coronary heart disease, and metabolic syndrome [33-35]. Importantly, as the Rotterdam Study found, low serum triglycerides levels were also associated with an increased risk of ICH [36]. To evaluate the influence of those factors on the genotype of CD36 in this study, we conduct subgroups analysis according to the BMI, TC, TG, HDL, and LDL. However, for rs1194182, there was no significant difference among lipid subgroups found in the analysis. These findings may indicate that the CD36 increased risk of ICH is not through dyslipidemia pathways.

To our knowledge, this study was the first to report an association between SNPs of CD36 and ICH in a Chinese Han population. This association may be stronger for those individuals labeled as ICH. The SNP rs1194182 in CD36 can be recognized as molecular markers of ICH even though the mechanism is not clear. However, the main limitations of this study are the small sample size and the mechanisms of the CD36 gene in ICH are still unknown. These results should be validated in a large population and in different ethnicities. In our future studies, we will work on the mechanisms of the CD36 gene in ICH with a larger sample size in more diverse areas. As the association between SNPs of CD36 and ICH has been confirmed in a larger sample size and more diverse areas, we would estimate individual risk of ICH just according the venous blood sample.

https://doi.org/10.1155/2017/5352071

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors' Contributions

Qiu-Wen Gong, Mao-Fan Liao, and Liang Liu contributed equally to this work.

Acknowledgments

The authors acknowledge the Shanghai Genesky Bio-Tech Genetic Core Lab for assistance in genotyping techniques. This study was supported by grants from the National Natural Science Fund for Distinguished Young Scholars (81525008).

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Qiu-Wen Gong, Mao-Fan Liao, Liang Liu, Xiao-Yi Xiong, Qin Zhang, Qi Zhong, Kai Zhou, Yuan-Rui Yang, Zhao-You Meng, Chang-Xiong Gong, Rui Xu, and Qing-Wu Yang

Department of Neurology, Xinqiao Hospital, The Third Military Medical University, No. 183, Xinqiao Main Street, Shapingba District, Chongqing 400037, China

Correspondence should be addressed to Qing-Wu Yang; yangqwmlys@163.com

Received 20 March 2017; Accepted 12 June 2017; Published 19 July 2017

Academic Editor: Serge Marbacher
Table 1: Demographic and clinical characteristics of ICH patients
and controls.

Characteristics        Patients (n = 292)     Controls (n = 298)

Age (years            57.12 [+ or -] 12.52   58.84 [+ or -] 13.61
  [+ or -] SD)
Gender, (M/F)              149 (143)              151 (147)
BMI (kg/[m.sup.2])    24.06 [+ or -] 3.46    23.44 [+ or -] 3.43
Hypertension, n (%)        227 (7774)            108 (36.24)
Coronary heart              27 (265)               21 (238)
  disease
Diabetes mellitus           36 (256)               40 (258)
Smoking history             69 (223)               59 (239)
Drinking history            47 (245)               31 (267)
Family history              12 (280)                4(294)
TC (mmol/L)            4.65 [+ or -] 1.05     4.10 [+ or -] 1.05
TG (mmol/L)            2.23 [+ or -] 5.41     1.58 [+ or -] 1.4
HDL-c (mmol/L)         1.14 [+ or -]0.33      1.13 [+ or -] 0.38
LDL-c (mmol/L)         3.03 [+ or -]0.76      2.73 [+ or -] 0.88

Characteristics       P value

Age (years             0.112
  [+ or -] SD)
Gender, (M/F)          0.931
BMI (kg/[m.sup.2])     0.029
Hypertension, n (%)      0
Coronary heart         0.231
  disease
Diabetes mellitus      0.692
Smoking history        0.259
Drinking history       0.041
Family history         0.039
TC (mmol/L)              0
TG (mmol/L)            0.049
HDL-c (mmol/L)         0.712
LDL-c (mmol/L)           0

Table 2: Characteristics of CD36 gene polymorphisms investigated in
the study.

SNPs         Chromosome   Position   SNP property   Length   Alleles

rs1049654        7        80275455      5'UTR        379       A>C
rs1049673        7        80306350      3'UTR        308       C>G
rs1194182        7        80231504      5'UTR        256       G>C
rs12666644       7        80308199      3'UTR        279       G>A
rs12706949       7        80307224      3'UTR        354       g>t
rs12706950       7        80307502      3'UTR        244       G>A
rs13223096       7        80307624      3'UTR        244       c>t
rs13226433       7        80308171      3'UTR        279       C>A
rs13246513       7        80306751      3'UTR        250       c>t
rs1334512        7        80267904      5'UTR        288       g>t
rs7755           7        80306271      3'UTR        308       G>A

SNPs         MAF (CHB_1000 g)

rs1049654         0.325
rs1049673         0.485
rs1194182         0.335
rs12666644        0.087
rs12706949        0.427
rs12706950        0.422
rs13223096        0.427
rs13226433        0.485
rs13246513        0.422
rs1334512         0.272
rs7755            0.485

Table 3: Comparison of the genotype and allele frequencies of
associated CD36 gene variants between ICH patients and controls.

                                Frequency

SNPs         Genotype       ICH         Control
                        patients, n   patients, n

                          292(%)        298(%)

                AA      135 (46.2)    115 (38.6)
rsl049654       AC      124 (42.5)    143 (48.0)

                CC       33 (11.3)     40 (13.4)
                CC       60 (20.5)     63 (21.2)
rsl049673       CG      145 (49.7)    150 (50.3)

                GG       87 (29.8)     85 (28.5)
                GG      137 (46.9)    118 (39.6)
rsll94182       GC      124 (42.5)    142 (47.7)

                CC       31 (10.6)     38 (12.7)
                GG      258 (88.3)    245 (82.2)
rsl2666644      GA       32 (11.0)     52 (17.5)

                AA        2 (0.7)       1 (0.3)
                GG       83 (28.4)     88 (29.5)
rsl2706949      GT      149 (51.0)    147 (49.3)

                TT       60 (20.6)     63 (21.2)
                GG       82 (28.1)     88 (29.5)
rsl2706950      GA      150 (51.4)    146 (49.0)

                AA       60 (20.5)     64 (21.5)
                CC       83 (28.4)     88 (29.5)
rsl3223096      CT      149 (51.0)    147 (49.3)

                TT       60 (20.6)     63 (21.2)
                CC       59 (20.2)     62 (20.8)
rsl3226433      CA      146 (50.0)    151 (50.7)

                AA       87 (29.8)     85 (28.5)
                CC       83 (28.4)     89 (29.9)
rsl3246513      CT      150 (51.4)    146 (49.0)

                TT       59 (20.2)     63 (21.1)
                GG      172 (58.9)    188 (63.1)
rsl3344512      GT       97 (33.2)    101 (33.9)

                TT       23 (7.9)       9 (3.0)
                GG       60 (20.6)     63 (21.2)
rs7755          GA      144 (49.3)    150 (50.3)

                AA       88 (30.1)     85 (28.5)

                            Additive model          Dominant model

SNPs         Genotype
                          P      OR (95% Cl)      P      OR (95% Cl)

                AA      0.112       0.798       0.069       0.698
rsl049654       AC
                                (0.604-1.054)           (0.474-1.028)
                CC
                CC      0.847       1.027       0.894       0.972
rsl049673       CG
                                (0.783-1.346)           (0.638-1.48)
                GG
                GG      0.072       0.772       0.046       0.674
rsll94182       GC
                                (0.583-1.024)           (0.457-0.992)
                CC
                GG      0.208       0.723       0.131       0.664
rsl2666644      GA
                                (0.436-1.198)            (0.39-1.13)
                AA
                GG      0.787       0.963       0.509       0.868
rsl2706949      GT
                                (0.734-1.264)           (0.57-1.322)
                TT
                GG      0.788       0.963       0.531       0.874
rsl2706950      GA
                                (0.734-1.264)           (0.574-1.332)
                AA
                CC      0.787       0.963       0.509       0.868
rsl3223096      CT
                                (0.734-1.264)           (0.57-1.332)
                TT
                CC      0.833       1.03        0.869       0.965
rsl3226433      CA
                                (0.785-1.351)           (0.634-1.469)
                AA
                CC      0.798       0.965       0.568       0.885
rsl3246513      CT
                                (0.735-1.267)           (0.528-1.346)
                TT
                GG      0.11        1.302       0.246       1.259
rsl3344512      GT
                                 (0.942-1.8)            (0.583-1.859)
                TT
                GG      0.922       1.014       0.78        0.942
rs7755          GA
                                (0.773-1.328)           (0.619-1.433)
                AA

                           Reccessive model

SNPs         Genotype                           Allele
                          P      OR (95% Cl)

                AA      0.569       0.849         A
rsl049654       AC
                                (0.484-1.49)      C
                CC
                CC      0.632       1.121         C
rsl049673       CG
                                (0.703-1.787)     G
                GG
                GG      0.481       0.814         G
rsll94182       GC
                                (0.459-1.445)     C
                CC
                GG      0.381       3.211         G
rsl2666644      GA
                                (0.237-43.56)     A
                AA
                GG      0.786       1.067         G
rsl2706949      GT
                                (0.667-1.707)     T
                TT
                GG      0.814       1.058         G
rsl2706950      GA
                                (0.662-1.69)      A
                AA
                CC      0.786       1.067         C
rsl3223096      CT
                                (0.667-1.707)     T
                TT
                CC      0.584       1.14          C
rsl3226433      CA
                                (0.713-1.825)     A
                AA
                CC      0.482       1.049         C
rsl3246513      CT
                                (0.655-1.68)      T
                TT
                GG      0.09        2.203         G
rsl3344512      GT
                                (0.884-5.487)     C
                TT
                GG      0.632       1.121         G
rs7755          GA
                                (0.703-1.787)     A
                AA

                               Frequency

SNPs         Genotype       ICH         Control
                        patients, n   patients, n
                            (%)           (%)

                AA      394 (67.5)    373 (62.6)
rsl049654       AC
                        190 (32.5)    223 (37.4)
                CC
                CC      265 (45.4)    276 (46.3)
rsl049673       CG
                        319 (54.6)    320 (53.7)
                GG
                GG      398 (68.2)    378 (63.4)
rsll94182       GC
                        186 (31.8)    218 (36.6)
                CC
                GG      548 (93.8)    542 (90.9)
rsl2666644      GA
                         36 (6.2)      54 (9.1)
                AA
                GG      315 (53.9)    323 (54.2)
rsl2706949      GT
                        269 (46.1)    273 (45.8)
                TT
                GG       314(53.8)    322 (54.0)
rsl2706950      GA
                        270 (46.2)    274 (46.0)
                AA
                CC      315 (53.9)    323 (54.2)
rsl3223096      CT
                        269 (46.1)    273 (45.8)
                TT
                CC      264 (45.2)    275 (46.1)
rsl3226433      CA
                        320 (54.8)     321(53.9)
                AA
                CC      316 (54.1)    324 (54.4)
rsl3246513      CT
                        268 (45.9)    272 (45.6)
                TT
                GG      441 (75.5)    477 (80.0)
rsl3344512      GT
                        143 (24.5)    119 (20.0)
                TT
                GG      264 (45.2)    276 (46.3)
rs7755          GA
                        320 (54.8)    320 (53.7)
                AA

                         Multiplicative model

SNPs         Genotype
                          P      OR (95% Cl)

                AA      0.079       1.24
rsl049654       AC
                                (0.975-1.576)
                CC
                CC      0.748       0.963
rsl049673       CG
                                (0.766-1.211)
                GG
                GG      0.087       0.81
rsll94182       GC
                                (0.637-1.031)
                CC
                GG      0.979       1.517
rsl2666644      GA
                                (0.979-2.35)
                AA
                GG      0.93        0.99
rsl2706949      GT
                                (0.787-1.244)
                TT
                GG      0.929       0.99
rsl2706950      GA
                                (0.787-1.244)
                AA
                CC      0.93        0.99
rsl3223096      CT
                                (0.787-1.244)
                TT
                CC      0.747       0.963
rsl3226433      CA
                                (0.766-1.211)
                AA
                CC      0.931       0.99
rsl3246513      CT
                                (0.787-1.245)
                TT
                GG      0.062       0.769
rsl3344512      GT
                                (0.584-1.013)
                TT
                GG      0.704       0.957
rs7755          GA
                                (0.761-1.203)
                AA

Cf confidence interval; OR, odds ratio; SNP, single nucleotide
polymorphism. Additive model: heterozygotes and minor allele
homozygotes were weighed 2 and 1, respectively, to major allele
homozygotes. Dominant model: major allele homozygotes versus
heterozygotes plus minor allele homozygotes. Recessive model: major
allele homozygotes plus heterozygotes versus minor allele
homozygotes.

Table 4: The genotype distributions and allele frequencies of the CD36
gene rs1194182 polymorphisms in the hypertension and normotensive
groups.

                    rs1194182         Additive model
Subgroup
                 GG    GC    CC     P      OR (95% Cl)

Male             69    64    16   0.613       0.905
(ICH)(149)                                (0.161-1.331)
Male             62    67    22
(control)(151)
Female           68    60    15   0.601       0.682
(ICH)(143)                                (0.33-1.409)
Female           56    75    16
(control)(147)
Hypertension     105   100   22   0.053       0.696
(ICH)(227)                                (0.482-1.005)
Hypertension     36    53    18
(control)(107)
Normotensive     32    24    9    0.982       1.005
(ICH)(65)                                  (0.64-1.58)
Normotensive     82    89    20
(control)(191)
Lobar            18    19    3    0.973       1.018
(ICH)(40)                                 (0.761-1.631)
Lobar            118   142   38
(control)(298)
Nonlobar         119   105   28   0.063       0.778
(ICH)(252)                                (0.311-1.592)
Nonlobar         118   142   38
(control)(298)

                      Dominant model         Recessive model
Subgroup
                   P      OR (95% Cl)      P      OR (95% Cl)

Male             0.601       0.866       0.79         0.9
(ICH)(149)               (0.505-1.485)           (0.415-1.95)
Male
(control)(151)
Female           0.201       0.498       0.791       0.83
(ICH)(143)               (0.171-1.45)            (0.209-3.293)
Female
(control)(147)
Hypertension     0.042       0.578       0.333        0.7
(ICH)(227)               (0.341-0.98)            (0.334-1.441)
Hypertension
(control)(107)
Normotensive     0.539       0.828       0.307       1.602
(ICH)(65)                (0.453-1.513)           (0.648-3.96)
Normotensive
(control)(191)
Lobar            0.604       0.947       0.423       1.098
(ICH)(40)                (0.622-1.846)           (0.874-2.96)
Lobar
(control)(298)
Nonlobar         0.043       0.645       0.512       0.797
(ICH)(252)               (0.461-0.997)           (0.384-1.524)
Nonlobar
(control)(298)

                   Allele    Multiplicative model
Subgroup
                  G     C      P      OR (95% CI)

Male             202   96    0.242       1.223
(ICH)(149)                           (0.873-1.713)
Male             191   111
(control)(151)
Female           196   90    0.21        1.246
(ICH)(143)                           (0.883-1.759)
Female           187   107
(control)(147)
Hypertension     310   144   0.012       1.533
(ICH)(227)                           (1.095-2.145)
Hypertension     125   89
(control)(107)
Normotensive     88    42    0.76        1.068
(ICH)(65)                            (0.699-1.633)
Normotensive     253   129
(control)(191)
Lobar            55    25    0.351       1.269
(ICH)(40)                            (0.769-2.095)
Lobar            378   218
(control)(298)
Nonlobar         343   161   0.107       1.229
(ICH)(252)                           (0.956-1.579)
Nonlobar         378   218
(control)(298)

Table 5: The BMI and serum lipid data of ICH patients and control
subjects stratified by CD36 genotypes.

Parameter                                     Genotype
                         GG                      GC

BMI (ICH)       24.043 [+ or -] 3.423   24.311 [+ or -] 3.563
BMI (control)   23.367 [+ or -]3.053    23.479 [+ or -] 3.602
TC (ICH)        4.7045 [+ or -] 1.112   4.649 [+ or -] 1.041
TC (control)    4.117 [+ or -] 1.154    4.134 [+ or -] 0.927
TG (ICH)        1.926 [+ or -] 1.671    1.927 [+ or -] 1.223
TG (control)     1.64 [+ or -] 1.52     1.609 [+ or -] 1.413
HDL (ICH)       1.174 [+ or -] 0.338    1.111 [+ or -] 0.316
HDL (control)   1.113 [+ or -] 0.295    1.152 [+ or -] 0.451
LDL (ICH)       3.076 [+ or -] 0.768    3.037 [+ or -] 0.794
LDL (control)   2.739 [+ or -] 0.843    2.728 [+ or -] 0.919

Parameter                                      P value
                         CC             (multiple comparison)

BMI (ICH)       23.651 [+ or -] 4.042           0.59
BMI (control)   23.533 [+ or -] 3.916           0.952
TC (ICH)        4.394 [+ or -] 0.748            0.332
TC (control)    3.908 [+ or -] 1.173            0.489
TG (ICH)        1.886 [+ or -] 1.109            0.989
TG (control)     1.292 [+ or -] 0.88            0.391
HDL (ICH)       1.149 [+ or -] 0.303            0.295
HDL (control)   1.131 [+ or -] 0.317            0.711
LDL (ICH)        2.841 [+ or -] 0.61            0.303
LDL (control)   2.627 [+ or -] 0.841            0.717
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Title Annotation:Research Article
Author:Gong, Qiu-Wen; Liao, Mao-Fan; Liu, Liang; Xiong, Xiao-Yi; Zhang, Qin; Zhong, Qi; Zhou, Kai; Yang, Yu
Publication:BioMed Research International
Article Type:Report
Date:Jan 1, 2017
Words:5494
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