The relationship of miR-146a gene polymorphism with carotid atherosclerosis in Chinese patients with type 2 diabetes mellitus

Thrombosis Research 136 (2015) 1149–1155

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The relationship of miR-146a gene polymorphism with carotid atherosclerosis in Chinese patients with type 2 diabetes mellitus Jing Shen, Min Zhang, Mingfang Sun, Kang Tang, Bo Zhou ⁎ Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, PR China

a r t i c l e

i n f o

Article history: Received 30 June 2015 Received in revised form 20 September 2015 Accepted 7 October 2015 Available online 9 October 2015 Keywords: miR-146a gene p300 gene Single nucleotide polymorphism Atherosclerosis Type 2 diabetes mellitus

a b s t r a c t Objective: Atherosclerosis (AS) is regarded as the major cause of disability and death in diabetic patients. However, its precise pathogenesis is not entirely clear. Recent genome-wide association studies (GWAS) have revealed AS is related to some epigenetic changes. This study aimed to investigate the possible associations of miR-146a and transcriptional coactivator p300 polymorphisms with carotid atherosclerosis in type 2 diabetes mellitus. Method: This case–control study included 596 type 2 diabetes mellitus patients with carotid atherosclerosis and 379 patients without carotid atherosclerosis. Genotyping of miR-146a and p300 polymorphisms was performed by allelic discrimination assay with TaqMan-MGB probes. Results: The CC genotype of rs2910164 in miR-146a was found to be associated with an increased risk of carotid vulnerable plaque in the Chinese type 2 diabetes mellitus patients, but this association was not found in the type 2 diabetes mellitus patients with carotid atherosclerosis or in the plaque load group. In addition, no significant difference in transcriptional coactivator p300 genotype distribution was observed between the type 2 diabetes mellitus patients with and without carotid atherosclerosis, plaque stability or plaque load, respectively. Stratified analyses revealed that the miR-146aCC genotype was associated with an increased risk of vulnerable plaque in subjects who were older, females, those with diabetes duration of more than 10 years, and those with hypertension. The gene-gene interactions between the miR-146a rs2910164 and p300 rs20551 polymorphisms were further analysed, but no combined effects of these two genes on enhancing the risk of carotid atherosclerosis, plaque stability, or plaque load were detected. Conclusion: The miR-146a rs2910164 polymorphism might be associated with carotid vulnerable plaque risk in Chinese type 2 diabetes mellitus patients, particularly in older patients, females, those with diabetes duration of more than 10 years and those with hypertension. The transcriptional coactivator p300 rs20551 polymorphism may not be a risk factor for the development or progression of atherosclerosis in type 2 diabetes mellitus. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction Atherosclerosis (AS), a chronic inflammatory disease [1], is regarded as a major cause of disability and death in diabetic patients. Although hyperglycaemia is one of risk factors for AS, not all diabetic patients develop it during their lifetime, suggesting that complex interactions between different genetic [2–4] and environmental factors [1,5] are important in determining susceptibility to AS in diabetic patients. Recently, it has become clear that some of the gene-environment interactions relevant to complex diseases are regulated by epigenetic mechanisms. An epigenetic alteration is a heritable biological change in gene expression that occurs without a change in DNA sequence [6, 7]. Moreover, recent genome-wide association studies (GWAS) have revealed that AS is related to epigenetic changes in some genes

⁎ Corresponding author. E-mail address: [email protected] (B. Zhou).

http://dx.doi.org/10.1016/j.thromres.2015.10.013 0049-3848/© 2015 Elsevier Ltd. All rights reserved.

[8]. The most frequently occurring epigenetic changes include DNA methylation, histone modifications, and those involving non-coding RNAs, including microRNAs (miRNAs) and long intergenic noncoding RNAs (lincRNAs) [9]. miRNAs are single-stranded, small (approximately 22 nucleotides in length), non-coding RNAs that negatively regulate gene expression by binding to the 3′-untranslated regions (3′-UTRs) of target mRNAs to inhibit translation and/or promote mRNA degradation. To date, numerous miRNAs have been found to be involved in the full range of processes of cardiovascular diseases and diabetes [10,11]. MicroRNA-146a (miR146a) has been extensively researched in recent years and has been proven to play significant roles in the occurrence and development of AS, including involvement in endothelial activation and inflammatory response [12], ox-LDL accumulation in macrophages [13] and vascular smooth muscle cell proliferation [14]. In addition, several studies have shown the miR-146a expression is significantly up-regulated in atherosclerotic plaques [15,16]. And other finding has further indicated that the up-regulation of miR-146a expression in peripheral blood

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Table 1 PCR primers, probe, and annealing temperatures used for detection of the p300 and miR-146a polymorphisms. Gene

SNP ID

Sequence(5′–3′)

p300

rs20551

miR-146a

rs2910164

Forward primer Reverse primer Probe1 Probe2 Forward primer Reverse primer Probe1 Probe2

Tm (°C) CAACCAGAACCAGCAGATACTCA TCAAACAGTAATTGCCCTACTCTCA FAM-CCGGAGGATaTTT-MGB VIC-CCGGAGGATgTTT-MGB TGGACTGCAAGGAGGGGTC CCTCAAGCCCACGATGACA FAM-TCAGACCTcTGAAATT-MGB VIC-TCAGACCTgTGAAATT-MGB

60

63

Tm: annealing temperature.

mononuclear cells was positively correlated with the plasma concentrations of IFN-γ and TNF-α in patients with acute coronary syndrome [17]. Because of these properties, this miRNA may play pivotal roles in the initiation, progression or complications of atherosclerosis. Recently, a common functional single nucleotide polymorphism (SNP) in the premiRNA sequence of miR-146a (miR-146aC N G, rs2910164) has been shown to increase susceptibility to cancer [18,19], inflammatory bowel disease [20], and atherosclerosis-related ischaemic events [21, 22]. However, to our knowledge, the association of the miR-146a rs2910164 gene polymorphism with AS in diabetic patients has not been evaluated to date. The transcriptional coactivator p300 is a histone acetyltransferase (HAT) family member that is involved in regulating diverse biological pathways, including inflammation [23,24]. Several studies have shown that p300 may be necessary during smooth muscle cell phenotypic switching and differentiation [25,26]. Recently, Feng et al. [27] have indicated that p300 likely regulates miR-146a, which participates in mediating extracellular matrix protein production in the retina, heart and kidneys of diabetic rats. However, there is lack of relevant clinical evidence of the correlation between p300 and miRNAs in diabetic patients. Several mutations in the p300 gene have been reported, including a SNP (GNA, rs20551) in the coding sequence that results in a change of the amino acid at position 997 from isoleucine (Ile) to valine (Val). Analysis of Ensembl data (http://asia.ensembl.org) has revealed that this amino acid is located closely upstream of the bromodomain and may be responsible for HAT activity. Thus, this polymorphism may affect the functioning of p300. To date, few studies have investigated the roles or interactive effects of miR-146a (CNG) and p300 (GN A) polymorphisms in type 2 diabetes mellitus (T2DM) with carotid atherosclerosis. In this study, we aimed to investigate the associations of these genetic polymorphisms with carotid atherosclerosis by conducting a case–control study. We also evaluated the synergistic effects of the miR-146a(C N G) and p300 (G NA) genotypes on clinical and biological variables in Chinese T2DM patients with and without carotid atherosclerosis. 2. Materials and methods 2.1. Study population The study group included 975 subjects with T2DM recruited from the Department of Endocrinology of the First Affiliated Hospital of Chongqing Medical University, China between April 2013 and November 2014. All subjects enrolled in this study were of the Han nationality and were not related. The following inclusion criteria were applied: 1) a diagnosis of T2DM based on the WHO definition of diabetes [28]; 2) an age of N 40 years; and 3) the presence (or not) of carotid artery atherosclerosis. The exclusion criteria were as follows: 1) a history of previous carotid endarterectomy and artery stenting; 2) carotid aneurism; 3) carotid stenosis of more than 50%; 4) severe aortic stenosis or heart failure; 5) any systemic infection; 6) cardiovascular events (such as myocardial infarction or cerebral stroke within the past 3 months);

7) severe trauma (within the past 3 months); 8) pregnant or breastfeeding; 9) diagnosis with malignancy of various types; 10) autoimmune disease (such as rheumatoid arthritis, systemic lupus erythematosus, or scleroderma); and 11) severe renal or liver dysfunction. None of the patients had a history of ketoacidosis or hyperosmolar nonketotic syndrome. We studied the following clinical and laboratory variables: age, sex, body mass index (BMI; kg/m2), waist circumference, smoking status, diabetes duration, blood pressure, fasting blood glucose, glycosylated haemoglobin (HbA1C), total cholesterol, triglycerides (TGs), lowTable 2 Baseline characteristics of type 2 diabetes patients with and without carotid atherosclerosis. Characteristic

Sample size Demographic parameters Age (years) Gender (male/female) Current smokers (%) Diabetes duration (years) History of hypertension (%) BMI (kg/m2) Waist circumference (cm) Systolic BP (mm Hg) Diastolic BP (mm Hg) Pulse pressure (mm Hg) Biochemical parameters FBG (mmol/L) HbA1c (%) Total cholesterol (mmol/L) Triglycerides (mmol/L) HDL-c (mmol/L) LDL-c (mmol/L) Non-HDL-c (mmol/L)⁎ ApoA1 (g/L) ApoB (g/L) ApoA1/ApoB LP(a) (mg/L) hs-CRP (mg/L) Fibrinogen (g/L) Medication (%) Antihypertensive drugs ACEI/ARBs CCBs Others⁎⁎ Statins

T2DM

P value

Carotid atherosclerosis (−)

Carotid atherosclerosis (+)

379

596

58.39 ± 9.73 204/175 129 (34.1) 9.76 ± 6.03 176 (46.5) 25.23 ± 3.26 88.50 ± 8.11 131.01 ± 18.13 78.13 ± 10.07 52.88 ± 13.97

66.12 ± 10.00 336/260 186 (31.2) 11.55 ± 7.03 388 (65.0) 24.87 ± 3.41 88.41 ± 8.94 136.34 ± 19.56 76.46 ± 10.93 59.88 ± 14.28

0.000a 0.446 0.371 0.000a 0.000a 0.120 0.553 0.000a 0.025b 0.000a

8.20 (6.70–11.10) 7.9 (6.8–9.7) 4.43 (3.79–5.29) 1.59 (1.07–2.75) 1.16 ± 0.34 2.61 ± 0.92 3.31 (2.62–4.09) 1.31 ± 0.26 0.86 ± 0.24 1.53 (1.19–1.94) 157.0 (78.5–332.5) 0.82 (0.45–1.43) 2.91 ± 0.67

8.40 (6.70–10.90) 8.0 (6.9–9.6) 4.3 2(3.58–5.28) 1.44 (1.00–2.12) 1.15 ± 0.33 2.73 ± 1.01 3.18 (2.42–4. 06) 1.29 ± 0.25 0.89 ± 0.28 1.49 (1.16–1.89) 209.0 (98.0–423.0) 1.03 (0.51–2.20) 3.16 ± 0.77

0.888 0.485 0.237 0.000a 0.774 0.088 0.351 0.508 0.222 0.295 0.002b 0.000a 0.000a

139 (36.8) 85 71 12 48 (12.6)

319 (53.5) 212 189 18 110 (18.5)

0.000a

0.022b

BMI: body mass index, BP: blood pressure, HbA1C: haemoglobin A1C, HDL-c: high-density lipoprotein cholesterol, LDL-c: low-density lipoprotein cholesterol, non-HDL-c: non-HDL cholesterol, ApoA1: apolipoprotein A1, Apo B: apolipoprotein B, LP(a): lipoprotein (a), hs-CRP: high-sensitivity C-reactive protein, ACEI/ARB: angiotensin-converting enzyme inhibitor/angiotensin receptor blocker, and CCB: calcium channel blocker. ⁎ Non-HDL-c = total cholesterol − HDL-c. ⁎⁎ Other antihypertensive drugs, including diuretics and α/β receptor blockers. a P value b 0.001. b P value b 0.05.

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Table 3 Associations of miR-146a and transcriptional coactivator p300 genotypes with carotid atherosclerosis in diabetic patients. Characteristics

T2DM Carotid atherosclerosis (−)

miR-146a CNG GG GC CC Dominant model (GG vs. GC + CC) Recessive model (GG + GC vs. CC) G allele C allele p300 GNA AA AG GG Dominant model (AA vs. AG + GG) Recessive model (AA + AG vs. GG) A allele G allele

OR (95% CI)

49 (12.9) 177 (46.8) 153 (40.3)

96 (16.1) 283 (47.5) 217 (36.4)

275 (36.3) 483 (63.7)

475 (39.8) 717 (60.2)

318 (83.8) 59 (15.6) 2 (0.6)

509 (85.4) 81 (13.6) 6 (0.9)

695 (91.7) 63 (8.3)

1099 (92.2) 93 (7.8)

density lipoprotein cholesterol (LDL-c), high-density lipoprotein cholesterol (HDL-c), apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), lipoprotein (a) [LP(a)], high-sensitivity C-reactive protein (hs-CRP), and fibrinogen. Subjects with a blood pressure of ≥140/90 mm Hg and those who were using antihypertensive drugs were considered to be hypertensive. All biochemical analyses were performed at the hospital laboratory by standard procedures. This study was approved by the ethics committees of the participating medical centres, and written informed consent was obtained from each participant in the study.

P value

Carotid atherosclerosis (+) 1.000 (reference) 0.817 (0.540–1.236) 0.728 (0.477–1.113) 0.776 (0.525–1.148) 0.850 (0.643–1.124) 1.00 (reference) 0.859 (0.712–1.037)

1.00 (reference) 0.859 (0.585–1.260) 1.559 (0.300–8.090) 0.884 (0.608–1.287) 1.594 (0.308–8.264) 1.00 (reference) 0.934 (0.669–1.303)

0.339 0.143 0.205 0.253 0.114

0.437 0.597 0.521 0.578 0.686

media thickening of ≥1.5 mm. To further analyse the diabetic patients with different carotid plaque phenotypes, we divided them into two groups based on plaque stability, including a stable plaque group (with uniformly echogenic or predominantly calcified plaques) and a vulnerable plaque group (with predominately echolucent plaques). Moreover, carotid plaque loads were evaluated by determining plaque scores (PSs). The PSs were calculated by summing the maximal plaque thicknesses on both sides (the CCAs, CBs, and ICAs) [30]. We divided the diabetic patients with carotid plaques into the following two groups based on the 50th percentile of the PSs: [1] those with a PS of ≤3.5; and [2] those with a PS of N 3.5.

2.2. Carotid ultrasonography 2.3. SNP genotyping Carotid artery ultrasonography was performed on each subject by the same experienced sonographer with a linear 3–10 MHz probe in B-mode (SXFL012-IU22; Philips Ultrasound, Inc.). Patients were examined in the supine position with the head tilted backwards. Highresolution images of the longitudinal lateral view of the area of the bilateral common carotid arteries (CCAs), carotid bulbs (CBs), and internal carotid arteries (ICAs) were obtained according to the recommendations of the American Society of Echocardiography Carotid IntimaMedia Thickness Task Force [29]. Based on the ultrasound results, the T2DM patients were divided into control (without carotid atherosclerosis) and case (with carotid atherosclerosis) groups. The control group included T2DM patients with a mean bilateral CCA intima-media thickness of ≤0.9 mm and the absence of atherosclerotic plaques in all carotid vessel segments. The case group included patients with an atherosclerotic plaque in any carotid vessel segment. A plaque was defined as a focal wall thickening of N 50% of the surrounding intima-media thickness or focal intima-

Genomic DNA was extracted from peripheral blood samples with a TIANamp Blood DNA Kit (TIANGEN Biotech Co., Ltd., Beijing, China). DNA concentration was measured with a Nanodrop2000 spectrophotometer, and the samples were stored at − 20 °C. In addition, 20 μl DNA samples were transferred to fresh tubes and standardized to a concentration of 20 ng/μl. miR-146a (rs2910164; C/G) and transcriptional coactivator p300 (rs20551; G/A) polymorphisms were analysed by TaqMan probe allelic discrimination assay using real– time PCR (Bio-Rad). The probes and primers used (Table 1) were designed and synthesized by Shanghai GeneCore BioTechnologies Co., Ltd. PCR was performed in a total volume of 10 μl containing 5 μl TaqMan Universal PCR Master Mix (GeneCore), 0.5 μl primer, 0.5 μl probe, 2 μl deionized water, and 1 μl DNA template. The amplification reaction parameters were as follows: 50 °C for 2 min and 95 °C for 10 min, followed by 40 cycles of 95 °C for 15 s and 60 °C (rs20551) or 63 °C (rs2910164) for 1 min. Fluorescence signals were recorded

Table 4 Binary logistic regression analysis of the associations of miR-146a and transcriptional coactivator p300 polymorphisms with carotid plaque phenotypes in diabetic patients according to ultrasound phenotypes of carotid atherosclerosis. Bold-faced values were P value b 0.05 and were considered statistically significant. miR-146a (GG + GC vs.CC) Carotid plaque type Stable plaque Vulnerable plaque Carotid plaque score⁎ Score (≤3.5) Score (N3.5)

AOR (95% CI)a 1.000 (reference) 1.629 (1.101–2.410) AOR (95% CI)b 1.000 (reference) 0.863 (0.593–1.258)

p300 (AA vs. AG + GG) P value 0.015 P value 0.444

AOR (95% CI)a 1.000 (reference) 0.987 (0.563–1.731) AOR (95% CI)b 1.000 (reference) 0.978 (0.584–1.638)

P value 0.965 P value 0.934

⁎ The two groups of plaque scores are based on the 50th percentile scores. a Adjusted for age, sex, diabetes duration, smoking status, history of hypertension, pulse pressure, total cholesterol, triglycerides, HDL-cholesterol, LDL-cholesterol, and antihypertensive drugs. b Adjusted for age, sex, smoking status, history of hypertension, pulse pressure, HDL-cholesterol, and antihypertensive drugs.

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Table 5 Stratified analysis of associations of miR-146a polymorphisms with plaque stability. Subgroup Age ≤65 N65 Gender Male Female

Diabetes duration ≤10 N10 Hypertension No Yes

Vulnerable plaque

AOR (95%CI)⁎

Genotype

Stable plaque

GG + GC CC GG + GC CC

65 (66.3) 33 (33.7) 106 (73.1) 39 (26.9)

93 (60.4) 61 (39.6) 76 (55.5) 61 (44.5)

1.000 (reference) 1.355 (0.755–2.432) 1.000 (reference) 1.971 (1.156–3.361)

GG + GC CC GG + GC CC

71 (70.3) 30 (29.7) 100 (70.4) 42 (29.6)

117 (58.5) 83 (41.5) 52 (57.1) 39 (42.9)

1.000 (reference) 1.507 (0.878–2.584) 1.000 (reference) 1.881 (1.039–3.408)

GG + GC CC GG + GC CC

77 (64.2) 43 (35.8) 94 (76.4) 29 (23.6)

106 (60.2) 70 (39.8) 63 (54.8) 52 (45.2)

1.000 (reference) 1.267 (0.745–2.155) 1.000 (reference) 2.427 (1.336–4.407)

GG + GC CC GG + GC CC

51 (73.9) 18 (26.1) 120 (69.0) 54 (31.0)

75 (64.1) 42 (35.9) 94 (54.0) 80 (46.0)

1.000 (reference) 1.358 (0.634–2.908) 1.000 (reference) 1.801 (1.125–2.884)

P value

0.308 0.013

0.137 0.037

0.382 0.004

0.431 0.014

⁎ Adjusted for age, gender, diabetes duration, smoking status, history of hypertension, pulse pressure, total cholesterol, triglycerides, HDL-cholesterol, LDL-cholesterol, and antihypertensive drugs.

and automatically analysed to identify the SNP genotypes. We checked random results from TaqMan probe allelic discrimination assay for concordance for each genotype by DNA sequencing, obtaining a concordance rate of 100%.

patients without carotid atherosclerosis. With regard to medication history, a greater number of T2DM patients with carotid atherosclerosis were on active antihypertensive and statin therapies compared with the controls (P b 0.001 and P = 0.022).

2.4. Statistical analysis

3.2. Genotype distributions

The measurement data are presented as the mean ± standard deviation (for normally distributed data) or the median (for data with a skewed distribution). Enumeration data are expressed as frequencies (percentages). Variables that were not normally distributed were log10-transformed to generate approximately normal distributions. Differences between groups were compared using unpaired Student's t test or analysis of variance (ANOVA) for normally distributed continuous variables and the Pearson χ2 test for categorical variables. Associations of miR-146a or p300 polymorphisms with atherosclerosis risk were assessed by unconditional logistic regression analysis and adjusted for possible confounders identified by univariate analysis. Interactions between the genotypes and clinical parameters were evaluated using stepwise logistic regression analysis for categorical variables (atherosclerotic occurrence and plaque stability) and linear stepwise regression analysis for continuous variables (plaque load). A P value of b 0.05 was considered statistically significant. All statistical analyses were performed using SPSS software (version 19.0).

The miR-146aC N G and p300G NA genotype distributions in the T2DM patients with carotid atherosclerosis and controls are shown in Table 3. The genotype frequencies for the two polymorphisms were consistent with Hardy–Weinberg equilibrium in all of the studied groups. There were no significant differences in the miR-146aCNG and p300GN A genotype distributions between the T2DM patients with carotid atherosclerosis and the control subjects (Table 3). To investigate the effects of the two polymorphisms on plaque stability and plaque load, we further analysed their relationships with different phenotypes and the carotid plaque score. Analysis of their effects on plaque stability revealed that the recessive model of the miR-146aCC genotype was associated with a significantly increased risk of vulnerable plaque compared with the GG + GC genotype (GG + GC versus CC: AOR, 1.629, 95% CI, 1.101–2.410), while the comparison of the miR-146aC N G genotype distributions with regard to plaque load showed no statistically significant difference (P = 0.444). We next performed stratified analysis to determine the clinical significance of differences in miR-146a genotype distribution on plaque stability (Table 5). We found that the increased risk of vulnerable plaque associated with the miR-146aCC genotype was confined to the older patients (GG + GC versus CC: AOR, 1.971, 95% CI, 1.156–3.361), females (GG + GC versus CC: AOR, 1.881, 95% CI, 1.039–3.408), those with a diabetes duration of more than 10 years (GG + GC versus CC: AOR, 2.427, 95% CI, 1.336–4.407), and those in the hypertension subgroup (GG + GC versus CC: AOR, 1.801, 95% CI, 1.125–2.884). In addition, the transcriptional coactivator p300GN A polymorphism was not significantly associated with plaque stability or plaque load. The relevant results are listed in Table 4.

3. Results 3.1. Description of the study population The main demographic and biochemical characteristics of the T2DM patients with and without carotid atherosclerosis are summarized in Table 2. Of the 975 patients, 379 control subjects and 596 T2DM patients with carotid atherosclerosis were enrolled in the study. The T2DM patients with carotid atherosclerosis were significantly older and had a higher systolic blood pressure, pulse pressure and diabetes duration compared with the controls (P b 0.001, respectively). The percentage of smokers was not significantly different between the two groups (P = 0.371). In addition, biochemical parameter analysis showed that the subjects in the case group had significantly higher levels of LP(a) (P = 0.002), hs-CRP (P b 0.001) and fibrinogen (P b 0.001), whereas the triglyceride level was significantly lower than in the T2DM

3.3. Analyses of synergistic effects of gene-environment and gene-gene interactions We performed stepwise regression analysis to evaluate the interaction effects of various environmental factors and the two gene

J. Shen et al. / Thrombosis Research 136 (2015) 1149–1155 Table 6 Stepwise regression analysis to estimate the risk factors for carotid atherosclerosis, plaque stability, and plaque load in type 2 diabetes patients. Independent variables

B

SE

P value

OR

Lower 95% CI

Upper 95% CI

Risk factors for atherosclerotic occurrence⁎ Age 0.080 0.010 0.000 LDL-c 0.261 0.105 0.012 hs-CRP 0.282 0.083 0.001

1.084 1.062 1.298 1.058 1.325 1.127

1.105 1.594 1.559

Risk factors for plaque stability⁎ Sex (male) 1.216 0.195 0.000 Apo B 0.956 0.353 0.007 MiR-146a (CC) 0.471 0.202 0.020

3.373 2.303 2.601 1.301 1.602 1.077

4.941 5.197 2.381

Risk factors for plaque load⁎⁎ Age 0.034 Smoking status 1.091 Pulse pressure 0.024 Apo A1 −1.136

– – – –

– – – –

0.012 0.252 0.008 0.434

0.004 0.000 0.002 0.009

– – – –

⁎ Binary stepwise logistic regression analysis was used to estimate the risk factors. ⁎⁎ Linear stepwise regression analysis was used to estimate the risk factors.

polymorphisms on carotid atherosclerosis in T2DM patients. The results are listed in Table 6. First, binary logistic regression analysis showed that three variables (age, LDL-c, and hs-CRP) were identified as risk factors in the carotid atherosclerosis group but not in the type 2 diabetes patients without carotid atherosclerosis. Moreover, male gender, Apo B, and miR-146a CC were identified as risk factors in the vulnerable plaque group but not in the stable plaque group. Lastly, we analysed the variants influencing the PS. The results showed that age, smoking, and pulse pressure were risk factors for plaque load. However, ApoA1 was a protective factor in this regard. We further analysed the gene-gene interaction between the miR146a (CN G) and p300 (GN A) polymorphisms, but we did not find any combined effect of these two genes on enhancing the risk of carotid atherosclerosis, plaque stability, or plaque load. The relevant data are shown in Supplemental Table S1. 4. Discussion Atherosclerotic cardiovascular disease (ASCVD) remains an important cause of disability and death for diabetic patients in China, despite the variety of effective interventions available for decreasing cardiovascular events. ASCVD is partly hereditary [2], which has prompted genetic studies exploring novel therapeutic targets. Recent large-scale GWAS have uncovered a complex genetic architecture of ASCVD susceptibility [31,32]. The GWAS studies have discovered that ASCVD is not only associated with loci related to lipid metabolism, inflammation, and the immune response but also post-translational protein modifications [8, 31]. Epigenetic modifications are important to the development of ASCVD [6]. In the present study, we investigated the relationship between the SNPs of two epigenetic genes in T2DM patients with carotid atherosclerosis in a Chinese population. Here, we found that the CC genotype of rs2910164 in miR-146a was associated with an increased risk of carotid vulnerable plaque in the Chinese T2DM patients but that this association was not present in the T2DM patients with carotid atherosclerosis or in the plaque load group. In addition, no significant differences in transcriptional coactivator p300 (G NA, rs20551) genotype distribution were observed between the T2DM groups with and without carotid atherosclerosis, plaque stability or plaque load. It is generally known that arterial wall alterations precede clinical cardiovascular events, which then reflect advanced atherosclerotic disease, and that the stability of atherosclerotic plaques is primarily related to acute ischaemic events. Recently, some studies have demonstrated the associations of miR-146a SNPs with the risk of atherosclerosisrelated acute ischaemic events [21,22,33–35], whereas this is the first study to investigate the direct associations of miR-146a polymorphisms

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with atherosclerosis risk in absence of previous clinical events in T2DM patients. We found that the CC genotype of rs2910164 in the miR-146a gene was significantly associated with an increased risk of vulnerable plaque in the T2DM patients. In agreement with our findings, Zhu et al. and Chen et al. have found that the CC genotype of rs2910164 is associated with increased risks of large-artery atherosclerotic stroke and myocardial infarction in the Chinese Han population [22,33]. However, previous studies of the Korean population have discovered that the GG genotype of rs2910164 is associated with an increased risk of large-artery atherosclerotic stroke, whereas the CC genotype is associated with a decreased risk of small-vessel stroke [21]. A potential reason for this difference may be the varying genetic backgrounds among races. The effects of miR-146a polymorphisms on the pathogenesis of vulnerable plaque are currently unknown. In recent years, several studies have suggested that the rs2910164 polymorphism in pre-miR-146 decreases the expression of mature miR-146a and thus affects target mRNA binding [36,37]. In addition, to gain further insights into the manner by which miR-146a participates in vulnerable plaque development, we searched available databases for its potential mRNA targets. Through searches of three target prediction databases, TargetScan (http://www. targetscan.org/), microRNA.org (http://www.microrna.org/microrna/ getGeneForm.do), and MirBase (http://www.microrna.org/search. shtml), we identified six target genes that may be involved in the occurrence and development of vulnerable plaque, including target genes related to the inflammatory response [interleukin-1 receptor-associated kinase 1 (IRAK1), tumour necrosis factor receptor-associated factor 6 (TRAF6), and NADPH-oxidizing enzyme 4 (NOX4)], lipid uptake [tolllike receptor 4 (TLR4)], and VSMC proliferation and migration [Krüppel-like factor 4 (KLF4) and calcium-sensing receptor (CaSR)]. Previous reports have also confirmed the effects of adjustment of the target genes on the occurrence and development of vulnerable plaque [13,38–40]. In addition, the knockdown of miR-146a significantly promotes the apoptotic capacity of VSMCs in vitro [14]. Nevertheless, to our surprise, the processes of inflammation and VSMC proliferation and migration, which miR-146a plays roles in, are active during the occurrence of atherosclerosis. We did not observe a significant association of rs2910164 in miR-146a with the occurrence of carotid atherosclerosis or plaque load. This observation indicated that different stages of atherosclerosis might exhibit different gene expression patterns and be differentially influenced by environmental and genetic factors. However, these findings need to be verified in future studies. We also performed stratified analyses of plaque stability according to age, gender, diabetes duration, and hypertension, revealing that the risk of vulnerable plaque in patients with the miR-146aCC genotype was more pronounced in older subjects, females, those with diabetes duration of more than 10 years, and those with hypertension. Ageing, which is a major risk factor for cardiovascular disease, often leads to a state of chronic low-grade inflammation that has been defined as inflammaging [41]. Recently, Vasa-Nicotera et al. have revealed that miR-146a prevents senescence by mediating the expression of NOX4, which is the major source of reactive oxygen species in human endothelial cells [40]. MiR-146a as an ageing-related microRNA may play direct roles in inducing or contributing to the inflammatory response and to age-related diseases [42]. In addition, we observed that the miR-146a CC genotype was associated with an increased risk of vulnerable plaque in the female subgroup. A difference in miR-146a genotype distribution between the genders has also been observed in a previous study [43]. Recently, a case–control study of postmenopausal monozygotic twins has revealed that serum miR-21 and miR-146a levels are lower in oestrogen-based hormone replacement therapy users compared with their non-using twins, demonstrating relationships of miR-21 and miR-146a with oestrogen [44]. Nevertheless, their relationships with atherosclerosis require further study. Transcriptional coactivator p300, a histone acetyltransferase, plays a pivotal role in diabetic vascular disease [27,45]. Furthermore, in VSMCs undergoing differentiation, recent studies have found that the p300

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protein could be involved in VSMC phenotypic modulation [26]. Thus, p300 may play a partial role in atherosclerosis through the regulation of the inflammatory response and VSMC phenotypic switching. However, in this study, we failed to find any connection between the rs20551 polymorphism in p300 and the risk of carotid atherosclerosis in T2DM. Even analyses of plaque stability and load did not reveal any associations. Recently, rs20551 of p300 has been found to increase the risk of hepatocellular carcinoma in the Moroccan population [46]. However, there is currently a lack of relevant evidence for the correlation of this polymorphism with vascular disease. Therefore, the role of p300 polymorphisms in atherosclerosis should be further researched. In summary, our study provides the first evidence that the miR-146a rs2910164 polymorphism might be associated with carotid vulnerable plaque risk in Chinese T2DM patients, particularly among older patients, females, those with diabetes duration of more than 10 years and those with hypertension. The transcriptional coactivator p300 rs20551 polymorphism may not be a risk factor for the development or progression of atherosclerosis. Replication of the associations in independent genetic studies with larger sample sizes is required, and further investigations involving functional assessments are needed to elucidate the underlying mechanism. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.thromres.2015.10.013. Declaration of interest The authors declare that there are no conflicts of interest. Acknowledgements This study was supported by the National Natural Science Foundation of China (No. 81370940). References [1] H. Mangge, G. Almer, M. Truschnig-Wilders, A. Schmidt, R. Gasser, D. Fuchs, Inflammation, adiponectin, obesity and cardiovascular risk, Curr. Med. Chem. 17 (2010) 4511–4520. [2] D.M. Lloyd-Jones, B.H. Nam, R.S. D'Agostino, D. Levy, J.M. Murabito, T.J. Wang, P.W. Wilson, C.J. O'Donnell, Parental cardiovascular disease as a risk factor for cardiovascular disease in middle-aged adults: a prospective study of parents and offspring, JAMA 291 (2004) 2204–2211. [3] K.J. Hunt, R. Duggirala, H.H. Goring, J.T. Williams, L. Almasy, J. Blangero, D.H. O'Leary, M.P. Stern, Genetic basis of variation in carotid artery plaque in the San Antonio Family Heart Study, Stroke 33 (2002) 2775–2780. [4] C. Dong, A. Beecham, S. Slifer, L. Wang, S.H. Blanton, C.B. Wright, T. Rundek, R.L. Sacco, Genomewide linkage and peakwide association analyses of carotid plaque in Caribbean Hispanics, Stroke 41 (2010) 2750–2756. [5] R. Malin, A. Loimaala, A. Nenonen, M. Mercuri, I. Vuori, M. Pasanen, P. Oja, G. Bond, T. Koivula, T. Lehtimaki, Relationship between high-density lipoprotein paraoxonase gene M/L55 polymorphism and carotid atherosclerosis differs in smoking and nonsmoking men, Metabolism 50 (2001) 1095–1101. [6] C. Napoli, V. Crudele, A. Soricelli, M. Al-Omran, N. Vitale, T. Infante, F.P. Mancini, Primary prevention of atherosclerosis: a clinical challenge for the reversal of epigenetic mechanisms? Circulation 125 (2012) 2363–2373. [7] M.A. Reddy, R. Natarajan, Epigenetic mechanisms in diabetic vascular complications, Cardiovasc. Res. 90 (2011) 421–429. [8] Y. Yamada, T. Nishida, H. Horibe, M. Oguri, K. Kato, M. Sawabe, Identification of hypo- and hypermethylated genes related to atherosclerosis by a genome-wide analysis of DNA methylation, Int. J. Mol. Med. 33 (2014) 1355–1363. [9] J.K. Kim, M. Samaranayake, S. Pradhan, Epigenetic mechanisms in mammals, Cell. Mol. Life Sci. 66 (2009) 596–612. [10] B. Schroen, S. Heymans, Small but smart-microRNAs in the centre of inflammatory processes during cardiovascular diseases, the metabolic syndrome, and ageing, Cardiovasc. Res. 93 (2012) 605–613. [11] S.Y. Park, H.J. Jeong, W.M. Yang, W. Lee, Implications of microRNAs in the pathogenesis of diabetes, Arch. Pharm. Res. 36 (2013) 154–166. [12] H.S. Cheng, N. Sivachandran, A. Lau, E. Boudreau, J.L. Zhao, D. Baltimore, P. DelgadoOlguin, M.I. Cybulsky, J.E. Fish, MicroRNA-146 represses endothelial activation by inhibiting pro-inflammatory pathways, EMBO Mol. Med. 5 (2013) 949–966. [13] K. Yang, Y.S. He, X.Q. Wang, L. Lu, Q.J. Chen, J. Liu, Z. Sun, W.F. Shen, MiR-146a inhibits oxidized low-density lipoprotein-induced lipid accumulation and inflammatory response via targeting toll-like receptor 4, FEBS Lett. 585 (2011) 854–860. [14] S. Dong, W. Xiong, J. Yuan, J. Li, J. Liu, X. Xu, MiRNA-146a regulates the maturation and differentiation of vascular smooth muscle cells by targeting NF-kappaB expression, Mol. Med. Rep. 8 (2013) 407–412.

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