Molecular Medicine Reports Molecular Medicine Reports 1791-2997 1791-3004 D.A. Spandidos 10.3892/mmr.2016.5495 mmr-14-03-2328 Articles miRNA polymorphisms (miR-146a, miR-149, miR-196a2 and miR-499) are associated with the risk of coronary artery disease SungJung-Hoon1 KimSang-Hoon1 YangWoo-In1 KimWon-Jang1 MoonJae-Youn1 KimIn Jai1 ChaDong-Hun1 ChoSeung-Yun1 KimJung Oh2 KimKyeong Ah2 KimOk-Joon3 LimSang-Wook1* KimNam-Keun2* Department of Cardiology, CHA Bundang Medical Center, CHA University, Seongnam, Gyeonggi 463-712 Department of Biomedical Science, College of Life Science, CHA University, Seongnam, Gyeonggi 463-400 Department of Neurology, CHA Bundang Medical Center, CHA University, Seongnam, Gyeonggi 463-712, Republic of Korea Correspondence to: Professor Nam-Keun Kim, Department of Biomedical Science, College of Life Science, CHA University, 335 Pangyo-ro, Bundang-gu, Seongnam, Gyeonggi 463-400, Republic of Korea, E-mail: nkkim@cha.ac.kr; namkkim@naver.com Professor Sang-Wook Lim, Department of Cardiology, CHA Bundang Medical Center, CHA University, 351 Yatap-dong, Bundang-gu, Seongnam, Gyeonggi 463-712, Republic of Korea, E-mail: swlim@cha.ac.kr

Contributed equally

 09 2016 11 07 2016 14 3 2328 2342 15 09 2015 01 06 2016 Copyright © 2016, Spandidos Publications 2016

Small non-coding microRNAs (miRNAs) are not only important for heart and vascular development but are also important in cardiovascular pathophysiology and diseases, such as ischemia and atherosclerosis-related diseases. However, the effect of miR-146a, miR-149, miR-196a2 and miR-499 polymorphisms on coronary artery disease (CAD) susceptibility remain unknown. The aim of the present study was to examine the genotype frequencies of miR-146a, miR-149, miR-196a2 and miR-499 polymorphisms in patients with CAD, and assess their clinical applications for diagnosing and monitoring CAD. Using polymerase chain reaction-amplified DNA, microRNA polymorphisms were analyzed in 522 patients with CAD and 535 control subjects. The miR-149 rs2292832 C>T and miR-196a2 rs11614913 T>C polymorphisms were shown to be significantly associated with CAD prevalence. In subgroup analyses according to disease severity, the miR-146a rs2910164GG genotype was significantly associated with CAD risk in the stent ≥2 group. In addition, miR-146aG/-149T/-196a2C/-499 G allele combination was significantly associated with CAD prevalence (G-T-C-G and G-C-C-G of miR-146a/-149/-196a2/-499). The combination genotypes of miR-146aGG/149TC+CC and miR-149CC/196a2TC were significantly associated with CAD incidence. In subgroup analyses, miR-146a rs2910164 C>G increased the risk of developing CAD in non-smoking, hypertensive and nondiabetic subgroups. Furthermore, miR-149 rs2292832 C>T and miR-196a2 rs11614913 T>C was shown to increase CAD risk in females and patients aged >63 years old. The miR-149T allele, miR-196a2C allele and miR-146aG/-149T/-196a2C/-499 G allele combination were associated with CAD pathogenesis. The combined effects of environmental factor and genotype combination of miRNA polymorphisms may contribute to CAD prevalence.

 coronary artery disease microRNA polymorphism risk factor haplotype combined genotype Introduction

Cardiovascular disease is the single most prevalent health problem. It is associated with the highest rates of mortality and morbidity worldwide, accounting for >14% of all fatalities, and is predicted to remain so until 2030 (1). Coronary artery disease (CAD) is the most common type of cardiovascular disease, in which a plaque builds up inside the coronary arteries that can lead to a complete blockage of blood flow to the heart, resulting in a heart attack. Moreover, plaque build up narrows coronary arteries, which results in decreased blood flow to the heart that can cause chest pain (angina), shortness of breath, or other symptoms. Previous epidemiological studies have identified the role of several modifiable and non-modifiable risk factors in the pathogenesis and prognosis of CAD, including age, gender, smoking, obesity, diet, life style, and genetic factors (2).

One of the major challenges in cardiovascular disease is the identification of reliable clinical biomarkers that can be routinely measured in blood plasma. MicroRNAs (miRNAs), hold promise as novel biomarkers for clinical diagnosis and can be found in a number of bodily fluids, including blood, urine, saliva, plasma and serum. They are protected from degradation in the circulation through association with lipids, proteins or microparticles, rendering them an attractive disease biomarker candidate (3). miRNAs are short non-coding RNA sequences that regulate the expression of multiple target genes, predominantly by binding to the 3′-untranslated region of mRNA transcripts, resulting either in translational inhibition or mRNA degradation (4). In the cardiovascular system, miRNAs are not only important for heart and vascular development but are also essential in cardiovascular pathophysiology and cardiovascular diseases, such as arrhythmia, ischemia and coronary atherogenesis (5). miRNAs have been increasingly implicated in the control of various biological processes, including cell differentiation, cell proliferation, cell growth and apoptosis, and numerous pathological processes, such as cancer, Alzheimer's disease and cardiovascular disease (3). Polymorphic miRNA-mediated gene regulation and mutations in the corresponding sequence space (machinery, miRNA precursors and the target sites) are likely to make a significant contribution to phenotypic variation, including the susceptibility to diseases, such as cancer and cardiovascular disease (6). A single nucleotide polymorphism (SNP) is a DNA sequence variation of a single nucleotide, adenine (A), thymine (T), cytosine (C) or guanine (G), on genomic DNA. An SNP in an miRNA sequence may alter miRNA expression and/or maturation and have been shown to be associated with the progression of CAD. miR-146aG>C, miR-149C>T, miR-196a2T>C and miR-499A>G polymorphisms have been reported to be associated with lung, breast, thyroid, colon and gastric cancer (7). Recently, four well-known miRNA polymorphisms in pre-miRNA sequences [miR-146aC>G (rs2910164; chromosome 5, 159912418), miR-149T>C (rs2292832; chromosome 2, 241395503), miR-196a2T>C (rs11614913; chromosome 12, 54385599) and miR-499A>G (rs3746444; chromosome 20, 33578251)] have been investigated in a variety of diseases and were found to contribute to pathogenesis (8).

In particular, significantly increased levels of miR-146a (6.25-fold) compared with controls and patients with the wild type variant were associated with the CC genotype in patients with CAD (P<0.0001) (9). hsa-miR-149 may be involved in congenital heart disease by regulating methylenetetrahydrofolate reductase (MTHFR), but which can be altered by regulation of MTHFR, because the binding site has an rs4846049 polymorphism in the MTHFR 3′-untranslated region (10). In addition, it has been suggested that the plasma miR-499 concentration may be a biomarker of myocardial infarction in humans (11,12). Further follow-up case-control studies, identified two SNPs (rs11614913 and rs3746444) in hsa-miR-196a2 and hsa-miR-499 that are associated with an increased risk of developing cancer (13–15), congenital heart disease (16) and dilated cardiomyopathy. Thus, it was hypothesized that SNPs in these miRNAs may also contribute to susceptibility to and unfavorable prognosis of CAD.

According to recent data, the miR-146aG, miR-149T, miR-196a2C and miR-499 G alleles are possible genetic predisposing factors in various diseases (17–19). Moreover, these four miRNAs have been shown to affect vascular damage responses, such as those in abortion, cancer, ischemic stroke and cardiovascular disease (8,17,18,20,21). The miR-146a, -149, -196a2 and -499 alleles are closely associated with the regulation of tumor necrosis factor-α (TNF-α), MTHFR, Annexin A1 (ANXA1), and C-reactive protein (CRP), respectively (8). Furthermore, in the circulatory system, these miRNA targets are important in thrombosis and inflammatory signaling pathways. Recent advances in genetic research have systematically identified and analyzed human polymorphisms in miRNAs and/or miRNA target sites (22,23). However, the majority of these studies focus on SNPs in the target sites and their effects on disease-related miRNAs (8,22–24). One of these studies has mentioned the interplay effects between miRNAs' SNP and target gene SNPs in disease (24). There are currently no data regarding the role of miRNA polymorphisms in CAD pathogenesis. Therefore, the present study aimed to investigate a miRNA-miRNA synergistic effect associated with CAD by genetic association analyses of these four well known miRNA variants and CAD patients, with or without percutaneous coronary intervention (PCI) according to disease severity.

 Materials and methods Study population

The study subjects were recruited from the South Korean provinces of Seoul and Kyeonggi-do between 2006 and 2015 from the Department of Cardiology at the CHA Bundang Medical Center in Seongnam, South Korea. The study was approved by the Institutional Review Board of CHA Bundang Medical Center (Seongnam, Korea). In total, 522 patients with CAD were referred from the Department of Cardiology at CHA Bundang Medical Center, CHA University. All patients who presented with stable coronary artery disease or acute coronary syndromes (including unstable angina with or without ST-segment elevation) and at least one coronary lesion with >50% stenosis in a vessel with a diameter of 2.25–4.00 mm between 2006 and 2015, were screened for eligibility. No restrictions were placed on the total number of treated lesions, which vessels were treated, lesion length, or the number of stents implanted. Exclusion criteria were history of acute myocardial infarction and life expectancy <1 year. All patients underwent coronary angiography and electrocardiography. Diagnoses were made by coronary angiography, and were confirmed by at least 1 independent experienced cardiologist.

In total, 535 gender- and age (±5 years)-matched control subjects from patients presented at the Department of Cardiology at the CHA Bundang Medical Center (Seongnam, Korea) during the same period for health examinations, including biochemical testing, electrocardiograms, and coronary computed tomography scans. Exclusion criteria were the same as those used in the patient group, as well as a recent history of anginal symptoms. Hypertension was defined as systolic pressure >140 mmHg and diastolic pressure >90 mmHg on >1 occasion and included patients currently taking hypertensive medications. Diabetes mellitus was defined as a fasting plasma glucose level >126 mg/dl (7.0 mmol/l) and included patients taking diabetic medications. Smoking refers to patients who currently smoke. Hyperlipidemia was defined as a high fasting serum total cholesterol (TC) level (≥240 mg/dl) or an antihyperlipidemic agent treatment history.

 Genetic analyses

DNA was extracted from peripheral blood leukocytes using the G-dex II Genomic DNA Extraction kit (iNtRON Biotechnology, Inc., Seongnam, Korea), according to the manufacturer's instructions. Polymerase chain reaction (PCR) restriction fragment length polymorphism assays to analyze the miR-146aC>G, miR-196a2T>C and miR-499A>G polymorphisms. Genotyping of the miR-149T>C polymorphism was determined using quantitative PCR (RG-3000, Corbett Research, Mortlake, Australia) for allelic discrimination. Primers and TaqMan probes were designed using Primer Express Software (version 2.0; Thermo Fisher Scientific, Inc., Waltham, MA, USA), and synthesized and supplied by Applied Biosystems (Foster City, CA, USA). The reporter dyes used were 5-carboxyfluorescein (FAM) and 2′,7′-dimethoxy-4′,5′-dichloro-6-carboxyfluorescein (JOE). The primer sequences for amplification are as follows: forward: 5′-CAT GGG TTG TGT CAG TGT CAG AGC T-3′ and reverse: 5′-TGC CTT CTG TCT CCA GTC TTC CAA-3′ for miR-146aC>G; forward: 5′-CTG GCT CCG TGT CTT CAC TC-3′ and reverse: 5′-CAA CTC GCC CAG CCG-3′ for miR-149T>C; forward: 5′-CCC CTT CCC TTC TCC TCC AGA TA-3′ and reverse: 5′-CGA AAA CCG ACT GAT GTA ACT CCG-3′ for miR-196a2T>C; and forward: 5′-CAA AGT CTT CAC TTC CCT GCC A-3′ and reverse: 5′-GAT GTT TAA CTC CTC TCC ACG TGA TC-3′ for miR-499A>G. The selected probes were 5′-FAM-TGG GGC AGC CGG AAC AAC-TAMRA-3′ (C allele detecting probe) and 5′-JOE-TGG GGC AGC TGG AAC AAC-TAMRA-3′ (T allele detecting probe) for miR-149T>C. Underlined bases in the primers above are mismatches with the complementary sequence. The miR-146aC>G and miR-196a2T>C polymorphisms were digested by SacI and MspI, respectively, for 16 h at 37°C (New England BioLabs, Beverly, MA, USA). The miR-499A>G polymorphism was digested with BclI for 16 h at 50°C (New England Biolabs). The PCR conditions were as follows: Initial denaturation at 94°C for 5 min/denaturation at 94°C for 30 sec; annealing at 58°C for 30 sec, extension at 72°C for 30 sec with 32 amplification cycles/final extension at 72°C for 5 min for the miR-146aG>C, miR-196a2C>T and miR-499C>T polymorphisms. The miR-149T>C was designed for real-time quantitative PCR (RT-qPCR): Initial denaturation at 95°C for 15 min/denaturation at 95°C for 30 sec/annealing at 58°C for 50 sec with 50 amplification cycles. The reaction product (12 μl) was run on a 3.0% ethidium bromide-stained agarose gel and directly visualized under ultraviolet illumination. Approximately 10% of the PCR assays were randomly repeated for each of the miRNA polymorphisms and the results were checked for concordance by DNA sequencing using an automatic sequencer (ABI 3730x l DNA analyzer; Applied Biosystems). The concordance of the quality control samples was 100%.

 Statistical analysis

To estimate the relative risk of the various genotypes for CAD, the odds ratio (OR) and 95% confidence interval (CI) were calculated. Case and control groups were compared using Student's t-test for continuous variables, and the χ2 test for categorical variables. For multivariate analyses, logistic regression analyses were used to adjust for possible confounders, including age, gender, hypertension, Diabetes mellitus, hyperlipidemia and smoking. P<0.05 was considered to indicate a statistically significant difference. Multiple hypotheses testing was performed using the Benjamini-Hochberg method to control for false discovery rate (FDR) in the logistic regression analysis. Calculating the FDR is a way to address the problems associated with multiple comparisons, and FDR provides a measure of the expected proportion of false-positives among data. Analyses were performed using GraphPad Prism 4.0 (GraphPad Software, Inc., San Diego, CA, USA), StatsDirect Statistical Software Version 2.4.4 (StatsDirect Ltd., Altrincham, UK), and MedCalc (Version 7.4 for Windows; MedCalc, Ostend, Belgium). The multifactor dimensionality reduction (MDR) method was proposed by Ritchie et al (25), and implemented by Hahn et al (26). The MDR method has been described in detail previously (25,26). Briefly, the MDR is comprised of 2 steps. The best combination of multifactors is initially selected and the genotype combinations are classified into high and low-risk groups (26). Interaction analyses were performed in the open-source MDR software package (v.2.0) available from www.epistasis.org. Using MDR analyses, all possible allele combinations were constrcted for gene-gene interactions. HAPSTAT software was used to estimate the frequencies of allele combinations for the polymorphisms selected by MDR analysis with strong synergistic effects. HAPSTAT allows testing of the haplotype (or allele combination) effects by maximizing the likelihood (from the observed data) that properly accounts for phase uncertainty and study design. Current versions of the HAPSTAT software (v.3.0) are available from www.bios.unc.edu/~lin/hapstat/.

 Results Characteristics of the study population

Baseline characteristics of the CAD patients and controls are shown in Table I. No significant differences in the age and gender distribution were identified between the patients with CAD and controls, suggesting that our frequency-matching on age and gender was satisfactory. The average body mass index (BMI) of CAD patients was significantly higher than that of the controls (P<0.0001). In addition, the serum total cholesterol (TC) and TG levels were significantly higher and those of high density lipoprotein-cholesterol (HDL-C) were significantly lower in the patients with CAD compared with the controls (P=0.022, P=0.004 and P=0.001, respectively); however, no difference was identified in the level of serum low density lipoprotein-cholesterol (LDL-C) between the two groups (P=0.116). For the disease history, 135 (25.9%) patients had a history of Diabetes mellitus (DM), which was significantly higher compared with controls (P=0.0003), however, no significant difference was identified in the hypertension history between CAD patients and controls. Furthermore, no significant difference was identified in smoking (P=0.165), the hyperlipidemia history (P=0.226), plasma homocysteine (HCY) level (P=0.315), serum folate level (P=0.288) and serum vitamin B12 level (P=0.097) were shown between CAD patients and controls.

 miR-149 and miR-196a2 polymorphisms are significantly correlated with CAD

miR-146aC>G, -149T>C, -196a2T>C, and -499A>G polymorphisms were investigated and their genotype distributions in CAD patients and control subjects were determined (Table II). The adjusted odds ratio (AOR) from logistic regression analyses with respect to age, gender, hypertension, diabetes mellitus and hyperlipidemia was calculated. The miRNA genotype frequencies of controls were consistent with Hardy-Weinberg equilibrium. The miR-149 rs2292832C>T polymorphism was significantly different between patients with CAD and control subjects (TT vs. TC: COR, 1.312, 95% CI, 1.018–1.692; AOR, 1.336, 95% CI, 1.031–1.731). The miR-196a2 rs11614913 T>C polymorphism was significantly different between CAD patients and control subjects (TT vs. TC: COR, 0.736, 95% CI, 0.558–0.971; TT vs. TC+CC: COR, 0.768, 95% CI, 0.592–0.996). However, miR-146a rs2910164C>G, and miR-499 rs3746444A>G polymorphisms were not significantly different between CAD patients and control subjects (Table II).

 Genotype frequencies of the miRNA polymorphisms between CAD patients with/without PCI

The miR-146aC>G, -149T>C, -196a2T>C, and -499A>G polymorphisms in CAD patients with or without PCI and in control subjects was determined. The incidence of the miR-146a rs2910164 C>G polymorphism was significantly different between patients with CAD with a stent and control subjects (CC+CG vs. GG: COR, 1.499, 95% CI, 1.036–2.169; CC+CG vs. GG: AOR, 1.473, 95% CI, 1.005–2.159), and between CAD patients without stent and control subjects (CC vs. GG: COR, 0.531, 95% CI, 0.282–0.998, AOR, 0.517, 95% CI, 0.267–1.000; CC+CG vs. GG: COR, 0.495, 95% CI, 0.272–0.900; CC+CG vs. GG: AOR, 0.479, 95% CI, 0.258–0.891). The miR-149 rs2292832 C>T polymorphism was significantly different between CAD patients with a stent and control subjects (TT vs. TC: COR, 1.392, 95% CI, 1.042–1.861, AOR, 1.381, 95% CI, 1.023–1.864; TT vs. TC+CC: COR, 1.338, 95% CI, 1.015–1.765). However, the miR-196a2 rs11614913 T>C was significantly different between patients with CAD without a stent and control subjects (TT vs. TC: COR, 0.678, 95% CI, 0.471–0.975, TT vs. CC: COR, 0.510, 95% CI, 0.308–0.844, AOR, 0.523, 95% CI, 0.313–0.875; TT vs. TC+CC: COR, 0.630, 95% CI, 0.446–0.890, AOR, 0.647, 95% CI, 0.455–0.919; TT+TC vs. CC: AOR, 0.621, 95% CI, 0.388–0.993). The miR-499 rs3746444 A>G polymorphisms were not identified to be significantly different between patients with CAD with or without a stent and control subjects (Table III).

 Analysis of the number of stents after PCI according to disease severity

To examine whether the effect of each polymorphism is related to the number of implanted stents for a given patient, the patients with stents were group divided into two subgroups (=1 and ≥2) according to disease severity. In subgroup analyses, the miR-146a rs2910164 C>G polymorphism was significantly associated with increased disease severity in the stent ≥2 group (CC vs. GG: COR, 1.875, 95% CI, 1.013–3.468; CC+CG vs. GG: COR, 1.796; 95% CI, 1.043–3.094). The miR-149 rs2292832 C>T showed significant association with stent=1 group (TT vs. CC: COR, 1.399, 95% CI, 1.011–1.937; TT vs. TC+CC: COR, 1.366, 95% CI, 1.001–1.863). However, miR-196a2 rs11614913 T>C, and miR-499 rs3746444 A>G polymorphisms were not observed to be significantly different between the 2 subgroups (=1 and ≥2) and control subjects (Table IV).

 Subgroup analyses

To determine the additional clinical significance, stratified analyses according to age, gender, hypertension, diabetes mellitus, hyperlipidemia and smoking status. In the stratified analyses, it was demonstrated that the miR-146a rs2910164 C>G polymorphism showed significant increases in the incidence of CAD compared with the CC genotype in the hypertension (CC+CG vs. GG: AOR, 1.933, 95% CI, 1.133–3.298), nondiabetic (CC+CG vs. GG: AOR, 1.619, 95% CI, 1.047–2.504), and non-smoking subgroups (CC vs. GG: AOR=1.860, 95% CI, 1.099–3.147). Furthermore, the miR-149 rs2292832 C>T polymorphism showed significant increases in the incidence of CAD compared with the TT genotype in the older age (age ≥63) (TT vs. TC: AOR, 1.516, 97% CI, 1.041–2.209), female (TT vs. TC: AOR, 1.947, 95% CI, 1.208–3.138; TT vs. TC+CC: AOR, 1.976, 95% CI, 1.244–3.138) and nonsmoking subgroups (TT vs. TC: AOR, 1.604, 95% CI, 1.102–2.333; TT vs. TC+CC: AOR, 1.549, 95% CI, 1.078–2.225; Table V). For CAD, the miR-196a2 rs11614913T>C genotype was associated with a significantly increased risk of CAD in the older subgroup (age ≥63) (TT vs. TC: AOR, 0.656, 95% CI, 0.435–0.988), and female subgroup (TT+TC vs. CC: AOR, 1.860, 95% CI, 1.097–3.154; Table VI).

 Gene-gene interaction analyses using the MDR method

The possible allele combinations of miR-146a, miR-149, miR-196a2, and miR-499 were constructed to analyze gene-gene interactions (Table VII). As a result, miR-146a/149, miR-146a/499, miR-146a/149/196a2, miR-146a/149/499, miR-146a/196a2/499, miR-149/196a2/499, and miR-146a/149/196a2/499 models were selected by the MDR method. Several allele combination frequencies were significantly different between patients with CAD and controls. When patients with CAD were compared with controls, miR-146a/149 (C-T vs. G-C:OR, 1.459, 95% CI, 1.075–1.981), miR-146a/499 (C-A vs. C-G:OR, 0.707, 95% CI, 0.507–0.985), miR-146a/149/196a2 (C-T-T vs. G-C-T:OR, 1.713, 95% CI, 1.136–2.584), miR-146a/149/499 (C-T-A vs. C-T-G:OR, 0.559, 95% CI, 0.370–0.844), miR-146a/196a2/499 (C-T-A vs. C-T-G:OR, 0.595, 95% CI, 0.367–0.966), miR-149/196a2/499 (T-T-A vs. T-T-G: OR, 0.603, 95% CI, 0.387–0.940), and miR-146a/149/196a2/499 (C-T-T-A vs. G-T-C-A: OR, 0.684, 95% CI, 0.475–0.983; G-T-C-G: OR, 43.55, 95% CI, 2.575–736.512; G-C-T-G: OR, 3.437, 95% CI, 1.378–8.572; G-C-C-G: OR, 0.048, 95% CI, 0.003–0.817) were significantly associated with disease prevalence (Table VII).

 Allele combinations of miR-146a, -149, -196a2 and -499 polymorphisms with synergistic effects

To investigate the genes without environmental interaction, the combined effects between miRNA polymorphisms and the prevalence of CAD was analyzed. The AOR from the logistic regression analyses with respect to the age, gender, hypertension, diabetes mellitus and hyperlipidemia was calculated. There were significant combined gene (miR-146a/149) effects when the environmental influence was excluded in CAD risk. (CG/TT: AOR, 0.677, 95% CI, 0.473–0.970; GG/TC: AOR, 1.683, 95% CI, 1.013–2.797; GG/CC: AOR, 4.200, 95% CI, 1.082–16.306; GG/TC+CC: AOR, 1.939, 95% CI, 1.206–3.118), miR-149/196a2 (CC/TC: AOR, 0.436, 95% CI, 0.198–0.961; Table VIII).

 Discussion

Circulating miRNAs have many of the essential characteristics to be a good biomarker of noninvasive measurability. For example, a high degree of sensitivity and specificity, which allows the early detection of pathological states, including the time-related changes during the course of disease, a long half-life within the sample, and rapid and cost-effective laboratory detection (22). miRNAs are important in a number of physiological and pathological processes, including tumorigenesis, proliferation, metabolism, immune function and epigenetics (27–30). Emerging evidence has indicated that circulating miRNAs may be biomarkers for cardiovascular diseases, including essential hypertension, heart failure, Diabetes mellitus, stroke, coronary artery disease, acute myocardial infarction and acute pulmonary embolism (15,31–34). To the best of our knowledge, this is the first study to provide evidence that four miRNA polymorphisms are involved in the predisposition to CAD with or without prior PCI. In this study, the miR-146aC>G (rs2910164), miR-149T>C (rs2292832), miR-196a2T>C (rs11614913), and miR-499A>G (rs3746444) polymorphisms were investigated in CAD patients with or without PCI. Furthermore, the results also demonstrated that miR-149T>C (rs2292832) and miR-196a2T>C (rs11614913) polymorphisms were significantly associated with the development of CAD in females and patients over the age of 63 years old. The results also suggest that the miR-146aC>G (rs2910164) polymorphism was significantly associated with hypertension, whereas there was no correlation of the smoking and diabetes mellitus groups with an increased risk of developing CAD. Moreover, the severity of CAD was positively correlated with the number of stents implanted: Patients with CAD that had undergone ≥2 stent treatments showed an increased CAD incidence compared with patients having one stent treatment for the miR-146a rs2910164 C>G polymorphism (Table IV). The precise mechanisms of miRNA-mediated gene expression and maturation are largely unknown; however, studies have suggested several mechanisms, including genetic and epigenetic mechanisms (DNA methylation, histone modification and non-coding RNAs) (28,35). In addition, small variations in the quantity of miRNAs may have an effect on thousands of target mRNAs and result in diverse functional consequences. The most common genetic variations, such as SNPs, in miRNA sequences may also be functional and therefore may represent ideal candidate biomarkers for cancer and cardiovascular diseases, including CAD.

miR-146a, -149, -196a2 and -499 can regulate TNF-α, methylenetetrahydrofolate reductase, ANXA1 and CRP, respectively (10,36,37). According to previously published studies, TNF-α, methylenetetrahydrofolate reductase, ANXA1, and CRP were well-known risk factors for cerebral ischemia (38–40). TNF-α is associated with increased plasminogen activator inhibitor-1 protein levels (30); methylenetetrahydrofolate reductase dysfunction is associated with plasma total HCY accumulation (29); ANXA1 is connected to decreased TNF-α levels (41); and CRP can elevate blood pressure, body mass index, insulin resistance and TG levels (42). In addition, CRP and TNF-α are simultaneously activated in stress conditions (43). These data suggest that TNF-α, ANXA1 and CRP may be closely linked. In fact, MDR analyses in the present study indicated genetic interactions between miR-146a/-196a2/-499 and miR-146a/-196a2. There have been limited studies regarding the functions of miRNA polymorphisms. The miR-146aG and miR-196a2T allele were shown to be associated with decreased mature miRNA levels (44,45). miR-149T>C and miR-499A>G were located in a pre-miRNA structure, not the mature miRNA form. However, these polymorphisms were affected by miRNA biogenesis. In addition, there was no mature miRNA expression, according to miR-149T>C and miR-499A>G. However, miR-499AA is associated with decreased plasma CRP concentrations (46). Based on these results, it is hypothesized that miR-146aC, miR-196a2T and miR-499A alleles have protective roles in vascular pathogenesis through inhibition of TNF-α and CRP levels. However, it was not possible to measure miRNA, TNF-α and CRP levels in the present study.

Recent advances in genetic research have systematically identified and analyzed human polymorphisms in miRNAs and/or miRNA target sites (2,23). However, the majority of these studies focused on SNPs in the target sites and their effects on disease-related miRNAs, while only a few studies have reported the understanding the synergistic regulation of miRNAs and their potential targeted SNPs cooperative effects contributing to disease progression. With the rapid identification of disease-related miRNAs, there is a requirement to determine their functional relationships contributing to diseases at a systems biology level.

Data from the present study indicate that specific combinations of miRNA haplotypes are correlated with the incidence of CAD. For example, the G-T-C-G polymorphism of miR-146a/149/196a2/499 correlates the most strongly with CAD (OR, 43.55, 95% CI, 2.575–736.5, P<0.0001), followed by the G-C-C-G (OR, 0.048, 95% CI, 0.003–0.817, P=0.001) haplotype. The C-T-G polymorphism in miR-146a/149/499 exhibited the strongest correlation with CAD incidence among all three miRNA haplotypes (OR, 0.559, 95% CI, 0.370–0.844, P=0.006). Thus, the specific combination of miRNA polymorphisms appear to provide synergistic effects.

There are several limitations of the present study. It is not yet clear which genetic polymorphisms predict the phenotypes associated with CAD and disease severity. The present study population comprised of only Korean individuals, and these results require validation in other ethnic groups. This was a hospital-based case-control study that had a relatively small sample size. However, the recruitment of >1,000 individuals from an ethnically homogeneous population (Koreans have a low degree of interracial marriage) is enough to give reliable data.

In conclusion, the miR-146aC>G and miR-149T>C polymorphisms were associated with an increased risk of CAD in the Korean population. The present study marks the first report of an association between stroke and SBI and miRNA polymorphisms (miR-146aC>G, -149T>C, -196a2T>C and -499A>G) in the Korean population. Therefore, additional studies of other racial and ethnic populations regarding the biological functions of miRNA are required to fully understand the role of miRNA polymorphisms in CAD risk.

 Acknowledgments

This study was supported by a National Research Foundation (NRF) of Korea Grant funded by the Korean Government, Ministry of Education (grant no. NRF-2013R1A1A2008177), and supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare (grant no. HI16C1559), Republic of Korea.

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Baseline characteristics between controls and patients with CAD.

Characteristic Controls CAD patients P-value
N 535 522
Age (years, mean ± SD) 60.68±11.59 60.75±11.61 0.932
Male, n (%) 267 (50.0) 261 (50.0) 0.972
Hypertension, n (%) 243 (45.4) 227 (43.5) 0.736
SBP (mmHg, mean ± SD) 131.65±17.00 128.46±22.76 0.010
DBP (mmHg, mean ± SD) 80.64±11.68 79.09±13.63 0.048
Diabetes millitus, n (%) 79 (14.8) 135 (25.9) 0.0003
FBS (mg/dl) 112.46±37.37 140.90±62.24 <0.0001
BMI ≥25 kg/m2, n (%) 124 (23.2) 263 (50.4) <0.0001
TG (mg/dl, mean ± SD) 140.86±86.07 158.79±110.12 0.004
LDL-C (mg/dl, mean ± SD) 116.97±40.17 112.08±39.52 0.116
HDL-C (mg/dl, mean ± SD) 46.72±14.73 43.65±11.10 0.001
Smokers (%) 150 (28.0) 176 (33.7) 0.165
Hyperlipidemia (%) 122 (22.8) 142 (27.0) 0.226
HCY (μmol/l, mean ± SD) 9.83±3.97 10.12±5.27 0.315
Folate (nmol/l, mean ± SD) 8.99±8.01 8.38±9.15 0.288
Vitamin B12 (pg/ml, mean ± SD) 753.09±707.34 906.47±1327.12 0.097
Total cholesterol (mg/dl, mean ± SD) 192.38±43.25 186.03±45.69 0.022

P-values were calculated by a two-sided t-test for continuous variables and χ2 test for categorical variables. Bold indicates significant values. CAD, coronary artery disease; SD, standard deviation; BP, blood pressure, FBS, fasting blood sugar; HTN, hypertension; DM, Diabetes mellitus; BMI, body mass index; TG, triglycerides; LDL/HDL-C, low/high density lipoprotein-cholesterol; HCY, homocysteine.

Genotype frequencies of miRNA polymorphisms between patients with CAD and control subjects.

Genotype Controls (n=535) CAD (n=522) COR (95% CI) P-value AOR (95% CI) P-value
miR-146a rs2910164 C>G
 CC 202 (37.8) 203 (38.9) 1.000 (reference) 1.000 (reference)
 CG 260 (48.6) 242 (46.4) 0.926 (0.713–1.204) 0.566 0.930 (0.711–1.216) 0.594
 GG 73 (13.6) 77 (14.8) 1.050 (0.722–1.527) 0.800 0.995 (0.676–1.464) 0.978
 Dominant (CC vs. CG+GG) 0.953 (0.744–1.222) 0.705 0.941 (0.730–1.214) 0.641
 Recessive (CC+CG vs. GG) 1.095 (0.775–1.547) 0.607 1.058 (0.743–1.508) 0.755
 HWE-P 0.460 0.724
miR-149 rs2292832 C>T
 TT 263 (49.2) 227 (43.5) 1.000 (reference) 1.000 (reference)
 TC 219 (40.9) 248 (47.5) 1.312 (1.018–1.692) 0.036 1.336 (1.031–1.731) 0.029
 CC 53 (9.9) 47 (9.0) 1.027 (0.668–1.581) 0.902 1.027 (0.659–1.601) 0.905
 Dominant (TT vs. TC+CC) 1.257 (0.986–1.601) 0.065 1.276 (0.996–1.634) 0.054
 Recessive (TT+TC vs. CC) 0.900 (0.596–1.356) 0.616 0.890 (0.585–1.356) 0.589
 HWE-P 0.456 0.073
miR-196a2 rs11614913 T>C
 TT 153 (28.6) 179 (34.3) 1.000 (reference) 1.000 (reference)
 TC 274 (51.2) 236 (45.2) 0.736 (0.558–0.971) 0.030 0.786 (0.591–1.044) 0.096
 CC 108 (20.2) 107 (20.5) 0.847 (0.601–1.194) 0.343 0.858 (0.606–1.217) 0.391
 Dominant (TT vs. TC+CC) 0.768 (0.592–0.996) 0.046 0.801 (0.614–1.046) 0.103
 Recessive (TT+TC vs. CC) 1.019 (0.756–1.375) 0.900 0.965 (0.710–1.311) 0.820
 HWE-P 0.465 0.078
miR-499 rs3746444 A>G
 AA 354 (66.2) 358 (68.6) 1.000 (reference) 1.000 (reference)
 AG 168 (31.4) 155 (29.7) 0.912 (0.701–1.187) 0.494 0.910 (0.695–1.192) 0.495
 GG 13 (2.4) 9 (1.7) 0.685 (0.289–1.622) 0.389 0.733 (0.297–1.813) 0.502
 Dominant (AA vs. AG+GG) 0.896 (0.693–1.159) 0.403 0.895 (0.687–1.165) 0.410
 Recessive (AA+AG vs. GG) 0.705 (0.299–1.662) 0.424 0.747 (0.304–1.837) 0.526
 HWE-P 0.182 0.091

Adjusted for age, gender, hypertension, diabetes mellitus and hyperlipidemia. Bold indicates significant values. CAD, coronary artery disease; COR, crude odds ratio; CI, confidence interval, AOR, adjusted odds ratio.

Genotype frequencies of miRNA polymorphisms between CAD patients and control subjects.

Genotype Controls(n=535) Stent(n=329) COR(95% CI) P-value AOR(95% CI) P-value Non-stent(n=193) COR(95% CI) P-value AOR(95% CI) P-value
miR-146a rs2910164C>G
 CC 202(37.8) 130(39.5) 1.000(reference) 1.000(reference) 73(37.8) 1.000(reference) 1.000(reference)
 CG 260(48.6) 136(41.3) 0.813(0.601–1.100) 0.179 0.803(0.585–1.102) 0.174 106(54.9) 1.128(0.795–1.601) 0.500 1.158(0.811–1.653) 0.420
 GG 73(13.6) 63(19.2) 1.341(0.897–2.006) 0.153 1.293(0.852–1.963) 0.228 14(7.3) 0.531(0.282–0.998) 0.049 0.517(0.267–1.000) 0.050
 Dominant (CC vs. CG+GG) 0.929(0.701–1.231) 0.606 0.909(0.678–1.218) 0.521 0.997(0.710–1.400) 0.987 1.015(0.719–1.435) 0.931
 Recessive (CC+CG vs. GG) 1.499(1.036–2.169) 0.032 1.473(1.005–2.159) 0.047 0.495(0.272–0.900) 0.021 0.479(0.258–0.891) 0.020
 HWE-P 0.460 0.013 0.003
miR-149 rs2292832C>T
 TT 263(49.2) 138(42.0) 1.000(reference) 1.000(reference) 89(46.1) 1.000(reference) 1.000(reference)
 TC 219(40.9) 160(48.6) 1.392(1.042–1.861) 0.025 1.381(1.023–1.864) 0.035 88(45.6) 1.187(0.841–1.677) 0.329 1.257(0.885–1.785) 0.202
 CC 53(9.9) 31(9.4) 1.115(0.684–1.817) 0.663 1.084(0.648–1.813) 0.758 16(8.3) 0.892(0.485–1.640) 0.713 0.944(0.510–1.746) 0.854
 Dominant (TT vs. TC+CC) 1.338(1.015–1.765) 0.039 1.327(0.996–1.769) 0.053 1.130(0.812–1.572) 0.468 1.204(0.860–1.685) 0.279
 Recessive (TT+TC vs. CC) 0.946(0.594–1.508) 0.816 0.923(0.570–1.496) 0.746 0.822(0.458–1.476) 0.512 0.873(0.483–1.577) 0.652
 HWE-P 0.456 0.112 0.373
miR-196a2 rs11614913T>C
 TT 153(28.6) 104(31.6) 1.000(reference) 1.000(reference) 75(38.9) 1.000(reference) 1.000(reference)
 TC 274(51.2) 145(44.1) 0.779(0.565–1.072) 0.126 0.874(0.625–1.223) 0.433 91(47.1) 0.678(0.471–0.975) 0.036 0.706(0.487–1.024) 0.067
 CC 108(20.2) 80(24.3) 1.090(0.744–1.596) 0.659 1.146(0.773–1.699) 0.498 27(14.0) 0.510(0.308–0.844) 0.009 0.523(0.313–0.875) 0.014
 Dominant (TT vs. TC+CC) 0.8670.643–1.168) 0.347 0.948(0.694–1.293) 0.734 0.630(0.446–0.890) 0.009 0.647(0.455–0.919) 0.015
 Recessive (TT+TC vs. CC) 1.270(0.915–1.765) 0.154 1.213(0.862–1.706) 0.268 0.643(0.407–1.017) 0.059 0.621(0.388–0.993) 0.047
 HWE-P 0.465 0.039 0.943
miR-499 rs3746444A>G
 AA 354(66.2) 228(69.3) 1.000(reference) 1.000(reference) 130(67.4) 1.000(reference) 1.000(reference)
 AG 168(31.4) 95(28.9) 0.878(0.649–1.187) 0.398 0.871(0.637–1.192) 0.389 60(31.1) 0.973(0.681–1.390) 0.878 0.946(0.657–1.362) 0.764
 GG 13(2.4) 6(1.8) 0.717(0.269–1.912) 0.506 0.941(0.346–2.559) 0.905 3(1.5) 0.628(0.176–2.241) 0.474 0.444(0.097–2.029) 0.295
 Dominant(AA vs. AG+GG) 0.866 (0.645–1.164) 0.340 0.874 (0.644–1.187) 0.389 0.948 (0.668–1.345) 0.764 0.907 (0.633–1.299) 0.594
 Recessive(AA+AG vs. GG) 0.746(0.281–1.982) 0.557 0.998(0.369–2.699) 0.997 0.634(0.179–2.249) 0.481 0.439(0.097–1.988) 0.285
 HWE-P 0.182 0.274 0.180

Adjusted for age, gender, hypertension, diabetes mellitus, and hyperlipidemia. Bold indicates significant values. CAD, coronary artery disease; miR, microRNA; COR, crude odds ratio; AOR, adjusted odds ratio; CI, confidence interval; HWE-P, Hardy-Weinberg equilibrium P-value.

Genotype frequencies of miRNA polymorphisms between CAD patients and control subjects.

Genotype Controls(n=535) Stent=1(n=234) COR(95% CI) P-value AOR(95% CI) P-value Stent ≥2(n=95) COR(95% CI) P-value AOR(95% CI) P-value
miR-146a rs2910164C>G
 CC 202(37.8) 93(39.7) 1.000(reference) 1.000(reference) 31(32.6) 1.000(reference) 1.000(reference)
 CG 260(48.6) 99(42.3) 0.730(0.521–1.023) 0.067 0.722(0.508–1.025) 0.068 43(45.3) 1.078(0.656–1.772) 0.768 1.049(0.624–1.765) 0.857
 GG 73(13.6) 42(17.9) 1.174(0.749–1.840) 0.484 1.088(0.682–1.734) 0.724 21(22.1) 1.875(1.013–3.468) 0.045 1.825(0.966–3.448) 0.064
 Dominant(CC vs. CG+GG) 0.827(0.605–1.131) 0.235 0.807(0.584–1.116) 0.195 1.252(0.788–1.990) 0.341 1.217(0.755–1.962) 0.421
 Recessive(CC+CG vs. GG) 1.384(0.914–2.097) 0.125 1.341(0.872–2.060) 0.181 1.796(1.043–3.094) 0.035 1.748(0.996–3.070) 0.052
miR-149 rs2292832C>T
 TT 263(49.2) 97(41.5) 1.000(reference) 1.000(reference) 41(43.2) 1.000(reference) 1.000(reference)
 TC 219(40.9) 114(48.7) 1.399(1.011–1.937) 0.043 1.375(0.985–1.920) 0.062 47(49.5) 1.377(0.873–2.171) 0.169 1.318(0.822–2.114) 0.251
 CC 53(9.9) 23(9.8) 1.228(0.719–2.097) 0.453 1.170(0.669–2.048) 0.582 7(7.4) 0.847(0.361–1.990) 0.704 0.832(0.341–2.032) 0.687
 Dominant(TT vs. TC+CC) 1.366(1.001–1.863) 0.049 1.339(0.973–1.844) 0.074 1.274(0.820–1.977) 0.282 1.242(0.788–1.957) 0.350
 Recessive(TT+TC vs. CC) 1.039(0.625–1.729) 0.882 1.003(0.593–1.696) 0.991 0.723(0.319–1.643) 0.439 0.739(0.319–1.713) 0.481
miR-196a2 rs11614913T>C
 TT 153(28.6) 74(31.6) 1.000(reference) 1.000(reference) 30(31.6) 1.000(reference) 1.000(reference)
 TC 274(51.2) 105(44.9) 0.792(0.554–1.133) 0.202 0.897(0.618–1.302) 0.568 40(42.1) 0.745(0.446–1.244) 0.260 0.846(0.493–1.451) 0.543
 CC 108(20.2) 55(23.5) 1.053(0.687–1.614) 0.813 1.120(0.720–1.741) 0.615 25(26.3) 1.181(0.658–2.119) 0.578 1.212(0.663–2.218) 0.533
 Dominant(TT vs. TC+CC) 0.866(0.621–1.209) 0.398 0.957(0.677–1.351) 0.801 0.868(0.542–1.391) 0.556 0.955(0.583–1.565) 0.855
 Recessive(TT+TC vs. CC) 1.215(0.840–1.756) 0.301 1.166(0.797–1.706) 0.429 1.412(0.854–2.335) 0.179 1.292(0.768–2.173) 0.335
miR-499 rs3746444A>G
 AA 354(66.2) 164(70.1) 1.000(reference) 1.000(reference) 64(67.4) 1.000(reference) 1.000(reference)
 AG 168(31.4) 65(27.8) 0.835(0.594–1.175) 0.300 0.845(0.594–1.203) 0.350 30(31.6) 0.988(0.617–1.582) 0.959 0.991(0.609–1.611) 0.970
 GG 13(2.4) 5(2.1) 0.830(0.291–2.368) 0.728 1.097(0.378–3.188) 0.865 1(1.1) 0.426(0.055–3.310) 0.414 0.568(0.072–4.514) 0.593
 Dominant(AA vs. AG+GG) 0.835(0.599–1.164) 0.287 0.856(0.607–1.206) 0.373 0.947(0.595–1.508) 0.820 0.968(0.599–1.564) 0.895
 Recessive(AA+AG vs. GG) 0.877(0.309–2.488) 0.805 1.137(0.394–3.283) 0.812 0.427(0.055–3.304) 0.415 0.597(0.076–4.704) 0.624

Adjusted for age, gender, hypertension, diabetes mellitus, and hyperlipidemia. Bold indicates significant values. CAD, coronary artery disease; miR, microRNA; COR, crude odds ratio; AOR, adjusted odds ratio; CI, confidence interval.

Stratified effects of miRNA polymorphisms on CAD risk.

Variable miR-146a rs2910164C>G
miR-149 rs2292832C>T
GG
Recessive (CC+CG vs. GG)
TC
Dominant (TT vs. TC+CC)
AOR (95% CI) P-value AOR (95% CI) P-value AOR (95% CI) P-value AOR (95% CI) P-value
Age (years)
 <63 0.712 (0.411–1.231) 0.224 0.805 (0.489–1.325) 0.393 1.160 (0.805–1.671) 0.426 1.111 (0.784–1.573) 0.555
 ≥63 1.421 (0.805–2.510) 0.226 1.405 (0.834–2.365) 0.201 1.516 (1.041–2.209) 0.030 1.415 (0.987–2.029) 0.059
Gender
 Male 1.154 (0.667–1.999) 0.609 1.383 (0.845–2.262) 0.197 1.021 (0.682–1.528) 0.920 0.942 (0.640–1.386) 0.760
 Female 1.477 (0.752–2.901) 0.257 1.576 (0.838–2.963) 0.158 1.947 (1.208–3.138) 0.006 1.976 (1.244–3.138) 0.004
Hypertension
 Negative 0.945 (0.510–1.749) 0.856 1.086 (0.614–1.919) 0.777 1.408 (0.906–2.189) 0.129 1.225 (0.802–1.871) 0.347
 Positive 1.701 (0.942–3.070) 0.078 1.933 (1.133–3.298) 0.016 1.437 (0.946–2.183) 0.089 1.480 (0.994–2.205) 0.054
Diabetes mellitus
 Negative 1.389 (0.863–2.237) 0.176 1.619 (1.047–2.504) 0.030 1.395 (0.987–1.971) 0.059 1.305 (0.936–1.819) 0.117
 Positive 0.978 (0.403–2.372) 0.961 1.033 (0.458–2.331) 0.937 1.134 (0.598–2.152) 0.699 1.218 (0.663–2.237) 0.526
Hyperlipidemia
 Negative 1.245 (0.762–2.033) 0.382 1.496 (0.952–2.350) 0.081 1.415 (0.993–2.015) 0.054 1.339 (0.954–1.877) 0.091
 Positive 1.390 (0.605–3.194) 0.438 1.398 (0.658–2.970) 0.384 1.207 (0.661–2.202) 0.540 1.172 (0.662–2.074) 0.586
Smoking status
 Nonsmokers 1.860 (1.099–3.147) 0.021 1.924 (1.194–3.101) 0.007 1.604 (1.102–2.333) 0.014 1.549 (1.078–2.225) 0.018
 Smokers 0.584 (0.276–1.234) 0.159 0.858 (0.440–1.673) 0.652 1.117 (0.654–1.907) 0.686 1.050 (0.634–1.739) 0.849

Adjusted for age, gender, hypertension, diabetes mellitus and hyperlipidemia. Bold indicates significant values. CAD, coronary artery disease; miR, microRNA; AOR, adjusted odds ratio; CI, confidence interval.

Stratified effects of miRNA polymorphisms on CAD risk.

Variable miR-196a2 rs11614913 T>C
miR-499 rs3746444 A>G
TC
Recessive (TT+TC vs. CC)
TC
Recessive (AA+AG vs. GG)
AOR (95% CI) P-value AOR (95% CI) P-value AOR (95% CI) P-value AOR (95% CI) P-value
Age (years)
 <63 0.970 (0.649–1.450) 0.883 0.931 (0.604–1.436) 0.747 1.617 (0.421–6.220) 0.484 1.672 (0.437–6.389) 0.453
 ≥63 0.656 (0.435–0.988) 0.044 1.038 (0.666–1.620) 0.868 0.391 (0.102–1.500) 0.171 0.393 (0.103–1.498) 0.171
Gender
 Male 0.872 (0.555–1.372) 0.554 0.844 (0.529–1.348) 0.478 1.657 (0.478–5.741) 0.426 1.844 (0.535–6.361) 0.333
 Female 0.967 (0.567–1.651) 0.903 1.860 (1.097–3.154) 0.021 0.547 (0.063–4.713) 0.583 0.595 (0.070–5.097) 0.636
Hypertension
 Negative 0.908 (0.551–1.496) 0.705 1.492 (0.894–2.489) 0.126 0.895 (0.259–3.092) 0.861 0.937 (0.275–3.201) 0.918
 Positive 0.862 (0.544–1.366) 0.526 1.047 (0.659–1.664) 0.845 1.171 (0.187–7.347) 0.866 1.230 (0.199–7.589) 0.824
Diabetes mellitus
 Negative 0.847 (0.571–1.257) 0.409 1.116 (0.749–1.664) 0.590 1.098 (0.396–3.049) 0.857 1.167 (0.421–3.234) 0.766
 Positive 1.067 (0.535–2.125) 0.855 1.482 (0.711–3.091) 0.294 N/A 0.994 N/A 0.994
Hyperlipidemia
 Negative 0.910 (0.610–1.358) 0.645 1.357 (0.913–2.017) 0.131 0.740 (0.224–2.444) 0.622 0.839 (0.256–2.752) 0.772
 Positive 0.787 (0.410–1.509) 0.471 0.871 (0.430–1.764) 0.702 1.997 (0.251–15.907) 0.514 2.149 (0.277–16.666) 0.464
Smoking status
 Smokers 0.738 (0.487–1.120) 0.153 1.456 (0.940–2.257) 0.093 1.230 (0.397–3.805) 0.720 1.319 (0.431–4.042) 0.627
 Nonsmokers 1.317 (0.722–2.402) 0.369 0.884 (0.498–1.568) 0.673 0.512 (0.048–5.450) 0.579 0.522 (0.049–5.572) 0.591

Adjusted for age, gender, hypertension, diabetes mellitus and hyperlipidemia. Bold indicates significant values. CAD, coronary artery disease; miR, microRNA; AOR, adjusted odds ratio; CI, confidence interval.

Frequencies of miR-146a, miR-149, miR-196a2 and miR-499 haplotypes in patients with CAD and in controls.

Haplotype Overall Control CAD OR (95% CI) P-value
miR-146a/149 0.018
 C-T 0.4227 0.4240 0.4195 1.000 (reference)
 G-C 0.1258 0.1072 0.1551 1.459 (1.075–1.981)
miR-146a/499 0.042
 C-A 0.4997 0.4934 0.5087 1.000 (reference)
 C-G 0.1137 0.1272 0.0931 0.707 (0.507–0.985)
miR-146a/149/196a2 0.011
 C-T-T 0.2461 0.2480 0.2427 1.000 (reference)
 G-C-T 0.0699 0.0541 0.0910 1.713 (1.136–2.584)
miR-146a/149/499 0.006
 C-T-A 0.3441 0.3323 0.3616 1.000 (reference)
 C-T-G 0.0789 0.0926 0.0561 0.559 (0.370–0.844)
miR-146a/196a2/499 0.038
 C-T-A 0.2830 0.2848 0.2815 1.000 (reference)
 C-T-G 0.0559 0.0673 0.0392 0.595 (0.367–0.966)
miR-149/196a2/499 0.026
 T-T-A 0.3088 0.3052 0.3170 1.000 (reference)
 T-T-G 0.0685 0.0778 0.0490 0.603 (0.387–0.940)
miR-146a/149/196a2/499
 C-T-T-A 0.2007 0.1956 0.2107 1.000 (reference)
 G-T-C-A 0.1141 0.1341 0.0992 0.684 (0.475–0.983) 0.045
 G-T-C-G 0.0158 0.0000 0.0212 43.55 (2.575–736.512) <0.0001
 G-C-T-G 0.0121 0.0068 0.0250 3.437 (1.378–8.572) 0.008
 G-C-C-G 0.0069 0.0142 0.0000 0.048 (0.003–0.817) 0.001

Insignificant data were removed from table. Bold indicates significant values. miR, microRNA; CAD, coronary artery disease;OR, odds ratio; CI, confidence interval.

Genotype combination of microRNA polymorphisms.

Combined genotype Controls (n=535) CAD (n=329) AOR (95% CI) P-value
miR-146a/149
 CG/TT 129 (24.1) 57 (17.3) 0.677 (0.473–0.970) 0.033
 GG/TC 35 (6.5) 35 (10.6) 1.683 (1.013–2.797) 0.044
 GG/CC 3 (0.6) 8 (2.4) 4.200 (1.082–16.31) 0.038
 GG/TC+CC 3 (0.6) 43 (13.1) 1.939 (1.206–3.118) 0.006
miR-149/196a2
 CC/TC 30 (5.6) 9 (2.7) 0.436 (0.198–0.961) 0.039

Insignificant data were removed from table. Adjusted for age, gender, hypertension, diabetes mellitus and hyperlipidemia. Bold indicates significant values. CAD, coronary artery disease; miR, microRNA; AOR, adjusted odds ratio; CI, confidence interval.