Associations Between microRNA Polymorphisms and Development of Coronary Artery Disease: A Case

Abstract

Coronary artery disease (CAD), a common cardiovascular disease, has become a vital cause of mortality worldwide. Genetic microRNA (miR) polymorphisms might contribute to CAD susceptibility. In this study, we selected miR-146a, miR-196a2, and miR-499 single nucleotide polymorphisms and conducted a case–control study. In total, 505 CAD cases and 1109 controls were recruited. We used SNPscan™ genotyping assay to obtain genotyping of miR rs2910164, rs11614913, and rs3746444 variants. We found that miR-196a2 rs11614913 T > C decreased the susceptibility of myocardial infarction (MI) (TC vs. TT: adjusted p = 0.007 and CC/TC vs. TT: adjusted p = 0.012). In female subgroup, our results indicated that miR-196a2 rs11614913 T > C variants might also decrease the susceptibility of CAD (TC vs. TT: adjusted p = 0.017 and TC/CC vs. TT: adjusted p = 0.015). In summary, these results suggest that miR-196a2 rs11614913 T > C locus decreases the susceptibility of CAD in female and MI subgroups. However, further studies are needed to validate the potential associations of miR-196a2 rs11614913 locus with CAD.

Introduction

Coronary artery disease (CAD) manifests in middle and old age populations worldwide (Newton et al., 2015). The etiology of CAD remains unclear. Traditional factors, such as smoking, drinking, obesity, overweight, hypertension, type 2 diabetes mellitus (T2DM), and hyperlipidemia, have contributed to the susceptibility and progression of CAD (Moon et al., 2015; Li et al., 2016; Albrektsen et al., 2017; Bokma et al., 2017; Lerman et al., 2017; Lim, 2017; Lui et al., 2017; Zhang et al., 2017). Nevertheless, some potential susceptibility factors promoting CAD development are needed to be studied. Recently, accumulating molecular epidemiological investigations have highlighted that hereditary factors are important for CAD, and the individual's single nucleotide polymorphisms (SNPs) are attracting more attention (Gui et al., 2014).

microRNAs (miRs), a group of noncoding RNAs, are ∼22 nucleotides in length (Noonan et al., 2010; Panarelli and Yantiss, 2011) and regulate gene expression at the post-translational level (Vasudevan et al., 2007). miRs are correlated with human diseases, including CAD, diabetes, cancer, and so on. Accumulating evidences have indicated the vital roles of miRs in regulating fundamental biological processes of CAD, such as proliferation, differentiation and stress resistance, and abnormal apoptosis (Klingelhoffer et al., 2016; Brunquell et al., 2017; Lu et al., 2017; Wang et al., 2017; Yu et al., 2017). In addition, several studies identified that miRs were implicated in the regulation of energy metabolism, oxidative stress, inflammation, and proliferation of smooth muscle cells (Maves et al., 1989; McDonald et al., 2015; Afzal et al., 2016; Jin et al., 2017), which are associated with the development of CAD.

SNPs located on pre-miR genes and certain miR-binding sites in target genes may influence the interaction of specific miR and mRNA target site and then lead to the abnormal expression of these genes (Sun et al., 2010). In view of the potential roles of miRs in regulating translation, genetic miR SNPs were thought to contribute to CAD susceptibility. Previous studies have focused on the correlation of some common miR SNPs (miR-146a rs2910164 C>G, miR-196a2 rs11614913 T>C, and miR-499 rs3746444 A>G) with CAD development. Recently, these important polymorphisms in miR were under investigation to explore the possible association to CAD, but the results remained conflicting. Thus, in this study, we tried to identify the correlation of miR SNPs with the susceptibility to CAD. We selected rs2910164, rs11614913, and rs3746444 SNPs and conducted a case–control study in Chinese population.

Materials and Methods

Study population and ethical approval

A total of 505 CAD patients who had presented to the Fujian Union Hospital from October 2014 to May 2016 were enrolled in the study. And 1109 controls were recruited at the same time. The criterion of included CAD cases was coronary stenosis ≥50%, which was confirmed based on coronary angiography (Kucukhuseyin et al., 2013). The major criteria of healthy controls were as follows: (1) without history of CAD, (2) without any symptom of CAD, and (3) without myocardial ischemia in electrocardiogram test (Kucukhuseyin et al., 2013).

The related risk factors and demography data, such as smoking, alcohol consumption, weight, height, body mass index (BMI), sex, age, fasting blood glucose (FBG), history of T2DM, hypertension, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), and total cholesterol (TC), were collected. The definition of smoker and drinker was presented in our previous studies (Tang et al., 2014c, 2016). The diagnosis of T2DM was based on WHO1999 criteria. The systolic pressure ≥140 mmHg and/or diastolic pressure ≥90 mmHg or history of antihypertensive therapy was defined as hypertension (Soderstrom et al., 2013). BMI ≥24 was defined as overweight or obese (Zhai et al., 2010). Hyperlipidemia was diagnosed according to Chinese Medical Association 2016 criteria (Luo et al., 2019).

The written informed consent was signed by each participant. The present study was approved by Ethics Committee of Fujian Medical University (Approval No. 2016KY006).

miR SNP selection

To analyze the correlation of miR SNPs with the risk of CAD, we chose rs2910164, rs11614913, and rs3746444 SNPs according to the previous publications; these polymorphisms were found to be associated with cancer (Chen et al., 2016; Yin et al., 2017a; Tang et al., 2019; Thakur et al., 2019), ischemic stroke (Huang et al., 2015b; Zhu et al., 2017a), T2DM (Alipoor et al., 2016; Kaidonis et al., 2016; Ciccacci et al., 2018), autoimmune diseases (Ciccacci et al., 2016; Park et al., 2016; Toraih et al., 2016; Assmann et al., 2017; Shaker et al., 2018), and CAD (Zhi et al., 2012; Chen et al., 2014, 2017b; Xiong et al., 2014; Li et al., 2015; Bastami et al., 2016; Fragoso et al., 2019).

Isolation of DNA and genotyping

Two-milliliter venous blood specimens were drawn into a test tube containing ethylene diamine tetraacetic acid. Genomic DNA was extracted from venous blood samples using the DNA Kit (Promega, Madison). We used SNPscan™ genotyping assay (Genesky Biotechnologies, Inc., Shanghai, China) to obtain genotyping of rs2910164, rs11614913, and rs3746444 polymorphisms. To ensure the accuracy of our results, 65 randomly selected DNA samples were tested repeatedly, and the results were unchanged.

Statistical analyses

The continuous variables (e.g., FBG, age, height, weight, BMI, TG, TC, LDL-C, and HDL-C) are presented as the mean ± standard deviation. Differences in age, sex, hypertension, T2DM, hyperlipidemia, smoking, drinking, BMI, and genotypes of selected polymorphisms were analyzed using χ ² test. Student's t-test was harnessed to determine the potential difference between two groups. The relationship of rs2910164, rs11614913, and rs3746444 genotypes with CAD susceptibility was determined by odds ratios and their 95% confidence intervals. Hardy–Weinberg equilibrium (HWE) was calculated by an Internet HWE-analysis software (online at http://ihg.gsf.de/cgi-bin/hw/hwa1.pl) (Tang et al., 2013, 2014a, 2014b, 2017; Wang et al., 2015; Qiu et al., 2016; Zhu et al., 2017b). In this study, SAS 9.4 software (SAS Institute, Cary, NC) was used for statistical analyses.

Results

Population characteristics

The frequency distributions of drinking, BMI, age, sex, smoking, hypertension, T2DM, and hyperlipidemia information for the 505 CAD patients (64.57 ± 9.91 years) and 1109 healthy controls (64.75 ± 10.20 years) are presented in Table 1. The gender and age had no significant difference between two groups (p = 0.704 and 0.942, respectively). The differences of BMI, hypertension, T2DM, smoking, drinking, and hyperlipidemia between CAD patients and controls were significant (p < 0.05). Among the CAD patients, 147 were myocardial infarction (MI) cases and 358 were non-MI cases.

Table 1.

Distribution of Selected Demographic Variables and Risk Factors in Coronary Artery Disease Cases and Controls

Variable	Cases (n = 505)		Controls (n = 1109)		p ^a
Variable	n	%	n	%	p ^a
Age (years)	64.57 (±9.91)		64.75 (±10.20)		0.729
Age (years)
<65	249	49.31	549	49.50	0.942
≥65	256	50.69	560	50.50	0.942
Sex
Male	395	78.22	858	77.37	0.704
Female	110	21.78	251	22.63	0.704
Tobacco use
Never	255	50.50	773	69.70	<0.001
Ever	250	49.50	336	30.30	<0.001
Alcohol use
Never	383	78.54	974	87.83	<0.001
Ever	122	21.46	135	12.17	<0.001
BMI (kg/m²)
<24	231	45.74	596	53.74	0.003
≥24	274	54.26	513	46.26	0.003
Hypertension
Yes	352	69.70	660	59.51	<0.001
No	153	30.30	449	40.49	<0.001
Fasting glucose (mM)	6.17 (±2.04)		5.78 (±1.67)		<0.001
Type 2 diabetes mellitus
Yes	157	31.09	171	15.42	<0.001
No	348	68.91	938	84.58	<0.001
TC (mM)	4.26 (±1.22)		4.84 (±1.05)		<0.001
TG (mM)	1.71 (±1.17)		1.61 (±1.01)		0.081
HDL-C (mM)	1.03 (±0.30)		1.23 (±0.36)		<0.001
LDL-C (mM)	2.72 (±1.09)		3.08 (±0.86)		<0.001
Hyperlipidemia
Yes	374	74.06	754	67.99	0.014
No	131	25.94	355	32.01	0.014
Type of CAD
Non-MI	358	70.89
MI	147	29.11

Bold values are statistically significant (p < 0.05).

Two-sided χ ² test and Student's t-test.

BMI, body mass index; CAD, coronary artery disease; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MI, myocardial infarction; TC, total cholesterol; TG, triglyceride.

Primary information of miR SNPs

The primary information of rs2910164, rs11614913, and rs3746444 SNPs is listed in Table 2. These SNPs were noncoding variants. Genotype distributions of three miR SNPs in controls were in accord with HWE (p > 0.05). All of the genotyping successful rates were 99.50% (Table 2). All data were presented in Supplementary Data.

Table 2.

Primary Information for miR- 146 a rs2910164 C>G, miR- 196 a 2 rs11614913 T>C, and miR- 499 rs3746444 A>G Polymorphisms

Genotyped SNPs	miR-146a rs2910164 C > G	miR-196a2 rs11614913 T > C	miR-499 rs3746444 A>G
Chromosome	5	12	20
Function	nc-transcript-variant	nc-transcript-variant	nc-transcript-variant
Chr Pos (NCBI Build 38)	160485411	53991815	3499048
MAF for Chinese in database	0.35	0.34	0.15
MAF in our controls (n = 1109)	0.37	0.44	0.15
p value for HWE test in our controls	0.946	0.052	0.649
Genotyping method	SNPscan	SNPscan	SNPscan
% genotyping value	99.50	99.50	99.50

HWE, Hardy–Weinberg equilibrium; MAF, minor allele frequency; miR, microRNA; SNPs, single nucleotide polymorphisms.

Association between miR SNPs and CAD risk

The genotype frequencies of miR polymorphisms in different groups are summarized in Table 3. The frequencies of rs2910164 CC, CG, and GG genotypes were 38.80%, 49.20%, and 12.00% in CAD cases and 39.42%, 46.65%, and 13.92% in controls, respectively. Adjusting for BMI, hypertension, smoking, drinking, age, sex, T2DM, and hyperlipidemia, we identified that miR-146a rs2910164 was not associated with the risk of CAD (CG vs. CC: adjusted p = 0.709; GG vs. CC: adjusted p = 0.732; CG/GG vs. CC: adjusted p = 0.843; GG vs. CC/CG: adjusted p = 0.607; Table 4).

Table 3.

The Frequencies of miR- 146 a rs2910164 C>G, miR- 196 a 2 rs11614913 T>C, and miR- 499 rs3746444 A>G Polymorphisms in Coronary Artery Disease Patients and Controls

Genotype	Overall CAD cases (n = 505)		Non-MI cases (n = 358)		MI cases (n = 147)		Controls (n = 1109)
Genotype	n	%	n	%	n	%	n	%
miR-146a rs2910164 C > G
CC	194	38.80	132	37.39	62	42.18	436	39.42
CG	246	49.20	176	49.86	70	47.62	516	46.65
GG	60	12.00	45	12.75	15	10.20	154	13.92
G allele	366	36.60	266	37.68	100	34.01	824	37.25
miR-499 rs3746444 A>G
AA	357	71.40	245	69.41	112	76.19	800	72.33
AG	138	27.60	103	29.18	35	23.81	279	25.23
GG	5	1.00	5	1.42	0	0	27	2.44
G allele	148	14.80	113	16.01	35	11.90	333	15.05
miR-196a2 rs11614913 T > C
TT	163	32.60	105	29.75	58	39.46	335	30.29
TC	247	49.40	184	52.12	63	42.86	576	52.08
CC	90	18.00	64	18.13	26	17.69	195	17.63
C allele	427	42.70	312	44.19	115	39.12	966	43.67

Table 4.

Overall and Stratified Analyses of miR- 146 a rs2910164 C>G, miR- 196 a2 rs11614913 T>C, and miR- 499 rs3746444 A>G Polymorphisms with Coronary Artery Disease

Genotype	Overall CAD cases (n = 505) vs. controls (1109)				Non-MI cases (n = 358) vs. controls (1109)				MI cases (n = 147) vs. controls (1109)
Genotype	Crude OR (95% CI)	p	Adjusted OR ^a (95% CI)	p	Crude OR (95% CI)	p	Adjusted OR ^a (95% CI)	p	Crude OR (95% CI)	p	Adjusted OR ^a (95% CI)	p
miR-146a rs2910164 C > G
CG vs. CC	1.07 (0.85–1.35)	0.552	1.05 (0.82–1.33)	0.709	1.13 (0.87–1.46)	0.367	1.11 (0.84–1.45)	0.462	0.95 (0.66–1.37)	0.800	0.92 (0.63–1.34)	0.647
GG vs. CC	0.88 (0.62–1.23)	0.448	0.94 (0.65–1.35)	0.732	0.97 (0.66–1.42)	0.857	1.04 (0.69–1.55)	0.865	0.69 (0.38–1.24)	0.212	0.75 (0.40–1.38)	0.356
GG/CG vs. CC	1.03 (0.83–1.28)	0.814	1.02 (0.82–1.29)	0.843	1.09 (0.85–1.40)	0.496	1.09 (0.84–1.42)	0.506	0.89 (0.63–1.27)	0.521	0.88 (0.61–1.26)	0.489
GG vs. CC/CG	0.84 (0.61–1.16)	0.294	0.92 (0.65–1.28)	0.607	0.90 (0.63–1.29)	0.575	0.98 (0.67–1.42)	0.908	0.70 (0.40–1.23)	0.218	0.79 (0.44–1.40)	0.414
miR-499 rs3746444 A>G
AG vs. AA	1.11 (0.87–1.41)	0.399	1.05 (0.82–1.36)	0.685	1.21 (0.92–1.58)	0.171	1.16 (0.87–1.53)	0.317	0.90 (0.60–1.34)	0.594	0.84 (0.55–1.28)	0.412
GG vs. AA	0.42 (0.16–1.09)	0.073	0.49 (0.18–1.32)	0.157	0.61 (0.23–1.59)	0.307	0.70 (0.26–1.90)	0.487	—	—	—	—
GG/AG vs. AA	1.05 (0.83–1.32)	0.699	1.01 (0.79–1.29)	0.944	1.15 (0.89–1.50)	0.288	1.12 (0.85–1.47)	0.425	0.82 (0.55–1.22)	0.324	0.78 (0.51–1.18)	0.231
GG vs. AA/AG	0.40 (0.16–1.05)	0.064	0.48 (0.18–1.30)	0.149	0.57 (0.22–1.50)	0.258	0.68 (0.25–1.83)	0.439	—	—	—	—
miR-196a2 rs11614913 T > C
TC vs. TT	0.88 (0.69–1.12)	0.301	0.86 (0.67–1.11)	0.239	1.02 (0.77–1.34)	0.893	1.00 (0.75–1.34)	0.991	0.63 (0.43–0.93)	0.018	0.58 (0.39–0.86)	0.007
CC vs. TT	0.95 (0.69–1.30)	0.740	0.98 (0.71–1.36)	0.903	1.05 (0.73–1.50)	0.800	1.09 (0.75–1.58)	0.660	0.77 (0.47–1.26)	0.301	0.76 (0.46–1.28)	0.306
CC/TC vs. TT	0.90 (0.72–1.13)	0.354	0.89 (0.70–1.13)	0.335	1.03 (0.79–1.33)	0.847	1.02 (0.78–1.35)	0. 873	0.67 (0.47–0.95)	0.025	0.62 (0.43–0.90)	0.012
CC vs. TT/TC	1.03 (0.78–1.35)	0.857	1.08 (0.81–1.44)	0.620	1.04 (0.76–1.41)	0.830	1.09 (0.78–1.51)	0.618	1.00 (0.64–1.58)	0.987	1.05 (0.66–1.68)	0.831

Bold values are statistically significant (p < 0.05).

Adjusted for age, sex, smoking, drinking, BMI, hypertension, T2DM, and hyperlipidemia.

95% CI, 95% confidence interval; OR, odds ratio; T2DM, type 2 diabetes mellitus.

The frequencies of rs3746444 AA, AG, and GG variants were 71.40%, 27.60%, and 1.00% in CAD group and 72.33%, 25.23%, and 2.44% in controls, respectively. When we adjusted BMI, hypertension, smoking, drinking, age, sex, T2DM, and hyperlipidemia, the null association of miR-499 rs3746444 A>G locus with CAD risk was also found (AG vs. AA: adjusted p = 0.685; GG vs. AA: adjusted p = 0.157; AG/GG vs. AA: adjusted p = 0.944; GG vs. AA/AG: adjusted p = 0.149; Table 4).

The frequencies of rs11614913 TT, TC, and CC genotypes were 32.60%, 49.40%, and 18.00% in CAD group and 30.29%, 52.08%, and 17.63% in controls, respectively. When we adjusted BMI, hypertension, smoking, drinking, age, sex, T2DM, and hyperlipidemia, the results suggested that rs11614913 genotype was not different between CAD cases and controls (TC vs. TT: adjusted p = 0.239; CC vs. TT: adjusted p = 0.903; TC/CC vs. TT: adjusted p = 0.335; CC vs. TT/TC: adjusted p = 0.620; Table 4).

Association of miR SNPs with CAD risk in different CAD types

The genotype frequencies of rs2910164, rs11614913, and rs3746444 polymorphisms in different stratification groups are summarized in Table 3. To determine whether the role of rs2910164, rs11614913, and rs3746444 variants was influenced by CAD type, we performed a stratified analysis. We identified that rs11614913 SNP was associated with a decreased susceptibility of MI (TC vs. TT: adjusted p = 0.007 and CC/TC vs. TT: adjusted p = 0.012). However, we found that rs2910164 and rs3746444 loci were not associated with the susceptibility of CAD (Table 4).

Association of miR SNPs with CAD risk in a stratification analysis

Table 5 summarized rs11614913 variant frequencies in different drinking, BMI, hypertension, age, smoking, T2DM, sex, and hyperlipidemia subgroups. In female subgroup, we identified that rs11614913 SNP decreased the risk of CAD (TC vs. TT: adjusted p = 0.017 and TC/CC vs. TT: adjusted p = 0.015). However, no difference was shown in distribution of rs2910164 and rs3746444 variants among CAD patients and controls in other subgroups (Tables 6 and 7).

Table 5.

Stratified Analyses Between miR- 196 a2 rs11614913 T > C Polymorphism and Coronary Artery Disease Risk by Sex, Age, Body Mass Index, Smoking Status, and Alcohol Consumption

Variable	miR-196a2 rs11614913 T > C (case/control) ^a			Adjusted OR ^b (95% CI); p
Variable	TT	TC	CC	TC vs. TT	CC vs. TT	CC/TC vs. TT	CC vs. TT/TC
Sex
Male	122/268	198/440	71/147	0.98 (0.74–1.31); 0.904	1.13 (0.77–1.64); 0.535	1.02 (0.77–1.34); 0.903	1.14 (0.82–1.59); 0.440
Female	41/67	49/136	19/48	0.51 (0.29–0.88); 0.017	0.57 (0.28–1.16); 0.121	0.52 (0.31–0.88); 0.015	0.85 (0.45–1.62); 0.624
Age
<65	84/172	122/278	42/97	0.84 (0.58–1.22); 0.360	0.93 (0.57–1.51); 0.765	0.86 (0.61–1.22); 0.408	1.03 (0.67–1.59); 0.892
≥65	79/163	125/298	48/98	0.86 (0.60–1.22); 0.403	1.02 (0.65–1.61); 0.919	0.90 (0.64–1.26); 0.539	1.13 (0.76–1.67); 0.556
Smoking status
Never	79/235	126/392	46/144	0.90 (0.64–1.26); 0.537	0.89 (0.58–1.38); 0.602	0.90 (0.65–1.24); 0.504	0.95 (0.65–1.40); 0.798
Ever	84/100	121/184	44/51	0.79 (0.54–1.16); 0.222	1.12 (0.66–1.88); 0.678	0.85 (0.59–1.23); 0.400	1.30 (0.82–2.06); 0.268
Alcohol consumption
Never	121/293	188/506	69/173	0.86 (0.65–1.14); 0.280	0.99 (0.69–1.43); 0.959	0.89 (0.68–1.16); 0.389	1.09 (0.79–1.51); 0.596
Ever	42/42	59/70	21/22	0.84 (0.47–1.49); 0.547	0.93 (0.43–2.01); 0.862	0.86 (0.50–1.49); 0.592	1.04 (0.52–2.06); 0.913
BMI (kg/m²)
<24	81/183	108/299	40/112	0.81 (0.57–1.16); 0.250	0.87 (0.55–1.39); 0.562	0.83 (0.59–1.16); 0.268	0.99 (0.65–1.50); 0.956
≥24	82/152	139/277	50/83	0.92 (0.64–1.32); 0.653	1.12 (0.70–1.78); 0.648	0.97 (0.69–1.36); 0.842	1.18 (0.78–1.77); 0.438
Type 2 diabetes mellitus
Yes	50/49	81/90	26/32	0.94 (0.55–1.58); 0.805	0.87 (0.43–1.74); 0.693	0.92 (0.56–1.52); 0.742	0.91 (0.49–1.67); 0.754
No	113/286	166/486	64/163	0.84 (0.63–1.12); 0.242	1.04 (0.71–1.51); 0.858	0.89 (0.68–1.17); 0.397	1.15 (0.83–1.60); 0.405
Hypertension
Yes	114/202	174/340	60/117	0.90 (0.66–1.22); 0.501	0.97 (0.65–1.45); 0.875	0.92 (0.69–1.23); 0.558	1.03 (0.72–1.48); 0.859
No	49/133	73/236	30/78	0.78 (0.50–1.23); 0.289	0.99 (0.56–1.76); 0.972	0.83 (0.54–1.28); 0.404	1.15 (0.70–1.91); 0.581
Hyperlipidemia
Yes	119/223	185/390	67/138	0.85 (0.63–1.15); 0.301	0.98 (0.66–1.45); 0.912	0.88 (0.66–1.18); 0.398	1.08 (0.77–1.52); 0.658
No	44/112	62/186	23/57	0.87 (0.54–1.38); 0.546	1.02 (0.55–1.88); 0.955	0.90 (0.58–1.40); 0.649	1.11 (0.64–1.92); 0.710

Bold values are statistically significant (p < 0.05).

For miR-196a2 rs11614913 T>C, the genotyping was successful in 500 (99.01%) CAD cases and 1106 (99.73%) controls.

Adjusted for age, sex, smoking, drinking, BMI, hypertension, T2DM, and hyperlipidemia (besides stratified factors accordingly) in a multiple logistic regression model.

Table 6.

Stratified Analyses Between miR- 146 a rs2910164 C > G Polymorphism and Coronary Artery Disease Risk by Sex, Age, Body Mass Index, Smoking Status, and Alcohol Consumption

Variable	miR-146a rs2910164 C > G (case/control) ^a			Adjusted OR ^b (95% CI); p
Variable	CC	CG	GG	CG vs. CC	GG vs. CC	GG/CG vs. CC	GG vs. CC/CG
Sex
Male	150/331	190/402	51/122	1.03 (0.78–1.35); 0.862	0.98 (0.65–1.46); 0.912	1.01 (0.78–1.32); 0.916	0.96 (0.67–1.40); 0.849
Female	44/105	56/114	9/32	1.22 (0.72–2.05); 0.460	0.83 (0.35–1.98); 0.674	1.14 (0.69–1.88); 0.614	0.75 (0.33–1.70); 0.483
Age
<65	95/232	123/244	30/71	1.05 (0.74–1.49); 0.800	1.07 (0.63–1.84); 0.798	1.05 (0.75–1.47); 0.768	1.05 (0.63–1.73); 0.859
≥65	99/204	123/271	30/83	0.98 (0.70–1.37); 0.902	0.84 (0.51–1.39); 0.503	0.95 (0.69–1.30); 0.746	0.85 (0.54–1.35); 0.501
Smoking status
Never	98/304	122/354	31/113	1.05 (0.77–1.45); 0.750	0.93 (0.58–1.49); 0.756	1.03 (0.76–1.39); 0.875	0.90 (0.58–1.40); 0.644
Ever	96/132	124/162	29/41	1.03 (0.71–1.48); 0.881	0.95 (0.54–1.67); 0.861	1.01 (0.71–1.44); 0.945	0.94 (0.55–1.58); 0.806
Alcohol consumption
Never	150/388	183/449	45/135	1.04 (0.79–1.35); 0.802	0.94 (0.63–1.41); 0.772	1.02 (0.79–1.31); 0.911	0.92 (0.63–1.35); 0.683
Ever	44/48	63/67	15/19	1.03 (0.58–1.80); 0.932	0.84 (0.37–1.91); 0.680	0.98 (0.57–1.69); 0.949	0.83 (0.39–1.76); 0.624
BMI (kg/m²)
<24	90/233	115/279	24/82	1.04 (0.74–1.46); 0.833	0.84 (0.48–1.46); 0.517	1.00 (0.72–1.38); 0.978	0.82 (0.49–1.36); 0.441
≥24	104/203	131/237	36/72	1.06 (0.75–1.49); 0.748	1.07 (0.65–1.75); 0.785	1.06 (0.77–1.46); 0.721	1.04 (0.66–1.64); 0.870
Type 2 diabetes mellitus
Yes	61/68	78/85	18/18	1.04 (0.64–1.70); 0.877	1.28 (0.58–2.82); 0.537	1.08 (0.67–1.73); 0.750	1.26 (0.60–2.63); 0.547
No	133/368	168/431	42/136	1.06 (0.80–1.40); 0.685	0.89 (0.59–1.34); 0.568	1.02 (0.78–1.33); 0.889	0.86 (0.59–1.26); 0.438
Hypertension
Yes	136/253	172/311	40/95	1.04 (0.78–1.40); 0.774	0.84 (0.54–1.31); 0.450	1.00 (0.76–1.32); 0.992	0.82 (0.55–1.24); 0.355
No	58/183	74/205	20/59	1.03 (0.67–1.58); 0.883	1.18 (0.63–2.20); 0.602	1.06 (0.71–1.59); 0.765	1.16 (0.65–2.07); 0.615
Hyperlipidemia
Yes	146/302	179/352	46/97	1.04 (0.78–1.38); 0.787	1.05 (0.69–1.61); 0.827	1.04 (0.80–1.37); 0.767	1.03 (0.69–1.53); 0.897
No	48/134	67/164	14/57	1.11 (0.71–1.75); 0.654	0.71 (0.35–1.43); 0.340	1.01 (0.66–1.56); 0.957	0.67 (0.35–1.28); 0.226

For miR-146a rs2910164 C>G, the genotyping was successful in 500 (99.01%) CAD cases and 1106 (99.73%) controls.

Adjusted for age, sex, smoking, drinking, BMI, hypertension, T2DM, and hyperlipidemia (besides stratified factors accordingly) in a multiple logistic regression model.

Table 7.

Stratified Analyses Between miR- 499 rs3746444 A>G Polymorphism and Coronary Artery Disease Risk by Sex, Age, Body Mass Index, Smoking Status, and Alcohol Consumption

Variable	miR-499 rs3746444 A>G (case/control) ^a			Adjusted OR ^b (95% CI); p
Variable	AA	AG	GG	AG vs. AA	GG vs. AA	GG/AG vs. AA	GG vs. AA/AG
Sex
Male	274/615	113/217	4/23	1.09 (0.82–1.45); 0.557	0.45 (0.15–1.37); 0.159	1.03 (0.78–1.37); 0.812	0.44 (0.15–1.33); 0.146
Female	83/185	25/62	1/4	0.82 (0.46–1.46); 0.507	0.45 (0.04–5.67); 0.538	0.80 (0.46–1.42); 0.446	0.47 (0.04–5.91); 0.562
Age
<65	174/401	70/133	4/13	1.08 (0.74–1.56); 0.696	0.88 (0.26–2.99); 0.838	1.06 (0.74–1.53); 0.744	0.86 (0.26–2.92); 0.813
≥65	183/399	68/146	1/14	0.99 (0.70–1.41); 0.974	0.16 (0.02–1.26); 0.082	0.92 (0.65–1.30); 0.650	0.16 (0.02–1.26); 0.082
Smoking status
Never	189/567	58/184	4/20	0.91 (0.64–1.30); 0.607	0.63 (0.20–1.96); 0.423	0.89 (0.63–1.25); 0.488	0.64 (0.21–2.00); 0.445
Ever	168/233	80/95	1/7	1.20 (0.83–1.74); 0.329	0.21 (0.02–1.81); 0.155	1.14 (0.79–1.64); 0.490	0.20 (0.02–1.71); 0.140
Alcohol consumption
Never	270/709	103/239	5/24	1.11 (0.84–1.47); 0.473	0.64 (0.23–1.76); 0.388	1.07 (0.81–1.41); 0.628	0.63 (0.23–1.71); 0.359
Ever	87/91	35/40	0/3	0.85 (0.49–1.50); 0.580	—	0.80 (0.46–1.39); 0.425	—
BMI (kg/m²)
<24	170/438	58/139	1/17	0.98 (0.68–1.43); 0.926	0.16 (0.02–1.24); 0.079	0.90 (0.62–1.30); 0.571	0.16 (0.02–1.24); 0.079
≥24	187/362	80/140	4/10	1.09 (0.77–1.55); 0.614	0.82 (0.24–2.84); 0.755	1.08 (0.77–1.51); 0.674	0.80 (0.23–2.76); 0.724
Type 2 diabetes mellitus
Yes	118/125	38/42	1/4	0.85 (0.50–1.46); 0.552	0.30 (0.03–2.88); 0.297	0.80 (0.47–1.37); 0.421	0.31 (0.03–2.99); 0.313
No	239/675	100/237	4/23	1.10 (0.83–1.47); 0.510	0.56 (0.19–1.67); 0.301	1.06 (0.80–1.40); 0.692	0.55 (0.18–1.63); 0.278
Hypertension
Yes	251/474	92/168	5/17	1.03 (0.75–1.40); 0.875	0.74 (0.26–2.12); 0.577	1.00 (0.74–1.36); 0.982	0.74 (0.26–2.10); 0.568
No	106/326	46/111	0/10	1.09 (0.70–1.69); 0.709	—	1.00 (0.65–1.55); 0.987	—
Hyperlipidemia
Yes	265/547	102/187	4/17	1.07 (0.79–1.44); 0.681	0.51 (0.16–1.61); 0.254	1.02 (0.76–1.37); 0.888	0.50 (0.16–1.58); 0.241
No	92/253	36/92	1/10	1.01 (0.63–1.62); 0.959	0.38 (0.05–3.16); 0.373	0.97 (0.61–1.53); 0.880	0.38 (0.05–3.14); 0.370

For miR-499 rs3746444 A>G, the genotyping was successful in 500 (99.01%) CAD cases and 1106 (99.73%) controls.

Adjusted for age, sex, smoking, drinking, BMI, hypertension, T2DM, and hyperlipidemia (besides stratified factors accordingly) in a multiple logistic regression model.

Combination analysis of three miR-polymorphisms (rs11614913, rs2910164, and rs3746444)

Table 8 showed the combined analysis of rs11614913, rs2910164, and rs3746444 polymorphisms. Three different combinations, rs11614913/rs2910164, rs11614913/rs3746444, and rs2910164/rs3746444, were made to explore the interaction of different genotypes of miR-SNPs and their potential roles on the development of CAD. However, we did not find any association between combined genotypes and the risk of CAD.

Table 8.

Combination Analysis of microRNA Polymorphisms (rs11614913, rs2910164, and rs3746444) in Coronary Artery Disease Cases and Controls

Genotype	Case		Control		OR (95% CI)	p
Genotype	n	%	n	%	OR (95% CI)	p
rs11614913/rs2910164
TT/CC	63	12.60	129	11.66	1.00
TT/CG	82	16.40	161	14.56	1.04 (0.70–1.56)	0.838
TT/GG	18	3.60	45	4.07	0.82 (0.44–1.53)	0.530
TC/CC	94	18.80	230	20.80	0.84 (0.57–1.23)	0.365
TC/CG	122	24.40	265	23.96	0.94 (0.65–1.36)	0.754
TC/GG	31	6.20	81	7.32	0.78 (0.47–1.31)	0.350
CC/CC	37	7.40	77	6.96	0.98 (0.60–1.61)	0.949
CC/CG	42	8.40	90	8.14	0.96 (0.59–1.54)	0.851
CC/GG	11	2.20	28	2.53	0.80 (0.38–1.72)	0.574
rs11614913/rs3746444
TT/AA	110	22.00	239	21.61	1.00
TT/AG	51	10.20	90	8.14	1.23 (0.82–1.86)	0.321
TT/GG	2	0.40	6	0.54	0.72 (0.14–3.65)	0.694
TC/AA	184	36.80	424	38.34	0.94 (0.71–1.25)	0.685
TC/AG	60	12.00	137	12.39	0.95 (0.65–1.39)	0.797
TC/GG	3	0.60	15	1.36	0.43 (0.12–1.53)	0.294
CC/AA	63	12.60	137	12.39	1.00 (0.69–1.45)	0.996
CC/AG	27	5.40	52	4.70	1.13 (0.67–1.89)	0.647
CC/GG	0	0.0	6	0.54	0.17 (0.01–2.99)	0.183
rs2910164/rs3746444
CC/AA	144	28.80	317	28.66	1.00
CC/AG	48	9.60	109	9.86	0.97 (0.65–1.44)	0.877
CC/GG	2	0.40	10	0.90	0.44 (0.10–2.04)	0.358
CG/AA	168	33.60	365	33.00	1.01 (0.77–1.33)	0.924
CG/AG	76	15.20	141	12.75	1.19 (0.84–1.67)	0.326
CG/GG	2	0.40	10	0.90	0.44 (0.10–2.04)	0.358
GG/AA	45	9.00	118	10.67	0.90 (0.61–1.32)	0.578
GG/AG	14	2.80	29	2.62	1.06 (0.55–2.07)	0.858
GG/GG	1	0.20	7	0.63	0.31 (0.04–2.58)	0.445

Discussion

Several case–control studies have regarded miR rs2910164, rs11614913, and rs3746444 polymorphisms as promising candidates for CAD. However, the association between these SNPs and CAD was conflicting. The present study in Eastern Chinese Han populations failed to confirm this relationship in overall comparison. However, we found that rs11614913 locus might decrease the susceptibility of CAD in MI and female subgroups. To our knowledge, this is the first study suggesting the protective role of miR-196a2 rs11614913 T > C polymorphism to MI.

Luthra et al. (2008) indicated that miR-196a2 could regulate annexin A1 (ANXA1). ANXA1 is important for controlling inflammation. An increased expression of ANXA1 might be a protective factor for CAD (Bergstrom et al., 2017). Thus, miR-196a2 might be associated with the development of CAD. Mir-196a2 rs11614913 T → C variant locates in the 3p strand of mature miR regions, which could influence the pre-miR maturation and the interaction between 3p mature miRs and target mRNAs (Landgraf et al., 2007). Sung et al. (2016) suggested that rs11614913 T > C polymorphism was a protective factor for CAD. However, the others reported null association (Zhi et al., 2012; Chen et al., 2014; Xiong et al., 2014; Huang et al., 2015a).

Recently, a quantitative assessment of the relationship between miR-196a2 rs11614913 T > C polymorphism and susceptibility of CAD in Asians indicated that this SNP may contribute to a decreased susceptibility of CAD (Wang et al., 2018). In this study, our findings were similar to these reports. Some functional studies reported that rs11614913 T → C variant might be associated with a higher expression miR-196a2 in tumor tissues (Zhao et al., 2016; Yin et al., 2017b). In glioma tissue, a previous study found that the rs11614913 T > C SNP could influence the expression of target gene HOXC8 (Sibin et al., 2017). In liver cirrhosis patients with hepatopulmonary syndrome, compared to the patients carrying rs11614913 TT genotype, the expression of miR-196a increased in individuals with rs11614913CT/CC genotype (Chen et al., 2017a).

In addition, Xu et al. (2009) suggested that miR-196a2 rs11614913 CC could promote the expression of mature miR-196a in cardiac tissue, and the higher expression of miR-196a might lead to a decreased mRNA target of homeobox B8. Thus, miR-196a2 rs11614913 T > C polymorphism may influence the risk of CAD by increasing the expression of miR-196a2 and decreasing mRNA target of homeobox B8. It is noticeable, however, that the relatively few studies were performed on the relationship of rs11614913 with CAD risk, and the sample size of these studies was moderate. In addition, we only found the association between rs11614913 with decreased CAD risk in MI and female subgroups. It could be explained that the association of rs11614913 T > C SNP with the risk of CAD might be influenced by environment factor and subtype of disease.

Considering a fact that most of the SNPs make a small-to-moderate contribution to the development of CAD, the potential role of the rs11614913 T > C locus in miR-196a2 may be diluted or masked by interaction of gene–environment factors. In the future, more studies with detailed environment factors are needed to support our findings.

Nevertheless, some potential limitations should be considered. First, the CAD cases and controls were both recruited from local hospital, and these participants could not be well representative of the Chinese, and bias might have occurred. Second, the SNPs studied in the present study were selected based on literatures and could not obtain an extensive view of the heritable variations of miRs. Further case–control studies are needed to explore the mechanism of CAD development by a functional study. Third, for the moderate sample size in MI and female subgroup, the power might be insufficient in these genetic models. Finally, we did not collect detailed information on cardiovascular event, which restricted further assess of the roles of the miR rs2910164, rs11614913, and rs3746444 polymorphisms in CAD progression and prognosis.

To sum up, this study highlights that rs11614913 locus might decrease the susceptibility of CAD in MI and female subgroups. However, further studies are needed to confirm these potential associations between rs11614913 polymorphism and CAD development.

Footnotes

Acknowledgments

The authors appreciate all subjects who participated in this study. The authors thank Dr. Yan Liu (Genesky Biotechnologies, Inc., Shanghai, China) for technical support.

Disclosure Statement

No competing financial interests exist.

Funding Information

The project was supported by Research Foundation for Senior Talents of Jiangsu University (Grant no. 16JDG066) and Zhenjiang Social Development Science and Technology Project (SH2014087).

Supplementary Material

Supplementary Data

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Supplementary Material

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Associations Between microRNA Polymorphisms and Development of Coronary Artery Disease: A Case–Control Study

Abstract

Introduction

Materials and Methods

Study population and ethical approval

miR SNP selection

Isolation of DNA and genotyping

Statistical analyses

Results

Population characteristics

Primary information of miR SNPs

Association between miR SNPs and CAD risk

Association of miR SNPs with CAD risk in different CAD types

Association of miR SNPs with CAD risk in a stratification analysis

Combination analysis of three miR-polymorphisms (rs11614913, rs2910164, and rs3746444)

Discussion

Footnotes

Acknowledgments

Disclosure Statement

Funding Information

Supplementary Material

References

Supplementary Material