Role of polymorphisms and microRNA levels in predicting cardiovascular events in patients with acute myocardial infarction

Abstract

Acute myocardial infarction (AMI) remains a leading global cause of morbidity and mortality, with high risk of recurrent adverse cardiovascular events. Conventional diagnostic markers often lack the sensitivity needed for early detection and prognostic stratification. Recent advances highlight the role of microRNAs (miRNAs) and their genetic polymorphisms in regulating inflammation, fibrosis, and endothelial function in atherosclerotic disease. This review summarizes evidence on circulating miRNA expression and miRNA-related single nucleotide polymorphisms as biomarkers in AMI. Literature from PubMed, Scopus, and Web of Science was evaluated, focusing on pathways involving NF-κB, interleukin-1 receptor/toll-like receptors, and JAK/STAT signaling. Circulating miRNAs such as miR-150, miR-208, miR-26a, and miR-483-5p demonstrate strong diagnostic accuracy, while polymorphisms, particularly rs2910164 in miR-146a, are consistently associated with AMI susceptibility and adverse outcomes. These findings suggest that miRNAs and their variants may serve as non-invasive tools for diagnosis and risk prediction, supporting future integration into precision cardiovascular medicine.

Key Words: Acute myocardial infarction; Circulating microRNA; Major adverse cardiovascular event; Coronary artery disease; MicroRNA polymorphism

Core Tip: Acute myocardial infarction (AMI) remains a leading cause of morbidity and mortality worldwide, and traditional biomarkers offer limited prognostic value. This review highlights the emerging significance of circulating microRNAs (miRNAs)-particularly miR-150, miR-208, miR-26a, and miR-483-5p and genetic polymorphisms such as rs2910164 in miR-146a in predicting AMI risk and major adverse cardiovascular events. These biomarkers regulate inflammation and endothelial dysfunction through key pathways, nuclear factor-kappaB, and nterleukin-1 receptor/toll-like receptors signaling. Integrating miRNA profiling with clinical assessment may enhance early diagnosis and enable personalized risk stratification in AMI patients.

INTRODUCTION

Coronary artery disease (CAD) is the leading cause of mortality among atherosclerotic cardiovascular diseases, accounting for approximately 1 million deaths and 8.2 million cases of angina annually in East Asia and the Pacific region, according to the World Health Organization[1]. Acute myocardial infarction (AMI), one of the most severe complications of CAD, is increasingly observed in younger populations. A study by Salari et al[2] reported an AMI incidence of 3.8% in individuals under 60 years of age, rising to 9.5% in those over 60. Data from the YOUNG-MI program (2020) further revealed that 20.5% of AMI hospitalizations occurred in patients younger than 40-years-old, highlighting a concerning trend toward earlier onset[3].

In addition to its clinical impact, AMI imposes a significant economic burden. In South Korea, the total economic loss attributed to AMI in 2012 was estimated at $1.18 billion United States dollars, with 51.6% representing direct costs, such as medical expenses, transportation and caregiving, and the remainder due to productivity losses[4].

Despite advances in early intervention and coronary revascularization, patients remain at elevated risk for major adverse cardiovascular events (MACE), including death, recurrent MI, and coronary stent restenosis. A study conducted in Bangladesh reported an in-hospital MACE rate of 37.0%, increasing to 45.0% at 30 days post-intervention. In-hospital rates of all-cause mortality, post-procedural restenosis, and recurrent MI were 19.0%, 18.0%, and 0.0%, respectively. At 30 days, these rates were 15.0%, 26.0% and 4.0%[5]. Long-term follow-up data from Tini et al[6] showed that over a mean period of 3.06 ± 2.04 years, the MACE incidence was 11.2% in patients under 45 and 24.2% in those over 45. These findings underscore the urgent need for early and accurate risk stratification strategies to improve outcomes in AMI patients.

Recent advances in genetics and molecular biology have led to the discovery of small regulatory RNAs that modulate gene expression at the post-transcriptional level. Among these, microRNAs (miRNAs) are short, non-coding RNAs that bind to the 3’-untranslated region (UTR) of target mRNAs to suppress gene expression[7]. Growing evidence suggests that both circulating miRNA expression levels and miRNA-related genetic polymorphisms hold prognostic value in AMI. Notably, elevated circulating miRNA levels and the presence of the rs2910164 polymorphism have been associated with increased AMI risk and poorer clinical outcomes. Carriers of the CC + GC genotype at this locus demonstrated a 3.189-fold higher risk of cardiovascular events [odds ratio (OR) = 3.189, 95%CI: 1.450-7.015, P = 0.004]. Multivariate analysis further identified miRNA expression as an independent risk factor for MACE in AMI patients [hazard ratio (HR) = 2.712, 95%CI: 1.450-5.072, P = 0.002][8].

In addition, several miRNAs have been implicated in post-AMI cardiac remodeling and mortality prediction. For example, miRNA-150 suppresses myocardial hypertrophy and fibrosis by targeting serum response factor and c-Myb[9]. These findings underscore the significant potential of miRNAs and their polymorphisms as biomarkers for risk stratification in AMI. Given the high risk of mortality and complications both during hospitalization and after discharge, there is growing interest in the prognostic utility of these molecular markers. Consistent with this trend, we conducted a comprehensive review to evaluate the association between miRNA expression and genetic polymorphisms with AMI incidence and post AMI burden, particularly the risk of MACE.

This minireview was conducted through a comprehensive search of peer-reviewed articles published between January 2010 and April 2025. The databases of PubMed, Scopus, and Web of Science were queried using the following keywords and Boolean operators: ("microRNA" OR "miRNA") AND ("acute myocardial infarction" OR "AMI") AND ("polymorphism" OR "SNP") AND ("prognosis" OR "diagnosis" OR "biomarker"). Only articles published in English were considered. Both original research studies and systematic reviews were included if they met the following inclusion criteria: (1) The study focused on circulating miRNAs or miRNA-related genetic polymorphisms in patients with AMI; (2) The study evaluated diagnostic or prognostic performance (e.g., sensitivity, specificity, HR, OR); and (3) The study population consisted of human subjects with a confirmed diagnosis of AMI. Additionally, reference lists of key articles were manually screened to identify further relevant studies. Data were extracted and synthesized qualitatively to summarize the current state of evidence on miRNA biomarkers and polymorphisms in relation to AMI and MACE.

MIRNA AND ITS ASSOCIATION WITH AMI AND POST-AMI MAJOR ADVERSE CARDIAC EVENTS

RNA, miRNA and circulating miRNA

RNA is a fundamental biological molecule involved in transcription, translation, gene regulation, and expression. Structurally, RNA consists of a single strand of nucleotide monomers composed of adenine (A), guanine (G), cytosine (C), and uracil (U). Three major types of RNA play critical roles in genetic processes, namely messenger RNA (mRNA), transfer RNA, and ribosomal RNA[10].

Advancements in genetics have led to the discovery of a novel class of small RNA molecules. In 1993, Ambros and Ruvkun first described a small RNA in Caenorhabditis elegans that regulates gene expression post-transcriptionally. Their studies identified a small RNA, lin-4, which downregulates lin-14 gene expression by binding to its 3’-UTR, thereby inhibiting its translation. The term "microRNA" was later introduced by Tuschl and Bartel to describe this class of small non-coding RNA molecules that act as post-transcriptional regulators[7]. Nearly a decade after their discovery, miRNAs were identified in higher organisms, including mammals, and have since been implicated in a wide range of human diseases[11,12].

Molecular structural analysis has shown that miRNAs are small, single-stranded non-coding RNA molecules ranging from 19 to 25 nucleotides in length. They play a critical role in post-transcriptional gene regulation[13]. In most cases, miRNAs inhibit gene expression by binding to the 3’-UTR of target mRNAs, resulting in either mRNA degradation or translational repression. However, recent studies have also identified miRNA interactions with the 5'-UTR. For example, Nitschke et al[14] demonstrated that miRNA-760 functions as a negative regulator by binding to the 5'-UTR of the ATXN-1 gene, which is implicated as a cause of spinocerebellar ataxia type 1 (commonly known as SCA1), leading to RNA degradation and translational inhibition. Furthermore, under specific conditions, miRNAs have been shown to activate gene expression, translocate between subcellular compartments to modulate translation rates, and even influence transcriptional activity[15].

Circulating miRNAs are small, non-coding RNAs detectable in extracellular body fluids such as plasma, serum, synovial fluid, urine and saliva. Emerging evidence suggests that these miRNAs are transported through the circulatory system via small vesicles (e.g., exosomes), Argonaute protein complexes, or high-density lipoprotein cholesterol (HDL-c) particles[16]. Secretion mechanisms of miRNAs involve the ceramide-dependent pathway in COS7 and HEK293 cells. Ceramide, a bioactive sphingolipid whose biosynthesis is tightly controlled by neutral sphingomyelinase 2, triggers exosome secretion (Figure 1). Inhibition of this enzyme reduces miRNA secretion, while its overexpression increases miRNA secretion[17].

Figure 1  MicroRNA biogenesis and secretion pathway.

More recently, HDL-c has been identified as another carrier of endogenous miRNAs. With an average size of 8 nm to 12 nm-significantly smaller than exosomes–HDL-c contains lipids such as phosphatidylcholine, which can form stable ternary complexes with nucleic acids. Apolipoprotein A1, a major protein component of HDL-c, has also been shown to facilitate systemic delivery of miRNAs in animal models.

The physical and biochemical stability of circulating miRNAs makes them promising biomarkers for disease diagnosis and therapeutic monitoring. These molecules exhibit a long half-life (approximately 5 days in serum), resist RNase degradation, and remain stable at room temperature and under harsh conditions, including multiple freeze-thaw cycles[16].

Role of miRNAs and their polymorphisms in AMI and post-AMI MACE prognosis

Association between circulating miRNA levels and AMI diagnosis and prognosis of post-AMI burden (MACE): In recent years, several miRNAs have been implicated in the pathogenesis of atherosclerosis, largely through their regulation of genes involved in inflammatory responses (Figure 2). Beyond their diagnostic and therapeutic potential, circulating miRNAs have also demonstrated prognostic value in patients with AMI[18].

Figure 2  MicroRNA regulation of inflammation and fibrosis in acute myocardial infarction.

Multiple miRNAs have been shown to predict post-AMI outcomes, such as mortality and left ventricular remodeling. For example, miRNA-150 has been found to play a cardioprotective role; its upregulation inhibits myocardial hypertrophy and fibrosis by targeting serum response factor and c-Myb, and it also suppresses pro-apoptotic gene expression. Conversely, downregulation of miRNA-150 is associated with adverse outcomes, including left ventricular hypertrophy, myocardial rupture, and maladaptive remodeling following ST-elevation MI (STEMI). Additionally, miRNA-133a and miRNA-208 were among the first circulating miRNAs to be identified as prognostic markers; elevated levels of these miRNAs at 6 months post-AMI have been correlated with increased all-cause mortality[18].

A growing number of studies have demonstrated that circulating miRNAs are closely associated with CAD and AMI. Chen et al[19] identified four specific miRNAs– miRNA-1291, miRNA-217, miRNA-455-3p, and miRNA-566–as potential biomarkers for the early diagnosis of AMI. In a cohort comprising 80 AMI patients and 80 controls, these miRNAs were significantly downregulated in AMI patients. Their individual diagnostic performance yielded area under the curve (AUC) values of 0.82, 0.79, 0.82, and 0.83, respectively. When combined, the four miRNAs produced a composite AUC of 0.87, with a sensitivity of 83% and a specificity of 87%. Notably, their peak expression occurred earlier than that of traditional biomarkers such as troponin I and creatine kinase-MB (CK-MB).

Similarly, Zhang et al[20] reported that miRNAs including miRNA-32–3p, miRNA-3149 and miRNA-26a-5p have strong diagnostic value for severe CAD requiring intervention. MiRNA-146 has been shown to suppress inflammatory cytokines via the IRAK-1 pathway and plays a dual role in regulating inflammatory responses and low-density lipoprotein cholesterol (LDL-c) clearance from the liver. Meanwhile, miRNA-126, which is highly expressed in endothelial cells, regulates apoptosis and is critical to vascular homeostasis. Both miRNAs have emerged as key contributors to CAD pathogenesis[21]. Among these, miRNA-146 is one of the most extensively studied due to its critical role in immune regulation[22]. The miRNA-146 family includes miRNA-146a and miRNA-146b, located in a non-coding region on chromosome 5 (5q33.3) and an intergenic region on chromosome 10 (10q24.32), respectively[23].

Atherosclerotic CAD results from cholesterol deposition in the arterial intima, leading to luminal narrowing and ischemia. Inflammation and fibrosis are key features of this process, and miRNAs are central regulators. Raitoharju et al[24] and Takahashi et al[25] both found that circulating miRNA levels were elevated in patients with atherosclerosis and CAD compared to healthy controls, implicating the interleukin (IL)-1 receptor (IL-1R)/Toll-like receptors (TLRs)-nuclear factor kappa B (NF-κB) signaling pathway as a primary mediator of miRNA-induced inflammation. Additionally, circulating miRNA levels correlate with disease severity, with higher concentrations observed in patients with acute coronary syndrome (ACS) compared to those with stable angina[26].

Recent findings by Xiao et al[27] support the use of circulating miRNAs as prognostic biomarkers for MACE in STEMI patients. Other studies have reinforced the diagnostic performance of specific miRNAs in AMI (Table 1)[28-32]. Zhang et al[28] identified miRNA-486 and miRNA-150 with AUC values ranging from 0.678-0.771 (P < 0.001), while Peng et al[29] found miRNA-133 (AUC = 0.912, P < 0.001), miRNA-1291 (AUC = 0.695, P < 0.001) and miRNA-663b (AUC = 0.611, P < 0.01) to be promising candidates.

Table 1 Diagnostic value of circulating microRNA levels in acute myocardial infarction.

Ref.	Patients (n)	miRNA			Standard	AUC	95%CI	P value
Zhang et al[28], 2015	110 AMI	miRNA-486			Quantitative real-time PCR	0.731	-	< 0.001
		miRNA-150				0.678	-	< 0.001
		miRNA-486 miRNA-150				0.771	-	< 0.001
Peng et al[29], 2014	186 AMI	miRNA -133			Quantitative reverse transcriptase-PCR	0.912	-	< 0.001
		miRNA-1291				0.695		< 0.001
		miRNA-663b				0.611		< 0.01
Wang et al[30], 2014	45 AMI	miRNA-361-5p		0 hours	Quantitative real-time PCR	0.881	0.777–0.985	< 0.001
				4 hours		0.883	0.777–0.989	< 0.001
				24 hours		0.838	0.716–0.961	< 0.001
		miRNA-21-5p		0 hour		0.949	0.872–1.000	< 0.001
				4 hours		0.947	0.000–1.000	< 0.001
				24 hours		0.791	0.655–0.927	0.001
		miRNA-519e-5p		0 hours		0.798	0.663–0.934	0.001
				4 hours		0.801	0.668–0.934	0.002
				24 hours		0.908	0.818–0.997	< 0.001
		Combined score		0 hour		0.989	0.000–1.000	< 0.001
				4 hours		1.000	0.000–1.000	< 0.001
				24 hours		0.995	0.000–1.000	< 0.001
Zhao et al[31], 2023	183 ACS	miRNA-483-5p			Quantitative reverse transcriptase-PCR	0.919	0.881-0.957	-
	78 AMI					0.867	0.800-0.933	-
Xue et al[32], 2019	31 AMI	miRNA-26a-1	Pre-PCI		Quantitative real-time PCR	0.965	-	< 0.001
			Post-PCI			0.939	-	< 0.001
		miRNA-146a	Pre-PCI			0.911	-	< 0.001
			Post-PCI			0.932	-	< 0.001
		miRNA-199a-1	Pre-PCI			0.855	-	< 0.001
			Post-PCI			0.823	-	< 0.001
		Combined	Pre-PCI			0.913	-	< 0.001
			Post-PCI			0.890	-	< 0.001

ACS: Acute coronary syndrome; AMI: Acute myocardial infarction; AUC: Area under the curve; CI: Confidence interval; PCI: Percutaneous coronary intervention.

Wang et al[30] further reported that miRNA-361-5p, miRNA-21-5p, and miRNA-519e-5p had AUCs > 0.7, all statistically significant (P < 0.01). Zhao et al[31] determined a diagnostic cutoff for circulating miRNA-483-5p in AMI; the values were 1.292 (AUC = 0.919, 95%CI: 0.881-0.957) for ACS and 1.536 (AUC = 0.867, 95%CI: 0.800-0.933) for AMI. Finally, Xue et al[32] reported that miRNA-26a-1, miRNA-146a, and miRNA-199a-1, individually and in combination, demonstrated high diagnosis accuracy in AMI patients undergoing percutaneous coronary intervention (PCI) (Table 1).

Several miRNAs have been identified as prognostic indicators of mortality and left ventricular remodeling following AMI. For example, miRNA-150 regulates myocardial hypertrophy and fibrosis by modulating serum response factor and c-Myb, while also inhibiting apoptotic gene expression. Additionally, miRNA-133a and miRNA-208 were the among the first miRNAs identified as prognostic markers, with levels at 6 months post-AMI correlating significantly with increased all-cause mortality[18].

MiRNAs have also emerged as independent predictors of ACS, regardless of traditional cardiovascular risk factors[26]. Xiao et al[27] demonstrated that circulating miRNA levels can predict MACE in patients with STEMI. Similarly, a multicenter study by Jakob et al[33] involving 1002 STEMI patients found that individuals who experienced MACE had significantly decreased levels of miRNA-26b-5p levels (P = 0.038) and increased levels of miRNA-320a (P = 0.047) and miRNA-660-5p (P = 0.01). These miRNAs were linked to distinct pathophysiological mechanisms: MiRNA-26b-5p was associated with decreased risk of adverse myocardial hypertrophy, miRNA-320a promoted cardiomyocyte death and apoptosis, and miRNA-660-5p correlated with increased platelet activity. In Cox regression models adjusted for age and sex, the AUCs were 0.707 for miRNA-26b-5p, 0.683 for miRNA-660-5p, and 0.672 for miRNA-320a. The combination of the three yielded an improved AUC of 0.718. Notably, incorporating these miRNAs into the GRACE risk score enhanced its AUC from 0.679 to 0.720, reinforcing their prognostic utility in ACS and AMI.

Yang et al[34] conducted a prospective case-control study with 932 STEMI patients undergoing primary PCI and found that patients who experienced MACE had significantly lower levels of miRNA-26a-5p, miRNA-21-5p, and miRNA-191-5p compared to those without MACE (P < 0.001). Multivariate logistic regression revealed that all three miRNAs were inversely associated with MACE risk (P < 0.01), supporting their role as prognostic biomarkers and potential therapeutic targets in AMI[34].

In another study, Alavi-Moghaddam et al[35] identified a cut-off value of 12.38 for miRNA-208b in screening for MACE at 6 months after discharge. Patients with levels above this threshold had an HR of 5.08 (95%CI: 1.13–22.82, P = 0.03) for experiencing MACE (Table 2). Consistently, Zhao et al[31] reported that elevated miRNA-483-5p levels were significantly associated with MACE in AMI patients, with an HR of 5.955 (95%CI: 1.928-18.389, P = 0.002) (Table 2).

Table 2 Prognostic value of microRNA levels in predicting post-acute myocardial infarction major adverse cardiovascular events.

Ref.	Patients, (n)	miRNA	Follow-up period in months	Standard	Cut off (%)	HR (95%CI)	P value
Xiao et al[27], 2021	192 AMI	miRNA-146a	40	Quantitative real-time PCR	-	1.329 (1.060–1.664)	0.01
Alavi-Moghaddam et al[35], 2018	21 AMI	miRNA-208b	6	Quantitative real-time PCR	12.38	5.08 (1.13–22.82)	0.03
Zhao et al[31], 2023	183 ACS	miRNA-483-5p	6	Quantitative reverse transcriptase-PCR	-	5.955 (1.928-18.389)	0.002

ACS: Acute coronary syndrome; AMI: Acute myocardial infarction; HR: Hazard ratio.

The above studies demonstrate that, beyond their role in post-transcriptional gene regulation, circulating miRNAs also have significant diagnostic and prognostic value in patients with AMI. These findings highlight the potential of miRNA-based molecular profiling to support individualized treatment strategies for AMI patients[36].

Association between miRNA polymorphisms, AMI diagnosis accuracy and prognosis of post-AMI MACE: Genetic variation plays a fundamental role in genomic diversity. While mutation refers to rare genetic sequence variations occurring in less than 1% of the population, more common variants are classified as polymorphisms. Among these, SNPs are the most prevalent, involving a single base-pair alteration in the genome. SNPs occur at an estimated frequency of 1 per 300 base pairs, resulting in approximately 10 million SNPs across the human genome[37].

MiRNAs are key regulators of inflammation and fibrosis, and structural variations within miRNA sequences can lead to functional impairments, contributing to various pathologies, particularly atherosclerosis-related cardiovascular diseases. Several SNPs in miRNAs have been linked to increased susceptibility to CAD. For instance, a meta-analysis conducted in China revealed that the rs2910164 SNP, particularly the GG and GG + GC genotypes and the G allele, was significantly associated with a heightened risk of CAD[38].

Another notable polymorphism, rs11614913T>C in miRNA-196a2 has shown potential in predicting CAD severity. In a study by Zhi et al[39], the CC and CC/CT genotypes were found to be associated with a 35% increased risk of severe CAD (HR = 1.34, 95%CI: 1.02–1.75 for CC; HR = 1.35, 95%CI: 1.03–1.75 for CC/CT) compared to the TT genotype. Cox regression analysis further identified age, smoking status, rs11614913T>C and diabetes as significant factors influencing severe CAD outcomes, suggesting that this SNP may serve as a prognostic marker in the Chinese population.

Qiao et al[40] also highlighted miRNA-146a and the rs2910164 SNP as modifiers of MACE risk in patients with coronary syndrome. Moreover, elevated plasma expression of miRNA-146a and its rs2910164 variant have been proposed as biomarkers for early STEMI diagnosis and shown to correlate with disease severity and prognosis[8]. Additional studies have demonstrated that miRNA-146a overexpression in patients with unstable angina correlated with CAD severity and predicted poorer clinical outcomes[41]. However, contradictory findings have been reported. For example, Huang et al[42] observed that the rs2910164C>G variant was associated with a reduced risk of ACS in a Chinese cohort, highlighting the need for larger-scale studies to validate these findings. A study in Vietnam identified SNPs rs2431697 and rs2910164 in the miRNA-146a gene as being associated with clinical severity in AMI patients. Specifically, the C allele of rs2431697 and the G allele of rs2910164 were found to be independent risk factors for severe complications following AMI[43].

Recent studies have increasingly focused on evaluating miRNA polymorphisms as prognostic markers for MACE following discharge in AMI patients (Table 3)[40,44]. Among these, miRNA-146a polymorphisms have been implicated in post-AMI prognosis. For example, Qiao et al[40] identified the rs2910164 (G/C) polymorphism as a risk factor for both AMI incidence and adverse outcomes in ACS patients. In a dominant model, the rs2910164 variant was associated with a significantly increased risk of post-PCI MACE (CG + GG vs CC, HR = 1.405, P = 0.038). Biochemical analyses further revealed that carriers of the G allele exhibited increased inflammatory markers and oxidative stress, leading to enhanced NF-κB activation and pro-inflammatory signaling in atherosclerotic plaques. In a broader analysis, Li et al[44] investigated several miRNA-related gene polymorphisms, including miRNA-149 rs71428439, miRNA-146a rs2910164, miRNA-499 rs3746444, miRNA-423 rs6505162, miRNA-4513 rs2168518 and FABP2 rs2168518. The primary endpoints included MI, stroke, heart failure, coronary revascularization or coronary artery bypass grafting, cardiovascular death and all-cause mortality. Among these, the rs2910164 polymorphism was initially associated with an increased risk of death; however, after multivariable adjustment, this association lost its statistical significance. Liu et al[8] conducted a study on the plasma distribution of miRNA146a and its rs2910164 polymorphism in 92 STEMI patients and 100 healthy controls, tracking MACE over a 28-month follow-up. They found a significantly higher prevalence of the CC + GC genotype among AMI patients, with carriers demonstrating a 3.189-fold increased risk of MACE (P = 0.004). Circulating miRNA-146a levels were markedly elevated in AMI patients (P = 0.0001), with an AUC of 0.742 for AMI diagnosis. Specifically, patients with higher miRNA gene expression levels had a significantly greater incidence of MACE compared to those with lower miRNA gene expression levels (P = 0.001), confirming miRNA gene expressions as an independent predictor of post-AMI MACE in STEMI patients (HR = 2.712, P = 0.002).

Table 3 Prognostic value of microRNA polymorphism in predicting post-acute myocardial infarction major adverse cardiovascular events.

Ref.	Patients (n)	Genetic models	Analyzed model	Population-follow-up period	Standard	HR (95%CI)	P value
Qiao et al[40], 2023	612 ACS	miRNA-146a, rs2910164	Dominant (CC vs CG + GG)	42 months screening for MACE	ABI PRISM3730 DNA Sequencer	1.405 (1.018-1.939)	0.038
			Recessive (CC + CG vs GG)			1.107 (0.821–1.492)	0.506
Li et al[44], 2015	1004 CAD	miRNA-146a, rs2910164	Dominant (CC vs CG + GG)	5 years screening for MACE	PCR-based method	1.119 (0.848-1.478)	0.427
			Recessive (CC + CG vs GG)			1.095 (0.787-1.524)	0.591
			Dominant (CC vs CG + GG)	5 years screening for mortality		0.762 (0.485-1.197)	0.396
			Recessive (CC + CG vs GG)			0.757 (0.398-1.439)	0.203

ABI: Applied Biosystems, Inc; ACS: Acute coronary syndrome; CI: Confidence interval; HR: Hazard ratio; MACE: Major adverse cardiovascular events; miRNA: MicroRNA.

PATHOPHYSIOLOGY OF MIRNA RELATED TO AMI

Before AMI onset, patients typically experience a prolonged period of atherosclerotic plaque development within the coronary arteries. This process is driven by multiple pathological mechanisms, endothelial dysfunction, infiltration and migration of inflammatory cells, comprised vascular cell integrity, and accumulation of vulnerable plaques[11]. The initial phase begins with the subendothelial deposition and gradual accumulation of LDL-c, which undergoes oxidative modification, aggregation and transformation into oxidized LDL-c. Oxidized LDL-c stimulates endothelial and vascular smooth muscle cells to express adhesion molecules, pro-inflammatory cytokines, and growth factors. These molecules interact with circulating monocytes, promoting their adhesion, transmigration into the intima, and differentiation into macrophages and dendritic cells. These immune cells internalize oxidized LDL-c via phagocytosis, becoming foam cells. The progressive accumulation of foam cells in the proteoglycan-rich intimal layer results in the formation of fatty streaks, which evolve into atherosclerotic plaques. This plaque formation is parallel by chronic inflammatory responses. When an atherosclerotic plaque becomes unstable and ruptures, acute intraluminal thrombus formation induces thrombogenesis, leading to vascular occlusion or severe coronary artery stenosis. This ultimately results in impaired myocardial perfusion downstream of the affected coronary artery, finally leading to MI and cardiomyocyte necrosis[45]. Together, these findings underscore the critical role of pathological lipid accumulation, systemic inflammation, and thrombosis in the initiation and progression of AMI.

MiRNA-31, miRNA-181b, miRNA-10a/b, miRNA-126, and miRNA-17-3p have been implicated in endothelial dysfunction by inhibiting endothelial protective mediators. Moreover, miRNA-122 and miRNA-33a/b are key regulators of cholesterol homeostasis. Lipid accumulation is an essential step in atherosclerosis progression and has been linked to increased miRNA-26a, miRNA-221, miRNA-155, miRNA-21 and miRNA-125a-5p expression. Furthermore, miRNA-145, miRNA-127, miRNA-100 and miRNA-133a/b contributes to plaque instability, thereby facilitating ACS development. During atherosclerosis, oxidized LDL-containing foam cells secrete inflammatory cytokines and pro-angiogenic mediators, and several miRNAs, including miRNA-210, miRNA-222, miRNA-155, miRNA-27a/b, and miRNA-221 are associated with these processes[11]. SIRT1 regulates cellular aging by protecting cells against oxidative stress. MiRNA-34a targets SIRT1 within atherosclerotic arteries, where it exacerbates endothelial dysfunction. Furthermore, miRNA-217 modulates SIRT1 expression by binding to its 3′-UTR, thereby inhibiting protein secretion and reducing SIRT1’s protective effects against aging and endothelial homeostasis. Beyond their roles in endothelial function and cellular aging, miRNAs regulate vascular inflammation by modulating leukocyte activation and infiltration through endothelial barriers via miRNA-126 and miRNA-mediated inflammatory signaling pathways. MiRNA-126 regulates vascular inflammation by inhibiting vascular cell adhesion molecule-1 (VCAM-1), and its inhibition leads to increased tumor necrosis factor-alpha (TNF-α) expression, NF-κB activity, and enhanced VCAM-1-mediated leukocyte-endothelium interactions, thereby promoting inflammatory vascular injury[46]. MiRNAs modulate inflammatory responses through multiple signaling axes. In the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway, Tang et al[47] found that miRNA-146a reduces STAT1 gene expression, thereby controlling inflammatory responses via the interferon (IFN) pathway. Further analysis demonstrated that miRNA-mediated STAT1 suppression in macrophages inhibited IFN-γ-driven macrophage differentiation into M1 macrophages, suggesting a novel anti-inflammatory mechanism for miRNAs through M1 macrophage suppression[48]. Similarly, miRNA-146 regulates inflammation via the IL-1R/TLR/NF-κB axis. This signaling cascade is mediated through interleukin-1 receptor-associated kinase 1 (IRAK1) and tumor necrosis factor receptor-associated factor 6[47,49]. Li et al[50] found that miRNA reduces IRAK1 gene expression by binding to its 3’-UTR, thereby modulating inflammation by inactivating the NF-κB pathway and inhibiting inflammatory cell infiltration.

Beyond its role in inflammation, miRNA regulates fibrosis. Recent evidence suggests that therapeutic interventions targeting miRNA significantly ameliorate fibrosis in multiple organ systems[51]. Excessive extracellular matrix (ECM) production is a hallmark of fibrotic disorders across various organs, predominantly driven by chronic inflammatory responses and epithelial-to-mesenchymal transition. Briefly, activated fibroblasts deposit ECM components, causing increased tissue stiffness, impaired oxygen and nutrient diffusion, and subsequent cellular injury[22]. In murine models of hepatic fibrosis, miRNAs are downregulated. In contrast, miRNA overexpression suppressed fibrogenesis by inhibiting the transforming growth factor-beta signaling pathway[52]. Additionally, miRNAs control fibrosis by regulating ECM production. Increased inflammation promotes fibroblast-mediated ECM deposition, leading to increased miRNA expression. This causes decreased production of alpha-smooth muscle actin (known as α-SMA), hyaluronic acid, and collagen-1 within fibroblasts, thereby attenuating ECM accumulation in affected tissues[22] (Table 3).

IMPLEMENTATION AND LIMITATIONS

Circulating miRNAs and miRNA polymorphisms hold clear potential in the early diagnosis and prognosis of AMI. Their early presence during atherosclerotic plaque formation, coupled with their demonstrated capacity to modulate fibrosis and inflammation through the IL-1R/TLRs/NF-κB and JAK-STAT pathways, underscores their crucial role. Notably, circulating miRNA levels for STEMI diagnosis significantly increase upon hospital admission, which is strongly correlated with the CAD severity and serves as an independent predictor of ACS. Additionally, HRs suggest an association between circulating miRNA and post-AMI burden. Recent studies have identified a strong relationship between SNPs of miRNAs with an increased risk of CAD, AMI and MACE. Among these, rs2910164 (G/C) has the highest risk of ACS and is the strongest predictor of MACE. Patients carrying the G allele exhibit higher inflammation levels and increased oxidative stress, leading to impaired base-pairing with the IκBα 3'-UTR, activating the NF-κB inflammatory pathway and contributing to atherosclerotic plaque instability. Additionally, rs57095329 (A>G) reduces miRNA expression by affecting transcription, whereas rs2431697 (T>C) increases CAD risk. However, its association with disease severity remains unclear, in part because some studies have reported contradictory findings. For example, a study by Huang et al[42] suggested that rs2910164C>G reduces ACS risk in the Chinese population. These inconsistencies highlight the need for larger population-based studies to validate the role of this variant. The combination of CYP2C19 polymorphisms and inflammatory cell indices such as neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, or monocyte-to-lymphocyte ratio, serve as strong independent prognostic factors for cardiac adverse events in patients with ACS[53]. Based on the literature, circulating miRNA and its polymorphisms, particularly rs2910164, hold significant potential in the screening and early diagnosis of high-risk AMI and MACE patients. Its use as a biomarker could enhance preventive strategies, optimize treatment, and reduce cardiovascular event rates (Table 4).

Table 4 Summary of diagnostic and prognostic roles of circulating microRNAs and single nucleotide polymorphisms in acute myocardial infarction.

miRNA/SNP	Biological function	AMI diagnostic value	MACE prognostic value	Associated pathway
miR-150	Anti-apoptotic, anti-fibrotic	AUC 0.678–0.771[28]	Predictor of mortality, LV remodeling[18]	SRF, c-Myb, apoptosis
miR-208b	Cardiac-specific, involved in muscle gene regulation	AUC > 0.9 within 3–6 hours[19]	HR = 5.08 for MACE at 6 months[35]	Myocardial contractility genes
miR-483-5p	Inflammation-related	AUC = 0.867–0.919[31]	HR = 5.955 for MACE[31]	NF-κB, cytokines
miR-146a	Regulates IRAK1/TRAF6	AUC = 0.91[32]	HR = 2.712 for MACE[8]	NF-κB, IL-1R/TLR
SNP rs2910164 (miR-146a)	Alters miRNA processing, G/C substitution	↑Risk of AMI[38,40]	HR = 1.405 for MACE (dominant model)[57]	Inflammatory amplification
SNP rs11614913 (miR-196a2)	Influences mature miRNA levels	Associated with severe CAD[39]	HR = 2.44 (recessive model)[58]	Vascular remodeling, SIRT1 signaling
SNP rs3746444 (miR-499)	Affects cardiac muscle gene regulation	Predictor of MI susceptibility[58]	HR = 2.05 for MACE (recessive model)[58]	Myocardial apoptosis and necrosis

AUC: Area under the curve; CAD: Coronary artery disease; HR: Hazard ratio; IL-1R: Interleukin-1 receptor; LV: Left ventricular; MACE: Major adverse cardiovascular events; MI: Myocardial infarction; miRNA: MicroRNA; NF-κB: Nuclear factor-kappa B; SNP: Single nucleotide polymorphism; TLR: Toll-like receptor; TRAF6: Tumor necrosis factor receptor-associated factor 6.

Despite its promising potential, the clinical application of miRNA in AMI diagnosis and prognosis faces several challenges. According to Koshiol et al[54], one challenge is the inconsistency between detection methods, particularly between microarray and qRT-PCR, where only 44% of miRNAs (4/9) in a study of 49 Lung cancer samples showed correlated results between the two techniques. This discrepancy stems from differences in sensitivity, specificity, and technical execution. Additionally, miRNAs are short (approximately 22 nucleotides) with low copy numbers, leading to low detection sensitivity, especially when using non-amplification methods such as cloning or in situ hybridization, which require at least 5–25 µg RNA per sample and have lengthy processing times and poor sensitivity in detecting low-abundance miRNAs. Meanwhile, amplification-based methods such as qRT-PCR and microarray are susceptible to errors due to replication biases. There is currently no gold-standard assay for miRNA detection, which complicates standardization and reproducibility across studies. Different studies employ various validation methods without a unified protocol, reducing data reliability for cross-study comparisons. While capable of identifying novel miRNAs, next-generation sequencing technology still presents significant challenges. First, it requires large amounts of RNA as input (2–10 µg), is expensive, relies upon specialized equipment, and involves a lengthy processing time of 2–5 days per sample. Additionally, amplification biases may lead to false miRNA expression profiles, compromising result accuracy. Moreover, miRNA assays are highly susceptible to RNA contamination, laboratory handling errors, and amplification biases in qRT-PCR-based methods. Another challenge is data processing, as sequencing generates millions of reads, requiring complex bioinformatics algorithms to analyze and filter background noise. Notably, this technique may lead to underrepresentation of low-abundance miRNAs due to preferential amplification of highly expressed miRNAs[54]. Therefore, while miRNAs represent promising biomarkers, standardizing protocols and optimizing detection technologies are essential to enhance accuracy and clinical applicability.

To harness the full potential of miRNAs in cardiovascular disease management and treatment, future research should focus on their application in personalized medicine by improving diagnostic accuracy and enabling early therapeutic decision-making to enhance patient prognosis. Additionally, expanding genetic research on other miRNA polymorphisms and evaluating their impact on cardiovascular pathology will provide a stronger foundation for future studies and clinical applications. Despite the growing role for miRNAs, traditional biomarkers such as troponin, aspartate aminotransferase, lactate dehydrogenase, and CK-MB remain essential for cardiovascular disease diagnosis, treatment, and prognosis, especially AMI. Thus, integrating miRNA with conventional biomarkers may optimize AMI detection time, enhance diagnostic precision, and improve MACE risk stratification. Lastly, limitations in miRNA testing continue to pose significant barriers to its widespread clinical application. Therefore, developing next-generation miRNA assays with enhanced stability, accuracy, and cost-effectiveness remains a priority in future research.

Integration of miRNAs with conventional and non-conventional cardiovascular risk factors

Recent evidence suggests that miRNAs act within a complex network of conventional and non-conventional cardiovascular risk factors. Conditions such as inflammation, oxidative stress, endothelial dysfunction, and metabolic comorbidities (e.g., diabetes mellitus, chronic kidney disease) affect miRNA expression and function[22,46,50]. For instance, IL-6 and TNF-α induce the upregulation of miR-146a, highlighting its regulatory role at the intersection of inflammation and gene expression via IRAK1/NF-κB signaling[24,49]. Several studies have also found that circulating miRNA levels correlate with established risk scoring systems, such as GRACE and Thrombolysis in MI (known as TIMI), suggesting their potential to enhance traditional prognostic models[33]. Moreover, coexistence of miRNA dysregulation with metabolic diseases (e.g., diabetes, dyslipidemia) may amplify the risk of MACE through the synergistic activation of NF-κB signaling and oxidative pathways[50]. Therefore, to optimize risk stratification and enable personalized therapeutic approaches, future research should aim to construct multivariable predictive models that incorporate miRNA expression profiles alongside conventional risk factors and comorbidities.

Integration of miRNAs into multifactorial models and evaluation of relative importance

It is increasingly clear that no single biomarker nor risk factor can fully predict adverse outcomes. Therefore, miRNAs should be interpreted in the context of both conventional and emerging risk factors, including age, diabetes, chronic kidney disease, inflammatory status, and genetic predisposition. Several studies utilized multivariable Cox regression models or integrated risk scoring systems (e.g., GRACE combined with miRNA panels) to assess the additional prognostic value of miRNAs. For example, Jakob et al[33] found that combining miR-26b-5p, miR-320a, and miR-660-5p expression with the GRACE score improved the AUC from 0.679 to 0.720, indicating an additive rather than substitutive value. Furthermore, individual miRNAs may reflect different pathophysiological mechanisms. MiR-146a is associated with inflammation through the IL-1R/TLR/NF-κB axis, miR-483-5p relates to endothelial dysfunction, and miR-208b correlates with myocardial stress. These distinctions emphasize the importance of analyzing both the individual and combined effects of miRNAs to better understand their prognostic contributions. In order to improve MACE prediction accuracy and promote personalized treatment strategies, future multivariable predictive models should quantify the independent and synergistic values of miRNAs when considered alongside comorbidities and clinical variables.

From a genetic perspective, evaluating the role of miRNA-related SNPs requires genotyping accuracy and appropriate model selection. Commonly used methods include PCR-restriction fragment length polymerase, real-time quantitative PCR, TaqMan SNP assays, and mass spectrometry-based genotyping platforms (e.g., matrix-assisted laser desorption/ionization time-of-flight mass spectrometry[55-57]. These techniques enable the detection of allelic variation and genotype distribution within different populations. To assess the relationship between genomic diversity and cardiovascular outcomes, studies typically apply dominant, recessive, or additive genetic models depending on the hypothesized mode of inheritance. Multivariable logistic regression and Cox proportional hazards models determine ORs, and HRs adjusted for co-variants such as age, sex, hypertension, and diabetes[40,58,59]. Other studies using similar methodologies that have evaluated different miRNA SNPs with carrier results-some significant, others inconclusive-are summarized in Table 5. Furthermore, linkage disequilibrium and haplotype analysis can offer deeper insight into allelic interaction and cumulative genetic risk, which are particularly useful in genome-wide association studies. These methods contribute to the quantification of genetic variation across miRNA loci and whether they contribute independently or synergistically to the risk of AMI and MACE.

Table 5 Diagnostic value of microRNA polymorphisms in acute myocardial infarction.

Ref.	Patients, n	miRNA	Analyzed model	Standard	OR (95%CI)	P value
Wang et al[55], 2017	353 CAD	microRNA-146a, rs2431697	T allele carriers	MALDI-TOF MS, Sequenom MassARRAY system	1.26 (1.04-1.53)	0.018
		microRNA-146a, rs2910164	G allele carriers		0.73 (0.62-0.86)	< 0.001
Tie et al[56], 2023	151 CAD	microRNA-146a, rs2910164	Dominant (CG + GG vs CC)	Real-time PCR	1.59 (0.76–2.81)	0.014
			Recessive (CC vs TT + TC)		0.91 (0.53–2.04)	0.320
		miRNA-146a, rs41291957	Dominant (AA + GA vs GG)		0.71 (0.53–1.35)	0.680
			Recessive (AA vs GG + GA)		0.66 (0.21–1.12)	0.17
Agiannitopoulos et al[58], 2020	80 MI	miRNA-146a, rs2910164	Dominant (CC + CG vs GG)	PCR-RFLP, HRM, Sanger Sequencing	0.97 (0.57-1.63)	1.000
			Recessive (CG + GG vs CC)		1.37 (0.55-3.37)	0.478
		miRNA-149a, rs2292832	Dominant (CT + TT vs CC)		1.03 (0.61-1.74)	1.000
			Recessive (CC + CT vs TT)		1.18 (0.56-2.47)	0.700
		miRNA-196aC, rs11614913	Dominant (CT + TT vs CC)		1.73 (1.02-2.92)	0.047
			Recessive (CC + CT vs TT)		2.44 (1.13-5.27)	0.031
		miRNA-499, rs3746444	Dominant (AG + GG vs AA)		1.87 (1.08-3.24)	0.031
			Recessive (AA + AG vs GG)		2.05 (1.07-3.90)	0.035
Huang et al[42], 2015	718 ACS	miRNA-146a, rs11614913	Dominant (TC + CC vs TT)	Real time quantitative PCR system	1.10 (0.84-1.44)	0.488
			Recessive (CC vs TT + TC)		1.00	-
	717 ACS	miRNA-146a, rs2910164	Dominant (CG + GG vs CC)		0.77 (0.60-0.99)	0.044
			Recessive (CC + CG vs GG)		1.07 (0.79-1.45)	0.673
Zhao et al[59], 2019	5202 CHD	miRNA-146a, rs2910164	Dominant (CC vs CG + GG)	Systematic review	0.8 (0.73–0.87)	< 0.001
			Recessive (CC + CG vs GG)		0.86 (0.76-0.98)	< 0.001

CAD: Coronary artery disease; CHD: Congenital heart disease; CI: Confidence interval; HRM: High-resolution melting analysis; MALDI-TOF MS: Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry; MI: Myocardial infarction; OR: Odds ratio; RFLP: Restriction fragment length polymerase.

CONCLUSION

With advances in genetics, circulating miRNAs and their polymorphisms have significant potential in managing AMI and in the prognosis of post-AMI burden. MiRNAs herald a transformative era in CAD management, offering advancements in diagnosis, treatment, and prognosis, thereby enhancing both the quality of community life and healthcare. Despite its promising potential, the application of miRNAs as biomarkers in clinical practice faces numerous challenges, with a major barrier being limitations in miRNA testing. Further in-depth studies are necessary to expand the role and application of miRNA in the diagnosis, prognosis, and treatment of cardiovascular diseases and specifically in AMI, aiming to effectively integrate this biomarker into clinical practice.

ACKNOWLEDGEMENTS

The authors would like to thank the Can Tho University of Medicine and Pharmacy for creating favorable conditions for this study to be performed. We sincerely thank Dr. Tran Lam Thai Bao and Dr. Vo Anh Kiet for assisting us in the synthesis and revision of this manuscript.