Next Article in Journal
Long-Term Measurements of Radon and Thoron Exhalation Rates from the Ground Using the Vertical Distributions of Their Activity Concentrations
Next Article in Special Issue
Population Health and Health Services: Old Challenges and New Realities in the COVID-19 Era
Previous Article in Journal
Role of Mesh Pore Size in Dynamic Membrane Bioreactors
Previous Article in Special Issue
The Identification of Diabetes Mellitus Subtypes Applying Cluster Analysis Techniques: A Systematic Review
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Mir-1, miR-122, miR-132, and miR-133 Are Related to Subclinical Aortic Atherosclerosis Associated with Metabolic Syndrome

Agnė Šatrauskienė
Rokas Navickas
Aleksandras Laucevičius
Tomas Krilavičius
Rūta Užupytė
Monika Zdanytė
Ligita Ryliškytė
Agnė Jucevičienė
1,2 and
Paul Holvoet
Clinic of Cardiac and Vascular Diseases, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, 08661 Vilnius, Lithuania
Centre of Cardiology and Angiology, Vilnius University Hospital, Santaros Klinikos, 08410 Vilnius, Lithuania
Experimental, Preventive, and Clinic Medicine Department, Centre for Innovative Medicine, 08406 Vilnius, Lithuania
Informatics Faculty, Vytautas Magnus University, 44248 Kaunas, Lithuania
Baltic Institute of Advanced Technology, 01124 Vilnius, Lithuania
Faculty of Mathematics and Informatics, Vilnius University, 03225 Vilnius, Lithuania
Department of Cardiology and Cardiovascular Medicine, Universität Tübingen, 72074 Tübingen, Germany
Experimental Cardiology, Department of Cardiovascular Sciences, KU Leuven, 3000 Leuven, Belgium
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(4), 1483;
Submission received: 15 December 2020 / Revised: 29 January 2021 / Accepted: 1 February 2021 / Published: 4 February 2021
(This article belongs to the Special Issue Population Health and Health Services)


Previously, miR-1, miR-122, miR-126, miR-132, miR-133, and miR-370 were found to be related to coronary artery disease (CAD) progression. However, their relationship with subclinical atherosclerosis, especially in subjects with metabolic syndrome, is unknown. Therefore, our aim was to determine their relationship with arterial markers of subclinical atherosclerosis. Metabolic syndrome subjects (n = 182) with high cardiovascular risk but without overt cardiovascular disease (CVD) were recruited from the Lithuanian High Cardiovascular Risk (LitHiR) primary prevention program. The ardio-ankle vascular index (CAVI), augmentation index normalized to a heart rate of 75 bpm (AIxHR75), aortic pulse wave velocity (AoPWV), and carotid artery stiffness were assessed. MicroRNAs (miRs) were analyzed in serum. Pearson correlation and a univariate linear regression t-test showed that miR-1, miR-133b, and miR-133a were negatively associated with CAVI mean, whereas miR-122 was positively associated. MiR-1, miR-133b and miR-133a, and miR-145 were negatively associated with AIxHR75. MiR-122 correlated negatively with AoPWV. In multivariate linear regression models, miR-133b and miR-122 predicted CAVImean, miR-133 predicted AIxHR75, and miR-122 predicted AoPWV. MiR-132 predicted right carotid artery stiffness, and miR-1 predicted left carotid artery stiffness. The addition of smoking to miR-133b and miR-122 enhanced the prediction of CAVI. Age and triglycerides enhanced the prediction of AoPWV by miR-122. A cluster of four miRs are related to subclinical atherosclerosis in subjects with metabolic syndrome. Combined, they may have a more substantial diagnostic or prognostic value than any single miR. Future follow-up studies are needed to establish their clinical relevance.

1. Introduction

Cardiovascular disease (CVD) remains the leading global cause of death and is estimated to cause more than 23.6 million deaths per year by 2030 [1]. CVD risk assessment, especially primary prevention and early detection of CVD, remains the essential daily clinical practice endeavors. Coronary artery disease (CAD) is the most common type of heart disease. CAD is a major cause of mortality and morbidity all over the world [2]. The main problem in CAD diagnosis is that clinical manifestations occur only late after subclinical atherosclerosis initiation. Indeed, atherosclerosis is a chronic and relatively benign disease, which progresses silently and is asymptomatically present in most middle-aged individuals [3].
To reduce the burden of CVDs, they should be either prevented or diagnosed at their earliest stages. At present, patients’ cardiovascular risk is calculated using scoring systems, taking into account the relative importance of risk factors and quantifying the absolute level of CVD risk for individuals without CVD history. A large number of risk scoring systems are based on data from the Framingham study [4,5]. More recently, a combination of risk factors that commonly cluster together has been termed metabolic syndrome (MetS). This cluster contains dyslipidemia, hypertension, and hyperglycemia, with our without obesity, according to the used definition. The vast majority of studies show that atherosclerosis progresses faster in patients with MetS [6,7]. Framingham cohort studies demonstrated that MetS alone predicted approximately 25% of all new onset CVD cases [8].
Another approach to detecting those at high risk is to look for the presence of subclinical atherosclerosis. Arterial markers, such as the cardio-ankle vascular index (CAVI) [9], augmentation index normalized to a heart rate of 75 bpm (AIxHR75) [10], mean arterial pressure (MAP) [11], aortic pulse wave velocity (AoPWV) [12], carotid artery stiffness [13,14], and flow-mediated dilatation (FMD) [15], are already used in daily clinical practice. They proved to be useful in determining the degree of subclinical carotid atherosclerosis or endothelial dysfunction and have the advantage of being non-invasive and safe. However, their applicability in routine risk estimation is limited because they are operator dependent, resource heavy, and requiring relatively expensive equipment. Therefore, there is a need for more straightforward, less time-consuming, and operator-independent biomarkers that would correlate with these arterial markers, substituting the currently used biomarkers.
We aim to introduce microRNAs (miRs) as novel biomarkers for improved risk classification as they are easy to measure, non-invasive, and cost-effective. miRs are endogenous, non-coding, and small (18–22 nucleotides) RNA molecules regulating gene expression at the post-transcriptional level by imperfect base pairing with the 3′- and 5′ untranslated region (UTR) of target mRNA, leading to mRNA degradation or translation repression [16]. Certain miRs in plasma or serum were proved to be elevated or decreased (already) in the early stages of obesity, MetS, Type 2 diabetes mellitus, hypertension, dyslipidemia, coronary artery disease, peripheral artery disease, heart failure, and acute coronary syndrome [17,18,19,20,21,22,23,24,25,26]. Our recent systematic review [27] showed that miR-1 and miR-145b are potential biomarkers of acute coronary syndromes (ACS). miR-1 has higher sensitivity for all acute myocardial infarction (AMI), and miR-145 has higher sensitivity for ST-elevation myocardial infarction (STEMI) and the worst AMI outcome. However, when miRs were studied across different ACS study populations, patients had varying degrees of coronary stenosis, an essential confounder that limited the ability to pool the study results quantitatively. In addition to miR-1 and miR-133, our systemic review identified miR-122, miR-126, miR-132, miR-133, and miR-370 to be related to disease progression when comparing non-CAD vs. CAD. Therefore, we aimed to study the association of these miRs with several arterial atherosclerosis markers, as described above. Because MetS is associated with increased cardiovascular risk [28,29,30], and information about miRs that predicts subclinical atherosclerosis in the background of MetS is even rarer, we mainly searched correlations between miRs and arterial markers in subjects with MetS, without any previously diagnosed CVD.

2. Materials and Methods

Patients included in our study were recruited between 2007 and 2014 from the Lithuanian High Cardiovascular Risk (LitHiR) primary prevention program [31]. This long-term program has focused on employable-aged women (aged 50–65) and men (aged 40–55) without overt CVD. CVD was defined as stable angina pectoris, coronary artery disease, acute myocardial infarction, coronary artery bypass grafting, percutaneous coronary intervention, transient ischemic attack or stroke, and peripheral artery disease. As part of the program, a two-level approach involving primary healthcare institutions (PHCIs) and specialized cardiovascular prevention units (CVPUs) was applied. Participants of the first level of the program were recruited in three ways. The first group consisted of subjects registered in PHCIs and invited by general practitioners to participate. The second group consisted of subjects who visited PHCIs for reasons other than cardiovascular problems. The third group included people who found out about the program via local mass media. The only participants matching the program criteria were recruited. After cardiovascular risk evaluation at the PHCI level, subjects with high cardiovascular risk were directed to the CVPUs (secondary level) for further examination and treatment. Patients at high risk had MetS with at least one or more of the following conditions: (1) Systematic Coronary Risk Evaluation (SCORE) [32] score of ≥11; (2) type 2 diabetes; (3) positive family history of cardiovascular disease; and/or (4) severe dyslipidemia.

2.1. Selecting Patients with Metabolic Syndrome

From 2006 to 2010, 266,391 patients were examined at the PHCIs. Among them, 3194 (1152 (36.1%) men and 2042 (63.9%) women) subjects were diagnosed with MetS, according to ATP III [33], and referred to the CVPU at the Vilnius University Hospital Santaros Klinikos for additional assessment, risk stratification, and creating an individual prevention plan. All participants gave written informed consent. Out of the 3194 initially screened patients with MetS, blood samples were taken. Patients were reevaluated after a minimum of 3 years of follow-up. Blood samples were taken from 2891 patients after a 12-h fast. We only included the first 178 patients due to limited human and financial recourses in our prospective study.

2.2. Measurements of Arterial Markers

Measurements of arterial markers were made during each assessment (every few years, when the patient was screened). Therefore, all patients would have at least two measurements in time (with ±3 years interval). PWV measurements were taken with a Sphygmacor device. The reading was registered when at least 10 consecutive heartbeats with pulse waves matching each other, with at least 95% accuracy, were recorded.

2.3. Sample Collection and miR Detection

Venous blood samples (3 EDTA tubes, 2 tubes containing clot activator and gel for serum separation, 1 tube containing sodium citrate and liquid density medium) were collected from MetS patients after a 12-h fast. All blood samples were transported to the clinical laboratory within 30 min, under room temperature. If the sample did not arrive within 30 min after collection, it was not registered and not analyzed. The register contained each sample with its position in the freezer, identified by a code. Registered and coded samples were delivered to the sample preparation room.

2.4. Total Blood Cell Preparation

EDTA blood was diluted in erythrocyte lysis (EL) buffer with ratio 1:5, incubated on ice for 10–15 min, and centrifuged (400× g for 10 min, 4 °C). After withdrawing the supernatant, the pellet was resuspended in EL buffer (1 volume of blood mixed with 2 volumes EL buffer) and centrifuged (400× g for 10 min, 4 °C). After withdrawing the supernatant, pellet blood cells were stored at −80 °C.

2.5. Serum Sample Preparation

Blood with clot activators was placed in a vertical position for 30 min and centrifuged (1100–1300× g, 10–15 min). The supernatant containing serum was stored under −80 °C. Plasma and serum miRs were selected for the study because they are recognized as the most suitable clinical application source, as sample collection is quick and quite well reproducible [34,35,36].

2.6. Sample Testing

miRs were isolated from 200 μL serum using the miRCURY™ RNA Isolation Kit –Biofluids (Exiqon A/S, Vedbaek, Denmark), according to the manufacturer’s instructions. To compare the yield of RNA obtained from serum samples, bacteriophage MS2 carrier RNA was added to each sample during the purification procedure. Reverse transcription (RT) was performed using the Universal cDNA Synthesis Kit II (Exiqon A/S) and the miRCURY LNA Universal RT microRNA PCR, according to the manufacturer’s instructions. Before using RNA samples for miR profiling, the yield of typical serum miRs, absence of polymerase chain reaction (PCR) inhibitors, and hemolysis in the samples was assessed by use of a miR QC PCR Panel (Exiqon A/S). The amplification was performed in a LightCycler 480 Real-Time PCR System (Roche) in 96-well plates with 45 amplification cycles employing the cycling parameters recommended by Exiqon. The amplification curves were analyzed using the Roche LC software to determine the quantification cycle (by the second derivative method) and for melting curve analysis. NormFinder and geNorm reference gene validation software (GenEx Version 6, MultiD Analyses) were used to identify the most stably expressed miRs. Target miRs were normalized with two internal housekeeping control miRs, including hsa-miR-93-5p and hsa-miR-191-5p, which were identified as the most suitable reference miRs [37,38,39,40,41,42,43]. All data were pre-processed in GenEx Enterprise software (MultiD Analyses) to obtain ΔCq values: ΔCq = (Cq target miRNA − mean Cq of the two reference miRs).

2.7. Statistical Methods

The primary data (baseline characteristics) was processed with the Excel 2013 (Microsoft Corporation, Redmond, Washington, USA) program. Further data was processed with the statistical software “R” (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables in two groups were compared with a t-test, whereas categorical variables were compared with the Chi-square test with Yates’ correction. To measure the linear dependence between miR and medical tests, we used the parametric Pearson correlation coefficient. To examine whether the linear relationship exists between continuous and categorical variables, we performed a simple linear regression and a t-test on the regression line’s slope. Additionally, binary logistic regression and evaluation of performance were performed. In order to evaluate the performance of linear regression models, we used two different measures: mean absolute error (MAE), i.e., the average difference between the predicted value and real observation, and mean absolute percentage error (MAPE), i.e., the average value of the unsigned percentage error [44]. Cut off value for carotid-femoral pulse wave velocity was determined at 9.6 m/s per the established practice of clinical norms based on the expert consensus (expert consensus document on the measurement of aortic stiffness in daily practice using carotid-femoral pulse wave velocity). For the formation of different schemes (models), stepwise regression was used. The results are considered to be statistically significant when p < 0.05 [44,45,46,47].

3. Results

Table 1 summarizes comparative baseline characteristics of two groups: the larger group (n = 3194), including all patients who were diagnosed with MetS in 2007–2014 participating in LitHiR, and the second group of patients (n = 178) randomly selected from the first group to perform miR testing. Components of MetS were defined according to ATP III: waist circumference, triglycerides, high-density lipoprotein cholesterol, blood pressure, and fasting plasma glucose.
The table shows that groups did not differ according to age, sex, smoking, or number of metabolic syndrome components. Waist circumference was not different, although the miR testing group’s body mass index (BMI) was somewhat lower. Lipid levels and blood pressure do not differ between groups. However, patients in the miR testing group tended to have hypertension less often (92% vs. 97%) and type 2 diabetes less often (9% vs. 20%). Positive family history of cardiovascular diseases was similar in both groups. Hs-C-reactive protein levels were somewhat higher in the miR testing group than in the whole cohort. Finally, the miR testing group was treated often with statins (4.9% vs. 16.9 %).
Table 2 shows different plasma levels of 10 selected miRs in the group of 178 MetS patients.
Pearson correlation (if both variables are continuous) or a linear regression analysis (in case one variable is continuous and another is categorical) were performed to determine the linear relationship between miRs and selected arterial markers (Table 3).
miR-1, miR-133b, and miR-133a were negatively associated with CAVI mean, whereas miR-122 was positively associated. MmiR-1, miR-133b and miR-133a, and miR-145 were also negatively associated with AIxHR75. miR-122 correlated negatively with AoPWV. None of the tested miRs correlated with MAP or FMD%. miR-1 was weakly associated with left carotid artery stiffness, whereas miR-132 was weakly associated with right carotid artery stiffness; none of them were associated with mean carotid artery stiffness in both carotid arteries. miR-126, miR-155, miR-195, and miR-370 were not associated with any of the arterial markers. miR-132 correlated with a plaque in the left carotid artery, whereas miR-370 correlated with a plaque in the right carotid artery.
Multivariate linear regression models predicting the association between miRs and the investigated arterial markers were built, including only miRs significantly related in univariate models. miR-133b and miR-122 predicted CAVImean. miR-133 predicted AIxHR75, and miR-122 predicted AoPWV. Finally, miR-132 predicted right carotid artery stiffness and miR-1 predicted left carotid artery stiffness (Table 4).
We then investigated whether the addition of other cardiovascular risk markers than miRs could improve the models. The only factors significantly related to arterial markers were incorporated in the model. In model 1, age, sex, and smoking were added to miR expressions. In model 2, low-density lipoprotein cholesterol (LDL-Ch), high-density lipoprotein cholesterol (HDL-Ch), and triglycerides (TG) concentrations were added to model 1. In model 3, C-reactive protein (hs-CRP) values were added to model 2. In model 4, diabetes mellitus diagnosis (presence/absence) was added.
The addition of age and smoking to miR-133b and miR-122 enhanced the prediction of CAVI (Table 5).
None of the additional factors risk factors improved the prediction of AixHR75 by miR-133b (Table 6).
Age and TG added to the prediction of AoPWV by miR-122 (Table 7).
None of the other risk factors added to the prediction of carotid artery stiffness by miR-1 or miR-132 (Table 8).

4. Discussion

Our study identified four miRs correlating with several subclinical atherosclerosis markers in patients with metabolic syndrome: miR-1, miR-122, miR-132, and miR-133. miR-122, miR-132, and miR-133 were related to markers of arterial stiffness. miR-1 and miR-132 were rather weakly related to carotid stiffness. In contrast, miR-126, miR-145, miR-155, and miR-195 were not related to any arterial markers. Thus, this study further supports the conclusions of our systematic review that miR-133 and miR-1, which belong to the same transcriptional unit, are among the most promising miR biomarkers for progressing stages of cardiovascular disease.
However, in contrast to the conclusion of our systematic review of published studies that miR-1 is specific for acute coronary syndromes, our current study shows that it is already decreased in patients with asymptomatic atherosclerosis. The relation of the selected miRs with arterial markers of atherosclerosis has been previously underscored by mechanistic data supporting their involvement in several pathogenic mechanisms in atherosclerosis. Indeed, they were found to regulate endothelial function and angiogenesis (miR-1, miR-133) [47,48], vascular smooth muscle cell differentiation (miR-133) [49], apoptosis (miR-1 and miR-133) [50,51,52], inflammation (miR-133) [52,53,54,55], cardiac myocyte differentiation (miR-1 and miR-133) [50,56], and cardiac fibrosis (miR-122 and miR-133) [57,58,59,60]. In addition, our current study [27] adds two other miRs: miR-122 and miR-132. They were both related to aortic stiffness. miR-122 is associated with cardiac fibrosis [61]. miR-122 is one of a series of miRs, which may play a mechanistic role in coupling lipid metabolism and atherosclerosis [61]. miR-122 may reduce nitric oxide levels and induce endothelial dysfunction, particularly in hypertension [62,63]. Additionally, miR-122 down-regulates miR-21, enhancing cholesterol synthesis by increasing HMGCR (3-hydroxy-3-methylglutaryl-co-enzyme A reductase) transcription and translation [64,65]. Additionally, it has been implicated in inflammation in general [66]. Wang et al. found out that miR-122 is associated with obesity and insulin resistance in young adults [67]. Gao et al. concluded that miR-122 and miR-370 are higher in patients with hyperlipidemia and associated with CAD [21]. miR-122 was also associated with glycemic status and insulin levels. It was significantly upregulated in individuals with type 2 diabetes mellitus, obesity, and in the pre-diabetic stage [68]. Interestingly, TG and smoking added to the predictive power of miR-122. miR-132 has been implicated in smooth muscle cell proliferation in intimal hyperplasia [69] and vascular endothelial inflammation [70]. Eskildsen et al. found that miR-132 is involved in angiotensin II-induced hypertension [71]. Altogether, the relation of miR-122 and miR-132 with inflammation may explain their additive value to the expected values of miR-1 and miR-133 as biomarkers.
Overall, our study adds to earlier studies, showing that a panel of miRs embodies the intricate mechanisms in different plaque progression stages [72]. Further, Ren et al. identified a cluster of miRs (miR-106b/25 cluster, miR-17/92a cluster, miR-21/590-5p family, miR-126* miR-451) to be dysregulated in patients with vulnerable CAD compared with controls [73]. D’Alessandra et al. demonstrated that miR-1, miR-133a, and miR-126 in unstable CAD and miR-1, miR-126, and miR-485-3p in stable CAD correctly classified patients vs. controls with an efficiency of 87% [74]. Previous studies revealed the importance of miRs in regulating critical signaling and lipid homeostasis pathways that alter the balance of atherosclerotic plaque progression and regression [61,75,76]. In brief, the identified miRs are related to many other atherogenic processes, underscoring their mechanistic and clinical relevance.
A recent review discussed other commonly used subclinical vascular markers in the clinical setting. Among them were the carotid intima-media thickness and plaque measured by ultrasound, coronary artery calcium detected by cardiac computed tomography, ankle–arm index pressure measured by distal pressure Doppler measurement, and aortic pulse wave velocity measured from carotid and femoral pressure wave recordings with a Doppler or mechanographic device. Positive testing for subclinical atherosclerosis was found to be associated with a moderately high to high coronary heart disease (CHD) risk. However, the different types of subclinical atherosclerosis tests had a different prognostic performance. Positive testing for intima-media thickness, ankle-arm index pressure, and aortic pulse wave velocity conveyed a 10-year CHD risk between 10% and 20%. In contrast, positive testing for carotid plaque or coronary calcium conveys a 10-year CHD risk superior to 20%. In contrast, negative testing for subclinical atherosclerosis conveyed a low CHD risk inferior to 10%, whatever the test considered. It was concluded that for individuals at intermediate risk, e.g., 10–20% 10-year Framingham risk of fatal and non-fatal CHD or 3–5% European SCORE risk of fatal cardiovascular disease, clinicians may consider testing for subclinical atherosclerosis, and, in those with a positive test, aggressive risk reduction intervention may be appropriate [77,78,79,80,81,82,83]. CAVI was found to increase linearly with age and was elevated even in mild arteriosclerotic disease. It could identify differences in the degree of arteriosclerosis among patients with severe arteriosclerotic disease and better reflected the severity of disease of the coronary artery than brachial-ankle pulse wave velocity. Patients with higher CAVI values showed a poor prognosis compared with those with lower CAVI values. Furthermore, CAVI could be lowered by controlling hyperglycemia and hypertension [84], essential factors of MetS. Given this data, the relation of miR-133b and miR-122 with CAVI is of interest. Nevertheless, the implementation of subclinical atherosclerosis testing in patients’ risk management is still considered dependent on a better knowledge of the comparative prognostic performance of various atherosclerosis tests currently available.
Surprisingly, miR-126, miR-45, and miR-155 were not associated with any arterial markers in our study. However, together with miR-30a and let-7, miR-126 may be more linked to ischemic cardiovascular disease, including strokes [85,86]. Exosomes enriched in miR-126 reduced ischemic injury and fibrosis in a preclinical model [87]. The relation of miR-126 expression with obesity and type 2 diabetes remains to be elucidated. Indeed, one study showed that miR-126 is significantly lower (~65%) in overweight and obese subjects [88]. Another study showed that miR-126 was up-regulated in obesity and down-regulated in the pre-diabetic stage and in type 2 diabetes mellitus [89].
Previously, low levels of circulating miR-145 and miR-155 were found to be associated with CAD, by comparing CAD patients with healthy controls [90]. However, it is possible that this relation was due to differences in inflammation. Obesity-induced inflammation is an important mechanism linking obesity to MetS, and miR-145 stimulates the expression of TNF-α in adipocytes [91]. Additionally, miR-155 increases leukocyte-endothelial cell interactions and endothelial cell migration [92] and amplifies inflammation in adipocytes [93]. miR-145 may be more related to acute coronary artery disease. Indeed, miR-145 was lower in AMI patients, through was in relation to TGF-β- and hypoxia-induced ischemia [86,94]. miR-155 expression correlated positively with the Gensini score [95]. The up-regulation of miR-155 by hypertension and inflammation is associated with endothelial dysfunction and loss of protection against ox-LDL-induced cytokine release and apoptosis [96,97,98,99,100]. Circulating miR-195 predicted adverse ischemic events after angioplasty with stent implantation [101], possibly preventing endothelial repair [102]. Thus, differences in the regulation of certain miRs according to disease stages may explain why particular miRs do not show a significant association with arterial markers in our study.
Our data is promising and suggests new means of biomarker-based risk stratification for subclinical atherosclerosis. However, the miR group’s relatively small size, the differences between the miR group and the whole cohort, and the lack of follow-up prevent establishing firm conclusions about their diagnostic power. The lack of data on plaque calcification is a further limitation. Notwithstanding these limitations, our study is unique because several markers of subclinical atherosclerosis have been included.

5. Conclusions

In conclusion, our study identified a cluster of four miRs (miR-1, miR-122, miR-132, and miR-133) related to subclinical atherosclerosis in MetS patients. Their value is underscored by published mechanistic data, implicating them in several atherogenic processes, such as endothelial dysfunction and inflammation, angiogenesis, vascular smooth muscle cell proliferation and differentiation, and apoptosis, hallmarks of early atherosclerosis. Further studies define the association between miRs and MetS as a set of components, and validation of those findings in a non-MetS population might be valuable.

Author Contributions

Conceptualization, R.N. and P.H.; Methodology, R.N. and A.Š.; Data curation, T.K., R.U. and A.J.; Formal analysis, R.U.; Funding acquisition, R.N.; Investigation, A.Š., and R.N.; Project administration, R.N.; Software, R.U.; Supervision, A.L., L.R. and P.H.; Writing– original draft, A.Š. and M.Z.; Writing–review & editing, A.Š., R.N. and P.H. All authors have read and agreed to the published version of the manuscript.


The study was funded by a grant (No. SEN-03/2016) from the Research Council of Lithuania.

Institutional Review Board Statement

The study was approved by Vilnius Regional Biomedical Research Ethics Committee (protocol code VD041, date of approval: 2013-06-11, Number of Ethical Approval: 158200-13-641-205).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Conflicts of Interest

The authors declare no conflict of interest.


  1. Update, S. Heart disease and stroke statistics—At-a-glance heart disease, stroke and other cardiovascular diseases heart disease. Stroke Cardiovasc. Dis. Risk Factors 2015, 1, 7–10. [Google Scholar]
  2. Watchira, J.K.; Stys, T.P. Cardiovascular disease and bridging the diagnostic gap. S. D. Med. J. South Dakota State Med. Assoc. 2013, 66, 366–369. [Google Scholar]
  3. Raitoharju, E.; Oksala, N.; Lehtimaki, T. MicroRNAs in the atherosclerosis plaque. Clin. Chem. 2013, 59, 1708–1721. [Google Scholar] [CrossRef] [Green Version]
  4. Dawber, T.R.; Meadors, G.F.; Moore, F.E.J. Epidemiological approaches to heart disease: The Framingham Study. Am. J. Public Health 1951, 41, 279–286. [Google Scholar] [CrossRef]
  5. Beswick, A.; Brindle, P. Risk scoring in the assessment of cardiovascular risk. Curr. Opin. Lipidol 2006, 17, 375–386. [Google Scholar] [CrossRef]
  6. Gami, A.S.; Witt, B.J.; Howard, D.E.; Erwin, P.J.; Gami, L.A.; Somers, V.K.; Montori, V.M. Metabolic syndrome and risk of incident cardiovascular events and death a systematic review and meta-analysis of longitudinal studies. J. Am. Coll. Cardiol. 2007, 49, 403–414. [Google Scholar] [CrossRef] [Green Version]
  7. Iglseder, B.; Cip, P.; Malaimare, L.; Ladurner, G.; Paulweber, B. The metabolic syndrome is a stronger risk factor for early carotid atherosclerosis in women than in men. Stroke 2005, 36, 1212–1217. [Google Scholar] [CrossRef] [Green Version]
  8. Grundy, S.M.; Brewer, H.B.; Cleeman, J.I.; Smith, S.C.; Lenfant, C. Definition of metabolic syndrome: Report of the national heart, lung, and blood institute/american heart association conference on scientific issues related to definition. Arter. Thromb Vasc Biol. 2004, 24, e13–e18. [Google Scholar]
  9. Sun, C.-K. Cardio-ankle vascular index (CAVI) as an indicator of arterial stiffness. Integrated Blood Pressure Control. 2013, 6, 27–38. [Google Scholar] [CrossRef] [Green Version]
  10. Wilhelm, B.; Klein, J.; Friedrich, C.; Frost, S.; Pfützner, A.; Kann, P.H.; Weber, M.M.; Forst, T. Increased arterial augmentation and augmentation index as surrogate parameters for arteriosclerosis in subjects with diabetes mellitus and nondiabetic subjects with cardiovascular disease. J. Diabetes Sci. Technol. 2007, 1, 260–263. [Google Scholar] [CrossRef] [Green Version]
  11. Zheng, L.; Sun, Z.; Li, J.; Zhang, R.; Zhang, X.; Liu, S.; Li, J.; Xu, C.; Hu, D.; Sun, Y. Pulse pressure and mean arterial pressure in relation to ischemic stroke among patients with uncontrolled hypertension in rural areas of china. Stroke 2008, 39, 1932–1937. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Amier, R.P.; Nijveldt, R. Clinical value of pulse wave velocity: A promising marker for arterial stiffness. Netherlands Heart J. 2015, 23, 491–492. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Della-Morte, D.; Gardener, H.; Denaro, F.; Boden-Albala, D.; Elkind, M.S.V.; Paik, M.C.; Sacco, R.L.; Rundek, T. Metabolic syndrome increases carotid artery stiffness: The northern Manhattan study. Int. J. Stroke Off. J. Int. Stroke Soc. 2010, 5, 138–144. [Google Scholar] [CrossRef] [PubMed]
  14. Sung, S.-H.; Liao, J.-N.; Yu, W.-C.; Cheng, H.-M.; Chen, C.-H. Common carotid artery stiffness is associated with left ventricular structure and function and predicts first hospitalization for acute heart failure. Pulse 2014, 2, 18–28. [Google Scholar] [CrossRef] [Green Version]
  15. Thijssen, D.H.J.; Black, M.A.; Pyke, K.E.; Padilla, A.; Atkinson, G.; Harris, R.A.; Parker, B.; Widlansky, M.E.; Tschakovsky, M.E.; Green, D.J. Assessment of flow-mediated dilation in humans: A methodological and physiological guideline. Am. J. Physiol. Heart Circ. Physiol. 2011, 300, H2–H12. [Google Scholar] [CrossRef] [Green Version]
  16. Bartel, D.P. MicroRNAs: Genomics, biogenesis, mechanism, and function. Cell 2004, 116, 281–297. [Google Scholar] [CrossRef] [Green Version]
  17. Fichtlscherer, S.; De Rosa, S.; Fox, H.; Schwietz, T.; Fischer, A.; Liebetrau, C.; Weber, M.; Hamm, C.W.; Röxe, T.; Müller-Ardogan, M.; et al. Circulating microRNAs in patients with coronary artery disease. Circ. Res. 2010, 107, 677–684. [Google Scholar] [CrossRef] [Green Version]
  18. Li, T.; Cao, H.; Zhuang, J.; Wan, J.; Guan, M.; Yu, B.; Li, X.; Zhang, W. Identification of miR-130a, miR-27b and miR-210 as serum biomarkers for atherosclerosis obliterans. Clin. Chim. Acta. Neth. 2011, 412, 66–70. [Google Scholar] [CrossRef]
  19. Weber, M.; Baker, M.B.; Patel, R.S.; Quyyumi, A.A.; Bao, G.; Searles, C.D. MicroRNA expression profile in CAD patients and the Impact of ACEI/ARB. Cardiology Res. Pr. 2011, 2011, 532915. [Google Scholar] [CrossRef] [Green Version]
  20. Taurino, C.; Miller, W.H.; McBride, M.W.; McClure, J.D.; Khanin, R.; Moreno, M.U.; Dymott, J.A.; Delles, C.; Dominiczak, A.F. Gene expression profiling in whole blood of patients with coronary artery disease. Clin. Sci. (Lond.) 2010, 119, 335–343. [Google Scholar] [CrossRef] [Green Version]
  21. Gao, W.; He, H.W.; Wang, Z.M.; Zhao, H.; Lian, X.Q.; Wang, Y.S.; Zhu, J.; Yan, J.J.; Zhang, D.G.; Yang, Z.J.; et al. Plasma levels of lipometabolism-related miR-122 and miR-370 are increased in patients with hyperlipidemia and associated with coronary artery disease. Lipids Health Dis. 2012, 11, 55. [Google Scholar] [CrossRef] [Green Version]
  22. Li, S.; Zhu, J.; Zhang, W.; Chen, Y.; Zhang, K.; Popescu, L.M.; Ma, X.; Lau, W.B.; Rong, R.; Yu, X.; et al. Signature microRNA expression profile of essential hypertension and its novel link to human cytomegalovirus infection. Circulation 2011, 124, 175–184. [Google Scholar] [CrossRef] [Green Version]
  23. Karolina, D.S.; Armugam, A.; Tavintharan, S.; Wong, M.T.K.; Lim, S.C.; Sum, C.F.; Jeyaseelan, K. MicroRNA 144 impairs insulin signaling by inhibiting the expression of insulin receptor substrate 1 in type 2 diabetes mellitus. PLoS ONE 2011, 6, e22839. [Google Scholar] [CrossRef]
  24. Karolina, D.S.; Tavintharan, S.; Armugam, A.; Sepramaniam, S.; Pek, S.L.T.; Wong, M.T.; Lim, S.C.; Sum, C.F.; Jeyaseelan, K. Circulating miRNA profiles in patients with metabolic syndrome. J. Clin. Endocrinol Metab. 2012, 97, E2271–E2276. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Zampetaki, A.; Kiechl, S.; Drozdov, I.; Willeit, P.; Mayr, U.; Prokopi, M.; Mayr, A.; Weger, S.; Oberhollenzer, F.; Bonora, E.; et al. Plasma MicroRNA profiling reveals loss of endothelial MiR-126 and other MicroRNAs in type 2 diabetes. Circ. Res. 2010, 107, 810–817. [Google Scholar] [CrossRef]
  26. Kong, L.; Zhu, J.; Han, W.; Jiang, X.; Xu, M.; Zhao, Y.; Dong, Q.; Pang, Z.; Guan, Q.; Gao, L.; et al. Significance of serum microRNAs in pre-diabetes and newly diagnosed type 2 diabetes: A clinical study. Acta Diabetol. 2011, 48, 61–69. [Google Scholar] [CrossRef]
  27. Navickas, R.; Gal, D.; Laucevičius, A.; Taparauskaitė, A.; Zdanytė, M.; Holvoet, P. Identifying circulating microRNAs as biomarkers of cardiovascular disease: A systematic review. Cardiovasc. Res. 2016, 111, 322–337. [Google Scholar] [CrossRef]
  28. Tune, J.D.; Goodwill, A.G.; Sassoon, D.J.; Mather, K.J. Cardiovascular consequences of metabolic syndrome. Transl Res. 2017, 183, 57–70. [Google Scholar] [CrossRef] [Green Version]
  29. Bundhun, P.K.; Wu, Z.J.; Chen, M.-H. Impact of modifiable cardiovascular risk factors on mortality after percutaneous coronary intervention: A systematic review and meta-analysis of 100 studies. Medicine 2015, 94, e2313. [Google Scholar] [CrossRef]
  30. Sperling, L.S.; Mechanick, J.I.; Neeland, I.J.; Herrick, C.J.; Després, J.P.; Ndumele, C.E.; Vijayaraghavan, K.; Handelsman, Y.; Puckrein, G.A.; Araneta, M.R.; et al. The cardiometabolic health alliance: Working toward a new care model for the metabolic syndrome. Am. Coll Cardiol. 2015, 66, 1050–1067. [Google Scholar] [CrossRef] [Green Version]
  31. Laucevicius, A.; Kasiulevicius, V.; Jatuzis, D.; Petrulioniene, Z.; Ryliskyte, L.; Rinkuniene, E.; Badariene, J.; Cypiene, A.; Gustiene, O.; Slapikas, R. Lithuanian high cardiovascular risk (lithir) primary prevention programme—rationale and design. Semin. Cardiovasc. Med. 2012, 18, 3. [Google Scholar] [CrossRef]
  32. Conroy, R.M.; Pyorala, K.; Fitzgerald, A.P.; Sans, S.; Menotti, A.; De Backer, G.; De Bacquer, D.; Ducimetière, P.; Jousilahti, P.; Keil, U.; et al. On behalf of the SCORE project group. Estimation of ten-year risk of fatal cardiovascular disease in Europe: The SCORE project. Eur. Heart, J. 2003, 24, 987–1003. [Google Scholar] [CrossRef]
  33. Cleeman, J.I.; Grundy, S.M.; Becker, D.; Clark, L. Expert panel on detection evaluation and treatment of high blood, cholesterol in adults: Executive summary of the third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). JAMA 2001, 285, 2486–2497. [Google Scholar]
  34. Sato, F.; Tsuchiya, S.; Terasawa, K.; Tsujimoto, G. Intra-platform repeatability and inter-platform comparability of microRNA microarray technology. PLoS ONE 2009, 4, e5540. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Wang, B.; Howel, P.; Bruheim, S.; Ju, J.; Owen, L.B.; Fodstad, O.; Xi, Y. Systematic evaluation of three microRNA profiling platforms: Microarray, beads array, and quantitative real-time PCR array. PLoS ONE 2011, 6, e17167. [Google Scholar] [CrossRef]
  36. Chen, Y.; Gelfond, J.A.; McManus, L.M.; Shireman, P.K. Reproducibility of quantitative RT-PCR array in miRNA expression profiling and comparison with microarray analysis. Bmc Genom. 2009, 10, 407. [Google Scholar] [CrossRef] [Green Version]
  37. Mestdagh, P.; Van Vlierberghe, P.; De Weer, A.; Muth, D.; Westermann, F.; Speleman, F.; Vandesompele, J. A novel and universal method for microRNA RT-qPCR data normalization. Genome Biol. 2009, 10, R64. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Hou, B.; Murata, M.; Said, A.S.; Sakaida, H.; Masuda, S.; Takahashi, T.; Zhang, Z.; Takeuchi, K. Changes of micro-RNAs in asymptomatic subjects sensitized to Japanese cedar pollen after prophylactic sublingual immunotherapy. Allergy Rhinol. 2015, 6, e33–e38. [Google Scholar] [CrossRef] [Green Version]
  39. Barry, S.E.; Chan, B.; Ellis, M.; Yang, Y.; Plit, M.L.; Guan, G.; Wang, X.; Britton, W.J.; Saunders, B.M. Identification of miR-93 as a suitable miR for normalizing miRNA in plasma of tuberculosis patients. J. Cell. Mol. Med. 2015, 19, 1606–1613. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Gharbi, S.; Shamsara, M.; Khateri, S.; Soroush, M.R.; Ghorbanmehr, N.; Tavallaei, M.; Nourani, M.R.; Mowla, S.J. Identification of reliable reference genes for quantification of micrornas in serum samples of sulfur mustard-exposed veterans. Cell J. (Yakhteh) 2015, 17, 494–501. [Google Scholar]
  41. Ouyang, X.; Jiang, X.; Gu, D.; Zhang, Y.; Kong, S.K.; Jiang, C.; Xie, W. Dysregulated serum MiRNA profile and promising biomarkers in dengue-infected patients. Int. J. Med Sci. 2016, 13, 195–205. [Google Scholar] [CrossRef] [Green Version]
  42. Wang, J.; Zhu, Y.; Jin, F.; Tang, L.; He, Z.; He, Z. Differential expression of circulating microRNAs in blood and haematoma samples from patients with intracerebral haemorrhage. Journal of International Medical Research 2016, 44, 419–432. [Google Scholar] [CrossRef] [Green Version]
  43. Schwarzenbach, H.; da Silva, A.M.; Calin, G.; Pantel, K. Data normalization strategies for MicroRNA quantification. J. Clin. Chem. 2015, 61, 1333–1342. [Google Scholar] [CrossRef] [PubMed]
  44. Hyndman, R.J.; Athanasopoulos, G. Forecasting: Principles and Practice. Chapter 2.5 Evaluating Forecast Accuracy. OTexts. 2013. Available online: (accessed on 25 April 2017).
  45. Hastie, T.; Tibshirani, R.; Friedman, J.J. The Elements of Statistical Learning: Data Mining, Inference, And Prediction, 2nd ed.; Springer Series in Statistics: New York, NY, USA, 2009; pp. 44–61. [Google Scholar]
  46. Menard, S.W. Applied Logistic Regression, 2nd ed.; SAGE: Newbury Park, CA, USA, 2002. [Google Scholar]
  47. Lin, C.-Y.; Lee, H.-C.; Fu, C.-Y.; Ding, Y.-Y.; Chen, J.-S.; Lee, M.-H.; Huang, W.-J.; Tsai, H.-J. MiR-1 and miR-206 target different genes to have opposing roles during angiogenesis in zebrafish embryos. Nat. Commun. 2013, 4, 2829. [Google Scholar] [CrossRef] [Green Version]
  48. Soufi-Zomorrod, M.; Hajifathali, A.; Kouhkan, F.; Mehdizadeh, M.; Rad, S.M.A.H.; Soleimani, M. MicroRNAs modulating angiogenesis: miR-129-1 and miR-133 act as angio-miR in HUVECs. Tumour Biol. J. Int. Soc. Oncodevelopmental Biol. Med. 2016, 37, 9527–9534. [Google Scholar] [CrossRef] [PubMed]
  49. Torella, D.; Iaconetti, C.; Catalucci, D.; Ellison, G.M.; Leone, A.; Waring, C.D.; Bochicchio, A.; Vicinanza, C.; Aquila, I.; Curcio, A.; et al. MicroRNA-133 controls vascular smooth muscle cell phenotypic switch in vitro and vascular remodeling in vivo. Circ. Res. 2011, 109, 880–893. [Google Scholar] [CrossRef] [Green Version]
  50. Glass, C.; Singla, D.K. MicroRNA-1 transfected embryonic stem cells enhance cardiac myocyte differentiation and inhibit apoptosis by modulating the PTEN/Akt pathway in the infarcted heart. AJP Heart Circ. Physiol. 2011, 301, H2038–H2049. [Google Scholar] [CrossRef] [Green Version]
  51. Xu, C.; Lu, Y.; Pan, Z.; Chu, W.; Luo, X.; Lin, H.; Xiao, J.; Shan, H.; Wang, Z.; Yang, B. The muscle-specific microRNAs miR-1 and miR-133 produce opposing effects on apoptosis by targeting HSP60, HSP70 and caspase-9 in cardiomyocytes. J. Cell Sci. 2007, 120 Pt 17, 3045–3052. [Google Scholar] [CrossRef] [Green Version]
  52. Zhang, L.; Cheng, H.; Yue, Y.; Li, S.; Zhang, D.; He, R. TUG1 knockdown ameliorates atherosclerosis via up-regulating the expression of miR-133a target gene FGF1. Cardiovasc Pathol. 2018, 33, 6–15. [Google Scholar] [CrossRef]
  53. Yu, S.Y.; Dong, B.; Tang, L.; Zhou, S.H. LncRNA MALAT1 sponges miR-133 to promote NLRP3 inflammasome expression in ischemia-reperfusion injured heart. Int. J. Cardiol. 2018, 254, 50. [Google Scholar] [CrossRef]
  54. Gast, M.; Rauch, B.H.; Nakagawa, S.; Haghikia, A.; Jasina, A.; Haas, J.; Nath, N.; Jensen, L.; Stroux, A.; Böhm, A.; et al. Immune system-mediated atherosclerosis caused by deficiency of long non-coding RNA MALAT1 in ApoE-/-mice. Cardiovasc Res. 2019, 115, 302–314. [Google Scholar] [CrossRef]
  55. Youyou, Y.; Bin, Z.; Ning, L.; Chao, Q.; Yanlong, X.; Xin, T.; Tianyi, L.; Bin, L. Circulating long non-coding RNA UCA1 as a novel biomarker of acute myocardial infarction. Biomed Res. Int. 2016, 2016, 7. [Google Scholar] [CrossRef] [Green Version]
  56. Carè, A.; Catalucci, D.; Felicetti, F.; Bonci, D.; Addario, A.; Gallo, P.; Bang, M.L.; Segnalini, P.; Gu, Y.; Dalton, N.D.; et al. MicroRNA-133 controls cardiac hypertrophy. Nat. Med. 2007, 13, 613–618. [Google Scholar] [CrossRef] [PubMed]
  57. Matkovich, S.J.; Wang, W.; Tu, Y.; Eschenbacher, W.H.; Dorn, L.E.; Condorelli, G.; Diwan, A.; Nerbonne, J.M.; Dorn, G.W. 2nd. MicroRNA-133a protects against myocardial fibrosis and modulates electrical repolarization without affecting hypertrophy in pressure-overloaded adult hearts. Circ. Res. 2010, 106, 166–175. [Google Scholar] [CrossRef] [Green Version]
  58. Castoldi, G.; Di Gioia, C.R.; Bombardi, C.; Catalucci, D.; Corradi, B.; Gualazzi, M.G.; Leopizzi, M.; Mancini, M.; Zerbini, G.; Condorelli, G.; et al. MiR-133a regulates collagen 1A1: Potential role of miR-133a in myocardial fibrosis in angiotensin II-dependent hypertension. J. Cell Physiol. 2012, 227, 850–856. [Google Scholar] [CrossRef] [PubMed]
  59. Chen, S.; Puthanveetil, P.; Feng, B.; Matkovich, S.J.; Dorn, G.W., 2nd; Chakrabarti, S. Cardiac miR-133a overexpression prevents early cardiac fibrosis in diabetes. J. Cell Mol. Med. 2014, 18, 415–421. [Google Scholar] [CrossRef] [Green Version]
  60. Beaumont, J.; López, B.; Hermida, N.; Schroen, B.; San José, G.; Heymans, S.; Valencia, F.; Gómez-Doblas, J.J.; De Teresa, E.; Díez, J.; et al. microRNA-122 down-regulation may play a role in severe myocardial fibrosis in human aortic stenosis through TGF-β1 up-regulation. Clin. Sci. (Lond.) 2014, 126, 497–506. [Google Scholar] [CrossRef]
  61. Novák, J.; Olejníčková, V.; Tkáčová, N.; Santulli, G. Mechanistic role of micrornas in coupling lipid metabolism and atherosclerosis. Adv. Exp. Med. Biol. 2015, 887, 79–100. [Google Scholar] [CrossRef] [Green Version]
  62. Nemecz, M.; Alexandru, N.; Tanko, G.; Georgescu, A. Role of MicroRNA in endothelial dysfunction and hypertension. Curr. Hypertens Rep. 2016, 18, 87. [Google Scholar] [CrossRef]
  63. Yang, Z.; Venardos, K.; Jones, E.; Morris, B.J.; Chin-Dusting, J. Identification of a novel polymorphism in the 3′UTR of the L-arginine transporter gene SLC7A1: Contribution to hypertension and endothelial dysfunction. Circulation 2007, 115, 1269–1274. [Google Scholar] [CrossRef] [Green Version]
  64. Sun, C.; Huang, F.; Liu, X.; Xiao, X.; Yang, M.; Hu, G.; Liu, H.; Liao, L. miR-21 regulates triglyceride and cholesterol metabolism in non-alcoholic fatty liver disease by targeting HMGCR. Int J. Mol. Med. 2015, 35, 847–853. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Valdmanis, P.N.; Kim, H.K.; Chu, K.; Zhang, F.; Xu, J.; Munding, E.M.; Shen, J.; Kay, M.A. miR-122 removal in the liver activates imprinted microRNAs and enables more effective microRNA-mediated gene repression. Nat. Commun. 2018, 9, 5321. [Google Scholar] [CrossRef]
  66. Noh, K.; Kim, M.; Kim, Y.; Kim, H.; Kim, H.; Byun, J.; Park, Y.; Lee, H.; Lee, Y.S.; Choe, J.; et al. miR-122-SOCS1-JAK2 axis regulates allergic inflammation and allergic inflammation-promoted cellular interactions. Oncotarget 2017, 8, 63155–63176. [Google Scholar] [CrossRef] [PubMed]
  67. Wang, R.; Hong, J.; Cao, Y.; Shi, J.; Gu, W.; Ning, G.; Zhang, Y.; Wang, W. Elevated circulating microRNA-122 is associated with obesity and insulin resistance in young adults. Eur. J. Endocrinol. 2015, 172, 291–300. [Google Scholar] [CrossRef] [PubMed]
  68. Mononen, N.; Lyytikäinen, L.P.; Seppälä, I.; Mishra, P.P.; Juonala, M.; Waldenberger, M.; Klopp, N.; Illig, T.; Leiviskä, J.; Loo, B.M.; et al. Whole blood microRNA levels associate with glycemic status and correlate with target mRNAs in pathways important to type 2 diabetes. Sci. Rep. 2019, 9, 8887. [Google Scholar] [CrossRef] [PubMed]
  69. Choe, N.; Kwon, J.S.; Kim, J.R.; Eom, G.H.; Kim, Y.; Nam, K.I.; Ahn, Y.; Kee, H.J.; Kook, H. The microRNA miR-132 targets Lrrfip1 to block vascular smooth muscle cell proliferation and neointimal hyperplasia. Atherosclerosis 2013, 229, 348–355. [Google Scholar] [CrossRef] [PubMed]
  70. Zhang, L.; Huang, D.; Wang, Q.; Shen, D.; Wang, Y.; Chen, B.; Zhang, J.; Gai, L. MiR-132 inhibits expression of SIRT1 and induces pro-inflammatory processes of vascular endothelial inflammation through blockade of the SREBP-1c metabolic pathway. Cardiovasc. Drugs 2014, 28, 303–311. [Google Scholar] [CrossRef]
  71. Eskildsen, T.V.; Jeppesen, P.L.; Schneider, M.; Nossent, A.Y.; Sandberg, M.B.; Hansen, P.B.; Jensen, C.H.; Hansen, M.L.; Marcussen, N.; Rasmussen, L.M.; et al. Angiotensin II regulates microRNA-132/-212 in hypertensive rats and humans. Int. J. Mol. Sci. 2013, 14, 11190–11207. [Google Scholar] [CrossRef] [Green Version]
  72. Jovanović, I.; Zivković, M.; Jovanović, J.; Djurić, T.; Stanković, A. The co-inertia approach in identification of specific microRNA in early and advanced atherosclerosis plaque. Med. Hypotheses 2014, 83, 11–15. [Google Scholar] [CrossRef] [PubMed]
  73. Ren, J.; Zhang, J.; Xu, N.; Han, G.; Geng, Q.; Song, J.; Li, S.; Zhao, J.; Chen, H. Signature of circulating microRNAs as potential biomarkers in vulnerable coronary artery disease. PLoS ONE 2013, 8, e80738. [Google Scholar] [CrossRef] [Green Version]
  74. D’Alessandra, Y.; Carena, M.C.; Spazzafumo, L.; Martinelli, F.; Bassetti, B.; Devanna, P.; Rubino, M.; Marenzi, G.; Colombo, G.I.; Achilli, F.; et al. Diagnostic potential of plasmatic MicroRNA signatures in stable and unstable angina. PLoS ONE 2013, 8, e80345. [Google Scholar]
  75. Feinberg, M.W.; Moore, K.J. MicroRNA regulation of atherosclerosis. Circ. Res. 2016, 118, 703–720. [Google Scholar] [CrossRef] [Green Version]
  76. Novák, J.; Bienertová-Vašků, J.; Kára, T.; Novák, M. MicroRNAs involved in the lipid metabolism and their possible implications for atherosclerosis development and treatment. Mediat. Inflamm. 2014, 2014, 275867. [Google Scholar] [CrossRef]
  77. Simon, A.; Chironi, G.; Levenson, J. Comparative performance of subclinical atherosclerosis tests in predicting coronary heart disease in asymptomatic individuals. Eur. Heart J. 2007, 28, 2967–2971. [Google Scholar] [CrossRef] [Green Version]
  78. Simon, A.; Levenson, J. May subclinical arterial disease help to better detect and treat high-risk asymptomatic individuals? J. Hypertens 2005, 23, 1939–1945. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  79. National Cholesterol Education Program (NCEP). Expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). Third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) final report. Circulation 2002, 106, 3143–3421. [Google Scholar]
  80. Taylor, A.J.; Merz, C.N.; Udelson, J.E. 34th Bethesda Conference: Executive summary—can atherosclerosis imaging techniques improve the detection of patients at risk for ischemic heart disease? J. Am. Coll Cardiol. 2003, 41, 1860–1862. [Google Scholar] [CrossRef] [Green Version]
  81. De Backer, G.; Ambrosion, i.E.; Borch-Johnsen, K.; Brotons, C.; Cifkova, R.; Dallongeville, J.; Ebrahim, S.; Faergeman, O.; Graham, I.; Mancia, G.; et al. Third joint task force of european and other societies on cardiovascular disease prevention in clinical practice. european guidelines on cardiovascular disease prevention in clinical practice. third joint task force of european and other societies on cardiovascular disease prevention in clinical practice. Eur. Heart J. 2003, 24, 1601–1610. [Google Scholar] [PubMed]
  82. Smith, S.C., Jr.; Greenland, P.; Grundy, S.M. AHA Conference proceedings. Prevention conference V: Beyond secondary prevention: Identifying the high-risk patient for primary prevention: Executive summary. American Heart Association. Circulation 2000, 101, 111–116. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Kuhler, L.H. Prevention of coronary heart disease and the national cholesterol education program. Circulation 2006, 113, 598–600. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Miyoshi, T.; Ito, H. Assessment of arterial stiffness using the cardio-ankle vascular index. Pulse 2016, 4, 11–23. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Long, G.; Wang, F.; Li, H.; Yin, Z.; Sandip, C.; Lou, Y.; Wang, Y.; Chen, C.; Wang, D.W. Circulating miR-30a, miR-126 and let-7b as biomarker for ischemic stroke in humans. BMC Neurol. 2013, 13, 178. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. Xue, S.; Liu, D.; Zhu, W.; Su, Z.; Zhang, L.; Zhou, C.; Li, P. Circulating MiR-17-5p, MiR-126-5p and MiR-145-3p are novel biomarkers for diagnosis of acute myocardial infarction. Front Physiol. 2019, 10, 123. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Luo, Q.; Guo, D.; Liu, G.; Chen, G.; Hang, M.; Jin, M. Exosomes from MiR-126-overexpressing adscs are therapeutic in relieving acute myocardial ischaemic injury. Cell Physiol. Biochem. 2017, 44, 2105–2116. [Google Scholar] [CrossRef]
  88. Hijmans, J.G.; Diehl, K.J.; Bammert, T.D.; Kavlich, P.J.; Lincenberg, G.M.; Greiner, J.J.; Stauffer, B.L.; DeSouza, C.A. Influence of overweight and obesity on circulating inflammation-related microRNA. Microrna 2018, 7, 148–154. [Google Scholar] [CrossRef]
  89. Vasu, S.; Kumano, K.; Darden, C.M.; Rahman, I.; Lawrence, M.C.; Naziruddin, B. MicroRNA signatures as future biomarkers for diagnosis of diabetes states. Cells 2019, 8, 1533. [Google Scholar] [CrossRef] [Green Version]
  90. Faccini, J.; Ruidavets, J.B.; Cordelier, P.; Martins, F.; Maoret, J.J.; Bongard, V.; Ferrières, J.; Roncalli, J.; Elbaz, M.; Vindis, C. Circulating miR-155, miR-145 and let-7c as diagnostic biomarkers of the coronary artery disease. Sci. Rep. 2017, 7, 42916. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  91. Lorente-Cebrián, S.; Mejhert, N.; Kulyté, A.; Laurencikiene, J.; Åström, G.; Hedén, P.; Rydén, M.; Arner, P. MicroRNAs regulate human adipocyte lipolysis: Effects of miR-145 are linked to TNF-α. PLoS ONE 2014, 9, e86800. [Google Scholar] [CrossRef]
  92. Zhu, N.; Zhang, D.; Chen, S.; Liu, X.; Lin, L.; Huang, X.; Guo, Z.; Liu, J.; Wang, Y.; Yuan, W.; et al. Endothelial enriched microRNAs regulate angiotensin II-induced endothelial inflammation and migration. Atherosclerosis 2011, 215, 286–293. [Google Scholar] [CrossRef]
  93. Karkeni, E.; Astier, J.; Tourniaire, F.; El Abed, M.; Romier, B.; Gouranton, E.; Wan, L.; Borel, P.; Salles, J.; Walrand, S.; et al. Obesity-associated inflammation induces microRNA-155 expression in adipocytes and adipose tissue: Outcome on adipocyte function. J. Clin. Endocrinol. Metab. 2016, 101, 1615–1626. [Google Scholar] [CrossRef]
  94. Mayorga, M.E.; Penn, M.S. miR-145 is differentially regulated by TGF-β1 and ischaemia and targets Disabled-2 expression and wnt/β-catenin activity. J Cell Mol. Med. 2012, 16, 1106–1113. [Google Scholar] [CrossRef] [PubMed]
  95. Qiu, X.K.; Ma, J. Alteration in microRNA-155 level correspond to severity of coronary heart disease. Scand. J Clin. Lab. Invest. 2018, 78, 219–223. [Google Scholar] [CrossRef] [PubMed]
  96. Klimczak, D.; Kuch, M.; Pilecki, T.; Żochowska, D.; Wirkowska, A.; Pączek, L. Plasma microRNA-155-5p is increased among patients with chronic kidney disease and nocturnal hypertension. J. Am. Soc. Hypertens. 2017, 11, 831–841.e4. [Google Scholar] [CrossRef] [PubMed]
  97. Wu, X.Y.; Fan, W.D.; Fang, R.; Wu, G.F. Regulation of microRNA-155 in endothelial inflammation by targeting nuclear factor (NF)-κB P65. J. Cell Biochem. 2014, 115, 1928–1936. [Google Scholar] [CrossRef] [PubMed]
  98. O’Connell, R.M.; Taganov, K.D.; Boldin, M.P.; Cheng, G.; Baltimore, D. MicroRNA-155 is induced during the macrophage inflammatory response. Proc. Natl. Acad. Sci. USA 2007, 104, 1604–1609. [Google Scholar] [CrossRef] [Green Version]
  99. Sun, H.X.; Zeng, D.Y.; Li, R.T.; Pang, R.P.; Yang, H.; Hu, Y.L.; Zhang, Q.; Jiang, Y.; Huang, L.Y.; Tang, Y.B.; et al. Essential role of microRNA-155 in regulating endothelium-dependent vasorelaxation by targeting endothelial nitric oxide synthase. Hypertension 2012, 60, 1407–1414. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  100. Li, S.; Sun, Y.; Zhong, L.; Xiao, Z.; Yang, M.; Chen, M.; Wang, C.; Xie, X.; Chen, X. The suppression of ox-LDL-induced inflammatory cytokine release and apoptosis of HCAECs by long non-coding RNA-MALAT1 via regulating microRNA-155/SOCS1 pathway. Nutr. Metab. Cardiovasc. Dis. 2018, 28, 1175–1187. [Google Scholar] [CrossRef] [PubMed]
  101. Stojkovic, S.; Jurisic, M.; Kopp, C.W.; Koppensteiner, R.; Huber, K.; Wojta, J.; Gremmel, T. Circulating microRNAs identify patients at increased risk of in-stent restenosis after peripheral angioplasty with stent implantation. Atherosclerosis 2018, 269, 197–203. [Google Scholar] [CrossRef]
  102. Yin, D.; Fu, C.; Sun, D. Silence of lncRNA uca1 represses the growth and tube formation of human microvascular endothelial cells through miR-195. Cell Physiol. Biochem. 2018, 49, 1499–1511. [Google Scholar] [CrossRef]
Table 1. Baseline characteristics.
Table 1. Baseline characteristics.
Baseline Characteristics.miRAll Patientsp-Value
Number of patients1783194
Male n (%)73 (41)1152 (36.1)0.210
Female n (%)105 (59)2042 (63.9)
Age, years (mean ± SD)53.23 ± 5.6954.12 ± 6.150.109
MetS components
Waist circumference men (mean ± SD)109.4 ± 7.02110.89 ± 9.880.102
Waist circumference women (mean ± SD)102.9 ± 9.1104.62 ± 10.650.142
Body mass index (mean ± SD)31.25 ± 3.9432.33 ± 4.830.026
Triglycerides (mean ± SD)2.63 ± 4.142.58 ± 2.420.489
High-density lipoprotein cholesterol (mean ± SD)1.26 ± 0.281.24 ± 0.330.204
Systolic blood pressure, mmHg (mean ± SD)135.64 ± 10.81142.04 ± 16.850.143
Diastolic blood pressure, mmHg (mean ± SD)83.18 ± 8.8886.79 ± 10.580.104
Fasting plasma glucose, mmol/L (mean ± SD)5.9 ± 0.726.31 ± 1.36<0.001
      Number of MetS components0.225
3 out of 5 n (%)74 (41.6)1197 (37.5)0.225
4 out of 5 n (%)60 (33.7)1147 (35.9)0.225
5 out of 5 n (%)33 (18.5)767 (23.1)0.225
Mean number of MetS components3.64 ± 0.873.85 ± 0.780.225
Other cardiovascular risk factors
Arterial hypertension (%)164 (92.1)3089 (96.7)0.003
Hyperlipidemia (%)178 (100)3157 (98.8)0.283
Positive family history (%)68 (38.2)1031 (32.3)0.119
Diabetes mellitus (%)16 (9)644 (20.2)0.0004
Current smoking (%)43 (24.2)734 (22.9)0.786
C-reactive protein (mean ± SD)3.5 ± 4.582.93 ± 3.670.003
Low-density lipoprotein cholesterol (mean ± SD)4.44 ± 1.264.34 ± 1.260.203
Use of statins30 (16.9)158 (4.9)<0.001
Metabolic syndrome (MetS), microRNA (miR).
Table 2. Plasma levels of miRs.
Table 2. Plasma levels of miRs.
Mean ± SD6.12 ± 1.332.05 ± 0.62.11 ± 0.675.79 ± 1.1−0.92 ± 1.37.98 ± 1.716.05 ± 1.184.58 ± 1.27.09 ± 1.434.66 ± 0.98
microRNA (miR), standard deviation (SD)
Table 3. Pearson correlation.
Table 3. Pearson correlation.
CAVI MeanAIxHR75AoPWVMAPFMD %Stiffness Left Carotid ArteryStiffness Right Carotid ArteryStiffness Carotid ArteryRight Carotid PlaqueLeft Carotid Plaque
miR-1r−0.218−0.2013.7 × 10−5−0.076−0.0860.154−0.0240.074−0.050.027
miR-370r−0.0589 × 10−40.0940.068−0.0040.0260.0950.0690.291−0.047
Augmentation index normalized to a heart rate of 75 bpm (AIxHR75), aortic pulse wave velocity (AoPWV), cardio-ankle vascular index (CAVI), flow-mediated dilatation (FMD), mean arterial pressure (MAP), microRNA (miR). Bolded values—statistically significant associations.
Table 4. Linear regression models.
Table 4. Linear regression models.
CAVImean9.21 − 0.31 miR-133b + 0.24 miR-1221.1217.16
AIxHR7538.55 − 3.14 miR-133b9.87145.89
AoPWV8.41 − 0.19 miR-1221.2514.31
Stiffness right carotid artery2.31 + 0.39 miR-1321.3539.01
Stiffness left carotid artery3.72 + 0.14 miR-11.2732.69
Augmentation index normalized to a heart rate of 75 bpm (AIxHR75), aortic pulse wave velocity (AoPWV), cardio-ankle vascular index (CAVI), mean absolute error (MAE), mean absolute percentage error (MAPE), microRNA (miR).
Table 5. Factors predicting CAVI.
Table 5. Factors predicting CAVI.
18.94–0.23 miR-122 − 0.3 age miR-133b + 0.82 smoking1.0716.18
20.6 + 0.22 miR-122 + 0.12 age + 1.1 smoking − 0.27TG − 0.78 HDL-Ch1.4320.07
30.6 + 0.22 miR-122 + 0.12 age + 1.1 smoking − 0.27TG − 0.78 HDL-Ch1.4320.07
40.6 + 0.22 miR-122 + 0.12 age + 1.1 smoking − 0.27TG − 0.78 HDL-Ch1.4320.07
Cardio-ankle vascular index (CAVI), high-density lipoprotein cholesterol (HDL-Ch), mean absolute error (MAE), mean absolute percentage error (MAPE), microRNA (miR), Triglycerides (TG). Bolded—significant relation.
Table 6. Factor predicting AIxHR75.
Table 6. Factor predicting AIxHR75.
137.49 − 2.06 miR-133b − 10.73 sex10.58151.72
237.49 − 2.06 miR-133b − 10.73 sex10.58151.72
337.49 − 2.06 miR-133b − 10.73 sex10.58151.72
437.49 − 2.06 miR-133b − 10.73 sex10.58151.72
Augmentation index normalized to a heart rate of 75 bpm (AIxHR75), mean absolute error (MAE), mean absolute percentage error (MAPE), microRNA (miR).
Table 7. Factors predicting AoPWV.
Table 7. Factors predicting AoPWV.
13.34–0.27 miR-122 + 0.09 age1.2914.7
23.34–0.26 miR-122 + 0.08 age + 0.63 TG1.2213.53
33.34–0.26 miR-122 + 0.08 age + 0.63 TG1.2213.53
43.34–0.26 miR-122 + 0.08 age + 0.63 TG1.2213.53
Aortic pulse wave velocity (AoPWV), mean absolute error (MAE), mean absolute percentage error (MAPE), microRNA (miR), Triglycerides (TG). Bolded—significant relation.
Table 8. Factors predicting carotid artery stiffness.
Table 8. Factors predicting carotid artery stiffness.
Stiffness right carotid artery
1−1.92 + 0.4 miR-132 + 0.07 age1.3738.07
2−1.92 + 0.4 miR-132 + 0.07 age1.3738.07
3−2.31 + 0.43 miR-132 + 0.08 age + 0.06 hs-CRP1.4138.03
4−2.31 + 0.43 miR-132 + 0.08 age + 0.06 hs-CRP1.4138.03
Stiffness left carotid artery
1−0.46 + 0.19 miR-1 + 0.07 age−0.02 smoking duration1.2931.73
2−0.46 + 0.19 miR-1 + 0.07 age−0.02 smoking duration1.2931.73
3−0.92 + 0.18miR-1 + 0.08 age−0.03 smoking duration + 0.07 hs-CRP1.3130.43
4−0.92 + 0.18miR-1 + 0.08 age−0.03 smoking duration + 0.07 hs-CRP1.3130.43
High sensitivity C-reactive protein (Hs-CRP), mean absolute error (MAE), mean absolute percentage error (MAPE), microRNA (miR).
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Šatrauskienė, A.; Navickas, R.; Laucevičius, A.; Krilavičius, T.; Užupytė, R.; Zdanytė, M.; Ryliškytė, L.; Jucevičienė, A.; Holvoet, P. Mir-1, miR-122, miR-132, and miR-133 Are Related to Subclinical Aortic Atherosclerosis Associated with Metabolic Syndrome. Int. J. Environ. Res. Public Health 2021, 18, 1483.

AMA Style

Šatrauskienė A, Navickas R, Laucevičius A, Krilavičius T, Užupytė R, Zdanytė M, Ryliškytė L, Jucevičienė A, Holvoet P. Mir-1, miR-122, miR-132, and miR-133 Are Related to Subclinical Aortic Atherosclerosis Associated with Metabolic Syndrome. International Journal of Environmental Research and Public Health. 2021; 18(4):1483.

Chicago/Turabian Style

Šatrauskienė, Agnė, Rokas Navickas, Aleksandras Laucevičius, Tomas Krilavičius, Rūta Užupytė, Monika Zdanytė, Ligita Ryliškytė, Agnė Jucevičienė, and Paul Holvoet. 2021. "Mir-1, miR-122, miR-132, and miR-133 Are Related to Subclinical Aortic Atherosclerosis Associated with Metabolic Syndrome" International Journal of Environmental Research and Public Health 18, no. 4: 1483.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop