Abstract
There is little known about the prognostic value of serum microRNAs (miRs) in diabetic patients with symptomatic internal carotid artery disease (ICAS) who underwent stent supported angioplasty (PTA) for ICAS. The present study aimed to investigate expression levels of selected miRs for future major adverse cardiac and cerebral events (MACCE) as a marker in diabetic patients following ICAS-PTA. The expression levels of 11 chosen circulating serum miRs were compared in 37 diabetic patients with symptomatic ICAS and 64 control group patients with symptomatic ICAS, but free of diabetes. The prospective median follow-up of 84 months was performed for cardiovascular outcomes. Diabetic patients, as compared to control subjects, did not differ with respect to age (p = 0.159), distribution of gender (p = 0.375), hypertension (p = 0.872), hyperlipidemia (p = 0.203), smoking (p = 0.115), coronary heart disease (p = 0.182), lower extremities arterial disease (LEAD, p = 0.731), and miRs expressions except from lower miR-16-5p (p < 0.001). During the follow-up period, MACCE occurred in 16 (43.2%) diabetic and 26 (40.6%) non-diabetic patients (p = 0.624). On multivariate Cox analysis, hazard ratio (HR) and 95% Confidence Intervals (95%CI) for diabetic patients associated with MACCE were miR-134-5p (1.12; 1.05–1.21, p < 0.001), miR-499-5p (0.16; 0.02–1.32, p = 0.089), hs-CRP (1.14; 1.02–1.28; p = 0.022), prior myocardial infarction (8.56, 1.91–38.3, p = 0.004), LEAD (11.9; 2.99–47.9, p = 0.005), and RAS (20.2; 2.4–167.5, p = 0.005), while in non-diabetic subjects, only miR-16-5p (1.0006; 1.0001–1.0012, p = 0.016), miR-208b-3p (2.82; 0.91–8.71, p = 0.071), and hypertension (0.27, 0.08–0.95, p = 0.042) were associated with MACCE. Our study demonstrated that different circulating miRs may be prognostic for MACCE in diabetic versus non-diabetic patients with symptomatic ICAS. Higher expression levels of miR-134 were prognostic for MACCE in diabetic patients, while higher expression levels of miR-16 were prognostic in non-diabetic patients.
Keywords: prognostic circulating miRs, recurrent myocardial infarction and ischemic stroke, biomarkers, diabetes, carotid artery stenosis, cardiovascular events
1. Introduction
Type 2 diabetes mellitus (T2DM) is a major risk factor for developing cardiovascular complications related to a progressive micro and macro angiopathy [1,2]. Hence, cardiovascular disease (CVD) and its cardiac and cerebral complications are the most prevalent causes of mortality and morbidity in diabetic populations [3,4].
Coronary heart disease is a leading cause of cardiovascular death in diabetic patients worldwide [1,3,4]. The second most frequent cause of death in diabetic patients is cerebral ischemia, either from the large or the small vessel disease with an incidence between 2.5 and 3.5 times higher in diabetic vs. non-diabetic patients [3,4]. Cerebral ischemia in diabetes is associated with high vascular dementia incidence, as well as high mortality and disability rate at short- and long-term follow-up [5]. However, limited number of tools to predict acute ischemic stroke (IS) outcome and the incidence of major cardiac and cerebral events (MACCE) in T2DM patients are available [6,7].
In all-comers populations, accumulating evidence has shown the existence of an intricate relationship between microRNAs (miRs) and the major mechanisms of IS, including energy failure, excitotoxicity, oxidative stress, inflammation, cell death, and blood–brain barrier (BBB) disruption [8,9]. For instance, middle cerebral artery occlusion was associated with the upregulation of miR-107 engaged in the excitotoxicity and with miR-126 engaged in the BBB disruption [9]. The involvement in the pathophysiology of cerebral ischemia was observed for miR-503 as an indicator of stroke severity and patients’ short-term outcome [10].
Consistently, in large vessel atherosclerotic disease, such as internal carotid artery stenosis (ICAS), several miRs have been postulated to have their role in symptom development, IS incidence, and further outcomes [11]. Among others, in patients with symptomatic ICAS, many miRs, including miR-17, miR-34a, mi-R-126, miR-133b, miR-155, miR-182 miR-208b, and miR-4909, were investigated for associations with risk of IS recurrence and MACCE incidence [12,13,14].
However, in patients with T2DM, unlike diabetes-free subjects, many differences in pathophysiology of ICAS and symptom development are observed [15].
We hypothesize that miR expression, among other factors, may differ in T2DM and diabetes-free patients with symptoms of cerebral ischemia attributed to ICAS despite performed carotid revascularization with stent supported angioplasty (PTA).
Therefore, in the present study, we aimed to compare expression levels of selected serum miRs in diabetic vs. non-diabetic patients as a potential biomarker of the outcome in patients with symptomatic ICAS referred to PTA.
2. Results
Compared with non-diabetic group, the T2DM group had lower expression of serum miR-16 (p < 0.001). There was no significant difference in age (p = 0.159), distribution of gender (p = 0.375), hypertension (p = 0.872), hyperlipidemia (p = 0.203), smoking (p = 0.115), prevalence of CHD (p = 0.182), LEAD (p = 0.731), levels of hs-CRP (p = 0.146), LDL-C (p = 0.292), serum creatinine (p = 0.361), and expression levels of the other investigated miRs between two groups, as shown in Table 1.
Table 1.
Parameter | All n = 101 |
Diabetic n = 37 |
Non-Diabetic n = 64 |
p-Value |
---|---|---|---|---|
Demographic data | ||||
Age, (median; IQR) | 69; 62–76 | 71; 63–78 | 67.5; 61.5–74 | 0.159 |
Male gender, n (%) | 63 (62.3%) | 21 (56.8%) | 42 (65.6%) | 0.375 |
Hypertension, n (%) | 96 (95.0%) | 35 (94.5%) | 61 (95.3%) | 0.872 |
Hypercholesterolemia, n (%) | 87 (86.1%) | 34 (91.8%) | 53 (82.8%) | 0.203 |
Smoking habit, n (%) | 62 (61.3%) | 19 (51.3%) | 43 (67.2%) | 0.115 |
Coronary artery disease, n (%) * | 54 (53.4%) | 23 (62.2%) | 31 (48.4%) | 0.182 |
Lower extremities arterial disease, n (%) * | 28 (27.7%) | 11 (29.7%) | 17 (26.6%) | 0.731 |
Prior myocardial infarction, n (%) | 20 (19.8%) | 8 (21.6%) | 12 (18.7%) | 0.727 |
Renal artery stenosis, n (%) * | 7 (6.9%) | 2 (5.4%) | 5 (7.8%) | 0.646 |
Laboratory results (serum) | ||||
Serum creatinine, μmol/L, (median; IQR) | 82; 70–100 | 85; 71–101 | 81; 68.5–99 | 0.361 |
C-Reactive Protein, g/L, (median; IQR) | 2.59; 1.99–250 | 3.15; 1.83–6.29 | 2.21; 1.27–4.45 | 0.146 |
Fibrinogen, g/L, (median; IQR) | 3.51; 3.01–4.30 | 3.78; 3.33–4.62 | 3.40; 3.00–4.00 | 0.062 |
LDL-cholesterol, mmol/L, (median; IQR) | 2.65; 1.99–3.04 | 2.59; 1.94–3.46 | 2.56; 2.03–2.95 | 0.292 |
microRNA | ||||
miR-1-3p, A.U., (median; IQR) | 0.17; 0.08–0.32 | 0.15; 0.08–0.24 | 0.19; 0.08–0.38 | 0.227 |
miR-122-5p, A.U., (median; IQR) | 48.05; 19.43–250.4 | 40.95; 12.72–142.4 | 52.82; 28.63–310.4 | 0.146 |
miR-124-3p, A.U., (median; IQR) | 0.23; 0.09–0.63 | 0.24; 0.09–0.57 | 0.22; 0.08–0.66 | 0.625 |
miR-133a-3p, A.U., (median; IQR) | 0.87; 0.63–1.22 | 0.85; 0.62–1.26 | 0.87; 0.63–1.15 | 0.805 |
miR-133b, A.U., (median; IQR) | 1.87; 1.19–2.53 | 1.87; 1.15–2.60 | 1.69; 1.24–2.47 | 0.766 |
miR-134-5p, A.U., (median; IQR) | 0.82; 0.33–2.80 | 0.90; 0.43–3.17 | 0.73; 0.29–1.87 | 0.357 |
miR-16-5p, A.U., (median; IQR) | 94.57; 37.57–263.7 | 45.32; 14.60–71.19 | 122.78; 64.96–543 | <0.001 |
miR-208b-3p, A.U., (median; IQR) | 0.005; 0.002–0.022 | 0.005; 0.002–0.018 | 0.005; 0.002–0.02 | 0.978 |
miR-34a-5p, A.U., (median; IQR) | 0.72; 0.31–1.06 | 0.76; 0.48–1.11 | 0.63; 0.28–1.04 | 0.145 |
miR-375, A.U., (median; IQR) | 3.53; 1.62–10.21 | 3.11; 1.49–6.71 | 4.48; 1.69–21.15 | 0.226 |
miR-499-5p, A.U., (median; IQR) | 0.02; 0.01–0.04 | 0.02; 0.01–0.07 | 0.02; 0.01–0.04 | 0.605 |
A.U., arbitrary units; *—defined as the presence of arterial stenosis exceeding 50% lumen reduction on angiography.
During the follow-up period, the rates of MACCE were similar for patients with vs. without diabetes (43.2% vs. 40.6%, p = 0.624). MACCE occurred in 16 T2DM patients including cardiovascular death in 11 (29.7%), non-fatal MI in 3 (8.1%), and non-fatal IS in 2 (5.4%), and in 26 non-diabetic patients, including cardiovascular death in 21 (32.8%), non-fatal MI in 1 (1.5%), and non-fatal IS in 4 (6.3%).
For T2DM patients, in univariate Cox proportional hazard analysis, MACCE risk was associated with a higher expression level of miR 134-5p (p = 0.02), miR-16-5p (p = 0.048), and miR-499-5p (p = 0.04). There was a trend towards statistical significance for miR-133a-3p (p = 0.089), miR-208b-3p (p = 0.071) and miR-34a-5p (p = 0.07), while there were no significant associations with the other studied miRs (Table 2). There was a significant association between MACCE incidence and prior MI (p = 0.029), LEAD (p = 0.004), and RAS (p = 0.028), but not with traditional cardiovascular risk factors (Table 2). Among the biochemical results, the association was found for hs-CRP (p = 0.025) and a trend to significance for creatinine level (p = 0.056).
Table 2.
Prognostic Factors | Diabetic Patients | Non-Diabetic Patients | ||
---|---|---|---|---|
HR (95% CI) | p-Value | HR (95% CI) | p-Value | |
microRNA | ||||
miR-1-3p | 3.93 (0.57–27.06) | 0.164 | 3.72 (1.21–11.5) | 0.022 |
miR-122-5p | 1.01 (0.99–1.02) | 0.678 | 1.00 (0.99–1.01) | 0.412 |
miR-124-3p | 0.81 (0.40–1.64) | 0.568 | 1.34 (0.76–2.38) | 0.305 |
miR-133a-3p | 2.13 (0.88–5.11) | 0.089 | 0.89 (0.60–1.32) | 0.573 |
miR-133b | 0.87 (0.53–1.42) | 0.581 | 0.98 (0.80–1.20) | 0.862 |
miR-134-5p | 1.04 (1.01–1.07) | 0.020 | 1.05 (0.98–1.13) | 0.140 |
miR-16-5p | 1.01 (1.00–1.02) | 0.048 | 1.0006 (1.0001–1.001) | 0.019 |
miR-208b-3p | 4.42 (0.87–22.25) | 0.071 | 2.77 (0.84–9.15) | 0.095 |
miR-34a-5p | 0.41 (0.15–1.07) | 0.070 | 1.04 (0.80–1.36) | 0.749 |
miR-375 | 1.02 (0.98–1.06) | 0.325 | 1.01 (0.99–1.02) | 0.856 |
miR-499-5p | 4.84 (1.07–21.89) | 0.040 | 0.36 (0.02–11.13) | 0.566 |
Demographic data | ||||
Age | 1.02 (0.96–1.08) | 0.457 | 1.02 (0.97–1.06) | 0.375 |
Male gender | 0.57 (0.19–1.69) | 0.317 | 0.84 (0.36–1.97) | 0.700 |
Hypertension | n.a. | n.a. | 0.32 (0.09–1.08) | 0.067 |
Hiperlipidemia | 1.56 (0.20–11.89) | 0.667 | 1.07 (0.37–3.13) | 0.897 |
Smoking habit | 1.12 (0.41–3.13) | 0.817 | 1.69 (0.31–2.53) | 0.369 |
CAD | 1.38 (0.46–4.08) | 0.558 | 0.97 (0.44–2.14) | 0.956 |
LEAD | 4.41 (1.57–12.42) | 0.004 | 2.04 (0.91–4.55) | 0.081 |
Prior MI | 3.39 (1.13–10.20) | 0.029 | 1.05 (0.39–2.82) | 0.921 |
Renal artery stenosis | 5.72 (1.20–27.21) | 0.028 | 1.25 (0.49–3.15) | 0.635 |
Laboratory results | ||||
Serum creatinine | 1.02 (0.99–1.01) | 0.056 | 1.00 (0.99–1.01) | 0.646 |
C-Reactive Protein | 1.10 (1.01–1.20) | 0.025 | 0.96 (0.90–1.04) | 0.392 |
Fibrinogen | 1.25 (0.82–1.90) | 0.309 | 0.60 (0.34–1.04) | 0.070 |
LDL-cholesterol | 0.90 (0.61–1.35) | 0.617 | 1.37 (0.83–2.29) | 0.221 |
CAD, Coronary Artery Disease; CI, Confidence Interval; HR, Hazard Ratio; LDL, Low Density Lipoprotein; LEAD, Lower Extremities Arterial Disease; MI, Myocardial Infarction.
For diabetes-free patients, MACCE risk was associated with higher expression levels of miR-1-3p (p = 0.022) and miR-16-5p (p = 0.019) Table 2. There was a trend to significance between MACCE incidence and arterial hypertension (p = 0.067) and fibrinogen level (p = 0.070), but not with the other cardiovascular risk factors. We found a trend of association between MACCE and LEAD (p = 0.081).
In the multivariate Cox analysis, HRs and 95% CIs for T2DM patients associated with MACCE were as follows: miR-134-5p (1.12; 1.05–1.21, p < 0.001), miR-499-5p (0.16; 0.02–1.32, p = 0.089), hs-CRP (1.14; 1.02–1.28; p = 0.022), prior MI (8.56, 1.91–38.3, p = 0.004), LEAD (11.9; 2.99–47.9, p = 0.005), and RAS (20.2; 2.4–167.5, p = 0.005).
In non-diabetic subjects, only miR-16-5p (1.0006; 1.0001–1.0012, p = 0.016), miR-208b-3p (2.82; 0.91–8.71, p = 0.071), and hypertension (0.27, 0.08–0.95, p = 0.042) were associated with MACCE. The detailed parameters of multivariate Cox hazard analysis are shown in Table 3.
Table 3.
Study Group | Prognostic Factors | HR (95% CI) | p-Value |
---|---|---|---|
Patients with diabetes | miR-134-5p | 1.12 (1.05–1.21) | 0.028 |
hs-CRP | 1.14 (1.01–1.28) | 0.022 | |
prior MI | 8.56 (1.91–38.25) | 0.004 | |
LEAD | 11.98 (2.99–48.0) | <0.001 | |
RAS | 20.24 (2.44–167.5) | 0.005 | |
miR-499-5p | 0.16 (0.02–1.32) | 0.089 | |
miR-133a-3p | 2.12 (0.51–8.91) | 0.302 | |
miR-16-5p | 1.01 (0.99–1.02) | 0.410 | |
miR-208b-3p | 5.91 (0.01–7.93) | 0.314 | |
miR 34a-5p | 0.65 (0.15–2.67) | 0.552 | |
Patients diabetes-free | miR-16-5p | 1.0006 (1.0001–1.0011) | 0.016 |
hypertension | 0.27 (0.07–0.95) | 0.042 | |
miR-208b-3p | 2.82 (0.91–8.71) | 0.071 | |
miR-1-3p | 0.58 (0.06–5.22) | 0.628 | |
Fibrinogen | 0.31 (0.08–1.12) | 0.171 |
3. Discussion
Patients with T2DM, unlike diabetes-free subjects, suffer more often from cardiovascular events [3,16]. Between the years 2007 and 2017, CVD was the cause of death in 9.9% of T2DM patients (representing 50.3% of all deaths) [3]. Despite improvements in cardiac care, T2DM still increases the risk of death, including all-cause (1.68; 95%CI 1.60 to 1.78), CVD (1.61; 95%CI 1.47 to 1.76), and MI (1.59; 95%CI 1.27 to 1.99) [16]. In the TECOS Trial, out of 530 cardiovascular deaths, sudden death accounted for 27.3% fatality cases, followed by stroke (12.3%), heart failure (12%), and MI (9%) [4].
Only few recent studies addressed the possible role of miRs modulation in patients with both symptomatic and asymptomatic ICAS and concomitant T2DM [17,18].
The novelty of the present study is the implication of the different diagnostic and prognostic circulating miRs associated with cerebral ischemia resulting from symptomatic ICAS in diabetic vs. non-diabetic patients.
We identified one miR (miR-16) that differed among T2DM vs. non-diabetic patients. We found higher miR-16 expression levels in patients without diabetes. Higher miR-16 expression was also identified as prognostic miRs of future MACCE, but only in diabetes-free patients.
As previously postulated, carotid or peripheral ischemia can negatively influence remote vascular remodeling [19,20]. The possible mechanism of miR-16 negative action can be through the injury promotion in a remote vascular district [19,20]. For example, in a study by Sorrentino et al., miR-16 was upregulated after vascular injury in a rat model in the presence of limb ischemia [21]. This was associated with a negative effect on endothelial repair reducing nitric oxide bioavailability [21]. Thus, limb ischemia affected negative carotid remodeling increasing neo-intima formation and delayed re-endothelialization after the injury [21]. Additionally, as previously shown, miR-16 can be associated with specific plaque features, such as plaque ulceration or calcification [22].
MiR-16-5p was also identified as upregulated in MI or coronary artery disease patients [23]. In another study, the acute MI patients in above the median levels of plasma miR-16 group suffered a 1.87-fold higher risk of MI recurrence compared to patients with a lower median value (p = 0.029) [24]. This is in line with our previous findings, where expression levels of miR-1-3p, miR-16-5p, and miR-122-5p were independent risk factors of MACCE [25].
We have observed two miRs to be prognostic for MACCE in T2DM patients, but not in the diabetes-free patients; for example, miR-134, which was associated with a 1.12-fold risk increase for MACCE, and miR-499, which showed a negative relationship with risk of future MACCE. MiR-134 after birth is restricted to the brain [26], and regulates ischemia/reperfusion injury-mediated neuronal cell death by targeting heat shock protein A12B (HSPA12B) and cyclic AMP response element-binding protein (CREB) [27,28]. There is a relationship between miR-134 and chronic inflammation, represented by hs-CRP and tumor necrosis factor alpha (TNF-α) [29]. In our present study, we have found elevated levels of hs-CRP to be associated with a 1.14-fold risk increase in MACCE in T2DM. Importantly, TNF-α, a cytokine produced in the adipose tissue, is associated with insulin resistance [30]. Induction of miR-134-5p by TNF-α has been observed and suggests a potential role for miR-134-5p in insulin-mediated glucose disposal and insulin sensitivity [29,30]. Furthermore, miR-134 plays role in developing diabetic nephropathy [31].
Altogether, a chronic low-grade inflammation, insulin-resistance, and diabetic nephropathy are risk factors for cardiovascular events [32]. However, to what extent miR-134 expression during cerebral ischemia exerts its impact on the future MACCE risk in diabetic patients and which mechanism plays a crucial role needs future investigations.
There is a link between miR-134 expression levels and stroke recurrence and MI incidence. A recent miR microarray analysis revealed that the expression of miR-19b, miR-134, and miR-186 were upregulated in patients with acute coronary syndrome compared to controls [33]. Interestingly, recent studies indicated that miR-134 might be used as a potential biomarker of coronary artery calcification, unstable coronary artery disease, or MI [34]. In line, carotid echolucent (unstable) plaques as compared to echogenic plaques differed in levels of miRs, including higher expression levels of miR-134-5p (p = 0.042) [22].
On the other hand, a study by Pielok et al. proved that miR-499 may be engaged in the hepatic insulin resistance and the development of metabolic diseases [35]. However, the clinical value of miR-499 for assessment of the MACCE incidence remains unclear.
The idea of ‘the otherness’ in miRs mechanisms for the outcome was also postulated in patients with various cardiovascular risk factors, e.g., in smokers in the context of the LEAD [36].
Our present study demonstrated a particularly huge role of the co-coexisting atherosclerotic lesions across the other vascular arterial beds in patients with T2DM. LEAD, coronary artery disease with a prior MI, and RAS were associated with 11.9-fold, 8.56-fold, and 20.2-fold higher MACCE incidence, respectively, in T2DM vs. non-diabetic patients. As previously reported, multiterritory atherosclerotic disease is frequent in diabetic patients [37,38].
Furthermore, arterial vasculopathy pattern differs substantially from that seen in patients with atherosclerotic disease but free from diabetes. Atherosclerotic lesions in diabetic coronary and peripheral arteries are much more disseminated and complex, leading to smaller vessels diameters, and as a consequence, suboptimal interventional treatment results [39]. Silent cardiac ischemia is more prevalent in T2DM vs. non-diabetic patients, accounting for 10–20% vs. 1–4%, respectively [40,41].
A major challenge associated with diabetes management for the reduction in cardiovascular events is the complex and multifaceted nature of the relationship linking diabetes to CVD [40]. Apart from traditional risk factors, such as age, male gender, dyslipidemia, hypertension, and smoking, in patients with T2DM, there are many non-traditional cardiovascular risk factors that elevate a person with diabetes to a higher risk category [41,42].
The diabetes-specific cardiovascular risk factors of atherosclerosis progression are the body fat distribution, metabolic syndrome, subclinical chronic inflammation, insulin resistance, increased glycosylation and oxidation, and disturbances in glucose metabolism [43]. Additionally, hyperinsulinemia and insulin resistance are associated with an increased free fatty-acid release promoting high triglycerides levels, high levels of ApoB and VLDL, and a low high-density lipoprotein (HDL) cholesterol level [43].
There is also otherness between specific circulating biomarkers and cytokines with the course of diabetes [43,44]. In diabetic patients, there is a greater role of inflammation for developing diabetes complications, such as hs-CRP, Interleukin-1 and -6, TNF-α, and expression of specific miRs, belonging to a family of small non-coding RNAs [18]. We observed the prognostic role of miR-134 and lower expression levels of miR-16 in our patients with diabetes and symptomatic ICAS.
Furthermore, diabetes exacerbates the proliferative phenotype of vascular smooth muscle cells (VSMCs), which underlies the very high rate of vascular complications in patients with diabetes. There is evidence that the contemporary miR-29c overexpression and miR-204 inhibition in the injured artery reduced coronary arterial stenosis in diabetic rats by preventing the exaggerated VSMCs growth upon injury [45]. In line with this, miR-503-5p might be a potential diagnostic biomarker for asymptomatic ICAS and overexpression of miR-503-5p may inhibit the proliferation of VSMCs and reduce stenosis severity [46].
Early detection of biomarkers associated with future outcome in patients with T2DM to prevent various cardiovascular events, such as MI, IS, and death, is of great interest. In the present study, we demonstrated higher expression levels of miR-134, a microRNA specific for cerebral ischemia injury, that showed prognostic role for future adverse cardiovascular events in T2DM. Moreover, patients with symptomatic ICAS and T2DM presented with lower expression levels of miR-16. Although high expression of miR-16 was prognostic for future MACCE in non-diabetic patients, the prognostic value of mentioned miR was not proven for patients with diabetes.
4. Materials and Methods
4.1. Study Population
In this prospective study, we evaluated 101 patients with hemodynamically significant symptomatic ICAS admitted to Endovascular and Vascular Surgery Department at our institution with the aim of PTA between January 2013 and January 2014. In all patients, revascularization of the target ICAS was performed by carotid artery stenting, followed by a 7-year follow-up period.
The study group comprised 37 patients with symptomatic ICAS causing 50–99% stenosis and concomitant T2DM. The control group consisted of 64 diabetes-free patients with symptomatic ICAS. Both diabetic and non-diabetic patients underwent PTA for symptomatic lesion according to the guidelines [47,48].
Inclusion criteria were: ICAS exceeding 50% lumen reduction confirmed by imaging studies (Doppler ultrasound or Computed tomography angiography (angio-CT) in the territory of cerebral ischemia with relevant brain imaging findings and/or neurological symptoms, confirming the association of carotid stenosis with cerebral ischemia as ensured by the consulting neurologist.
Exclusion criteria for both study and control groups included: acute heart failure or congestive heart failure in class III and IV of New York Heart Association (NYHA) classification, acute coronary syndrome, no direct association between ICAS and neurological symptoms or lesions on brain CT tomography, any active neoplastic disease, chronic or acute systemic inflammatory condition, and any known or suspected active infection.
The distribution of traditional cardiovascular risk factors (hyperlipidemia, arterial hypertension, former or active smoking), as well as the history of coronary artery disease including history of myocardial infarction (MI), renal artery stenosis (RAS), and peripheral extremities arterial disease (LEAD) were recorded. Definitions of the above were adopted from the scientific statements of the European Society of Cardiology [49,50,51]. The data on participants’ comorbidities were acquired from available medical history or based upon presented symptoms supported by diagnostic tools, e.g., Doppler ultrasonography, computed tomography or magnetic resonance imaging, and eventually angiography. All patients obtained peri- and post-procedural optimal medical treatment according to recommendations of respective societies [49,50,51].
The study complies with the Declaration of Helsinki and was approved by the Jagiellonian University Ethics Committee (KBET/21/B/2012; date of approval: 25 October 2012 with further extensions). All participants signed a written informed consent.
4.2. Biochemical Tests and miRs Extraction
All patients had fasting blood samples obtained on patient admission to the department, prior to PTA procedure, as soon as the signed informed consent was obtained. Serum blood tests included high-sensitivity C-reactive protein (hs-CRP), fibrinogen, creatinine, and low-density-lipoprotein (LDL) cholesterol levels.
Peripheral blood serum samples for profiling miRs were collected on patient admission before heparin treatment. Samples were left to coagulate for 30 min and centrifuged, and sera were frozen at –80 °C until analysis.
We used the miRNeasy Serum/Plasma Kit (cat. No. 217184, Qiagen, Hilden, Germany) with the beginning lysis by Trizol LS Reagent (cat. No. 10296-028, Invitrogen, Waltham, MA, USA) for the extraction of miRs. The RNA yield and concentrations were determined by capillary electrophoresis on the Agilent Bioanalyser 2100 with the Eukaryote Total RNA Pico Chip (Agilent Technologies, Inc, Santa Clara, CA, USA). An average of 60 ± 31.9 pg/μL total RNA from 300 μL of serum was collected.
The following sequence and catalog numbers for circulating miRs were used in each case: miR-1-3p (UGGAAUGUAAAGAAGUAUGUAU; EQ-204344), miR-34a-5p (UGGCAGUGUCUUAGCUGGUUGU; EQ-204486), miR-122-5p (UGGAGUGUGACAAUGGUGUUUG; EQ-205664), miR-124-3p (UUUGGUCCCCUUCAACCAGCUG; EQ-204788), miR-133a-3p (UUUGGUCCCCUUCAACCAGCUG; EQ-204788), miR-133b (UUUGGUCCCCUUCAACCAGCUA; EQ-204162), miR-134-5p (UGUGACUGGUUGACCAGAGGGG; EQ-205896), miR-208b-3p (AUAAGACGAACAAAAGGUUUGU; EQ-204636), miR-375 (UUUGUUCGUUCGGCUCGCGUGA; EQ-204362), and miR-499-5p (UUAAGACUUGCAGUGAUGUUU; EQ-205935). The endogenous miR-16-5p (UAGCAGCACGUAAAUAUUGGCG; EQ-204409) was used as a reference.
Analyzed miRs were taken into consideration based on the data regarding their potential relationship with development of atherosclerosis (PubMed, Bethesda, MD, USA), and their potential prognostic value.
At the time of the study, Exiqon LNA primers were used to quantify 10 mature miRs using the ViiA 7 real-time PCR system equipped with a 384-well reaction plate (Life Technologies, Carlsbad, CA, USA). RNA was converted to cDNA using the Universal cDNA Synthesis Kit (cat. No. EQ-203300, Exiqon, Vedbæk, Denmark). Before synthesis, RNAs were spiked with a synthetic miRNA that served as a control for the cDNA synthesis reaction. Real-time PCR was performed in triplicate with SYBR Green master mix Universal RT (cat. No. EQ-203400, Exiqon, Vedbæk, Denmark) using standard conditions.
Data were processed by the delta-Ct method, using a global normalization approach as implemented in the open source DataAssist software (Life Technologies, Carlsbad, CA, USA). The fold changes (RQ) were calculated, and statistically significant variations between group samples were filtered by the calculation of adjusted p-values using the Benjamini–Hochberg false discovery rate.
4.3. Carotid Artery Stenting Procedure
Between January 2013 and January 2014, 101 carotid artery stenting procedures were performed for symptomatic ICAS, exceeding at least 50% lumen reduction, according to the ‘tailored-CAS’ algorithm [52,53].
4.4. Follow-Up and Reporting of MACCE
The incidences of cardiovascular death, MI, and IS as well as composite endpoint (MACCE) were recorded prospectively during a follow-up period of 7 years. Adverse events were defined as fatal or non-fatal IS, fatal, or non-fatal MI or cardiovascular death (i.e., any sudden or unexpected death unless proven as non-cardiovascular on autopsy). MI was diagnosed according to criteria of the European Society of Cardiology. Diagnosis of IS was to be given by a neurologist to ensure reliability.
Final visits were done through telephone contact with a patient or appointed family member. One patient was lost to follow-up; however, the data on patient vital status were obtained from the national health registry.
4.5. Statistical Analysis
Continuous variables are presented as mean ± one standard deviation (SD) for variables with proven normal distribution by Shapiro–Wilk test, and median with interquartile range (IQR) for variables with no normal distribution. Categorical variables are expressed as frequencies and percentages (n, %). Means of analyzed parameters across groups were tested with the analysis of variance (ANOVA) test, and frequencies were compared by the chi-square test for independence. The potential independent prognostic markers of MACCE during the follow-up period were established from the clinical, biochemical, and miR variables with a Cox proportional hazard univariate analysis, and in case of a trend toward difference (p < 0.1), they were entered into a multivariate Cox proportional hazard analysis model. The results of uni- and multivariate Cox proportional hazard analysis were expressed as hazard ratio (HR) and 95% confidence interval (95%CI). Statistical analyses were performed with Statistica 13.0 software. Statistical significance was assumed at a p-value < 0.05.
5. Conclusions
Our study demonstrated that different circulating miRs may be prognostic for MACCE in diabetic versus non-diabetic patients with symptomatic ICAS. Higher expression levels of miR-134 were prognostic for MACCE in diabetic patients, while higher expression levels of miR-16 were prognostic for MACCE in non-diabetic patients.
6. Study Limitations
Our study has some limitations. The results are acquired form a single-center study with a relatively small group. The patients scheduled for surgical carotid endarterectomy were not enrolled in the present study. These factors could have an impact on the lower study power (0.762). Thus, future studies, preferably multicenter, are needed to assess the specific miRs as predictors of cardiovascular outcomes in patients with T2MD and symptomatic ICAS.
Acknowledgments
The authors would like to thank Francisco J. Enguita and Ewa Stępień for their support in the preparation, profiling, and assessment of miRs.
Author Contributions
Conceptualization, T.P. and A.K.-Z.; Data curation, R.B., A.R., J.L. and A.K.-Z.; Formal analysis, T.P., P.P., P.K. and K.Ż.; Funding acquisition, A.K.-Z.; Investigation, R.B., A.R., P.K. and A.K.-Z.; Methodology, R.B., T.P., A.R. and A.K.-Z.; Project administration, K.Ż.; Resources, T.P. and P.P.; Software, R.B.; Supervision, A.K.-Z.; Validation, J.L. and K.Ż.; Visualization, R.B.; Writing—original draft, R.B., A.R. and P.K.; Writing—review and editing, T.P., J.L., K.Ż. and A.K.-Z. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by grants from the Jagiellonian University to Anna Kabłak-Ziembicka and Piotr Pieniążek (grant number: N41/DBS/000752). This article was supported by the science fund of the John Paul II Hospital to Rafał Badacz, Cracow, Poland (no. FN/02/2022).
Institutional Review Board Statement
The study complies with the Declaration of Helsinki and was approved by the Jagiellonian University Ethics Committee (KBET/21/B/2012; date of approval: 25 October 2012 with further extensions).
Informed Consent Statement
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.
Conflicts of Interest
The authors declare that there is no conflict of interest regarding the publication of this article.
Sample Availability
The data presented in this study are available on request from the corresponding author.
Footnotes
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Gartner H.V., Eigentler T.K. Pathogenesis of diabetic macro- and microangiopathy. Clin. Nephrol. 2008;70:1–9. doi: 10.5414/CNP70001. [DOI] [PubMed] [Google Scholar]
- 2.Yang Z., Han B., Zhang H., Ji G., Zhang L., Singh B.K. Association of Lower Extremity Vascular Disease, Coronary Artery, and Carotid Artery Atherosclerosis in Patients with Type 2 Diabetes Mellitus. Comput. Math Methods Med. 2021;2021:6268856. doi: 10.1155/2021/6268856. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
- 3.Einarson T.R., Acs A., Ludwig C., Panton U.H. Prevalence of cardiovascular disease in type 2 diabetes: A systematic literature review of scientific evidence from across the world in 2007–2017. Cardiovasc. Diabetol. 2018;17:83. doi: 10.1186/s12933-018-0728-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Sharma A., Green J.B., Dunning A., Lokhnygina Y., Al-Khatib S.M., Lopes R.D., Buse J.B., Lachin J.M., Van de Werf F., Armstrong P.W., et al. Causes of Death in a Contemporary Cohort of Patients with Type 2 Diabetes and Atherosclerotic Cardiovascular Disease: Insights From the TECOS Trial. Diabetes Care. 2017;40:1763–1770. doi: 10.2337/dc17-1091. [DOI] [PubMed] [Google Scholar]
- 5.Hewitt J., Guerra C.L., Fernandez-Moreno M.D.C., Sierra C. Diabetes and stroke prevention: A review. Stroke Res. Treat. 2012;2012:673187. doi: 10.1155/2012/673187. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Young J.B., Gauthier-Loiselle M., Bailey R.A., Manceur A.M., Lefebvre P., Greenberg M., Lafeuille M.-H., Duh M.S., Bookhart B., Wysham C.H. Development of predictive risk models for major adverse cardiovascular events among patients with type 2 diabetes mellitus using health insurance claims data. Cardiovasc. Diabetol. 2018;17:118. doi: 10.1186/s12933-018-0759-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Baluja A., Rodríguez-Mañero M., Cordero A., Kreidieh B., Iglesias-Alvarez D., García-Acuña J.M., Martínez-Gómez A., Agra-Bermejo R., Alvarez-Rodríguez L., Abou-Jokh C., et al. Prediction of major adverse cardiac, cerebrovascular events in patients with diabetes after acute coronary syndrome. Diabetes Vasc. Dis. Res. 2020;17:1479164119892137. doi: 10.1177/1479164119892137. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Li Y., Liu Y., Wang Z., Hou H., Lin Y., Jiang Y. MicroRNA: Not Far from Clinical Application in Ischemic Stroke. ISRN Stroke. 2013;2013:1–7. doi: 10.1155/2013/858945. [DOI] [Google Scholar]
- 9.Kadir R.R.A., Alwjwaj M., Bayraktutan U. MicroRNA: An Emerging Predictive, Diagnostic, Prognostic and Therapeutic Strategy in Ischaemic Stroke. Cell. Mol. Neurobiol. 2020:1–19. doi: 10.1007/s10571-020-01028-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Sheikhbahaei S., Manizheh D., Mohammad S., Hasan T.M., Saman N., Laleh R., Mahsa M., Sanaz A.K., Shaghayegh H.J. Can MiR-503 be used as a marker in diabetic patients with ischemic stroke? BMC Endocr. Disord. 2019;19:42. doi: 10.1186/s12902-019-0371-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Bertoluci M.C., Rocha V.Z. Cardiovascular risk assessment in patients with diabetes. Diabetol. Metab. Syndr. 2017;9:25. doi: 10.1186/s13098-017-0225-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Gacoń J., Badacz R., Stępień E., Karch I., Enguita F.J., Żmudka K., Przewłocki T., Kabłak-Ziembicka A. Diagnostic and prognostic micro-RNAs in ischaemic stroke due to carotid artery stenosis and in acute coronary syndrome: A four-year prospective study. Kardiol. Pol. 2018;76:362–369. doi: 10.5603/KP.a2017.0243. [DOI] [PubMed] [Google Scholar]
- 13.Qi R., Liu H., Liu C., Xu Y., Liu C. Expression and short-term prognostic value of miR-126 and miR-182 in patients with acute stroke. Exp. Ther. Med. 2020;19:527–534. doi: 10.3892/etm.2019.8227. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kim J.-M., Jung K.-H., Chu K., Lee S.-T., Ban J., Moon J., Kim M., Lee S.K., Roh J.-K. Atherosclerosis-Related Circulating MicroRNAs as a Predictor of Stroke Recurrence. Transl. Stroke Res. 2015;6:191–197. doi: 10.1007/s12975-015-0390-1. [DOI] [PubMed] [Google Scholar]
- 15.Wang L.C.C., Hess C.N., Hiatt W.R., Goldfine A.B. Clinical Update: Cardiovascular Disease in Diabetes Mellitus Atherosclerotic Cardiovascular Disease and Heart Failure in Type 2 Diabetes Mellitus—Mechanisms, Management, and Clinical Considerations. Circulation. 2016;133:2459–2502. doi: 10.1161/CIRCULATIONAHA.116.022194. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Ballotari P., Ranieri S.C., Luberto F., Caroli S., Greci M., Rossi P.G., Manicardi V. Sex Differences in Cardiovascular Mortality in Diabetics and Nondiabetic Subjects: A Population-Based Study (Italy) Int. J. Endocrinol. 2015;2015:914057. doi: 10.1155/2015/914057. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Sardu C., Modugno P., Castellano G., Scisciola L., Barbieri M., Petrella L., Fanelli M., Macchia G., Caradonna E., Massetti M., et al. Atherosclerotic Plaque Fissuration and Clinical Outcomes in Pre-Diabetics vs. Normoglycemics Patients Affected by Asymptomatic Significant Carotid Artery Stenosis at 2 Years of Follow-Up: Role of microRNAs Modulation: The ATIMIR Study. Biomedicines. 2021;9:401. doi: 10.3390/biomedicines9040401. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Maitrias P., Meuth V.M.-L., Massy Z.A., M’Baya-Moutoula E., Reix T., Caus T., Metzinger L. MicroRNA deregulation in symptomatic carotid plaque. J. Vasc. Surg. 2015;62:1245–1250. doi: 10.1016/j.jvs.2015.06.136. [DOI] [PubMed] [Google Scholar]
- 19.Ambros V. The functions of animal microRNAs. Nature. 2004;431:350–355. doi: 10.1038/nature02871. [DOI] [PubMed] [Google Scholar]
- 20.Brevetti G., Piscione F., Cirillo P., Galasso G., Schiano V., Barbato E., Scopacasa F., Chiariello M. In concomitant coronary and peripheral arterial disease, inflammation of the affected limbs predicts coronary artery endothelial dysfunction. Atherosclerosis. 2008;201:440–446. doi: 10.1016/j.atherosclerosis.2008.01.014. [DOI] [PubMed] [Google Scholar]
- 21.Sorrentino S., Iaconetti C., De Rosa S., Polimeni A., Sabatino J., Gareri C., Passafaro F., Mancuso T., Tammè L., Mignogna C., et al. Hindlimb ischemia impairs endotelial recovery and increases neointimal proliferation in the carotid artery. Sci. Rep. 2018;8:761. doi: 10.1038/s41598-017-19136-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Badacz R., Przewłocki T., Gacoń J., Stępień E., Enguita F.J., Karch I., Żmudka K., Kabłak-Ziembicka A. Circulating miRNA levels differ with respect to carotid plaque characteristics and symptom occurrence in patients with carotid artery stenosis and provide information on future cardiovascular events. Postepy Kardiol Interwencyjnej. 2018;14:75–84. doi: 10.5114/aic.2018.74358. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Dégano I.R., Camps-Vilaró A., Subirana I., García-Mateo N., Cidad P., Muñoz-Aguayo D., Puigdecanet E., Nonell L., Vila J., Crepaldi F.M., et al. Association of Circulating microRNAs with Coronary Artery Disease and Usefulness for Reclassification of Healthy Individuals: The REGICOR Study. J. Clin. Med. 2020;9:1402. doi: 10.3390/jcm9051402. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Kong G., Hao X., Xing C. Increased plasma miR-16 is associated with poor prognosis for acute myocardial infarction. Int. J. Clin. Exp. Med. 2019;12:4070–4075. [Google Scholar]
- 25.Badacz R., Kleczyński P., Legutko J., Żmudka K., Gacoń J., Przewłocki T., Kabłak-Ziembicka A. Expression of miR-1-3p, miR-16-5p and miR-122-5p as Possible Risk Factors of Secondary Cardiovascular Events. Biomedicines. 2021;9:1055. doi: 10.3390/biomedicines9081055. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Schratt G.M., Tuebing F., Nigh E.A., Kane C.G., Sabatini M.E., Kiebler M., Greenberg M.E. A brain-specific microRNA regulates dendritic spine development. Nature. 2006;439:283–289. doi: 10.1038/nature04367. [DOI] [PubMed] [Google Scholar]
- 27.Huang W., Liu X., Cao J., Meng F., Li M., Chen B., Zhang J. MiR-134 regulates ischemia/reperfusion injury-induced neuronal cell death by regulating CREB signaling. J. Mol. Neurosci. 2015;55:821–829. doi: 10.1007/s12031-014-0434-0. [DOI] [PubMed] [Google Scholar]
- 28.Chi W., Meng F., Li Y., Li P., Wang G., Cheng H., Han S., Li J. Impact of microRNA-134 on neural cell survival against ischemic injury in primary cultured neuronal cells and mouse brain with ischemic stroke by targeting HSPA12B. Brain Res. 2014;1592:22–33. doi: 10.1016/j.brainres.2014.09.072. [DOI] [PubMed] [Google Scholar]
- 29.Lan G., Xie W., Li L., Zhang M., Liu D., Tan Y.-L., Cheng H.-P., Gong D., Huang C., Zheng X.-L., et al. MicroRNA-134 actives lipoprotein lipase-mediated lipid accumulation and inflammatory response by targeting angiopoietin-like 4 in THP-1 macrophages. Biochem. Biophys. Res. Commun. 2016;472:410–417. doi: 10.1016/j.bbrc.2015.10.158. [DOI] [PubMed] [Google Scholar]
- 30.Pirola L., Ferraz J.C. Role of pro- and anti-inflammatory phenomena in the physiopathology of type 2 diabetes and obesity. World J. Biol. Chem. 2017;8:120–128. doi: 10.4331/wjbc.v8.i2.120. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Qian X., Tan J., Liu L., Chen S., You N., Yong H., Pan M., You Q., Ding D., Lu Y. MicroRNA-134-5p promotes high glucose-induced podocyte apoptosis by targeting bcl-2. Am. J. Transl. Res. 2018;10:989–997. [PMC free article] [PubMed] [Google Scholar]
- 32.Kosmas C.E., Silverio D., Tsomidou C., Salcedo M.D., Montan P.D., Guzman E. The Impact of Insulin Resistance and Chronic Kidney Disease on Inflammation and Cardiovascular Disease. Clin. Med. Insights: Endocrinol. Diabetes. 2018;11:1179551418792257. doi: 10.1177/1179551418792257. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Wang K.-J., Zhao X., Liu Y.-Z., Zeng Q.-T., Mao X.-B., Li S.-N., Zhang M., Jiang C., Zhou Y., Qian C., et al. Circulating MiR-19b-3p, MiR-134-5p and MiR-186-5p are Promising Novel Biomarkers for Early Diagnosis of Acute Myocardial Infarction. Cell. Physiol. Biochem. 2016;38:1015–1029. doi: 10.1159/000443053. [DOI] [PubMed] [Google Scholar]
- 34.Liu W., Ling S., Sun W., Liu T., Li Y., Zhong G., Zhao D., Zhang P., Song J., Jin X., et al. Circulating microRNAs correlated with the level of coronary artery calcification in symptomatic patients. Sci. Rep. 2015;5:16099. doi: 10.1038/srep16099. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Pielok A., Marycz K. Non-Coding RNAs as Potential Novel Biomarkers for Early Diagnosis of Hepatic Insulin Resistance. Int. J. Mol. Sci. 2020;21:4182. doi: 10.3390/ijms21114182. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Pereira-Da-Silva T., Napoleão P., Costa M., Gabriel A., Selas M., Silva F., Enguita F., Ferreira R., Carmo M. Cigarette Smoking, miR-27b Downregulation, and Peripheral Artery Disease: Insights into the Mechanisms of Smoking Toxicity. J. Clin. Med. 2021;10:890. doi: 10.3390/jcm10040890. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Badacz R., Kabłak-Ziembicka A., Rosławiecka A., Rzeźnik D., Baran J., Trystuła M., Legutko J., Przewłocki T. The Maintained Glycemic Target Goal and Renal Function Are Associated with Cardiovascular and Renal Outcomes in Diabetic Patients Following Stent-Supported Angioplasty for Renovascular Atherosclerotic Disease. J. Pers. Med. 2022;12:537. doi: 10.3390/jpm12040537. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Przewłocki T., Kablak-Ziembicka A., Kozanecki A., Rzeźnik D., Pieniazek P., Musiałek P., Piskorz A., Sokołowski A., Rosławiecka A., Tracz W. Polyvascular extracoronary atherosclerotic disease in patients with coronary artery disease. Kardiologia Polska. 2009;67:978–984. [PubMed] [Google Scholar]
- 39.Leon B.M., Maddox T.M. Diabetes and cardiovascular disease: Epidemiology, biological mechanisms, treatment recommendations and future research. World J. Diabetes. 2015;6:1246–1258. doi: 10.4239/wjd.v6.i13.1246. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Hernández C., Candell-Riera J., Ciudin A., Francisco G., Aguadé-Bruix S., Simó R. Prevalence and risk factors accounting for true silent myocardial ischemia: A pilot case-control study comparing type 2 diabetic with non-diabetic control subjects. Cardiovasc. Diabetol. 2011;10:9. doi: 10.1186/1475-2840-10-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Abraham T.M., Pencina K.M., Pencina M.J., Fox C.S. Trends in Diabetes Incidence: The Framingham Heart Study. Diabetes Care. 2014;38:482–487. doi: 10.2337/dc14-1432. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Conroy R.M., Pyörälä K., Fitzgerald A.P., Sans S., Menotti A., De Backer G., De Bacquer D., Ducimetière P., Jousilahti P., Keil U., et al. SCORE project group. Estimation of ten-year risk of fatal cardiovascular disease in Europe: The SCORE project. Eur. Heart J. 2003;24:987–1003. doi: 10.1016/S0195-668X(03)00114-3. [DOI] [PubMed] [Google Scholar]
- 43.Chait A., den Hartigh L.J. Adipose Tissue Distribution, Inflammation and Its Metabolic Consequences, Including Diabetes and Cardiovascular Disease. Front. Cardiovasc. Med. 2020;7:22. doi: 10.3389/fcvm.2020.00022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Hamilton S.J., Watts G.F. Endothelial dysfunction in diabetes: Pathogenesis, significance, and treatment. Rev. Diabet. Stud. 2013;10:133–156. doi: 10.1900/RDS.2013.10.133. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Torella D., Iaconetti C., Tarallo R., Marino F., Giurato G., Veneziano C., Aquila I., Scalise M., Mancuso T., Cianflone E., et al. miRNA Regulation of the Hyperproliferative Phenotype of Vascular Smooth Muscle Cells in Diabetes. Diabetes. 2018;67:2554–2568. doi: 10.2337/db17-1434. [DOI] [PubMed] [Google Scholar]
- 46.Yan Z., Wang H., Liang J., Li Y., Li X. MicroRNA-503-5p improves carotid artery stenosis by inhibiting the proliferation of vascular smooth muscle cells. Exp. Ther. Med. 2020;20:85. doi: 10.3892/etm.2020.9213. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Aboyans V., Bartelink M.L., Baumgartner I., Clément D., Collet J.P., Cremonesi A., de Carlo M., Erbel R., European Stroke Organisation. ESC Committee for Practice Guidelines ESC Guidelines on the diagnosis and treatment of peripheral artery diseases: Document covering atherosclerotic disease of extracranial carotid and vertebral, mesenteric, renal, upper and lower extremity arteries: The Task Force on the Diagnosis and Treatment of Peripheral Artery Diseases of the European Society of Cardiology (ESC) Eur. Heart J. 2011;32:2851–2906. doi: 10.1093/eurheartj/ehr211. [DOI] [PubMed] [Google Scholar]
- 48.Aboyans V., Ricco J.B., Bartelink M.E.L., Björck M., Brodmann M., Cohnert T., Collet J.P., Czerny M., De Carlo M., Debus S., et al. 2017 ESC Guidelines on the Diagnosis and Treatment of Peripheral Arterial Diseases, in collaboration with the European Society for Vascular Surgery (ESVS): Document covering atherosclerotic disease of extracranial carotid and vertebral, mesenteric, renal, upper and lower extremity arteriesEndorsed by: The European Stroke Organization (ESO)The Task Force for the Diagnosis and Treatment of Peripheral Arterial Diseases of the European Society of Cardiology (ESC) and of the European Society for Vascular Surgery (ESVS) Eur. Heart J. 2018;39:763–816. doi: 10.1093/eurheartj/ehx095. [DOI] [PubMed] [Google Scholar]
- 49.Cosentino F., Grant P.J., Aboyans V., Bailey C.J., Ceriello A., Delgado V., Federici M., Filippatos G., Grobbee D.E., Hansen T.B., et al. 2019 ESC Guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur. Heart J. 2020;41:255–323. doi: 10.1093/eurheartj/ehz486. [DOI] [PubMed] [Google Scholar]
- 50.Mach F., Baigent C., Catapano A.L., Koskinas K.C., Casula M., Badimon L., Chapman M.J., De Backer G.G., Delgado V., Ference B.A., et al. 2019 ESC/EAS Guidelines for the management of dyslipidaemias: Lipid modification to reduce cardiovascular risk. Eur. Heart J. 2020;41:111–188. doi: 10.1093/eurheartj/ehz455. [DOI] [PubMed] [Google Scholar]
- 51.Williams B., Mancia G., Spiering W., Agabiti Rosei E., Azizi M., Burnier M., Clement D.L., Coca A., de Simone G., Dominiczak A., et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur. Heart J. 2018;39:3021–3104. doi: 10.1093/eurheartj/ehy339. [DOI] [PubMed] [Google Scholar]
- 52.Pieniazek P., Musialek P., Kablak-Ziembicka A., Tekieli L., Motyl R., Przewlocki T., Moczulski Z., Pasowicz M., Sokolowski A., Lesniak-Sobelga A., et al. Carotid artery stenting with patient- and lesion-tailored selection of the neuroprotection system and stent type: Early and 5-year results from a prospective academic registry of 535 consecutive procedures (TARGET-CAS) J. Endovasc. Ther. 2008;15:249–262. doi: 10.1583/07-2264.1. [DOI] [PubMed] [Google Scholar]
- 53.Musialek P., Pieniazek P., Tracz W., Tekieli L., Przewlocki T., Kablak-Ziembicka A., Motyl R., Moczulski Z., Stepniewski J., Trystula M., et al. Safety of embolic protection device-assisted and unprotected intravascular ultrasound in evaluating carotid artery atherosclerotic lesions. Med Sci. Monit. 2012;18:MT7–MT18. doi: 10.12659/MSM.882452. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.