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Mitochondrial function and integrity are vital for the maintenance of cellular homeostasis, particularly in high-energy demanding cells. Cardiomyocytes have a large number of mitochondria, which provide a continuous and bulk supply of the ATP necessary for cardiac mechanical function. More than 90% of the ATP consumed by the heart is derived from the mitochondrial oxidative metabolism. Decreased energy supply as the main consequence of mitochondrial dysfunction is closely linked to cardiovascular disease (CVD). The discovery of noncoding RNA (ncRNAs) in the mitochondrial compartment has changed the traditional view of molecular pathways involved in the regulatory network of CVD. Mitochondrial ncRNAs participate in controlling cardiovascular pathogenesis by regulating glycolysis, mitochondrial energy status, and the expression of genes involved in mitochondrial metabolism. Understanding the underlying mechanisms of the association between impaired mitochondrial function resulting from fluctuation in expression levels of ncRNAs and specific disease phenotype can aid in preventing and treating CVD. This review presents an overview of the role of mitochondrial ncRNAs in the complex regulatory network of the cardiovascular pathology. We will summarize and discuss (1) mitochondrial microRNAs (mitomiRs) and long noncoding RNAs (lncRNAs) encoded either by nuclear or mitochondrial genome which are involved in the regulation of mitochondrial metabolism; (2) the role of mitomiRs and lncRNAs in the pathogenesis of several CVD such as hypertension, cardiac hypertrophy, acute myocardial infarction and heart failure; (3) the biomarker and therapeutic potential of mitochondrial ncRNAs in CVD; (4) and the challenges inherent to their translation into clinical application.

The interest in non-coding RNAs, which started more than a decade ago, has still not weakened. A wealth of experimental and clinical studies has suggested the potential of non-coding RNAs, especially the short-sized microRNAs (miRs), to be used as the new generation of therapeutic targets and biomarkers of cardiovascular disease, an ever-growing public health issue in the modern world. Among the hundreds of miRs characterized so far, microRNA-1 (miR-1) and microRNA-21 (miR-21) have received some attention and have been associated with cardiac injury and cardioprotection. In this review article, we summarize the current knowledge of the function of these two miRs in the heart, their association with cardiac injury, and their potential cardioprotective roles and biomarker value. While this field has already been extensively studied, much remains to be done before research findings can be translated into clinical application for patient’s benefit.

Rapid and correct diagnosis of acute myocardial infarction (MI) has an important impact on patient treatment and prognosis. We compared the diagnostic performance of high-sensitivity cardiac troponin T (hs-cTnT) and cardiac enriched microRNAs (miRNAs) in patients with MI. Circulating concentrations of cardiac-enriched miR-208b and miR-499 were measured by quantitative PCR in a case-control study of 510 MI patients referred for primary mechanical reperfusion and 87 healthy controls. miRNA-208b and miR-499 were highly increased in MI patients (>10(5)-fold, P < 0.001) and nearly undetectable in healthy controls. Patients with ST-elevation MI (n= 397) had higher miRNA concentrations than patients with non-ST-elevation MI (n = 113) (P < 0.001). Both miRNAs correlated with peak concentrations of creatine kinase and cTnT (P < 10(-9)). miRNAs and hs-cTnT were already detectable in the plasma 1 h after onset of chest pain. In patients who presented <3 h after onset of pain, miR-499 was positive in 93% of patients and hs-cTnT in 88% of patients (P= 0.78). Overall, miR-499 and hs-cTnT provided comparable diagnostic value with areas under the ROC curves of 0.97. The reclassification index of miR-499 to a clinical model including several risk factors and hs-cTnT was not significant (P = 0.15). Circulating miRNAs are powerful markers of acute MI. Their usefulness in the establishment of a rapid and accurate diagnosis of acute MI remains to be determined in unselected populations of patients with acute chest pain.

Background Long non-coding RNAs (lncRNAs) constitute a novel class of non-coding RNAs. LncRNAs regulate gene expression, thus having the possibility to modulate disease progression. In this study, we investigated the changes of lncRNAs expression in the heart after myocardial infarction (MI). Results Adult male C57/BL6 mice were subjected to coronary ligation or sham operation. In a derivation group of 4 MI and 4 sham-operated mice sacrificed 24 hours after surgery, microarray analysis showed that MI was associated with up-regulation of 20 lncRNAs and down-regulation of 10 lncRNAs (fold-change >2). Among these, 2 lncRNAs, called myocardial infarction-associated transcript 1 (MIRT1) and 2 (MIRT2), showed robust up-regulation in the MI group: 5-fold and 13-fold, respectively. Up-regulation of these 2 lncRNAs after MI was confirmed by quantitative PCR in an independent validation group of 8 MI and 8 sham-operated mice (9-fold and 16-fold for MIRT1 and MIRT2, P < 0.001). In a time-course analysis involving 21 additional MI mice, the expression of both lncRNAs peaked 24 hours after MI and returned to baseline after 2 days. In situ hybridization revealed an up-regulation of MIRT1 expression in the left ventricle of MI mice. Expression of MIRT1 and MIRT2 correlated with the expression of multiple genes known to be involved in left ventricular remodeling. Mice with high level of expression of MIRT1 and MIRT2 had a preserved ejection fraction. Conclusion Myocardial infarction induces important changes in the expression of lncRNAs in the heart. This study motivates further investigation of the role of lncRNAs in left ventricular remodeling.

Dyslipidemia is a documented risk factor for cardiovascular diseases and other metabolic disorders. Therefore, the analysis of hyperlipidemia (HL)-related miRNAs is a potential approach for achieving new prognostic markers in lipid-metabolism related diseases. We aimed to analyze specific distribution of miRNAs in different tissues from HL animals. Golden Syrian hamsters were fed either regular chow (NL) or high-fat diet (HL) for 12 weeks. Microarray miRNAs profiling was performed in liver, heart and small intestine and data analyzed by R-studio software. Functional enrichment bioinformatics analysis was performed using miRWalk and DAVID tools. We observed a dysregulation of miRNAs in HL tissues evidencing a discrete distribution in the heart-liver axis and three lipid metabolism-related miRNAs were identified: hsa-miR-223-3p, hsa-miR-21-5p, and hsa-miR-146a-5p. Expression levels of these miRNAs were increased in HL livers and hearts. Functional bioinformatics analysis showed involvement of these miRNAs in the regulation of biological processes altered in HL conditions such as lipid metabolic process, fat cell differentiation, regulation of smooth muscle cells and cardiac septum development. We identified a set of miRNAs dysregulated in different tissues of HFD-induced HL hamsters. These findings motivate further studies aiming to investigate novel molecular mechanisms of lipid metabolism and atherogenic HL.

Background Cardiac troponins and natriuretic peptides are benchmark biomarkers for heart failure (HF) with reduced ejection fraction (HFrEF) but have limited prognostic performance for HF patients with preserved ejection fraction (HFpEF). Non-coding RNA-based biomarkers represent an innovative approach to risk-stratify patients and might address the unmet need for minimally invasive prognostic and predictive tools for HF development and HF-related outcomes. Our aim is to investigate the prognostic performance and risk stratification potential of circulating panels of microRNAs (miRNAs) in HFrEF and HFpEF. Methods A systematic search on PubMed, Web of Science, and Scopus databases was performed for studies reporting miRNAs as prognostic biomarkers in HF patients. A total of 22 studies pooling 5736 participants were included for quantitative analysis. KM-based individual patient data (IPD) analysis was performed in 12 studies (5064 participants). Results KM-based IPD analysis in HFrEF allowed the identification of a panel of four miRNAs (miR-27a-3p, miR-129-5p, miR-145-5p, and miR-590-3p) predicting the risk of all-cause death with hazard ratio (HR) 4.26 [2.68–6.76]. MiR-122-5p and miR-423-5p predicted cardiovascular death of HFrEF patients (HR 3.61 [2.67–4.87]). In HFpEF, miR-19a-3p predicted all-cause death of HFpEF patients with HR 2.23 [1.16–4.27]. Moreover, a panel of eight miRNAs (miR-17-5p, miR-20a-5p, miR-21, miR-23, miR-27, miR-106b-5p, miR-210, and miR-221) showed significant association with HF incidence (HR 2.14 [1.81–2.53]). Conclusions A comprehensive meta-analysis of KM-based IPD enabled the identification of unique miRNA panels predicting the incidence and severity of HFrEF and HFpEF, supporting the clinical usefulness of miRNA profiling for tailored healthcare and risk stratification in HF patients. Nonetheless, more rigorously designed longitudinal studies are needed to validate the clinical application of miRNAs as prognostic and predictive biomarkers. Graphical Abstract

Background We aimed to investigate the diagnostic performance of S100 as an outcome predictor after out-of-hospital cardiac arrest (OHCA) and the potential influence of two target temperatures (33 °C and 36 °C) on serum levels of S100. Methods This is a substudy of the Target Temperature Management after Out-of-Hospital Cardiac Arrest (TTM) trial. Serum levels of S100 were measured a posteriori in a core laboratory in samples collected at 24, 48, and 72 h after OHCA. Outcome at 6 months was assessed using the Cerebral Performance Categories Scale (CPC 1–2 = good outcome, CPC 3–5 = poor outcome). Results We included 687 patients from 29 sites in Europe. Median S100 values were higher in patients with a poor outcome at 24, 48, and 72 h: 0.19 (IQR 0.10–0.49) versus 0.08 (IQR 0.06–0.11) μg/ml, 0.16 (IQR 0.10–0.44) versus 0.07 (IQR 0.06–0.11) μg/L, and 0.13 (IQR 0.08–0.26) versus 0.06 (IQR 0.05–0.09) μg/L ( p  < 0.001), respectively. The ability to predict outcome was best at 24 h with an AUC of 0.80 (95% CI 0.77–0.83). S100 values were higher at 24 and 72 h in the 33 °C group than in the 36 °C group (0.12 [0.07–0.22] versus 0.10 [0.07–0.21] μg/L and 0.09 [0.06–0.17] versus 0.08 [0.05–0.10], respectively) ( p  < 0.02). In multivariable analyses including baseline variables and the allocated target temperature, the addition of S100 improved the AUC from 0.80 to 0.84 (95% CI 0.81–0.87) ( p  < 0.001), but S100 was not an independent outcome predictor. Adding S100 to the same model including neuron-specific enolase (NSE) did not further improve the AUC. Conclusions The allocated target temperature did not affect S100 to a clinically relevant degree. High S100 values are predictive of poor outcome but do not add value to present prognostication models with or without NSE. S100 measured at 24 h and afterward is of limited value in clinical outcome prediction after OHCA. Trial registration ClinicalTrials.gov identifier: NCT01020916 . Registered on 25 November 2009.

Prediction of neurological outcome is a crucial part of post cardiac arrest care and prediction in patients remaining unconscious and/or sedated after rewarming from targeted temperature management (TTM) remains difficult. Current guidelines suggest the use of serial measurements of the biomarker neuron-specific enolase (NSE) in combination with other predictors of outcome in patients admitted after out-of-hospital cardiac arrest (OHCA). This study sought to investigate the ability of NSE to predict poor outcome in patients remaining unconscious at day three after OHCA. In addition, this study sought to investigate if serial NSE measurements add incremental prognostic information compared to a single NSE measurement at 48 hours in this population. This study is a post-hoc sub-study of the TTM trial, randomizing OHCA patients to a course of TTM at either 33°C or 36°C. Patients were included from sites participating in the TTM-trial biobank sub study. NSE was measured at 24, 48 and 72 hours after ROSC and follow-up was concluded after 180 days. The primary end point was poor neurological function or death defined by a cerebral performance category score (CPC-score) of 3 to 5. A total of 685 (73%) patients participated in the study. At day three after OHCA 63 (9%) patients had died and 473 (69%) patients were not awake. In these patients, a single NSE measurement at 48 hours predicted poor outcome with an area under the receiver operating characteristics curve (AUC) of 0.83. A combination of all three NSE measurements yielded the highest discovered AUC (0.88, p = .0002). Easily applicable combinations of serial NSE measurements did not significantly improve prediction over a single measurement at 48 hours (AUC 0.58-0.84 versus 0.83). NSE is a strong predictor of poor outcome after OHCA in persistently unconscious patients undergoing TTM, and NSE is a promising surrogate marker of outcome in clinical trials. While combinations of serial NSE measurements may provide an increase in overall prognostic information, it is unclear whether actual clinical prognostication with low false-positive rates is improved by application of serial measurements in persistently unconscious patients. The findings of this study should be confirmed in another prospective cohort. NCT01020916.

Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82–0.84) and a balanced accuracy of 0.78 (95% CI 0.77–0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40–0.74). Quantitative PCR validated LEF1-AS1’s adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management. Identifying biomarkers associated with risk of severe COVID-19 disease could inform clinical management. Here, the authors identify a long noncoding RNA associated with severe disease using data from three European countries, and validate their finding in data from Canada.

Coronavirus disease-2019 (COVID-19) can be asymptomatic or lead to a wide symptom spectrum, including multi-organ damage and death. Here, we explored the potential of microRNAs in delineating patient condition and predicting clinical outcome. Plasma microRNA profiling of hospitalized COVID-19 patients showed that miR-144-3p was dynamically regulated in response to COVID-19. Thus, we further investigated the biomarker potential of miR-144-3p measured at admission in 179 COVID-19 patients and 29 healthy controls recruited in three centers. In hospitalized patients, circulating miR-144-3p levels discriminated between non-critical and critical illness (AUC miR-144-3p  = 0.71; p  = 0.0006), acting also as mortality predictor (AUC miR-144-3p  = 0.67; p  = 0.004). In non-hospitalized patients, plasma miR-144-3p levels discriminated mild from moderate disease (AUC miR-144-3p  = 0.67; p  = 0.03). Uncontrolled release of pro-inflammatory cytokines can lead to clinical deterioration. Thus, we explored the added value of a miR-144/cytokine combined analysis in the assessment of hospitalized COVID-19 patients. A miR-144-3p/Epidermal Growth Factor (EGF) combined score discriminated between non-critical and critical hospitalized patients (AUC miR-144-3p/EGF  = 0.81; p  < 0.0001); moreover, a miR-144-3p/Interleukin-10 (IL-10) score discriminated survivors from nonsurvivors (AUC miR-144-3p/IL-10  = 0.83; p  < 0.0001). In conclusion, circulating miR-144-3p, possibly in combination with IL-10 or EGF, emerges as a noninvasive tool for early risk-based stratification and mortality prediction in COVID-19.