Development of heart failure (HF) after myocardial infarction (MI) is responsible for premature death. Complex cellular and molecular mechanisms are involved in this process. A number of studies have linked the epitranscriptomic RNA modification N6-methyladenosine (m6A) with HF, but it remains unknown how m6A affects the risk of developing HF after MI. We addressed the regulation of m6A and its demethylase fat mass and obesity-associated (FTO) after MI and their association with HF. Using liquid chromatography coupled to mass spectrometry, we observed an increase of m6A content in the infarcted area of rat hearts subjected to coronary ligation and a decrease in blood. FTO expression measured by quantitative PCR was downregulated in the infarcted hearts. In whole blood samples collected at the time of reperfusion in MI patients, m6A content was lower in patients who developed HF as attested by a 4-month ejection fraction (EF) of ≤40% as compared to patients who did not develop HF (EF > 50%). M6A content was higher in females. These results show that m6A measured in blood is associated with HF development after MI and motivate further investigation of the potential role of m6A as a novel epitranscriptomics biomarker and therapeutic target of HF.

Cardiogenic shock (CS) is a life-threatening emergency. New biomarkers are needed in order to detect patients at greater risk of adverse outcome. Our aim was to assess the characteristics of miR-21-5p, miR-122-5p, and miR-320a-3p in CS and evaluate the value of their expression levels in risk prediction. Circulating levels of miR-21-5p, miR-122-5p, and miR-320a-3p were measured from serial plasma samples of 179 patients during the first 5–10 days after detection of CS, derived from the CardShock study. Acute coronary syndrome was the most common cause (80%) of CS. Baseline (0 h) levels of miR-21-5p, miR-122-5p, and miR-320a-3p were all significantly elevated in nonsurvivors compared to survivors (p < 0.05 for all). Above median levels at 0h of each miRNA were each significantly associated with higher lactate and alanine aminotransferase levels and decreased glomerular filtration rates. After adjusting the multivariate regression analysis with established CS risk factors, miR-21-5p and miR-320a-3p levels above median at 0 h were independently associated with 90-day all-cause mortality (adjusted hazard ratio 1.8 (95% confidence interval 1.1–3.0), p = 0.018; adjusted hazard ratio 1.9 (95% confidence interval 1.2–3.2), p = 0.009, respectively). In conclusion, circulating plasma levels of miR-21-5p, miR-122-5p, and miR-320a-3p at baseline were all elevated in nonsurvivors of CS and associated with markers of hypoperfusion. Above median levels of miR-21-5p and miR-320a-3p at baseline appear to independently predict 90-day all-cause mortality. This indicates the potential of miRNAs as biomarkers for risk assessment in cardiogenic shock.

Introduction: Hypertension is a major and modifiable risk factor for cardiovascular diseases. Essential, primary, or idiopathic hypertension accounts for 90–95% of all cases. Identifying novel biomarkers specific to essential hypertension may help in understanding pathophysiological pathways and developing personalized treatments. We tested whether the integration of circulating microRNAs (miRNAs) and clinical risk factors via machine learning modeling may provide useful information and novel tools for essential hypertension diagnosis and management. Materials and methods: In total, 174 participants were enrolled in the present observational case–control study, among which, there were 89 patients with essential hypertension and 85 controls. A discovery phase was conducted using small RNA sequencing in whole blood samples obtained from age- and sex-matched hypertension patients (n = 30) and controls (n = 30). A validation phase using RT-qPCR involved the remaining 114 participants. For machine learning, 170 participants with complete data were used to generate and evaluate the classification model. Results: Small RNA sequencing identified seven miRNAs downregulated in hypertensive patients as compared with controls in the discovery group, of which six were confirmed with RT-qPCR. In the validation group, miR-210-3p/361-3p/362-5p/378a-5p/501-5p were also downregulated in hypertensive patients. A machine learning support vector machine (SVM) model including clinical risk factors (sex, BMI, alcohol use, current smoker, and hypertension family history), miR-361-3p, and miR-501-5p was able to classify hypertension patients in a test dataset with an AUC of 0.90, a balanced accuracy of 0.87, a sensitivity of 0.83, and a specificity of 0.91. While five miRNAs exhibited substantial downregulation in hypertension patients, only miR-361-3p and miR-501-5p, alongside clinical risk factors, were consistently chosen in at least eight out of ten sub-training sets within the SVM model. Conclusions: This study highlights the potential significance of miRNA-based biomarkers in deepening our understanding of hypertension’s pathophysiology and in personalizing treatment strategies. The strong performance of the SVM model highlights its potential as a valuable asset for diagnosing and managing essential hypertension. The model remains to be extensively validated in independent patient cohorts before evaluating its added value in a clinical setting.

Finding novel biomarkers for Parkinson’s disease (PD) is crucial for early disease diagnosis, severity assessment and identifying novel disease-modifying drug targets. Our study aimed at investigating the GATA3 mRNA levels in whole blood samples of idiopathic PD (iPD) patients with different disease severities as a biomarker for iPD. The present study is a cross-sectional, case-control study, with samples obtained from the Luxembourg Parkinson’s cohort (LuxPARK). iPD (N = 319) patients, along with age-matched controls without PD (non-PD; N = 319) were included in this study. Blood GATA3 mRNA expression was measured using quantitative reverse transcription PCR (RT-qPCR) assays. The capacity of GATA3 expression levels to establish the diagnosis of iPD (primary end-point) and assess disease severity (secondary end-point) was determined. The blood levels of GATA3 were significantly lower in iPD patients, compared to non-PD controls (p ≤ 0.001). Logistic regression models showed a significant association of GATA3 expression with iPD diagnosis after adjustment for the confounders (p = 0.005). Moreover, the addition of GATA3 expression to a baseline clinical model improved its iPD diagnosis capacity (p = 0.005). There was a significant association of GATA3 expression levels with the overall disease severity (p = 0.002), non-motor experiences of daily living (nm-EDL; p = 0.003) and sleep disturbances (p = 0.01). Our results suggest that GATA3 expression measured in blood may serve as a novel biomarker and may help in the diagnosis of iPD and assessment of disease severity.