Serum microRNAs are key predictors of long-term heart failure and cardiovascular death after myocardial infarction

Screening for Heart Failure: Biomarkers to Detect Heightened Risk in the General Population

PURPOSE OF REVIEW

Heart failure (HF) represents a growing global burden of morbidity and mortality. Identifying individuals at risk for HF development is increasingly important, particularly given the advent of disease-modifying therapies for HF as well as its major risk factors such as obesity actalnd diabetes. We aim to review the key circulating biomarkers associated with future HF which may contribute to HF risk prediction.

RECENT FINDINGS

While current guidelines recommend the use of natriuretic peptides and cardiac troponins in HF risk stratification, there are a diverse array of other emerging protein, metabolic, transcriptomic, and genomic biomarkers of future HF development. These biomarkers not only lend insight into the underlying pathophysiology of HF, which spans inflammation to cardiac fibrosis, but also offer an opportunity to further refine HF risk in addition to established biomarkers. As evolving techniques in molecular biology enable an increased understanding of the complex biologic contributions to HF pathophysiology, there is an important opportunity to construct integrated clinical and multi-omic models to best capture HF risk. Moving forward, future studies should seek to understand the contributions of sex differences, underlying comorbidity burden, and HF subtypes to an individual's HF risk. Further studies are necessary to fully define the clinical utility of biomarker screening approaches to refine HF risk assessment, as well as to link risk assessment directly to preventive strategies for HF.

Differential gene expression patterns in ST-elevation Myocardial Infarction and Non-ST-elevation Myocardial Infarction

The ST-elevation Myocardial Infarction (STEMI) and Non-ST-elevation Myocardial Infarction (NSTEMI) might occur because of coronary artery stenosis. The gene biomarkers apply to the clinical diagnosis and therapeutic decisions in Myocardial Infarction. The aim of this study was to introduce, enrich and estimate timely the blood gene profiles based on the high-throughput data for the molecular distinction of STEMI and NSTEMI. The text mining data (50 genes) annotated with DisGeNET data (144 genes) were merged with the GEO gene expression data (5 datasets) using R software. Then, the STEMI and NSTEMI networks were primarily created using the STRING server, and improved using the Cytoscape software. The high-score genes were enriched using the KEGG signaling pathways and Gene Ontology (GO). Furthermore, the genes were categorized to determine the NSTEMI and STEMI gene profiles. The time cut-off points were identified statistically by monitoring the gene profiles up to 30 days after Myocardial Infarction (MI). The gene heatmaps were clearly created for the STEMI (high-fold genes 69, low-fold genes 45) and NSTEMI (high-fold genes 68, low-fold genes 36). The STEMI and NSTEMI networks suggested the high-score gene profiles. Furthermore, the gene enrichment suggested the different biological conditions for STEMI and NSTEMI. The time cut-off points for the NSTEMI (4 genes) and STEMI (13 genes) gene profiles were established up to three days after Myocardial Infarction. The study showed the different pathophysiologic conditions for STEMI and NSTEMI. Furthermore, the high-score gene profiles are suggested to measure up to 3 days after MI to distinguish the STEMI and NSTEMI.

Circulating miRNA 122-5p Expression Predicts Mortality and Cardiovascular Events in Chronic Hemodialysis Patients: A Multicentric, Pilot, Prospective Study

BACKGROUND

Despite patients undergoing chronic hemodialysis (HD) being notoriously prone to adverse cardiovascular (CV) events, risk prediction in this population remains challenging. miRNA 122-5p, a short, non-coding RNA predominantly involved in lipid and carbohydrate metabolism, has recently been related to the onset and progression of CV disease.

METHODS

We run a pilot, multicenter, longitudinal, observational study to evaluate the clinical significance and prognostic usefulness of circulating miRNA 122-5p in a multicentric cohort of 74 individuals on maintenance HD.

RESULTS

Patients displayed lower circulating miRNA 122-5p as compared to healthy controls (p = 0.004). At correlation analyses, ALT (β = 0.333; p = 0.02), E/e' (β = 0.265; p = 0.02) and CRP (β = -0.219; p = 0.041) were independent predictors of miRNA 122-5p levels. During a median follow-up of 22 months (range of 1-24), 30 subjects (40.5%) experienced a composite endpoint of all-cause mortality and fatal/non-fatal CV events. Baseline circulating miRNA 122-5p was higher in these subjects (p = 0.01) and it predicted a significantly higher risk of endpoint occurrence (Kaplan-Meier crude HR 3.192; 95% CI 1.529-6.663; p = 0.002; Cox regression adjusted HR 1.115; 95% CI 1.009-1.232; p = 0.03).

CONCLUSIONS

Altered miRNA 122-5p levels in HD patients may reflect hepatic and CV damage and may impart important prognostic information for improving CV risk prediction in this particular setting.

Peripheral blood mononuclear cells expression of miR-200c, miR-125b, miR-27b, miR-203, and miR-155 in patients with significant or insignificant coronary artery stenosis

Coronary artery disease (CAD) is one of the principal causes of death worldwide. Among several predisposing factors, inflammation and inflammatory genes play a significant role in disease pathogenesis. Inflammatory microRNAs, small noncoding RNAs involved in regulating inflammation, are promising candidates for understanding pathogenesis of CAD and developing diagnostic biomarkers. The aim of the study was to evaluate the alteration of miR-200c, miR-125b, miR-27b, miR-203 and, miR-155 in patients suffering from coronary artery stenosis and insignificant coronary artery stenosis compared to healthy subjects. In this study we compared expressions of five inflammatory miRNAs in peripheral blood mononuclear cells (PBMCs) of 72 patients suffering significant coronary artery stenosis (CAD), 74 individuals without coronary artery disease and 30 individuals with insignificant coronary artery stenosis (ICAD). After blood collection, PBMCs were isolated and RNA was extracted. Gene expression levels were assessed by SYBR green based real-time PCR. Statistical analysis was performed using R program. Expression levels of miR-200c, miR-203, and miR-155 were lower in subjects with ICAD than that in CAD patients and subjects of the control group. MiR-125b was downregulated in CAD and ICAD groups compared to the control group. PBMC miR-27b was upregulated in the CAD group as compared to the ICAD and control groups. Receiver operating characteristic curve analysis verified potential of three miRNAs in separating subjects with ICAD from CAD patients and healthy individuals. In conclusion, this original investigation suggested that altered expression of these five miRNAs may serve as a novel diagnostic biomarker discriminating clinical presentations of coronary artery diseases.

Predictive capacity of a genetic risk score for coronary artery disease in assessing recurrences and cardiovascular mortality among patients with myocardial infarction

Aim

This study aimed to evaluate the capacity of a genetic risk score (GRS) for coronary artery disease (CAD) independent of classical cardiovascular risk factors to assess the risk of recurrence in patients with first myocardial infarction. The secondary aim was to determine the predictive value of this GRS.

Methods

We performed a meta-analysis of individual data from three studies, namely, a prospective study including 75 patients aged <55 years, a prospective study including 184 patients with a mean age of 60.5 years, and a case-control study (77 cases and 160 controls) nested in a cohort of patients with first myocardial infarction. A GRS including 12 CAD genetic variants independent of classical cardiovascular risk factors was developed. The outcome was a composite of cardiovascular mortality and recurrent acute coronary syndrome.

Results

The GRS was associated with a higher risk of recurrence [hazard ratio = 1.24; 95% confidence interval (CI): 1.04-1.47]. The inclusion of the GRS in the clinical model did not increase the model's discriminative capacity (change in C-statistic/area under the curve: 0.009; 95% CI: -0.007 to 0.025) but improved its reclassification (continuous net reclassification index: 0.29; 95% CI: 0.08-0.51).

Conclusion

The GRS for CAD, independent of classical cardiovascular risk factors, was associated with a higher risk of recurrence in patients with first myocardial infarction. The predictive capacity of this GRS identified a subgroup of high-risk patients who could benefit from intensive preventive strategies.

A year in heart failure: updates of clinical and preclinical findings

We witnessed major advances in the management of heart failure (HF) in 2022. Results of recent clinical and preclinical investigations aid preventive strategies, diagnostic efforts, and therapeutic interventions, and collectively, they hold promises for a more effective HF care for the near future. Accordingly, currently available information extends the 2021 European Society of Cardiology guidelines and provides a solid background for the introduction of improved clinical approaches in the number of HF-related cases. Elaboration on the relationships between epidemiological data and risk factors lead to better understanding of the pathophysiology of HF with reduced ejection fraction and HF with preserved ejection fraction. The clinical consequences of valvular dysfunctions are increasingly interpreted not only in their haemodynamic consequences but also in association with their pathogenetic factors and modern corrective treatment possibilities. The influence of coronavirus disease 2019 pandemic on the clinical care of HF appeared to be less intense in 2022 than before; hence, this period allowed to refine coronavirus disease 2019 management options for HF patients. Moreover, cardio-oncology emerges as a new subdiscipline providing significant improvements in clinical outcomes for oncology patients. Furthermore, the introduction of state-of-the-art molecular biologic methods, multi-omic approaches forecast improved phenotyping and precision medicine for HF. All above aspects are addressed in this article that highlights a selection of papers published in ESC Heart Failure in 2022.

MicroRNAs: diagnostic, prognostic and therapeutic role in heart failure-a review

Heart failure is a leading cause of morbidity and mortality, with relevant social and economic burden on global healthcare system. Although the development of novel diagnostic tools and the advance in therapies have deeply influenced the diagnosis and treatment of this disease, improving both prognosis and life expectancy of patients, hospitalization is still high, and mortality remains considerable. MicroRNAs are small endogenous RNA molecules that post-transcriptionally regulate gene expression in both physiological and pathological processes. In recent years, microRNA have arisen as attractive therapeutic targets in the treatment of a wide spectrum of pathologies, including heart failure. In cardiac pathology, deregulation of microRNAs expression and function is associated to adverse outcome and heart failure progression. Circulating levels of specific microRNAs have emerged as useful biomarkers for the diagnosis of heart failure or as prognostic indicators. In the present review, we summarize the state of current research on the role of miRNAs as biomarkers for diagnosis and prognosis in patients with heart failure and their use as potential therapeutic targets for this condition.

The role of serial cardiac biomarkers in prognostication and risk prediction of chronic heart failure: additional scientific insights with hemodynamic feedback

INTRODUCTION

Heart failure (HF) is considered as a chronic long-term and lethal disease and will continue to be a major public health problem. Studying (circulating) biomarkers is a promising field of research and could be the first step toward HF tailored prognostic strategies as well as understanding the response to HF drugs in CHF patients.

AREAS COVERED

In literature, there has been considerable research on elevated biomarker levels that are related to a poor prognosis for HF. Since biomarker levels change over time, it is important to study serial (repeated) biomarker measurements which may help us better understand the dynamic course of HF illness. However, the majority of research focuses predominantly on baseline values of biomarkers. Additionally, remote monitoring devices, like sensors, can be used to link hemodynamic information to freshen biomarker data in order to further ameliorate the management of HF.

EXPERT OPINION

Novel biomarkers and additional scientific insights with hemodynamic feedback strongly aid in the prognostication and risk prediction of chronic HF.

From Classic to Modern Prognostic Biomarkers in Patients with Acute Myocardial Infarction

Despite all the important advances in its diagnosis and treatment, acute myocardial infarction (AMI) is still one of the most prominent causes of morbidity and mortality worldwide. Early identification of patients at high risk of poor outcomes through the measurement of various biomarker concentrations might contribute to more accurate risk stratification and help to guide more individualized therapeutic strategies, thus improving prognoses. The aim of this article is to provide an overview of the role and applications of cardiac biomarkers in risk stratification and prognostic assessment for patients with myocardial infarction. Although there is no ideal biomarker that can provide prognostic information for risk assessment in patients with AMI, the results obtained in recent years are promising. Several novel biomarkers related to the pathophysiological processes found in patients with myocardial infarction, such as inflammation, neurohormonal activation, myocardial stress, myocardial necrosis, cardiac remodeling and vasoactive processes, have been identified; they may bring additional value for AMI prognosis when included in multi-biomarker strategies. Furthermore, the use of artificial intelligence algorithms for risk stratification and prognostic assessment in these patients may have an extremely important role in improving outcomes.