Prognostication of Heart Failure Evolution: From Circulating Biomarkers to Genetic Risk Predictive Score, Volume II

The first volume of this Research Topic examined circulating protein biomarkers, non-coding RNAs, genetic risk scores, and multimarker models for heart failure (HF) diagnosis, risk stratification, and prognosis. It highlighted that, beyond conventionally used biomarkers such as natriuretic peptides and high-sensitivity cardiac troponins, few candidate biomarkers have transitioned into routine clinical practice, and that ESC and AHA/ACC guidelines still diverge on which markers add clinical value. Since 2024, multiple circulating biomarker predictive models, machine-learning risk models, multi-omics profiling, high-throughput proteomics, and polygenic risk scores have rapidly advanced. Alongside new heart failure therapies, including angiotensin receptor neprilysin inhibitors (ARNI), mineralocorticoid receptor antagonists (MRA), and sodium-glucose cotransporter 2 (SGLT2) inhibitors, there is a need for advanced scores to identify vulnerable patient populations and to apply personalized treatment algorithms with therapy response prediction. Another aspect of the discussion has been the discovery of optimal multiple-biomarker models for diagnosis, risk stratification, and efficacy monitoring in both sexes, depending on HF phenotypes and comorbidity profiles. Not least, economic burden and cost-effectiveness analyses are likely to be crucial factors for biomarker implementation across different HF therapies.Volume II aims to move the field from biomarker association toward clinical implementation. We seek contributions clarifying which circulating, genetic, and multi-omic markers offer genuine incremental value for diagnosing, stratifying, and monitoring heart failure, versus those that mainly reflect general processes such as inflammation, fibrosis, or biomechanical stress. Priority will be given to work addressing accuracy, specificity, reproducibility, cost-effectiveness, serial measurement, and performance across HF phenotypes (HFpEF, HFmrEF, HFrEF), and to integration of biomarkers with imaging, genetics, and electronic health records into actionable predictive models and practically useful algorithms. Submissions should provide detailed, in-depth comparisons among models based on clinical criteria, hemodynamic features, imaging findings, and biomarkers reflecting the various facets of HF pathogenesis. Biomarkers that support progress in personalized strategies in cardiac rehabilitation and cardiac surgical approaches are also within the scope of discussion.Themes include: novel and established circulating biomarkers for HF diagnosis and prognosis; multimarker panels and biomarker-guided therapy across HF phenotypes with different comorbidity signatures; side-by-side comparison of biomarker-tailored and patient-centered HF treatment; multi-omics approaches (proteomics, metabolomics, transcriptomics); non-coding RNAs and epigenetic biomarkers; genetic and polygenic risk scores; AI and machine-learning models integrating biomarkers with imaging and clinical data; extracellular vesicles, progenitor cells, and secretome-based biomarkers; biomarker responses to SGLT2 inhibitors, ARNI, and emerging HF therapies; and natriuretic peptide refinements and ESC versus AHA/ACC guideline comparisons. Topic editors: Alexander E Berezin, Salvatore De Rosa, Daniel Petrovič Source: https://www.frontiersin.org/research-topics/84352/prognostication-of-heart-failure-evolution-from-circulating-biomarkers-to-genetic-risk-predictive-score-volume-ii