Cardiology Research

Addressing Unmet Needs in Heart Failure with Preserved Ejection Fraction: Multi-Omics Approaches to Therapeutic Discovery

Heart failure with preserved ejection fraction (HFpEF) accounts for about half of heart failure cases and is linked to aging, obesity, diabetes, and multimorbidity, yet disease-modifying therapies remain limited. A major barrier is heterogeneity: HFpEF comprises overlapping inflammatory, fibrotic, cardiometabolic, and hemodynamic/vascular endophe-notypes embedded within systemic cardiorenal and cardiohepatic cross-talk, which con-ventional metrics such as left ventricular ejection fraction (LVEF), natriuretic peptides (NPs), and standard imaging capture incompletely. In this narrative review, we synthesize clinical, mechanistic, and trial data to describe HFpEF endophenotypes and their multi-organ interactions; critically appraise why traditional diagnostic and enrollment strategies contributed to neutral outcomes in landmark trials; and survey emerging cardiovascular multi-omics studies. We then outline an integrative systems-biology framework that applies (i) within-layer analyses and cross-layer integration, (ii) network-based driver nomination and biomarker discovery, and (iii) target nomination to link molecular programs with circulating markers and candidate therapies. Finally, we discuss practical challenges in implementing multi-omics HFpEF research and highlight future directions such as artificial intelligence (AI)-enabled multi-omics integration, cross-organ profiling, and biomarker-guided, endotype-enriched platform trials. Collectively, these advances position HFpEF as a proving ground for precision cardiology, in which therapies are matched to molecularly defined disease programs rather than ejection-fraction cutoffs alone.

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Executive Impact of HFpEF

Heart Failure with Preserved Ejection Fraction (HFpEF) represents a complex, growing challenge in cardiovascular medicine. Its multifaceted nature, characterized by diverse underlying biological pathways, demands a shift towards precision approaches to improve patient outcomes and alleviate the significant economic burden.

0 Share of all HF cases

0 5-year Mortality Rate (approx.)

0 Projected US HF Costs by 2030

0 Transformative Potential

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Clinical Heterogeneity & Traditional Diagnostics

Multi-Omics & Precision Approaches

Future Directions & Challenges

HFpEF's Diverse Etiologies

Despite shared clinical features (impaired relaxation, elevated filling pressures, and exercise intolerance), patients reach HFpEF through distinct biological routes (inflammatory, metabolic, fibrotic, and vascular) [10,30,31]. This recognition is crucial for developing targeted therapies that address specific underlying mechanisms.

Major HFpEF Endophenotypes

Endophenotype	Key Features	Therapeutic Approaches
Inflammatory	Obesity, diabetes, hypertension, CKD Elevated IL-6, TNF-α, CRP Endothelial activation; NO-cGMP-PKG impairment	Anti-inflammatory approaches SGLT2i; RAAS modulation Weight loss
Fibrotic/Increased Myocardial Stiffness	Elderly hypertensive women; LVH TGF-β activation; collagen deposition Altered titin phosphorylation; elevated PICP/PIIINP, galectin-3	Anti-fibrotics RAAS/ARNI in selected patients Intensive BP control
Cardiometabolic/Obese	Obesity, IR, dyslipidemia, NAFLD Lipotoxicity; mitochondrial dysfunction Altered acylcarnitines/BCAA metabolism; low NP levels	SGLT2i; GLP-1RA Weight loss Metabolic modulation
Hemodynamic/Vascular	Older age; long-standing HTN Arterial stiffness; pulmonary hypertension; RV dysfunction Abnormal ventricular-vascular coupling	Therapies targeting vascular stiffness Pulmonary vasodilators Exercise training

Why Clinical Trials Failed

The presence of multiple overlapping endophenotypes helps explain why many HFpEF clinical trials have produced neutral or modest results [18,64]. Historically, enrollment has relied on ejection-fraction thresholds and symptoms rather than underlying biological drivers [32,65]. This led to heterogeneous trial populations that diluted therapeutic effects.

50% of all heart failure cases

Limitations of Traditional Diagnostics

Conventional tools like LVEF, natriuretic peptides (NPs), and standard imaging parameters, while cornerstones of heart-failure evaluation, provide only partial insights into HFpEF pathophysiology [10,30]. They were optimized for systolic HF and often fail to resolve HFpEF mechanisms driven by diastolic dysfunction, inflammation, and multiorgan involvement [11,18,67].

Lessons from Major HFpEF Clinical Trials

Trial & Intervention	Implications for Omics-Guided Trial Design
TOPCAT (Spironolactone)	Need biologically consistent inclusion criteria. Use omics to define inflammatory/fibrotic subphenotypes most likely to benefit.
PARAGON-HF (Sacubitril-valsartan)	Rather than EF, future trials should stratify by vascular stiffness, fibrosis, and sex-specific pathways.
EMPEROR-Preserved (Empagliflozin)	Metabolic-renal pathways cut across EF. Omics can identify metabolic endotypes with strongest SGLT2i response.
DELIVER (Dapagliflozin)	Broad EF inclusion is viable when mechanism is systemic. Future trials should apply omics to cluster metabolic vs. fibrotic vs. inflammatory responders.

Need for Phenotype-Specific Enrichment

Moving forward, enrichment strategies that incorporate molecular and phenotypic markers—including omics biomarkers and imaging- or metabolism-derived signatures—will be essential for mechanism-aligned trial design [17,32,92,93]. Such approaches would align therapeutic mechanisms with the biological substrate of disease rather than with arbitrary EF thresholds [18].

The Promise of Multi-Omics

Advances in high-throughput technologies now enable a fully integrated multi-omics characterization of complex diseases, allowing investigators to capture biology across molecular layers [12]. Genomic analyses identify DNA variants, epigenomics reveals regulatory mechanisms, transcriptomics measures RNA expression, proteomics quantifies proteins, and metabolomics profiles small-molecule intermediates. Together, these provide a systems-level framework linking genotype to phenotype.

Oncology's Multi-Omics Success

In cancer research, large-scale initiatives such as The Cancer Genome Atlas integrated genomic, transcriptomic, and proteomic data to redefine tumors based on molecular signatures rather than organ of origin [96,97]. This molecular taxonomy directly guided the development of targeted therapies—HER2 amplification leading to trastuzumab in breast cancer [98], EGFR and ALK inhibitors in lung cancer [99], and immune-checkpoint therapies guided by tumor mutational burden [100].

Emerging Omics in HFpEF

Comparable integrative efforts are now underway in cardiovascular research. Early proteomic studies have identified multi-protein circulating signatures enriched for inflammation (e.g., LCN2, U-PAR, IL-1ra, Gal-9) and extracellular-matrix remodeling (e.g., TIMP-1, MMP7, MATN2) that differentiate HFpEF from HFrEF and, in some cohorts, predict HF hospitalization and mortality [103,104]. Metabolomic analyses have identified abnormalities in fatty-acid oxidation pathways and BCAA metabolism [35,105].

Integrative Multi-Omics Pipeline for HFpEF

Heterogeneous HFpEF Patients

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Multi-Omics & Systems Integration (Genomics, Epigenomics, Transcriptomics, Proteomics, Metabolomics, Imaging-omics)

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Endophenotype Discovery (Inflammatory, Fibrotic, Metabolic Modules)

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Biomarker Panels (Diagnostic, Prognostic, Pharmacodynamic)

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Target Nomination & Drug Matching (Hub genes/proteins, Signature Reversal)

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Precision Trial Design (Biomarker-guided Enrollment)

Driver Nomination

Genetic association signals can be linked to molecular regulation by performing expression quantitative trait locus (eQTL) and protein quantitative trait locus (pQTL) colocalization [109] as well as Transcriptome-Wide Association Studies (TWAS) [110]. These analyses identify variants that influence gene or protein abundance [17] and can be cross-referenced with myocardial single-cell or spatial transcriptomic maps to assign cell-type specificity [107].

Biomarker Discovery

Molecular network analyses group co-regulated genes, proteins, or metabolites into modules—sets of features that rise and fall together because they participate in shared bio-logical programs (e.g., inflammatory, fibrotic, or metabolic signaling). The overall activity of each module can then be summarized by its eigengene or latent factor, a single quantitative score capturing the dominant expression pattern within that module [112,113].

1X Multi-omics bridges discovery to efficacy

Target Nomination & Drug Matching

Candidate therapeutic targets are ranked by integrating network centrality—the position of genes or proteins within co-expression or protein–protein interaction networks [117-119]—with genetic evidence from eQTL or TWAS analyses [12,120], as well as druggability metrics reflecting ligandability or structural tractability [121]. Signature-matching approaches identify compounds predicted to reverse disease states [122,123].

Challenges in Cohort Design

Multi-omics studies in HFpEF often rely on small, heterogeneous cohorts with inconsistent diagnostic definitions and variable comorbidity burdens. These inconsistencies limit cross-study integration. Prospective studies should pre-specify biologically informed endophenotypes (e.g., inflammatory, fibrotic, metabolic, vascular) and adopt standardized phenotyping protocols [16,17].

Causality vs. Correlation

Most multi-omics associations describe correlation rather than true cause-and-effect relationships. To strengthen causal inference, genetic approaches such as Mendelian randomization or colocalization analysis can be used to test whether genetic variants linked to a molecular trait also predict disease outcomes [128]. Experimental perturbation—using CRISPR-based editing, RNA interference, or pharmacologic modulation in organoids or ex vivo tissue—can then validate whether altering a candidate gene or pathway modifies disease-relevant phenotypes or molecular programs [129,130].

AI & Machine Learning Integration

Artificial intelligence and machine learning approaches are increasingly essential for managing the scale and complexity of multi-omics data. By integrating heterogeneous datasets—genomics, transcriptomics, proteomics, metabolomics, and imaging—AI models can uncover hidden nonlinear relationships that traditional analyses miss. Deep learning architectures and graph neural networks are particularly well suited for identifying multi-layer molecular signatures that predict disease trajectory, drug response, or adverse outcomes.

Cross-Organ Multi-Omics

HFpEF exemplifies a disorder rooted not only in myocardial dysfunction but also in multi-organ cross-talk. Future research will rely on cross-organ multi-omics profiling—integrating cardiac, renal, hepatic, adipose, and skeletal-muscle datasets—to map the bidirectional interactions that sustain disease. Systems-level integration of these datasets will illuminate how organ-specific perturbations propagate through shared molecular pathways.

Precision-Guided Clinical Trial Design

Future clinical trials in HFpEF must evolve from population-level inclusion criteria toward biologically enriched, mechanism-guided designs. Multi-omics and imaging-derived biomarkers can stratify patients into actionable endotypes—such as inflammatory, fibrotic, or metabolic HFpEF—and serve as both eligibility criteria and pharmacodynamic readouts. Adaptive and platform trial structures can further test multiple interventions in parallel, aligning treatment arms to molecular profiles.

Precision Medicine Ground Zero Proving ground for precision cardiology

Leveraging Lessons from Oncology

The transformative potential of multi-omics has been best illustrated in oncology and nephrology [94,95]. In cancer research, large-scale initiatives such as The Cancer Genome Atlas integrated genomic, transcriptomic, and proteomic data to redefine tumors based on molecular signatures rather than organ of origin [96,97].

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Implementation Roadmap

Our phased approach ensures a seamless integration of multi-omics and AI into your cardiovascular research, moving from foundational analysis to actionable clinical translation.

Phase 1: Molecular Stratification

Establish standardized phenotyping protocols and conduct deep multi-omics profiling (genomics, transcriptomics, proteomics, metabolomics) in well-characterized HFpEF cohorts to identify distinct molecular endotypes. This builds the foundational understanding of disease heterogeneity.

Phase 2: Causal Driver Identification

Apply systems-biology frameworks and AI/ML algorithms to integrate multi-omics data. This phase focuses on network-based driver nomination, linking genetic associations to molecular regulation (eQTL, pQTL, TWAS), and identifying causal pathways. Biomarker panels for diagnosis, prognosis, and pharmacodynamics are developed and validated.

Phase 3: Biomarker-Guided Clinical Trials

Design and execute precision clinical trials in HFpEF using the identified molecular endotypes as stratification criteria. Implement biomarker-guided enrollment and mechanism-matched therapeutic interventions, such as anti-fibrotics for fibrotic endotypes or metabolic modulators for cardiometabolic endotypes. This increases the probability of detecting true therapeutic effects.

Phase 4: Cross-Organ Profiling & AI Integration at Scale

Expand multi-omics profiling to include cross-organ datasets (cardiac, renal, hepatic, adipose) to capture the systemic nature of HFpEF. Leverage advanced AI/ML for multi-omics integration and predictive modeling of disease trajectories and drug responses. This step refines target identification and drug matching.

Phase 5: Establish FAIR Data Ecosystems & Collaborative Infrastructure

Promote data standardization, interoperability, and equitable access by depositing datasets in public repositories (FAIR principles). Foster multi-center consortia and implement secure, privacy-preserving federated learning frameworks to scale research, validate findings, and ensure generalizability across diverse populations.

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Unlock the full potential of precision cardiology for Heart Failure with Preserved Ejection Fraction. Schedule a strategy session with our experts to discuss how multi-omics and AI can accelerate your therapeutic discoveries and clinical trial success.