Redefining Heart Failure Subtypes

🫀 Redefining Heart Failure Subtypes According to Skeletal Muscle Mass

Recent research suggests that heart failure classification is no longer limited to cardiac function alone. Skeletal muscle mass has emerged as a key factor that can refine how we understand and categorize heart failure.

🔍 Why does muscle mass matter?

Because it is closely linked to exercise capacity, inflammation levels, treatment response, and even survival rates in heart failure patients.

📌 What’s new?

Patients can now be better stratified based on:

Skeletal muscle strength and mass

Presence of muscle wasting (sarcopenia)

The interaction between cardiac health and muscular condition

💡 Who benefits from this?

Clinicians, researchers, patients, and anyone interested in prevention through improved fitness and muscle health.

🏋️‍♂️ Bottom line:

Enhancing skeletal muscle mass may not only improve general fitness—it could reshape the way heart failure is diagnosed and managed in the future.

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Background — Traditional classification of Heart Failure

Historically, HF has been grouped broadly as a clinical syndrome: when the heart fails to pump enough blood to meet the body’s needs, due to structural and/or functional abnormalities. 

Classical clinical subtypes were defined primarily based on the left ventricular ejection fraction (LVEF). By LVEF, HF has been divided into:

Heart Failure with Reduced Ejection Fraction (HFrEF) — where LVEF is significantly reduced. 

Heart Failure with Preserved Ejection Fraction (HFpEF) — where LVEF remains normal or near-normal, but other structural or functional abnormalities exist. 

Heart Failure with Mildly Reduced Ejection Fraction (HFmrEF) — with intermediate EF (e.g. 40–50%), a “middle ground” between reduced and preserved EF. 

This classification has guided diagnostic criteria, clinical decision-making, and — importantly — treatment strategies. 

However — as often in medicine — “one size fits all” has proved too simplistic.

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Why rethinking is needed — limitations of classic subtyping

HF as a syndrome is heterogeneous. Under the umbrella of HFpEF (or even HFmrEF), patients differ widely in terms of underlying causes, comorbidities (e.g. hypertension, obesity, kidney disease), structural heart changes, response to therapy, and prognosis. 

For many patients — especially with preserved or mid-range EF — standard therapies (developed mainly for HFrEF) often show limited benefit. 

The classic EF-based categories do not necessarily capture the underlying biology, comorbid conditions, or patient risk — so two patients both labeled “HFpEF” may have very different pathophysiology and prognoses. 

These limitations have encouraged researchers to move beyond “just EF” and toward a more nuanced, biologically and clinically meaningful classification.

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A New Paradigm: Phenotyping, Clustering & “Phenomics”

Recent years have seen a growing number of studies applying clustering methods, machine learning, and deep phenotyping to stratify HF patients into more refined subtypes — based on clinical features, comorbidities, biomarkers, outcomes — not just EF.

Key findings:

In a large, population-based study using data from three major UK datasets, researchers applied multiple unsupervised machine-learning methods and identified five reproducible HF subtypes: early onset, late onset, atrial fibrillation–related, metabolic, and cardiometabolic. These subtypes differed in demographics, comorbidities, prognosis (e.g. 1-year mortality), and genetic risk associations. 

A systematic review pooling clustering/phenotyping studies found that, across many investigations, clusters correspond well to about nine recurring phenotypes — for example: young/low comorbidity; metabolic; cardiorenal; atrial fibrillation; elderly female with AF; hypertensive; ischemic-male; valvular disease; device-implanted. 

In parallel, “phenomics” — integrating biomarker panels, proteomics, and machine learning — is showing promise. A recent proteomics-based study identified protein patterns that differ between patients who go on to develop HFpEF vs HFrEF, improving subtype prediction beyond standard clinical measures. 

Experts argue that deep phenotyping — combining clinical, hemodynamic, imaging, biomarker, and “omics” data — rather than just EF-based grouping, may be the way to identify therapeutically homogeneous subgroups amenable to targeted treatments. 

Thus: the new paradigm treats HF not as a few fixed categories, but as a spectrum of overlapping syndromes (or “phenogroups”) — each with distinct causes, risks, and therapeutic responses.

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The Genetic Angle: New Insights from Genomics

Beyond clinical/phenotypic clustering, genetic research is shedding light on why HF manifests so differently across individuals — and pointing to a genetic foundation for some subtypes.

A recent large-scale genome-wide association study (GWAS) of ~1.9 million individuals (including over 150,000 with HF) identified 66 genetic loci associated with HF and its subtypes — 37 of them previously unknown. 

Using functional analyses and network mapping, researchers assigned many of these loci to likely effector genes, connecting them to biological pathways relevant to etiologic disease clusters. 

Interestingly, in a more recent study of over 2.3 million individuals, researchers found 176 common-variant risk loci linked to all-cause HF; they clustered these into five broad “modules” based on pleiotropic associations (obesity/anthropometry, blood pressure/renal, atherosclerosis/lipids, immune activity, arrhythmias) — thus genetically linking different HF risk pathways. 

On the rare-variant side (exome sequencing of ~376,000 individuals), they also uncovered significant associations between rare loss-of-function variants in classic cardiomyopathy genes (e.g. TTN, BAG3, MYBPC3) and HF — showing that some HF cases derive from inherited cardiomyopathies, while others are due to polygenic risk. 

Importantly, the common-variant “background” (polygenic load) modified HF risk even among carriers of rare pathogenic variants — hinting at complex interplay between rare and common genetic factors. 

Implication: Genetic data supports that HF is not a monolithic disease. Depending on a person’s genetic makeup (common + rare variants), comorbidities, and environment, very different forms of HF may arise — some more akin to inherited cardiomyopathy, others linked to metabolic, vascular, or systemic disorders.

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Why This Redefinition Matters — Clinical and Therapeutic Implications

Redefining HF subtypes holds real potential to improve patient care, research, and outcomes:

Better prognostication & personalized risk prediction. Subtyping beyond EF (e.g. “atrial-fibrillation–related HF,” “metabolic HF,” “early-onset HF”) allows more accurate prediction of disease course, risks (mortality, hospitalization), and likely complications. 

More tailored treatment strategies. Since different phenotypes likely have distinct underlying mechanisms (e.g. comorbidity-driven inflammation, metabolic dysregulation, myocardial structural disease, renal involvement), grouping by phenotype rather than EF could help match patients to the most effective therapies — rather than one therapy-for-all. 

Improved clinical trial design. Using refined phenotypes can improve homogeneity of trial populations, ensure that interventions are tested on the right subgroups, and avoid diluting treatment effects by lumping together biologically disparate patients. 

Insight into underlying biology and genetics. Genetic and “-omics” research can reveal pathways driving HF in different subtypes — possibly opening doors to new drug targets, preventive strategies, and precision medicine approaches. 

Better patient communication and management planning. More precise subtyping allows clinicians to explain disease better to patients: what is driving their HF, what to expect, and which interventions (lifestyle, comorbidity management, medications) are likely to help.

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Challenges & What Still Needs to Be Done

Despite progress, redefining HF subtypes is not yet a solved problem. Some of the challenges:

Heterogeneity in clustering approaches. The many studies that use clustering / machine-learning vary widely in the variables used (clinical, lab, imaging), in methodology, and in validation — making standardization difficult. 

Lack of consensus on “phenogroups.” While recurring phenotypes appear (metabolic, AF-related, elderly, cardiorenal, etc.), there is not yet a universally accepted classification system; different studies produce different clusters. 

Translation to clinical practice is limited so far. Most phenotyping studies are retrospective and observational; prospective trials using phenotype-guided therapy approaches are still rare. 

“-Omics” and genetic findings need further validation. While promising, genetic loci and proteomic biomarkers need replication, functional validation, and integration with clinical data before they can guide routine care. 

Complexity and cost. Deep phenotyping (imaging, biomarkers, genetics) is resource-intensive, which may limit applicability in many clinical settings — especially low-resource environments.

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Conclusion — Toward a More Nuanced, Personalized View of Heart Failure

The way we define and classify heart failure is shifting. From simplistic EF-based subtypes, researchers and clinicians are moving toward a spectrum model — one that recognizes HF as a cluster of overlapping yet distinct syndromes, each with its own drivers, biology, prognosis, and therapeutic needs.

Harnessing data-driven clustering, deep phenotyping, proteomics, and genomics — we may be entering an era of precision cardiology for heart failure: where diagnosis is refined, risk is predicted per individual, and therapy is tailored to the patient’s subtype.

That said, the field is still evolving. While many promising studies exist, we need consensus in classification, prospective trials, and translation of “phenomics” into everyday clinical practice.