Visceral Fat, Not Pressure: How Adipokines May Drive HFpEF
Key Takeaways
- Sample Required: Waist-to-height ratio (clinical measurement)
- Fasting Rules: Not applicable
- Turnaround Time: Immediate
- Base Cost: Included in clinical consultation
Heart failure with preserved ejection fraction may be driven primarily by dysfunctional visceral fat and harmful adipokines—shifting screening toward waist-to-height ratio and treatment toward fat-targeting therapies, including GLP-1 agents.
What’s New
- Cardiologist Milton Packer proposes the Adipokine Hypothesis: HFpEF arises when internal fat (visceral adipose tissue) expands and undergoes a pathological shift in adipokine signaling that promotes inflammation, volume expansion, and fibrosis.
- Published in JACC and presented at ESC 2025, the framework is described by JACC editors as “bold” and “generative,” inviting validation and challenge through rigorous studies.
Fat Biology Over Pressure
- In health, adipokines protect the heart and kidneys by damping stress and inflammation and maintaining sodium/fluid balance; with excess visceral fat, this profile flips to pro-inflammatory, pro-fibrotic, volume-expanding signaling implicated in HFpEF.
- The model organizes adipokine “domains” whose imbalance explains hallmark HFpEF findings—systemic congestion, myocardial stiffness, and exercise intolerance—beyond a hypertension-centric view.
Who’s at Risk (Beyond BMI)
- BMI can miss visceral adiposity; the waist-to-height ratio (WHtR) better flags risk, with concern above 0.5 and many HFpEF patients exceeding 0.6 in cohorts cited by the framework.
- This aligns with public guidance encouraging waists under half of height, enabling earlier recognition when dyspnea is mislabeled as “just obesity.”
Treatment Implications
- Prioritize therapies that reduce fat mass and normalize adipokines; several approved HFpEF treatments may work partly via adipose biology but are underused in appropriate phenotypes.
- GLP-1 receptor agonists (e.g., semaglutide, tirzepatide) favorably influence adipokine release and have shown substantial reductions in HF hospitalization and all-cause death in contemporary analyses—mechanistically consistent with the hypothesis.
How to Measure (Simple and Scalable)
- Use WHtR at the point of care to stratify risk and phenotype candidates for adipose-targeted therapy; consider serial WHtR to track response alongside standard HFpEF metrics.
- Pair WHtR with clinical profiling (congestion, exercise capacity) to identify those most likely driven by adipokine imbalance.
Why This Matters
Historical Parallel
- Packer’s 1992 neurohormonal hypothesis transformed HFrEF management by shifting focus from mechanics to neurohormonal drivers; the adipokine framework aims to catalyze a similar shift for HFpEF.
- Editors published the new hypothesis because it “invites rigorous challenge,” signaling a field-level call to test and refine the model.
Information Gain: Adipokine Domains and Clinical Consequences
| Adipokine Domain | Healthy State | Dysfunctional State | Clinical Consequence |
|---|---|---|---|
| Anti-inflammatory | Adiponectin suppresses inflammation | Reduced adiponectin, increased TNF-α | Systemic inflammation and fibrosis |
| Volume regulation | Balanced natriuretic peptides | Aldosterone and renin activation | Fluid retention and congestion |
| Metabolic signaling | Insulin sensitivity | Insulin resistance and lipotoxicity | Myocardial stiffness and dysfunction |
FAQ
- What is the Adipokine Hypothesis?
A model positing HFpEF results from expansion and dysfunction of visceral fat that shifts adipokine signaling toward inflammation, volume expansion, and fibrosis. - Why not rely on BMI?
BMI misses visceral fat burden; WHtR > 0.5—and often > 0.6 in HFpEF—better reflects risk tied to adipokine dysfunction. - How might treatment change?
Greater emphasis on fat-targeting strategies and drugs that restore healthy adipokines (including GLP-1 RAs), alongside guideline-directed HFpEF care. - Is it proven?
It’s a generative framework; prospective trials by adiposity/adipokine phenotype are needed for validation and clinical algorithms.
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