Biomarker levels differ between HFpEF and HFrEF patients
Biomarker Profiles in Heart Failure Patients With Preserved and Reduced Ejection Fraction
Background
The fact that effective treatment options for heart failure with preserved ejection fraction (HFpEF) are lacking, highlights the poor understanding of the pathophysiology of this condition [1-4]. Differences in patient-specific biomarker profiles between HFpEF and heart failure with reduced ejection fraction (HFrEF) may provide insights to the differences in their pathophysiology and help develop effective treatment strategies for HFpEF [5].
In this study, levels of 33 biomarkers, patterns of correlation and the predictive value of these biomarkers were assessed in 460 patients with HFpEF or HFrEF at discharge after hospitalization for acute heart failure. Chosen biomarkers reflected different pathophysiological domains (inflammation, oxidative stress, remodelling, cardiac stretch, angiogenesis, arteriosclerosis, renal function).
Main results
- Levels of high-sensitive c-reactive protein (hs-CRP) were higher in HFpEF patients (3.6 mg/L vs 2.1 mg/L; P=0.001) and levels of pentraxin-3 were higher in HFrEF patients (3.9 ng/mL vs 3.2 ng/mL; P=0.009).
- Levels of cardiac stretch markers NT-proBNP (2988 pg/mL vs 1948 pg/mL; P<0.001) and proANP (21.9 pg/mL vs 17.0 pg/mL) were higher in HFrEF patients.
- The angiogenesis-specific marker VEGFR was higher in HFrEF patients (0.8 ng/mL vs 0.7 ng/mL; P=0.009).
- Results from the correlation analysis and associated heatmaps reveal that correlations between biomarkers in HFpEF patients are more associated with remodelling and inflammation, while in HFrEF patients angiogenesis is a more prominent feature.
- Network analysis further showed myeloperoxidase to be involved in many interactions in both HFrEF and HFpEF patients.
- Renal marker neutrophil gelatinase-associated lipocalin (NGAL) and blood urea nitrogen, as well as inflammation marker receptor of advanced glycation end-products (RAGE), were involved in biomarker associations in HFpEF patients.
- When examining the exclusive interactions between biomarkers, HFpEF patients revealed interactions, which were mainly associated with inflammation (interleukin-6; pentraxin-3; corrected P-value for difference <0.05).
- HFrEF showed exclusive interactions that were NT-proBNP mediated, indicating that biomarker interactions are more associated with cardiac stretch in HFrEF.
- Of the total cohort, 41% reached the clinical endpoint of death and/or heart failure rehospitalisation after 18 months (41% HFrEF vs 44.8% HFpEF, P=0.659). NT-proBNP was found to be equally predictive for the endpoint in HFrEF and HFpEF patients.
- A significant interaction in both univariable and multivariable analysis was found for heart failure status and neuropilin as well as osteopontin (both P<0.05). Both biomarkers were found only to be predictive in HFpEF patients.
- Osteopontin was predictive for heart failure re-hospitalisations (P=0.007) and all-cause mortality (P=0.031), but not in HFrEF patients.
- Neuropilin was predictive for all-cause mortality in both HFrEF (P=0.003) and HFpEF patients (P=0.023), as well as for heart failure re-hospitalisations in HFpEF patients (P=0.026).
Conclusion
Biomarker levels differ in HFpEF and HFrEF patients, mainly in the domains of cardiac stretch and inflammation. The angiogenesis marker neuropilin and remodeling marker osteopontin were found to only hold predictive value in HFpEF. The difference in biomarker levels may reflect the different pathophysiology of HFpEF and HFrEF.
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