The prevalence of heart failure with preserved ejection fraction (HFpEF) is increasing. This disease has posed diagnostic and therapeutic challenges; mortality and morbidity remain unacceptably high. Unbiased molecular studies are necessary to identify better diagnostic biomarkers.
Large-scale discovery proteomics with a multiplex immunoassay platform will identify novel molecular mechanisms that differentiate HFpEF from patients without heart failure (HF).
We performed proteomic analyses (Olink) of 552 proteins on 88 HFpEF cases and 88 no HF controls from individuals referred for cardiac catheterization at Duke University. HFpEF was defined as history of HF, ejection fraction >45% and diastolic dysfunction class ≥1; no-HF controls had no diastolic dysfunction. Univariate logistic regression was used to identify proteins associated with HFpEF; associated proteins at a false discovery rate <0.05 were then tested in a multivariable model adjusted for age, gender, body mass index (BMI), systolic blood pressure (SBP), diabetes (DM) and creatinine and included in a LASSO regression to create a sparse protein model. Incremental improvement in model prediction from the clinical model + NT-proBNP was assessed using change in the C-statistic. Gene Set Enrichment Analysis (GSEA) was used to identify pathways associated with HFpEF.
As expected, HFpEF cases were older and had higher BMI, SBP, creatinine and prevalence of DM (p<0.05) compared to no-HF controls. Univariate analysis identified 228 proteins associated with HFpEF; 77 remained nominally significant in the multivariable model (p<0.05). Key proteins included those involved in angiogenesis, fibrosis and inflammation. Most were more abundant in HFpEF cases than in no-HF controls, including Lysosomal integral membrane protein 2 (p=0.01), Neutrophil gelatinase-associated lipocalin (p=0.01), Urokinase plasminogen activator surface receptor (p=0.001), Serine/threonine-protein kinase receptor R3 (p=0.02) and Interleukin-1 receptor antagonist protein (p=0.002). LASSO yielded a 20-protein model; when added to the clinical model + NT-proBNP this LASSO proteomic model improved the C-statistic from 0.82 (Figure black line) to 0.92 (red line p=0.0001). GSEA revealed fatty acid metabolism and inflammatory pathways to be enriched.
Biomarkers of angiogenesis, fibrosis, fatty acid metabolism and inflammation are associated with HFpEF and improve discriminative capabilities on top of clinical factors and NT-proBNP. These findings highlight the importance of these pathways in HFpEF and identify potential novel circulating diagnostic biomarkers.
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