Identification of noval diagnostic biomarker for HFpEF based on proteomics and machine learning.

Background: Heart failure with preserved ejection fraction (HFpEF) is a complex syndrome that currently lacks effective biomarkers for early diagnosis and treatment. This study seeks to identify new potential biomarkers for HFpEF using proteomics and machine learning.

Methods: Plasma samples were collected from 20 patients newly diagnosed age, sex, BMI matched HFpEF and 20 healthy controls (HCs). Proteomic analysis was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-independent acquisition mode. Differentially expressed proteins (DEPs) were identified and analyzed through enrichment analyses and protein-protein interaction (PPI) network construction. Machine learning methods, including LASSO regression and the Boruta algorithm were used to select candidate biomarkers. The diagnostic value of these proteins was assessed using receiver operating characteristic (ROC) curves and nomogram construction. Expression of candidate proteins was analyzed in immune cells and tissues. Finally, enzyme-linked immunosorbent assay (ELISA) was used to validate the plasma levels of selected proteins.

Results: A total of 34 DEPs were identified between HFpEF patients and HCs. Enrichment analyses revealed involvement in acute-phase response and immune pathways. PPI network analysis identified nine hub proteins. Machine learning methods narrowed the candidates to four potential biomarkers: SERPINA1, AFM, SERPINA3, and ITIH4. Among these, SERPINA3 showed the highest diagnostic value with an area under the ROC curve (AUC) of 0.835. ELISA validation confirmed that plasma SERPINA3 levels were significantly elevated in HFpEF patients compared to HCs (p < 0.0001).

Conclusions: Our findings suggest that SERPINA3 could serve as a biomarker for HFpEF, Elevated plasma levels of SERPINA3 in HFpEF patients suggest its utility in early diagnosis and may provide insights into the disease's pathogenesis.