Serum proteomic profiling using data-independent acquisition mass spectrometry for familial hypercholesterolemia: a comparative study in a korean population

Background Familial hypercholesterolemia (FH) is a hereditary disorder characterized by elevated low-density lipoprotein cholesterol (LDL-C) levels, a strong family history, and increased cardiovascular disease (CVD) risk. Diagnosis relies on clinical criteria such as the Dutch Lipid Clinic Network or Simon Broome criteria, supplemented by genetic testing. However, genetic testing remains costly and time-consuming, and some FH patients lack known genetic mutations. Alternative diagnostic approaches are needed for early detection. This study investigates serum proteomic differences between FH patients and healthy individuals using advanced proteomic analysis. Purpose To identify differentially expressed proteins (DEPs) in FH patients compared to healthy controls, and assess their potential as biomarkers for FH risk stratification. Methods Serum samples from six FH patients and six healthy controls were obtained from a national registry. Extracellular vesicles (EVs) were isolated using strong anion exchange beads and processed via an automated system. EV-associated proteins were extracted, digested with trypsin, and analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using the data-independent acquisition (DIA) method. The acquired data were quantified using the MAXLFQ algorithm with median normalization. DEPs were identified based on a false discovery rate (FDR) < 0.05 and a fold change > 1.3. Functional enrichment analysis was performed using Gene Ontology (GO) categories. (Figure 1) Results A total of 4,861 proteins were analyzed, with statistically significant DEPs identified (FDR < 0.05). Principal component analysis demonstrated distinct clustering of FH patients and controls, suggesting a unique proteomic signature in FH. GO enrichment analysis revealed downregulation of proteins associated with blood coagulation and fibrinolysis, suggesting altered hemostatic balance in FH patients. Additionally, previously identified FH risk stratification biomarkers, including leucine-rich alpha-2-glycoprotein and histidine-rich glycoprotein, showed similar expression trends, supporting their potential clinical utility. (Figure 2) Conclusion Serum proteomic profiling revealed distinct proteomic alterations in FH patients, supporting the feasibility of proteomics-based FH diagnosis and risk stratification. These findings highlight potential biomarkers for early FH detection and cardiovascular risk assessment. Larger studies are needed to validate these results and translate them into clinical practice.