Untargeted Proteomic Profiling Identifies Candidate Biomarkers for Early Detection of Cardiovascular Disease and Mortality
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Background
The serum proteome can provide valuable insights into the development and progression of diseases. This is particularly important for cardiovascular disease (CVD), a leading cause of death worldwide. In this large-scale cohort study, we employ an untargeted mass-spectrometry-based approach to explore associations between highly expressed proteins, incident CVD (analysed as six individual outcomes and one composite outcome) and all-cause mortality.
Methods
The abundances of 439 proteins and protein groups quantified by mass spectrometry in serum were related to incident outcomes in 8,343 Generation Scotland participants (age 40–69 years), who were free of CVD at baseline (n all_cause_death =618, n composite_CVD =666, follow-up ≤17 years). Cox proportional hazards (PH) models were run before and after adjustment for pre-selected known CVD risk factors. Sex-specific effects were explored. A protein-based risk score for composite CVD outcome was developed using penalised regression.
Results
Forty-eight high abundance serum proteins and protein groups were significantly associated with incident CVD and death outcomes (P Bonferroni <1.14x10 - 4 ), including 24 associations not reported in the Open Targets database. Proteins involved in immune and oxidative stress responses were associated with composite CVD (Immunoglobulin heavy variable 3/OR16-9, Hazard Ratio per SD (HR)=0.85 [95%CI 0.79,0.92]) and death (Alpha-1-antitrypsin, HR=1.27 [1.17, 1.38]), while heart failure was linked to proteins playing a role in lipid metabolism (Apolipoprotein A-II, HR=0.70 [0.59, 0.84]) and complement cascade (Complement C1q subcomponent subunit B, HR=1.40 [1.18, 1.66]). Applied to the test set, the proteomic risk score improved 17-year incident CVD prediction over models including age, sex, and nine lifestyle and clinical risk factors (ΔAUC = 0.010, ROC P = 0.013).
Conclusion
The highly abundant serum proteome, readily assessed by mass spectrometry, reveals candidate biomarkers for incident CVD and provides predictive value for early risk stratification.
