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Coronary Progenitor Cells and Soluble Biomarkers in Cardiovascular Prognosis after Coronary Angioplasty
Published on: January 28, 2020
Proteomic predictors of mortality among people with Coronary Artery Disease
Chang Liu1, Qin Hui1,2, Arshed A Quyyumi3
1Department of Epidemiology, Emory University Rollins School of Public Health, 1518 Clifton Road NE, Atlanta, GA 30322 USA.
Insights
This study identified proteomic markers to predict mortality in coronary artery disease (CAD) patients. A novel proteomics score, derived from these markers, significantly improved prognostic accuracy in two independent cohorts.
Area of Science:
- Cardiovascular Medicine
- Proteomics
- Biomarker Discovery
Background:
- Coronary artery disease (CAD) presents a major global health challenge, necessitating improved prognostic tools.
- Proteomics offers insights into the molecular mechanisms driving CAD progression and mortality.
- Identifying reliable protein biomarkers is crucial for enhancing CAD patient outcomes.
Purpose of the Study:
- To investigate proteomic profiles in individuals with CAD.
- To identify specific protein biomarkers associated with all-cause mortality in CAD patients.
- To develop and validate a predictive proteomics score for CAD prognosis.
Main Methods:
- Analysis of proteomic data from 2768 CAD participants in the UK Biobank (UKB) and 91 in the Mental Stress Ischemia Prognosis Study (MIPS).
- Application of Cox proportional hazards models and LASSO regression for identifying mortality-predictive proteins.
- Development of a proteomics score based on LASSO-selected proteins and pathway enrichment analysis.
Main Results:
- 341 out of 1696 tested proteins were linked to all-cause mortality in the UKB cohort.
- Seventeen proteins were selected by LASSO, with GDF15 and ANGPT2 validated in the MIPS cohort.
- A one-standard deviation increase in the proteomics score was strongly associated with increased mortality in both UKB (HR 2.37) and MIPS (HR 6.93) cohorts.
Conclusions:
- The study identified novel proteomic biomarkers for predicting mortality in CAD.
- A validated proteomics score demonstrates potential for enhancing prognostic precision in CAD management.
- Findings contribute to a deeper understanding of CAD prognosis through proteomic profiling.
Background:
Coronary artery disease (CAD) continues to pose a significant global health challenge, highlighting the crucial need to enhance prognostic accuracy and improve mortality outcomes for CAD management. Proteomics offers a nuanced perspective on the molecular intricacies underlying CAD progression. This study investigates proteomic profiles in people with CAD, aiming to identify protein markers associated with mortality outcomes.
Methods:
Utilizing the UK Biobank (UKB) and the Mental Stress Ischemia Prognosis Study (MIPS), the study analyzed proteomic data from 2768 CAD participants in the UKB and 91 CAD participants in the MIPS. Cox proportional hazards models, LASSO regression, and pathway enrichment analysis were employed to identify proteomic predictors of all-cause mortality. A proteomics score was derived using the LASSO-selected proteins.
Results:
Out of 1696 tested proteins, 341 were associated with all-cause mortality in the UKB. Seventeen proteins were selected by LASSO, with GDF15 and ANGPT2 validated in the MIPS. One standard deviation higher in the proteomics score was associated with all-cause mortality in both the UKB (HR 2.37, 95 % CI 2.17 - 2.59, p 4.04 × 10-84) and the MIPS (HR 6.93, 95 % CI 3.29 - 14.58, p 3.52 × 10-7). Mortality-associated proteins revealed enrichment in pathways related to inflammation and cellular adhesion.
Conclusion:
This study expands our understanding of CAD prognosis by uncovering potential proteomic biomarkers. The development and validation of the proteomics score in two independent CAD cohorts in the UK and the US underscore the potential utility for enhancing prognostic precision in the context of CAD.
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