What is the clinical evidence from this study?

Study design: Cohort. Population: Coronary Artery Disease (n=2859). Intervention: Proteomics score vs. Per standard deviation lower score. Primary outcome: All-cause mortality (HR 2.37, 95% CI 2.17-2.59, p=<0.001).

What does this research mean for the field?

A higher LASSO-derived proteomics score is independently associated with an increased risk of all-cause mortality among individuals with coronary artery disease. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

November 8, 2025American Journal of Preventive CardiologyOpen Access

Proteomic predictors of mortality among people with Coronary Artery Disease

Q: What are the key findings of this study?

Each standard deviation increase in a LASSO-derived proteomics score was associated with a significantly higher risk of all-cause mortality (HR 2.37) among patients with coronary artery disease.

Q: What does this research mean for the field?

A higher LASSO-derived proteomics score is independently associated with an increased risk of all-cause mortality among individuals with coronary artery disease. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

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Key result

Higher proteomic score linked to ~2.4-fold higher all-cause mortality per SD in CAD.

HR 2.37
95% CI 2.17-2.59
P<0.001
n=2,859

Why the study?

Coronary artery disease poses a major global health challenge, creating a crucial need to enhance prognostic accuracy and improve mortality outcomes through molecular profiling.

Does a plasma proteomics score predict all-cause mortality in patients with coronary artery disease?

Study Design

Type

Cohort (n=2,859)

Multicenter

Yes

Structured PICO

Does a plasma proteomics score predict all-cause mortality in patients with coronary artery disease?

Population

2,859 participants with coronary artery disease from the UK Biobank and MIPS cohorts followed for a median of up to 14.1 years to evaluate proteomic predictors of mortality.

Exposure

Plasma proteomic profiling (specifically a proteomics score derived from 17 LASSO-selected proteins including GDF15 and ANGPT2)

Outcome

All-cause mortality at median 14.1 years (UKB) and 6.3 years (MIPS) follow-uphard clinical

Main Result

Hazard Ratio: 2.37 (95% CI 2.17–2.59)

p-value: p=<0.001

A proteomics score based on 17 circulating proteins significantly predicts long-term all-cause mortality in patients with coronary artery disease and improves predictive capability beyond traditional clinical risk factors.

Limitations

Residual and unmeasured confounding due to observational design and incomplete capture of variables like medication dose and CAD severity.
Relatively small number of participants and mortality events in the MIPS validation cohort.
UK Biobank cohort is predominantly White and healthier than the general population, which may limit generalizability outside the US and UK.
Predominant focus on mortality outcomes may not fully capture other long-term complications associated with CAD.
Proteomic profiling was based on a single baseline snapshot without serial measurements to capture dynamic changes.
Residual and unmeasured confounding
Potentially relevant variables were not harmonized or were incompletely captured across cohorts (e.g., medication dose, CAD severity, and cardiac function)
UKB baseline medications are self-reported without standardized dose or intensity
Relatively small number of participants in the MIPS cohort

Implication

May support proteomics-based risk stratification in CAD; leaves open whether scores should guide management or improve outcomes.

Abstract

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: ). 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.

Discussion

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Figures from the paper

Published by American Journal of Preventive Cardiology · CC BY-NC

Cite This Study

Liu et al. (2025) conducted a cohort in Coronary Artery Disease (n=2,859). Proteomics score vs. Per standard deviation lower score was evaluated on All-cause mortality (HR 2.37, 95% CI 2.17-2.59, p=<0.001). Each standard deviation increase in a LASSO-derived proteomics score was associated with a significantly higher risk of all-cause mortality (HR 2.37) among patients with coronary artery disease.

synapsesocial.com/papers/6a086fa8ef79633196e8b7c2 https://doi.org/10.1016/j.ajpc.2025.101347

Topics

Coronary artery disease

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Implication

May support proteomics-based risk stratification in CAD; leaves open whether scores should guide management or improve outcomes.

Proteomic predictors of mortality among people with Coronary Artery Disease

Study Design

Structured PICO

Main Result

Limitations

Implication

Abstract

Discussion

Figures from the paper

Cite This Study

Topics

Proteomic predictors of mortality among people with Coronary Artery Disease

Study Design

Structured PICO

Main Result

Limitations

Implication

Abstract

Discussion

Figures from the paper

Cite This Study

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Topics

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