Plasma proteomics reveals clinical and mechanistic heterogeneity among individuals who develop coronary artery disease
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
BACKGROUND
Individuals who develop coronary artery disease (CAD) are clinically and mechanistically heterogeneous, and understanding this variation is crucial for precise risk stratification and tailored interventions. However, the molecular mechanisms that connect these two kinds of heterogeneity remain unclear, limiting progress toward biologically grounded risk stratification and targeted interventions. Here, we investigated the heterogeneity of individuals who develop CAD by leveraging plasma proteomic signatures, placed individuals along continuous metabolic gradients and revealed the molecular programs underlying these patterns, thereby linking mechanistic variation to clinical heterogeneity.
METHODS AND RESULTS
From 42,803 UK Biobank participants, including 3,713 individuals who developed CAD within 10 years (incident CAD), we first identified a 320-protein panel from 2,923 baseline proteins that improved prediction of incident CAD beyond clinical risk scores. Using reverse graph embedding, we reduced the proteomic data to two dimensions and mapped each incident case onto the resulting two-dimensional latent proteomic space. These proteomic dimensions show significant associations with cardiometabolic and kidney-related clinical markers. The patterns were replicated in the EPIC-Norfolk study. Phenome-wide Cox regression analyses further linked these proteomic dimensions to 10-year incidence rates for various diseases, including type 2 diabetes, obesity, and chronic kidney disease (CKD). Furthermore, adding the proteomic dimensions to clinical variable-based Cox regression model improved prediction of 10-year incidence of CKD and other diseases, demonstrating the value of proteomic dimensions beyond conventional clinical risk factors. Moreover, individuals with prevalent CAD (diagnosed before proteomic sampling) exhibited high, metabolically adverse dimension values, indicating that these axes capture cumulative metabolic burden. Pathway enrichment analyses implicated altered extracellular matrix organization and immune programs among the proteins contributing to the proteomic dimensions.
CONCLUSIONS
Our findings demonstrate that plasma proteomic signatures can dissect the heterogeneity of individuals who develop CAD in continuous phenotypic gradients, improve prediction of CAD and comorbidities, and map underlying biological mechanisms.
Clinical Perspective
What is New?
-
In 42,803 UK Biobank participants, baseline plasma proteomics identified a protein panel that improved prediction of incident coronary artery disease (CAD) beyond conventional clinical risk scores, including AHA PREVENT, and defined two continuous proteomic dimensions that captured clinically relevant heterogeneity among individuals who later developed coronary artery disease; these patterns were replicated in EPIC-Norfolk cohort.
-
These proteomic dimensions captured distinct cardiometabolic and kidney-related patterns, improved prediction of multiple comorbidities, including chronic kidney disease, beyond clinical risk factors, and reflected biological programs involving metabolic, immune, and extracellular matrix pathways.
What Are the Clinical Implications?
-
Plasma proteomics can serve as a biologically grounded tool to improve prediction of CAD and related comorbidities, characterize clinically relevant heterogeneity, and provide disease-relevant information beyond standard clinical risk-factor measurements.
