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Updated: Sep 28, 2025
Identifying Coronary Artery Calcification on Non-gated Computed Tomography Scans
Published on: August 28, 2018
Identification of Specific Coronary Artery Disease Phenotypes Implicating Differential Pathophysiologies
Jona B Krohn1,2, Y Nhi Nguyen1,2, Mohammadreza Akhavanpoor3
1Department of Cardiology, Pulmonology and Angiology, University Hospital Heidelberg, Heidelberg, Germany.
Insights
Coronary artery disease (CAD) may represent multiple conditions, not a single entity. Analysis revealed four distinct CAD phenotypes with varying risk factors and prognoses, suggesting different disease processes.
Area of Science:
- Cardiology
- Genomics
- Biochemistry
Background:
- Coronary artery disease (CAD) risk factors are known, but CAD is often viewed as a single condition.
- The potential for distinct CAD phenotypes remains underexplored.
Purpose of the Study:
- To determine if coronary angiography can identify distinct coronary artery disease (CAD) phenotypes.
- To investigate associations between these phenotypes, major risk factors, and patient prognosis.
Main Methods:
- Cluster analysis of coronary angiography reports in 4,344 patients.
- Independent validation in 3,129 patients from the LURIC study.
Main Results:
- Four distinct CAD subgroups were identified based on stenosis patterns.
- Subgroups differed in demographics, cardiovascular risk, metabolic syndrome, inflammatory markers, and acute coronary syndrome presentation.
- Significant differences in all-cause and cardiovascular mortality were observed between clusters.
Conclusions:
- Four phenotypic subgroups of coronary artery disease (CAD) were identified.
- These subgroups exhibit unique characteristics and prognoses, suggesting CAD may encompass multiple distinct disease entities.
Background And Aims:
The roles of multiple risk factors of coronary artery disease (CAD) are well established. Commonly, CAD is considered as a single disease entity. We wish to examine whether coronary angiography allows to identify distinct CAD phenotypes associated with major risk factors and differences in prognosis.
Methods:
In a cohort of 4,344 patients undergoing coronary angiography at Heidelberg University Hospital between 2014 and 2016, cluster analysis of angiographic reports identified subgroups with similar patterns of spatial distribution of high-grade stenoses. Clusters were independently confirmed in 3,129 patients from the LURIC study.
Results:
Four clusters were identified: cluster one lacking critical stenoses comprised the highest percentage of women with the lowest cardiovascular risk. Patients in cluster two exhibiting high-grade stenosis of the proximal RCA had a high prevalence of the metabolic syndrome, and showed the highest levels of inflammatory biomarkers. Cluster three with predominant proximal LAD stenosis frequently presented with acute coronary syndrome and elevated troponin levels. Cluster four with high-grade stenoses throughout had the oldest patients with the highest overall cardiovascular risk. All-cause and cardiovascular mortality differed significantly between the clusters.
Conclusions:
We identified four phenotypic subgroups of CAD bearing distinct demographic and biochemical characteristics with differences in prognosis, which may indicate multiple disease entities currently summarized as CAD.
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