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Updated: Apr 20, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Simultaneous consideration of multiple candidate protein biomarkers for long-term risk for cardiovascular events
Sharif A Halim1, Megan L Neely1, Karen S Pieper1
1From the Division of Cardiology, Department of Medicine (S.A.H., S.H.S., W.E.K., R.M.C., C.B.G., L.K.N.), Department of Biostatistics and Bioinformatics (M.L.N.), Duke Clinical Research Institute (S.A.H., M.L.N., K.S.P., S.H.S., C.B.G., L.K.N.), Duke Center for Human Genetics (S.H.S., E.R.H.), and Duke Translational Medicine Institute (R.M.C.), Duke University School of Medicine, Durham, NC.
Insights
This study identified key protein biomarkers and clinical factors that predict cardiovascular events like death and myocardial infarction (MI). Combining multiple proteins offers a powerful approach to assess cardiovascular risk more accurately.
Area of Science:
- Cardiovascular Medicine
- Biomarker Discovery
- Proteomics
Background:
- Individual protein biomarkers are linked to cardiovascular risk.
- Simultaneous assessment of multiple proteins for risk prediction is underexplored.
Purpose of the Study:
- To identify panels of protein biomarkers that independently predict cardiovascular events (death/myocardial infarction).
- To evaluate the predictive value of protein panels in conjunction with clinical variables.
Main Methods:
- Nested case-control study of 2023 patients with suspected coronary disease.
- Plasma levels of 53 biomarkers measured; penalized logistic regression (elastic net) used for model fitting.
- Three models assessed: proteins alone, proteins with retained clinical variables, and proteins with selectable clinical variables.
Main Results:
- Model 1 identified 6 key proteins associated with death/MI.
- Model 2 showed only soluble CD40 ligand remained significant when clinical variables were retained.
- Model 3 identified a panel of 6 proteins and 5 clinical variables strongly associated with death/MI.
Conclusions:
- Simultaneous assessment of multiple protein biomarkers is valuable for identifying predictive panels.
- This approach aids in the further assessment of protein biomarkers for cardiovascular risk stratification.
Background:
Although individual protein biomarkers are associated with cardiovascular risk, rarely have multiple proteins been considered simultaneously to identify which set of proteins best predicts risk.
Methods And Results:
In a nested case-control study of 273 death/myocardial infarction (MI) cases and 273 age- (within 10 years), sex-, and race-matched and event-free controls from among 2023 consecutive patients (median follow-up 2.5 years) with suspected coronary disease, plasma levels of 53 previously reported biomarkers of cardiovascular risk were determined in a core laboratory. Three penalized logistic regression models were fit using the elastic net to identify panels of proteins independently associated with death/MI: proteins alone (Model 1); proteins in a model constrained to retain clinical variables (Model 2); and proteins and clinical variables available for selection (Model 3). Model 1 identified 6 biomarkers strongly associated with death/MI: intercellular adhesion molecule-1, matrix metalloproteinase-3, N-terminal pro-B-type natriuretic peptide, interleukin-6, soluble CD40 ligand, and insulin-like growth factor binding protein-2. In Model 2, only soluble CD40 ligand remained strongly associated with death/MI when all clinical risk predictors were retained. Model 3 identified a set of 6 biomarkers (intercellular adhesion molecule-1, matrix metalloproteinase-3, N-terminal pro-B-type natriuretic peptide, interleukin-6, soluble CD40 ligand, and insulin-like growth factor binding protein-2) and 5 clinical variables (age, red-cell distribution width, diabetes mellitus, hemoglobin, and New York Heart Association class) strongly associated with death/MI.
Conclusions:
Simultaneously assessing the association between multiple putative protein biomarkers of cardiovascular risk and clinical outcomes is useful in identifying relevant biomarker panels for further assessment.
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