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.
Abstract

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