Abstract 15927: Plasma Proteomic Profiling Identifies Multiple Biomarkers Identifying Increased Risk for Arrhythmic Death in the Prediction of ARrhythmic Events With Positron Emission Tomography (PAREPET) Study
Background: Selection of patients to receive a primary prevention ICD is solely based on LV EF. Plasma biomarkers may compliment this but most have not been evaluated in predicting arrhythmic death. We determined whether plasma proteomic profiling could afford an unbiased, discovery based approach to identify novel biomarkers to predict cause specific event rates from SCA (arrhythmic death or defibrillation for VT/VF).
Methods: Plasma samples were obtained at entry in 203 patients in the PAREPET study. Proteomic profiling was performed in a subset of 10 patients with SCA vs. 10 survivors matched for EF (26%), age (66 years) and creatinine (1.4). We used a tandem affinity depletion method (IgY14-SuperMix) to reduce high- and medium- abundance plasma proteins, followed by extensive ion current based biomarker discovery. Plasma proteins were expressed as SCA/survivors.
Results: SCA developed 1016±735 days after sampling. Stringent cutoff criteria (0.3% peptide identification FDR ; ≥2 unique peptides/protein) and a threshold change of ≥ 1.4 produced a subset of 89 proteins (of a total 468) that were differentially expressed (p<0.0001). Selected proteins and their differential expression are summarized in the Table. Both C-reactive protein and Galectin-3 (previously reported immunoassay biomarkers of cardiac mortality) were upregulated in patients with SCA. Ingenuity Pathway Analysis clustered proteins in the regulation of immune system process, inflammatory response, cell adhesion, lipid transport, regulation of cell proliferation and the response to stress.
Conclusions: Plasma proteomic profiling identifies a large number of novel candidate proteins that are differentially expressed many months prior to the development of SCA. This unbiased approach confirms two known cardiac biomarkers and identifies many others that can be tested retrospectively in larger populations as well as integrated into multiple biomarker risk models for the prediction of SCA.
- © 2013 by American Heart Association, Inc.