Multimarker proteomic profiling for the prediction of cardiovascular mortality in patients with chronic heart failure

Abstract

Risk stratification of patients with systolic chronic heart failure (HF) is critical to better identify those who may benefit from invasive therapeutic strategies such as cardiac transplantation. Proteomics has been used to provide prognostic information in various diseases. Our aim was to investigate the potential value of plasma proteomic profiling for risk stratification in HF. A proteomic profiling using surface enhanced laser desorption ionization - time of flight - mass spectrometry was performed in a case/control discovery population of 198 patients with systolic HF (left ventricular ejection fraction <45%): 99 patients who died from cardiovascular cause within 3 years and 99 patients alive at 3 years. Proteomic scores predicting cardiovascular death were developed using 3 regression methods: support vector machine, sparse partial least square discriminant analysis, and lasso logistic regression. Forty two ion m/z peaks were differentially intense between cases and controls in the discovery population and were used to develop proteomic scores. In the validation population, score levels were higher in patients who subsequently died within 3 years. Similar areas under the curves (0.66 - 0.68) were observed for the 3 methods. After adjustment on confounders, proteomic scores remained significantly associated with cardiovascular mortality. Use of the proteomic scores allowed a significant improvement in discrimination of HF patients as determined by integrated discrimination improvement and net reclassification improvement indexes. In conclusion, proteomic analysis of plasma proteins may help to improve risk prediction in HF patients.

Fig 1. Flow chart of the study.

Overview of the study design and analyses performed to build the proteomic scores and to test their validation. A Bonferroni correction was applied on the ion m/z peaks detected by SELDI-TOF analysis. Three different statistical regression methods (SVM, sPLS-DA and LASSO) were used to build the scores with the 42 differentially intense ion m/z peaks. The performance of the proteomic scores was then tested. LVEF: left ventricular ejection fraction, CPLL: Combinatorial peptide ligand library, SELDI-TOF-MS: Surface enhanced laser desorption ionization—time of flight—mass spectrometry.

Fig 2. Proteomic score values and ROC curves in the discovery population.

A: Three different regression methods (SVM, sPLS-DA and LASSO) were applied on the 42 ion m/z peaks differentially intense between cases and controls to calculate proteomic scores. The dark line inside the box plot indicates the median value whereas the extremities represent 75^th and 25^th percentiles. The whiskers above and below the dotted lines represent the maximum and minimum values except for outliers (either ≥ 1.5 times above the 3^rd quartile or ≤ 1.5 times below the 1^st quartile) that are represented by circles. B: ROC curves for performance of the proteomic scores. AUC indicates area under the curve.

Fig 3. Proteomic score values and ROC curves in the validation population.

A: The same regression methods (SVM, sPLS-DA and LASSO) were applied on the same 42 ion m/z peaks to calculate proteomic scores in the validation population. The dark line inside the box plot indicates the median value whereas the extremities represent 75^th and 25^th percentiles. The whiskers above and below the dotted lines represent the maximum and minimum values except for outliers (either ≥ 1.5 times above the 3^rd quartile or ≤ 1.5 times below the 1^st quartile) that are represented by circles. B: ROC curves for performance of the proteomic scores. AUC indicates area under the curve.

Fig 4. Independent predictors of cardiovascular death in the validation study.

Data are odds ratios and 95% confidence intervals.