Chronic heart failure (CHF) is a major public health care problem with a prevalence estimated at 1 to 2% of the adult population in developed countries. Although there have been important therapeutic improvements, the mortality of CHF remains high especially for the more severe forms of the disease. Risk stratification is an integral part in CHF management; indeed, high-risk patients can therefore be considered for implantable assist devices and/or cardiac transplantation. The aim of the present study is to find new circulating biomarkers that are associated with early cardiac mortality of CHF patients and that would carry additional information on top of “classic” extensive patient evaluation including NYHA class, left and right ventricular function, radionuclide angiography, BNP, and variables obtained during exercise testing (peak VO2).
CHF patients referred for evaluation of systolic HF have been included in Cardiology hospital in Lille. For the discovery phase, we designed a nested case/control test population including 99 stable CHF patients who died from cardiac causes within 3 years after blood sampling versus 99 stable CHF patients who had no event during the 3 years after sampling. For the validation phase, the proteomic analysis was repeated in an independent population of 344 consecutive HF patients including 266 patients with no event compared to 43 patients with cardiovascular death during the 3-year follow-up period.
We found 42 peaks with significant different intensity after Bonferroni’s correction (p=0.05/203=2.5 x 10-4) between cases and controls. We used these 42 SELDI peaks to build prognostic scores using 3 different statistical regression methods: the Support vector machine (SVM), the sparse partial least square discriminant analysis (sPLS-DA) technique and a lasso logistic regression (LASSO). The values of the proteomic scores were significantly higher in cases as compared to controls in the discovery population and they were validated in the validation population.
WP1 is the “Discovery of new biomarkers for predicting early cardiac mortality in HF patients”. We plan to purify 13 different m/z peaks including in the proteomic scores in both population using different biochemical techniques available in the laboratory to identify the corresponding proteins. WP2 is the“ Development of biomarkers based tools to validate the identified proteins in CHF patients”. For each protein, the best tool will be selected based on the specific affinity of the protein (western blot, ELISA, quantitative mass spectrometry) for the quantification and validation first in the test population and second in the population of validation. It is difficult to anticipate the post-translational modifications of the proteins identified. Depending on the identification of the proteins, we may need to develop a dedicated tool, if not available. The goal here is to develop biomarkers based tools easy to use in clinical practice to improve patient’s care in heart failure. WP3 is the “Development of models for prediction of risk of early death in CHF patients”. The predictive value of the potential biomarker(s) in addition to well-established risk factors (NYHA class, left ventricular ejection fraction, BNP, peak VO2) will be tested for a better categorization of HF patients using c-statistics. The overall statistical power (area under the ROC curve) will be assessed to determine the specificity and sensitivity. The incremental prognosis value of the biomarker(s) alone or by combination to predict the cardiovascular mortality, when added to models with established predictors of mortality will be estimated with the continuous net reclassification index (NRI) and the integrated discrimination improvement (IDI) in those populations.
Madame Florence PINET (U1167)
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
ES CHRU de Lille
Help of the ANR 224,025 euros
Beginning and duration of the scientific project: September 2015 - 24 Months