Premium
Circulating CXCL‐9, ‐10 and ‐11 Levels Improve the Discrimination of Risk Prediction Models for Left Ventricular Dysfunction
Author(s) -
Altara Raffaele,
Gu Yumei,
StruijkerBoudier Harry,
Staessen Jan,
Blankesteijn W Matthijs
Publication year - 2015
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.29.1_supplement.46.2
Subject(s) - subclinical infection , medicine , cardiology , biomarker , heart failure , inflammation , chemokine , biology , biochemistry
Detection of left ventricular dysfunction (LVD), a condition preceding heart failure, can be challenging. Because inflammation contributes to heart failure development, we measured serum levels of three related chemokines, CXCL‐9, ‐10 and ‐11, and NT‐proBNP in serum from subjects with LVD and controls and determined their improvement of risk prediction. Methods Subjects with subclinical (n=17) or advanced (N=14) LVD and age‐ and sex‐matched controls (n=31) were recruited from the large‐scale family‐based study on the genetic epidemiology of cardiovascular phenotypes (FLEMENGHO). Cytokine levels were determined by ELISA. Integration Discrimination Improvement (IDI) and Net Reclassification Improvement (NRI) were determined using SAS software. Results Increased serum levels of CXCL‐9, ‐10 and ‐11 were observed in the LVD groups (1.5, 1.3 and 1.8‐fold in the subclinical LVD and 2.2, 2.5 and 4‐fold in the advanced LVD group, respectively; p<0.01). NT‐proBNP was 1.5 and 1.8‐fold higher in the subclinical and advanced LVD groups; p<0.01). Adding the biomarker levels in a dichotomized way to a model already including established risk factors resulted in an improved NRI of 14.2% (P<0.01) and IDI of 155% (P<0.001) Conclusion Addition of CXCL‐9, ‐10 and ‐11 levels to established risk factors significantly improves the risk prediction models for LVD. This study underscores the importance of using a panel of biomarkers to better characterize subjects with LVD.