C-X-C Motif Chemokine Ligand 16 Is a Potent Predictor of Outcomes in Dialysis Patients

Abstract

Introduction: C-X-C motif chemokine ligand 16 (CXCL16) is an inflammatory marker that has been found to be predictive of outcomes in patients with cardiovascular disease. Our previous work has also demonstrated its relation to cardiac injury in dialysis patients. However, it is yet unclear whether there is an association between CXCL16 and adverse outcomes in dialysis patients. We aimed to evaluate its prognostic value along with several traditional inflammatory markers in the current study. Methods: This is a multicenter longitudinal study of prevalent dialysis patients. Circulating inflammatory markers including CXCL16, C-reactive protein (CRP), tumor necrosis factor-α, and interleukin-6 (IL-6) were measured using a multiplex assay. The primary outcomes were all-cause mortality and a composite of major adverse cardiovascular events (MACEs). The associations between biomarkers and outcomes were analyzed using Cox proportional hazards regression models. Results: Of the 366 participants with available plasma samples, the average age was 52.5 (±12.1) years, and there were 160 (43.7%) female participants. For all-cause mortality, logarithmically transformed CXCL16, IL-6, and CRP were independent predictors after adjustment for covariates. When the 3 markers were included in the same model, CXCL16 was the only one remaining its significance. For MACEs, logarithmically transformed CXCL16 and IL-6 were significant predictors when analyzed separately and CXCL16 was an independent predictor even after adjustment for IL-6. When the biomarkers were analyzed as categorical variables, only CXCL16 was associated with both outcomes. Adding CXCL16 to established risk factors improved risk prediction as revealed by Net Reclassification Index (NRI). Conclusion: Using a multimarker approach, we determined that CXCL16 is a potent predictor of all-cause mortality and cardiovascular events in dialysis patients. Our data suggest CXCL16 may improve risk stratification and could be a potential interventional target.