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Investigation of chronic kidney disease related biomarkers in association with clinical characteristics and outcomes in a large prospective CKD cohort
[Thesis]. Manchester, UK: The University of Manchester; 2017.
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Abstract
Chronic Kidney Disease (CKD) is common and is associated with increased risk of progression to end stage renal disease, cardiovascular disease and death. CKD is a heterogeneous condition and accurately predicting an individual’s risk for adverse outcomes remains a challenge. Over the past decade there has been a focus on the identification of novel biomarkers that may help improve risk stratification and the prediction of clinical endpoints in this population.The overall aim of this research project was to investigate a series of novel biomarkers in patients from the Chronic Renal Insufficiency Standards Implementation Study (CRISIS), a prospective observational study of outcome in all cause non-dialysis dependent CKD 3-5. The biomarkers selected for this project were Anti-Apolipoprotein A-1 (Anti-apoA-1 IgG), fetuin-A, fibroblast growth factor-23 (FGF23), high sensitivity cardiac troponin T (HS-cTnT), kidney injury molecule-1 (KIM-1), N-terminal pro-brain natriuretic peptide (NT-proBNP), neutrophil gelatinase associated lipocalin (NGAL) and osteoprotegerin (OPG). These biomarkers were chosen to address the three clinical endpoints of progression, cardiovascular disease and death with biomarkers considered both individually and as groups of related markers.The first aim of this project was to examine associations between the novel biomarkers and the clinical characteristics of the CRISIS population. The second aim was to investigate the associations between novel biomarkers and the study endpoints. In the case of FGF23 longitudinal measurements were analysed and in all other cases associations between baseline levels of the markers and clinical outcomes were considered. The third aim was to consider whether the biomarkers investigated in this project actually improve parameters of risk stratification and model discrimination, thereby demonstrating a potential to improve the prediction of outcome events in the CKD population.Many of the biomarkers were independently associated with one or all of the clinical outcomes considered. Despite these associations, it was more difficult to demonstrate clear improvement in risk classification or the prediction of clinical endpoints. Baseline models of standard biochemical and clinical parameters performed very well so even biomarkers that were strongly associated with clinical outcomes resulted in only small incremental improvements in the prediction of outcome events. It is now important to focus on defining how biomarkers may fit into clinical decision pathways.