Prediction and monitoring of chronic kidney disease

Chronic kidney disease (CKD) is defined as impaired kidney function or signs of structural kidney damage, that persist for longer than 3 months and that have implications for health. CKD has a prevalence of approximately 10%, and can lead to end stage renal disease (ESRD), cardiovascular disease and mortality. Given the large impact CKD has on global health, it is necessary to identify factors that can predict whether a patient with CKD is likely to have disease progression. To this end biomarkers are often used as a predictive factor. For CKD the estimated glomerular filtration rate (kidney function) and presence of albuminuria (protein in the urine) are the most important biomarkers. Patients with severe impairment of kidney function or severe albuminuria need treatment to slow kidney function decline. However, these biomarkers have limited predictive performance. Therefore, additional biomarkers have been investigated in this thesis. We have shown that unfortunately these biomarkers do not outperform kidney function and albuminuria for the prediction of CKD progression. We advise to not focus future research on individual biomarkers, which is currently common practice, but on combinations of markers (biomarker panels) to accomplish a more accurate risk prediction for CKD progression. In clinical practice, monitoring of kidney function is performed using creatinine. This method is criticized with regards to its precision. We have shown that novel kidney function markers are not better for monitoring kidney function decline. Therefore there is currently no reason to replace creatinine by other biomarkers.