URINE METABOLITE LEVELS OF CKD PATIENTS ARE ASSOCIATED WITH ADVERSE KIDNEY OUTCOMES AND MORTALITY

Abstract Background and Aims Chronic kidney disease (CKD) affects >10% of the adult population worldwide and is associated with increased risk of kidney failure (KF) and mortality. Mechanisms underlying the variable course of disease progression are incompletely understood. This study aimed at identifying novel metabolite biomarkers of adverse kidney outcomes and overall mortality, which may offer insights into pathophysiological mechanisms. Method Using measurements of 1,487 metabolites in urine of 5,087 CKD patients enrolled in the German Chronic Kidney Disease study, we evaluated the association of urine metabolite levels with adverse events. Main endpoints include KF, a combined endpoint of KF and acute kidney injury (KF+AKI), and overall mortality. Statistical analysis was based on a discovery-replication design (ratio 2:1) and multivariable adjusted Cox regression models. We performed cause-specific hazard regression as well as subdistribution hazard analyses with death of other causes as a competing event for the endpoints KF and KF+AKI. Statistical significance was defined using a Bonferroni correction for the number of tested metabolites per stage. An association was considered replicated if effect estimates from both stages were significant and direction-consistent. Results Median follow-up time was 4 years. At time of analysis, 362 patients died, 241 experienced KF, and 382 KF+AKI. Overall, we identified 55 urine metabolites whose levels were significantly and reproducibly associated with adverse kidney outcomes and/or mortality. Cause-specific and subdistribution hazard analyses showed almost identical results. Higher levels of the amino acid C-glycosyltryptophan in urine were associated with higher risk for all three endpoints (KF: hazard ratio 1.43, 95% confidence interval [1.27;1.61], KF+AKI: 1.40 [1.27;1.55], mortality: 1.47 [1.33;1.63]). The cumulative incidence function of KF was higher for each quartile of urine C-glycosyltryptophan levels and the effect were most pronounced in the highest quartile (see Figure). The replicated metabolites belong to different biochemical classes, and those belonging to the phosphatidylcholines pathway showed enrichment. Members of this pathway contributed to the improvement of the prediction performance for KF observed when multiple metabolites were added to the well-established kidney failure risk equation by Tangri. Conclusion This comprehensive screen of the association between urine metabolite levels and adverse kidney outcomes and mortality identified and replicated 55 urine metabolites associated with adverse kidney events, potentially providing new insights into the mechanisms of kidney disease progression. The study represents a valuable resource for future experimental studies of biomarkers of CKD progression.