Serum metabolomic profiling and incident ckd among african americans

Background and objectives Novel biomarkers that more accurately reflect kidney function and predict future CKD are needed. The human metabolome is the product of multiple physiologic or pathophysiologic processes andmay provide novel insight into disease etiology and progression. This study investigatedwhether estimated kidney functionwould be associatedwithmultiplemetabolites andwhether selectedmetabolomic factors would be independent risk factors for incident CKD. Design, setting, participants, &measurements In total, 1921 African Americans free of CKDwith amedian of 19.6 years follow-up among the Atherosclerosis Risk in Communities Study were included. A total of 204 serum metabolites quantified by untargeted gas chromatography–mass spectrometry and liquid chromatography– mass spectrometry was analyzed by both linear regression for the cross-sectional associations with eGFR (specified by the Chronic Kidney Disease Epidemiology Collaboration equation) and Cox proportional hazards model for the longitudinal associations with incident CKD. Results Forty named and 34 unnamed metabolites were found to be associated with eGFR specified by the Chronic Kidney Disease Epidemiology Collaboration equation with creatine and 3-indoxyl sulfate showing the strongest positive (2.8 ml/min per 1.73 m²per +1 SD; 95% confidence interval, 2.1 to 3.5) and negative association (214.2 ml/min per 1.73m2 per +1 SD; 95% confidence interval,217.0 to211.3), respectively. Two hundred four incident CKD events with a median follow-up time of 19.6 years were included in the survival analyses. Higher levels of 5-oxoproline (hazard ratio, 0.70; 95% confidence interval, 0.60 to 0.82) and 1,5-anhydroglucitol (hazard ratio, 0.68; 95% confidence interval, 0.58 to 0.80) were significantly related to lower risk of incident CKD, and the associations did not appreciably change when mutually adjusted. Conclusions These data identify a large number of metabolites associated with kidney function as well as two metabolites that are candidate risk factors for CKD and may provide new insights into CKD biomarker identification.