Early detection and proper management of chronic kidney disease (CKD) can delay progression to end-stage kidney disease. We applied metabolomics to discover novel biomarkers to predict the risk of deterioration in patients with different causes of CKD. We enrolled non-dialytic diabetic nephropathy (DMN, n = 124), hypertensive nephropathy (HTN, n = 118), and polycystic kidney disease (PKD, n = 124) patients from the KNOW-CKD cohort. Within each disease subgroup, subjects were categorized as progressors (P) or non-progressors (NP) based on the median eGFR slope. P and NP pairs were randomly selected after matching for age, sex, and baseline eGFR. Targeted metabolomics was performed to quantify 188 metabolites in the baseline serum samples. We selected ten progression-related biomarkers for DMN and nine biomarkers each for HTN and PKD. Clinical parameters showed good ability to predict DMN (AUC 0.734); however, this tendency was not evident for HTN (AUC 0.659) or PKD (AUC 0.560). Models constructed with selected metabolites and clinical parameters had better ability to predict CKD progression than clinical parameters only. When selected metabolites were used in combination with clinical indicators, random forest prediction models for CKD progression were constructed with AUCs of 0.826, 0.872, and 0.834 for DMN, HTN, and PKD, respectively. Select novel metabolites identified in this study can help identify high-risk CKD patients who may benefit from more aggressive medical treatment.

While a complex network of cellular and molecular events is known to be involved in the pathophysiological mechanism of chronic kidney disease (CKD), the divergence point between reversal and progression and the event that triggers CKD progression are still unknown. To understand the different mechanisms between reversible and irreversible kidney disease and to search for urinary biomarkers that can predict prognosis, a metabolomic analysis was applied to compare acute and chronic experimental glomerulonephritis (GN) models. Four metabolites, namely, epoxyoctadecenoic acid (EpOME), epoxyeicosatetraenoic acid (EpETE), α-linolenic acid (ALA), and hydroxyretinoic acid, were identified as predictive markers after comparing the chronic nephritis model with acute nephritis and control groups (false discovery rate adjusted p-value (q-value) < 0.05). Renal mRNA expression of cytochrome P450 and epoxide hydrolase was also identified as being involved in the production of epoxide metabolites from these polyunsaturated fatty acids (p < 0.05). These results suggested that the progression of chronic kidney disease is associated with abnormally activated epoxide hydrolase, leading to an increase in EpOME and EpETE as pro-inflammatory eicosanoids.