Metabolomic profiles in serum and urine uncover novel biomarkers in children with nephrotic syndrome

• DOI: 10.1111/eci.13978
• Publication Date: 2023-03-01T09:38:53Z
• Authors: Lidan Hu; Li Lin; Guoping Huang; Yi Xie; Zhaoyang Peng; Fei Liu; Guannan Bai; Wei Li; Langping Gao; Yan Wang; Qiuyu Li; Haidong Fu; Jingjing Wang; Qingnan Sun; Jianhua Mao
• Keywords: Adult; Nephrotic Syndrome; Tandem Mass Spectrometry; Humans; Metabolomics; Child; 6. Clean water; Chromatography, High Pressure Liquid; Biomarkers; 3. Good health; Chromatography, Liquid

AbstractBackgroundNephrotic syndrome is common in children and adults worldwide, and steroid‐sensitive nephrotic syndrome (SSNS) accounts for 80%. Aberrant metabolism involvement in early SSNS is sparsely studied, and its pathogenesis remains unclear. Therefore, the goal of this study was to investigate the changes in initiated SSNS patients‐related metabolites through serum and urine metabolomics and discover the novel potential metabolites and metabolic pathways.MethodsSerum samples (27 SSNS and 56 controls) and urine samples (17 SSNS and 24 controls) were collected. Meanwhile, the non‐targeted analyses were performed by ultra‐high‐performance liquid chromatography‐quadrupole time of flight‐mass spectrometry (UHPLC‐QTOF‐MS) to determine the changes in SSNS. We applied the causal inference model, the DoWhy model, to assess the causal effects of several selected metabolites. An ultraperformance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS) was used to validate hits (D‐mannitol, dulcitol, D‐sorbitol, XMP, NADPH, NAD, bilirubin, and α‐KG‐like) in 41 SSNS and 43 controls. In addition, the metabolic pathways were explored.ResultsCompared to urine, the metabolism analysis of serum samples was more clearly discriminated at SSNS. 194 differential serum metabolites and five metabolic pathways were obtained in the SSNS group. Eight differential metabolites were identified by establishing the diagnostic model for SSNS, and four variables had a positive causal effect. After validation by targeted MS, except XMP, others have similar trends like the untargeted metabolic analysis.ConclusionWith untargeted metabolomics analysis and further targeted quantitative analysis, we found seven metabolites may be new biomarkers for risk prediction and early diagnosis for SSNS.