Clinical metabolomics characteristics of diabetic kidney disease: A meta‐analysis of 1875 cases with diabetic kidney disease and 4503 controls

• Published in: Diabetes/metabolism research and reviews Vol. 40; no. 3; pp. e3789 - n/a
• Main Authors: Yuan, Yu, Huang, Liping, Yu, Lulu, Yan, Xingxu, Chen, Siyu, Bi, Chenghao, He, Junjie, Zhao, Yiqing, Yang, Liu, Ning, Li, Jin, Hua, Yang, Rongrong, Li, Yubo
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.03.2024
• Subjects: Aconitic acid; Amino acids; Biomarkers; Biomarkers - metabolism; Diabetes; Diabetes Mellitus; diabetic kidney disease; Diabetic Nephropathies - diagnosis; Diabetic Nephropathies - etiology; Diabetic Nephropathies - metabolism; Diabetic nephropathy; Drug therapy; Glycolic acid; Humans; Kidney diseases; Kidney Failure, Chronic; Lipid metabolism; Meta-analysis; Metabolic pathways; Metabolism; Metabolites; Metabolome; Metabolomics; Metabolomics - methods; Sensitivity analysis; systematic review; Uracil; diabetic kidney disease; meta-analysis; systematic review; metabolomics; biomarkers
• ISSN: 1520-7552; 1520-7560
• DOI: 10.1002/dmrr.3789

Aims Diabetic Kidney Disease (DKD), one of the major complications of diabetes, is also a major cause of end‐stage renal disease. Metabolomics can provide a unique metabolic profile of the disease and thus predict or diagnose the development of the disease. Therefore, this study summarises a more comprehensive set of clinical biomarkers related to DKD to identify functional metabolites significantly associated with the development of DKD and reveal their driving mechanisms for DKD. Materials and Methods We searched PubMed, Embase, the Cochrane Library and Web of Science databases through October 2022. A meta‐analysis was conducted on untargeted or targeted metabolomics research data based on the strategy of standardized mean differences and the process of ratio of means as the effect size, respectively. We compared the changes in metabolite levels between the DKD patients and the controls and explored the source of heterogeneity through subgroup analyses, sensitivity analysis and meta‐regression analysis. Results The 34 clinical‐based metabolomics studies clarified the differential metabolites between DKD and controls, containing 4503 control subjects and 1875 patients with DKD. The results showed that a total of 60 common differential metabolites were found in both meta‐analyses, of which 5 metabolites (p < 0.05) were identified as essential metabolites. Compared with the control group, metabolites glycine, aconitic acid, glycolic acid and uracil decreased significantly in DKD patients; cysteine was significantly higher. This indicates that amino acid metabolism, lipid metabolism and pyrimidine metabolism in DKD patients are disordered. Conclusions We have identified 5 metabolites and metabolic pathways related to DKD which can serve as biomarkers or targets for disease prevention and drug therapy.