Zeyu Zhang1,2, Maolin Luo1,3, Yongping Lu1, Weifeng Feng4, Hongwei Wu1, Lijing Fan1, Baozhang Guan1, Yong Dai2, Donge Tang2, Xiangnan Dong1, Chen Yun5, Berthold Hocher1,5,6, Haiping Liu7, Qiang Li8, Lianghong Yin9. 1. Department of Nephrology and Blood Purification, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, 510632, China. 2. The First Affiliated Hospital of Southern University of Science and Technology, The Second Clinical Medical College of Jinan University, Shenzhen People's Hospital, Shenzhen, CN, 518020, People's Republic of China. 3. Department of Endocrinology and Metabolism, People's Hospital of Liwan District, Guangzhou, 510380, People's Republic of China. 4. Department of Traditional Chinese Medicine, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, 510632, People's Republic of China. 5. Department of Nephrology, Charité -Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany. 6. Department of Medicine Nephrology, University Medicai Centre Mannheim, Heidelberg, Germany. 7. The Second People's Hospital of Lianping County, Guangdong, 517139, People's Republic of China. 3133975229@qq.com. 8. Dongguan Hospital of Guangzhou University of Traditional Chinese Medicine, Guangdong, 523000, People's Republic of China. dgzyysnk@126.com. 9. Department of Nephrology and Blood Purification, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, 510632, China. yin-yun@126.com.
Abstract
PURPOSE: Diabetic kidney disease (DKD) is the most common complication of type 2 diabetes mellitus (T2DM), and its pathogenesis is not yet fully understood and lacks noninvasive and effective diagnostic biomarkers. In this study, we performed urine metabolomics to identify biomarkers for DKD and to clarify the potential mechanisms associated with disease progression. METHODS: We applied a liquid chromatography-mass spectrometry-based metabolomics method combined with bioinformatics analysis to investigate the urine metabolism characteristics of 79 participants, including healthy subjects (n = 20), T2DM patients (n = 20), 39 DKD patients that included 19 DKD with microalbuminuria (DKD + micro) and 20 DKD with macroalbuminuria (DKD + macro). RESULTS: Seventeen metabolites were identified between T2DM and DKD that were involved in amino acid, purine, nucleotide and primarily bile acid metabolism. Ultimately, a combined model consisting of 2 metabolites (tyramine and phenylalanylproline) was established, which had optimal diagnostic performance (area under the curve (AUC) = 0.94). We also identified 19 metabolites that were co-expressed within the DKD groups and 41 metabolites specifically expressed in the DKD + macro group. Ingenuity pathway analysis revealed three interaction networks of these 60 metabolites, involving the sirtuin signaling pathway and ferroptosis signaling pathway, as well as the downregulation of organic anion transporter 1, which may be important mechanisms that mediate the progression of DKD. CONCLUSIONS: This work reveals the metabolic alterations in T2DM and DKD, constructs a combined model to distinguish them and delivers a novel strategy for studying the underlying mechanism and treatment of DKD.
PURPOSE: Diabetic kidney disease (DKD) is the most common complication of type 2 diabetes mellitus (T2DM), and its pathogenesis is not yet fully understood and lacks noninvasive and effective diagnostic biomarkers. In this study, we performed urine metabolomics to identify biomarkers for DKD and to clarify the potential mechanisms associated with disease progression. METHODS: We applied a liquid chromatography-mass spectrometry-based metabolomics method combined with bioinformatics analysis to investigate the urine metabolism characteristics of 79 participants, including healthy subjects (n = 20), T2DM patients (n = 20), 39 DKD patients that included 19 DKD with microalbuminuria (DKD + micro) and 20 DKD with macroalbuminuria (DKD + macro). RESULTS: Seventeen metabolites were identified between T2DM and DKD that were involved in amino acid, purine, nucleotide and primarily bile acid metabolism. Ultimately, a combined model consisting of 2 metabolites (tyramine and phenylalanylproline) was established, which had optimal diagnostic performance (area under the curve (AUC) = 0.94). We also identified 19 metabolites that were co-expressed within the DKD groups and 41 metabolites specifically expressed in the DKD + macro group. Ingenuity pathway analysis revealed three interaction networks of these 60 metabolites, involving the sirtuin signaling pathway and ferroptosis signaling pathway, as well as the downregulation of organic anion transporter 1, which may be important mechanisms that mediate the progression of DKD. CONCLUSIONS: This work reveals the metabolic alterations in T2DM and DKD, constructs a combined model to distinguish them and delivers a novel strategy for studying the underlying mechanism and treatment of DKD.
Authors: Pouya Saeedi; Inga Petersohn; Paraskevi Salpea; Belma Malanda; Suvi Karuranga; Nigel Unwin; Stephen Colagiuri; Leonor Guariguata; Ayesha A Motala; Katherine Ogurtsova; Jonathan E Shaw; Dominic Bright; Rhys Williams Journal: Diabetes Res Clin Pract Date: 2019-09-10 Impact factor: 5.602
Authors: Andrzej S Krolewski; Monika A Niewczas; Jan Skupien; Tomhito Gohda; Adam Smiles; Jon H Eckfeldt; Alessandro Doria; James H Warram Journal: Diabetes Care Date: 2013-08-12 Impact factor: 19.112