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ORIGINAL RESEARCH article
Front. Mol. Biosci.
Sec. Metabolomics
Volume 12 - 2025 | doi: 10.3389/fmolb.2025.1541440
Serum Metabolic Profiling of Patients with Diabetic Kidney Disease Based on Gas Chromatography-mass Spectrometry
Provisionally accepted
Xueyan Bian 1* Chenwen Wang 2 Majie Wang 3 Ailing Yin 4 Jiayan Xu 1 Mijia Liu 1 Hui Wang 2 Yating Cao 4 Xin Huang 4
Chenxue Qin 1*
Ye Zhang 2* Heming Yu 4*- 1 Department of Neurology, Ningbo First Hospital, Ningbo, Zhejiang Province, China
- 2 China Pharmaceutical University, Nanjing, China
- 3 Ningbo Kangning Hospital, Ningbo, Zhejiang Province, China
- 4 Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China
With the increasing incidence rate of diabetic kidney disease (DKD), the diagnosis and treatment of DKD in clinic is urgent. Serum samples from 56 DKD patients and 32 healthy controls (HC) in the first affiliated hospital of Ningbo university were collected and the metabolic profiling was analyzed by untargeted metabolomics using gas chromatography-mass spectrometry. The data was analyzed by principal components analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), Pearson correlation analysis and receiver operating characteristic curve (ROC). It was found that the serum metabolic profiling of DKD patients had significantly difference compared with HC. A total of 68 potential differential metabolites have been identified, which involved in arginine biosynthesis, ascorbate and aldarate metabolism and galactose metabolism etc. Moreover, a total of 31 differential metabolites were identified between early-stage (EDG) and late-stage (LDG) DKD patients. Additionally, 30 significant metabolic differences were observed among EDG, LDG, and HC. Through Pearson correlation analysis between abundance of differential metabolites and clinical markers (estimated glomerular filtration rate (eGFR), Blood Urea Nitrogen (BUN), Serum Creatinine (SCr) and urinary albumin/creatinine ratio (UACR)), together with area under ROC curve (AUROC) analysis, the AUROC values of myo-inositol and gluconic acid reached 0.992 and 0.991, can distinguish well between DKD patient and HC. The results indicated that myo-inositol and gluconic acid might be the biomarkers for DKD.
Keywords: diabetic kidney disease1, metabolomics2, Gas chromatography-mass spectrometry3, Biomarker4, Myo-inositol5, Gluconic acid6
Received: 07 Dec 2024; Accepted: 19 Feb 2025.
Copyright: © 2025 Bian, Wang, Wang, Yin, Xu, Liu, Wang, Cao, Huang, Qin, Zhang and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Xueyan Bian, Department of Neurology, Ningbo First Hospital, Ningbo, 315010, Zhejiang Province, China
Chenxue Qin, Department of Neurology, Ningbo First Hospital, Ningbo, 315010, Zhejiang Province, China
Ye Zhang, China Pharmaceutical University, Nanjing, China
Heming Yu, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu Province, China
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