Literature DB >> 30265543

Identification of Urinary Metabolite Biomarkers of Type 2 Diabetes Nephropathy Using an Untargeted Metabolomic Approach.

Chao-Jung Chen1,2, Wen-Ling Liao2,3, Chiz-Tzung Chang4,5, Yu-Ning Lin1, Fuu-Jen Tsai6,7,8.   

Abstract

Diabetic nephropathy (DN) is one of the most common complications of diabetes mellitus (DM). To discover early stage biomarkers of DN, untargeted liquid chromatography-mass spectrometry-based metabolomic analysis was performed in urine samples from healthy subjects and patients with micro- or macroalbuminuria due to nondiabetic disease (macro), type 2 DM without microalbuminuria (T2DM), and type 2 DM with microalbuminuria (T2DM+micro). Levels of four metabolites were significantly different among groups, and they were quantified in a larger group of 267 urine samples. Two metabolites were also discovered and validated in targeted metabolic study of amino acids. For diagnosis of nephropathy, N1-methylguanosine had the highest area-under-the-curve (AUC) value of 0.75 when compared to those of valine (0.68), xanthosine (0.67), and 7-methyluric acid (0.69). After combining fasting blood glucose and diastolic blood pressure (DBP) with N1-methylguanosine, the AUC increased to 0.987. To distinguish between T2DM and T2DM+micro conditions, xanthosine and N1-methylguanosine have AUC value of 0.612 and 0.624, respectively. After adjustment of HbA1c and DBP, AUC values of xanthosine and N1-methylguanosine increased to 0.716 and 0.723, respectively, and could be used to predict the development of nephropathy in T2DM patients.

Entities:  

Keywords:  biomarker; diabetic nephropathy; liquid chromatography; mass spectrometry; metabolomics; urine

Mesh:

Substances:

Year:  2018        PMID: 30265543     DOI: 10.1021/acs.jproteome.8b00644

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  16 in total

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2.  Identification of Insulin Resistance Biomarkers in Metabolic Syndrome Detected by UHPLC-ESI-QTOF-MS.

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Journal:  Metabolites       Date:  2022-06-01

3.  High-Throughput Metabolomics and Diabetic Kidney Disease Progression: Evidence from the Chronic Renal Insufficiency (CRIC) Study.

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Journal:  Am J Nephrol       Date:  2022-02-23       Impact factor: 4.605

4.  Urine metabolomics reveals biomarkers and the underlying pathogenesis of diabetic kidney disease.

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Journal:  Int Urol Nephrol       Date:  2022-10-18       Impact factor: 2.266

5.  Urine Metabolomics Analysis in Patients With Normoalbuminuric Diabetic Kidney Disease.

Authors:  Qian Feng; Yuanmeng Li; Yuwei Yang; Jiafu Feng
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Review 7.  Diabetic kidney diseases revisited: A new perspective for a new era.

Authors:  Haiyan Fu; Silvia Liu; Sheldon I Bastacky; Xiaojie Wang; Xiao-Jun Tian; Dong Zhou
Journal:  Mol Metab       Date:  2019-10-17       Impact factor: 7.422

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Authors:  Jiayi Wang; Wenzhe Yan; Xiang Zhou; Yu Liu; Chengyuan Tang; Youming Peng; Hong Liu; Lin Sun; Li Xiao; Liyu He
Journal:  PeerJ       Date:  2020-05-14       Impact factor: 2.984

9.  Identification of Potential Serum Metabolic Biomarkers of Diabetic Kidney Disease: A Widely Targeted Metabolomics Study.

Authors:  Hang Zhang; Jing-Jing Zuo; Si-Si Dong; Yuan Lan; Chen-Wei Wu; Guang-Yun Mao; Chao Zheng
Journal:  J Diabetes Res       Date:  2020-03-02       Impact factor: 4.011

10.  Urinary metabolomic investigations in vitiligo patients.

Authors:  Wei Liu; Xiao-Yan Liu; Yue-Tong Qian; Dong-Dong Zhou; Jia-Wei Liu; Tian Chen; Wei Sun; Dong-Lai Ma
Journal:  Sci Rep       Date:  2020-10-22       Impact factor: 4.379

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