Skip Navigation
Skip to contents

Diabetes Metab J : Diabetes & Metabolism Journal

Search
OPEN ACCESS

Author index

Page Path
HOME > Browse > Author index
Search
Yancheng Li 1 Article
Metabolic Risk/Epidemiology
Article image
Plasma Targeted Metabolomics Analysis for Amino Acids and Acylcarnitines in Patients with Prediabetes, Type 2 Diabetes Mellitus, and Diabetic Vascular Complications
Xin Li, Yancheng Li, Yuanhao Liang, Ruixue Hu, Wenli Xu, Yufeng Liu
Diabetes Metab J. 2021;45(2):195-208.   Published online March 9, 2021
DOI: https://doi.org/10.4093/dmj.2019.0209
  • 7,192 View
  • 221 Download
  • 15 Web of Science
  • 14 Crossref
Graphical AbstractGraphical Abstract AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
Background
We hypothesized that specific amino acids or acylcarnitines would have benefits for the differential diagnosis of diabetes. Thus, a targeted metabolomics for amino acids and acylcarnitines in patients with diabetes and its complications was carried out.
Methods
A cohort of 54 normal individuals and 156 patients with type 2 diabetes mellitus and/or diabetic complications enrolled from the First Affiliated Hospital of Jinzhou Medical University was studied. The subjects were divided into five main groups: normal individuals, impaired fasting glucose, overt diabetes, diabetic microvascular complications, and diabetic peripheral vascular disease. The technique of tandem mass spectrometry was applied to obtain the plasma metabolite profiles. Metabolomics multivariate statistics were applied for the metabolic data analysis and the differential metabolites determination.
Results
A total of 10 cross-comparisons within diabetes and its complications were designed to explore the differential metabolites. The results demonstrated that eight comparisons existed and yielded significant metabolic differences. A total number of 24 differential metabolites were determined from six selected comparisons, including up-regulated amino acids, down-regulated medium-chain and long-chain acylcarnitines. Altered differential metabolites provided six panels of biomarkers, which were helpful in distinguishing diabetic patients.
Conclusion
Our results demonstrated that the biomarker panels consisted of specific amino acids and acylcarnitines which could reflect the metabolic variations among the different stages of diabetes and might be useful for the differential diagnosis of prediabetes, overt diabetes and diabetic complications.

Citations

Citations to this article as recorded by  
  • Metabolomics Signature in Prediabetes and Diabetes: Insights From Tandem Mass Spectrometry Analysis
    Saad Ayyal Jabbar Al‐Rikabi, Ali Etemadi, Maher Mohammed Morad, Azin Nowrouzi, Ghodratollah Panahi, Mozhgan Mondeali, Mahsa Toorani‐ghazvini, Ensieh Nasli‐Esfahani, Farideh Razi, Fatemeh Bandarian
    Endocrinology, Diabetes & Metabolism.2024;[Epub]     CrossRef
  • Liquid Biopsy: A Game Changer for Type 2 Diabetes
    Gratiela Gradisteanu Pircalabioru, Madalina Musat, Viviana Elian, Ciprian Iliescu
    International Journal of Molecular Sciences.2024; 25(5): 2661.     CrossRef
  • Metabolomics for Clinical Biomarker Discovery and Therapeutic Target Identification
    Chunsheng Lin, Qianqian Tian, Sifan Guo, Dandan Xie, Ying Cai, Zhibo Wang, Hang Chu, Shi Qiu, Songqi Tang, Aihua Zhang
    Molecules.2024; 29(10): 2198.     CrossRef
  • Identification of FGF13 as a Potential Biomarker and Target for Diagnosis of Impaired Glucose Tolerance
    Qi Chen, Fangyu Li, Yuanyuan Gao, Fengying Yang, Li Yuan
    International Journal of Molecular Sciences.2023; 24(2): 1807.     CrossRef
  • Quantitative, Targeted Analysis of Gut Microbiota Derived Metabolites Provides Novel Biomarkers of Early Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients
    Lavinia Balint, Carmen Socaciu, Andreea Iulia Socaciu, Adrian Vlad, Florica Gadalean, Flaviu Bob, Oana Milas, Octavian Marius Cretu, Anca Suteanu-Simulescu, Mihaela Glavan, Silvia Ienciu, Maria Mogos, Dragos Catalin Jianu, Ligia Petrica
    Biomolecules.2023; 13(7): 1086.     CrossRef
  • Unlocking the Potential: Amino Acids’ Role in Predicting and Exploring Therapeutic Avenues for Type 2 Diabetes Mellitus
    Yilan Ding, Shuangyuan Wang, Jieli Lu
    Metabolites.2023; 13(9): 1017.     CrossRef
  • Untargeted metabolomics reveals dynamic changes in metabolic profiles of rat supraspinatus tendon at three different time points after diabetes induction
    Kuishuai Xu, Liang Zhang, Tianrui Wang, Zhongkai Ren, Tengbo Yu, Yingze Zhang, Xia Zhao
    Frontiers in Endocrinology.2023;[Epub]     CrossRef
  • Acylcarnitines: Can They Be Biomarkers of Diabetic Nephropathy?
    Xiaodie Mu, Min Yang, Peiyao Ling, Aihua Wu, Hua Zhou, Jingting Jiang
    Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2022; Volume 15: 247.     CrossRef
  • Targeted metabolomics analysis of amino acids and acylcarnitines as risk markers for diabetes by LC–MS/MS technique
    Shaghayegh Hosseinkhani, Babak Arjmand, Arezou Dilmaghani-Marand, Sahar Mohammadi Fateh, Hojat Dehghanbanadaki, Niloufar Najjar, Sepideh Alavi-Moghadam, Robabeh Ghodssi-Ghassemabadi, Ensieh Nasli-Esfahani, Farshad Farzadfar, Bagher Larijani, Farideh Razi
    Scientific Reports.2022;[Epub]     CrossRef
  • Identification of Insulin Resistance Biomarkers in Metabolic Syndrome Detected by UHPLC-ESI-QTOF-MS
    Leen Oyoun Alsoud, Nelson C. Soares, Hamza M. Al-Hroub, Muath Mousa, Violet Kasabri, Nailya Bulatova, Maysa Suyagh, Karem H. Alzoubi, Waseem El-Huneidi, Bashaer Abu-Irmaileh, Yasser Bustanji, Mohammad H. Semreen
    Metabolites.2022; 12(6): 508.     CrossRef
  • Serum Untargeted Metabolomics Reveal Potential Biomarkers of Progression of Diabetic Retinopathy in Asians
    Zongyi Wang, Jiyang Tang, Enzhong Jin, Yusheng Zhong, Linqi Zhang, Xinyao Han, Jia Liu, Yong Cheng, Jing Hou, Xuan Shi, Huijun Qi, Tong Qian, Li Yuan, Xianru Hou, Hong Yin, Jianhong Liang, Mingwei Zhao, Lvzhen Huang, Jinfeng Qu
    Frontiers in Molecular Biosciences.2022;[Epub]     CrossRef
  • Circulating amino acids and acylcarnitines correlated with different CAC score ranges in diabetic postmenopausal women using LC–MS/MS based metabolomics approach
    Shaghayegh Hosseinkhani, Pooneh Salari, Fatemeh Bandarian, Mojgan Asadi, Shapour Shirani, Niloufar Najjar, Hojat Dehghanbanadaki, Parvin Pasalar, Farideh Razi
    BMC Endocrine Disorders.2022;[Epub]     CrossRef
  • Metabolomic comparison followed by cross-validation of enzyme-linked immunosorbent assay to reveal potential biomarkers of diabetic retinopathy in Chinese with type 2 diabetes
    Zongyi Wang, Jiyang Tang, Enzhong Jin, Chi Ren, Siying Li, Linqi Zhang, Yusheng Zhong, Yu Cao, Jianmin Wang, Wei Zhou, Mingwei Zhao, Lvzhen Huang, Jinfeng Qu
    Frontiers in Endocrinology.2022;[Epub]     CrossRef
  • Urine Metabolites Enable Fast Detection of COVID-19 Using Mass Spectrometry
    Alexandre Varao Moura, Danilo Cardoso de Oliveira, Alex Ap. R. Silva, Jonas Ribeiro da Rosa, Pedro Henrique Dias Garcia, Pedro Henrique Godoy Sanches, Kyana Y. Garza, Flavio Marcio Macedo Mendes, Mayara Lambert, Junier Marrero Gutierrez, Nicole Marino Gra
    Metabolites.2022; 12(11): 1056.     CrossRef

Diabetes Metab J : Diabetes & Metabolism Journal
Close layer
TOP