Serum metabolomics model and its metabolic characteristics in patients with different syndromes of dyslipidemia based on nuclear magnetic resonance

被引:13
|
作者
Chen, Jing [1 ]
Ye, Chao [2 ]
Hu, Xiaomin [3 ]
Huang, Caihua [4 ]
Yang, Zheng [5 ]
Li, Pengyang [2 ]
Wu, Aiming [6 ]
Xue, Xiaolin [7 ]
Lin, Donghai [8 ]
Yang, Huimin [9 ]
机构
[1] Beijing Univ Chinese Med, Affiliated Hosp 3, Med Dept, Beijing, Peoples R China
[2] Beijing Univ Chinese Med, Dongzhimen Hosp, Orthoped Dept, Ward 2, Beijing, Peoples R China
[3] Univ Hong Kong, Li Ka Shing Fac Med, Dept Pathol, Hong Kong, Peoples R China
[4] Xiamen Univ Technol, Dept Phys Educ, Xiamen, Peoples R China
[5] Beijing Univ Chinese Med, SATCM Key Lab Renowned Phys & Class Formula, Beijing, Peoples R China
[6] Beijing Univ Chinese Med, Dongzhimen Hosp, Minist Educ & Beijing, Key Lab Chinese Internal Med, Beijing, Peoples R China
[7] Beijing Univ Chinese Med, Dept Diagnost Tradit Chinese Med, Sch Tradit Chinese Med, Beijing, Peoples R China
[8] Xiamen Univ, Coll Chem & Chem Engn, 11 North Third Ring East Rd, Xiamen 361005, Peoples R China
[9] Beijing Univ Chinese Med, Dongzhimen Hosp, Neurol Dept, Ward 3, Beijing, Peoples R China
基金
高等学校博士学科点专项科研基金;
关键词
Metabolomics; Dyslipidemia; Syndrome; Traditional Chinese medicine; TRADITIONAL CHINESE MEDICINE; TCM SYNDROME; PLASMA; BIOMARKERS;
D O I
10.1016/j.jpba.2018.12.042
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Dyslipidemia is known as a common clinical disease that affects the health of millions of people around the world. The treatment of dyslipidemia with traditional Chinese medicine (TCM) is generally based on the accurate identification of disease syndromes. TCM syndromes are judged by traditional four-diagnosis method, which is subjective and fuzzy. Additionally, the judgment of TCM syndromes highly depend on doctors' own clinical experience. In this present study, we used nuclear magnetic resonance (NMR)-based serum metabolomics patterns to figure out the metabolic characteristics of different syndromes in patients with dyslipidemia. In total, we enrolled 60 patients with dyslipidemia (30 cases with Spleen and Kidney Yang Deficiency syndrome (SKYD) and 30 cases with Phlegm-Dampness Retention syndrome (PDR)) and 20 healthy controls. Based on NMR technique, the serum metabolomics patterns of patients with different syndromes and healthy controls were analyzed, in the hope of screening the different metabolites among different syndromes and the differential metabolic pathway, as well as exploring the changes of metabolic network among different syndromes of dyslipidemia. The results suggested that the serum metabolomics patterns based on NMR was used to identify serum metabolites in patients with dyslipidemia of SKYD and PDR as well as healthy controls. Besides, it was found that the metabolic patterns of these three groups can be distinguished well and the different metabolites between different syndromes can be screened. From the point of view of metabolites, the metabolic characteristics of the patients with PDR were mainly the accumulation of noxious metabolites, while the metabolic characteristics of the patients with SKYD were mainly the lack of metabolites with protective function. From the point of view of metabolic mode, there were different metabolic patterns in patients with different syndromes of dyslipidemia in liver metabolism, oxidation, inflammatory reaction as well as energy metabolism, which reflects the difference of syndromes from different angles. The differences in metabolic outcomes among patients with different syndromes of dyslipidemia had a close association with to the effects of multiple signaling pathways. This study identified the characteristics of serum metabolic model of patients with different syndromes of dyslipidemia and the potential differential metabolites and characteristic metabolic characteristics of syndromes in order to understand the biological characteristics of patients with dyslipidemia of SKYD and PDR. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码:100 / 113
页数:14
相关论文
共 50 条
  • [31] Utility of magnetic resonance imaging and nuclear magnetic resonance-based metabolomics for quantification of inflammatory lung injury
    Serkova, Natalie J.
    Van Rheen, Zachary
    Tobias, Meghan
    Pitzer, Joshua E.
    Wilkinson, J. Erby
    Stringer, Kathleen A.
    AMERICAN JOURNAL OF PHYSIOLOGY-LUNG CELLULAR AND MOLECULAR PHYSIOLOGY, 2008, 295 (01) : L152 - L161
  • [32] Serum 1H nuclear magnetic resonance-based metabolomics of sole lesion development in Holstein cows
    Barden, Matthew
    Phelan, Marie M.
    Hyde, Robert
    Anagnostopoulos, Alkiviadis
    Griffiths, Bethany E.
    Bedford, Cherry
    Green, Martin
    Psifidi, Androniki
    Banos, Georgios
    Oikonomou, Georgios
    JOURNAL OF DAIRY SCIENCE, 2023, 106 (04) : 2667 - 2684
  • [33] Metabolomics comparison of serum and urine in dairy cattle using proton nuclear magnetic resonance spectroscopy
    Eom, Jun Sik
    Kim, Eun Tae
    Kim, Hyun Sang
    Choi, You Young
    Lee, Shin Ja
    Lee, Sang Suk
    Kim, Seon Ho
    Lee, Sung Sill
    ANIMAL BIOSCIENCE, 2021, 34 (12) : 1930 - 1939
  • [34] Nuclear magnetic resonance-based metabolomics in goat ovarian follicular fluid
    Arcce, Irving Mitchell Laines
    Silva, Lorena Mara Alexandre
    Canuto, Kirley Marques
    Alves Filho, Elenilson de Godoy
    de Sousa, Francisco Carlos
    Melo, Luciana Magalha
    Chaves, Maiana Silva
    van Tilburg, Mauricio Fraga
    Freotas, Vicente Jose de Fogieoredp
    SMALL RUMINANT RESEARCH, 2023, 223
  • [35] MagMet: A fully automated web server for targeted nuclear magnetic resonance metabolomics of plasma and serum
    Rout, Manoj
    Lipfert, Matthias
    Lee, Brian L. L.
    Berjanskii, Mark
    Assempour, Nazanin
    Fresno, Rosa Vazquez
    Cayuela, Arnau Serra
    Dong, Ying
    Johnson, Mathew
    Shahin, Honeya
    Gautam, Vasuk
    Sajed, Tanvir
    Oler, Eponine
    Peters, Harrison
    Mandal, Rupasri
    Wishart, David S. S.
    MAGNETIC RESONANCE IN CHEMISTRY, 2023, 61 (12) : 681 - 704
  • [36] Metabolomics analysis of serum in a rat heroin self-administration model undergoing reinforcement based on 1H-nuclear magnetic resonance spectra
    Tingting Ning
    Changlong Leng
    Lin Chen
    Baomiao Ma
    Xiaokang Gong
    BMC Neuroscience, 19
  • [37] Metabolomics analysis of serum in a rat heroin self-administration model undergoing reinforcement based on 1H-nuclear magnetic resonance spectra
    Ning, Tingting
    Leng, Changlong
    Chen, Lin
    Ma, Baomiao
    Gong, Xiaokang
    BMC NEUROSCIENCE, 2018, 19
  • [38] Complementary Nuclear Magnetic Resonance-Based Metabolomics Approaches for Glioma Biomarker Identification in a Drosophila melanogaster Model
    Maravat, Marion
    Bertrand, Marylene
    Landon, Celine
    Fayon, Franck
    Morisset-Lopez, Severine
    Sarou-Kanian, Vincent
    Decoville, Martine
    JOURNAL OF PROTEOME RESEARCH, 2021, 20 (08) : 3977 - 3991
  • [39] Nuclear Magnetic Resonance Based Metabolomics Study Identifies Highly Discriminatory Metabolites in 87 Systemic Sclerosis Patients
    Ahmed, Sakir
    Rai, Mohit Kumar
    Dubey, Durgesh
    Rawat, Atul
    Kumar, Dinesh
    Misra, Durga Prasanna
    Agarwal, Vikas
    ARTHRITIS & RHEUMATOLOGY, 2017, 69
  • [40] Application of nuclear magnetic resonance for analyzing metabolic characteristics of winter diatom blooms
    Jeong, Kwang-Seuk
    Jeong, Keon-Young
    Hong, Young-Shick
    Kim, Dong-Kyun
    Oh, Hye-Ji
    Chang, Kwang-Hyeon
    JOURNAL OF PLANKTON RESEARCH, 2020, 42 (01) : 31 - 39