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Frontiers of Medicine

Front Med    2013, Vol. 7 Issue (1) : 4-13     DOI: 10.1007/s11684-013-0248-4
REVIEW |
Metabolomics in human type 2 diabetes research
Jingyi Lu1, Guoxiang Xie2,3, Weiping Jia1, Wei Jia2,3()
1. Shanghai Diabetes Institute; Department of Endocrinology and Metabolism, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital; Shanghai Key Laboratory of Diabetes Mellitus; Shanghai Clinical Center for Diabetes, Shanghai 200233, China; 2. Center for Translational Medicine, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai 200233, China; 3. Center for Translational Biomedical Research, University of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, NC 28081, USA
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Abstract

The high prevalence of diabetes and diabetic complications has caused a huge burden on the modern society. Although scientific advances have led to effective strategies for preventing and treating diabetes over the past several decades, little progress has been made toward curing the disease or even getting it under control, from a public health and overall societal standpoint. There is still a lack of reliable biomarkers indicative of metabolic alterations associated with diabetes and different drug responses, highlighting the need for the development of early diagnostic and prognostic markers for diabetes and diabetic complications. The emergence of metabolomics has allowed researchers to systemically measure the small molecule metabolites, which are sensitive to the changes of both environmental and genetic factors and therefore, could be regarded as the link between genotypes and phenotypes. During the last decade, the progression made in metabolomics has provided insightful information on disease development and disease onset prediction. Recent studies using metabolomics approach coupled with statistical tools to predict incident diabetes revealed a number of metabolites that are significantly altered, including branched-chain and aromatic amino acids, such as isoleucine, leucine, valine, tyrosine and phenylalanine, as diagnostic or highly-significant predictors of future diabetes. This review summarizes the current findings of metabolomic studies in human investigations with the most common form of diabetes, type 2 diabetes.

Keywords metabolomics      type 2 diabetes      metabolic pathway      mass spectrometry      nuclear magnetic resonance (NMR)     
Corresponding Authors: Jia Wei,Email:w_jia@uncg.edu   
Issue Date: 05 March 2013
 Cite this article:   
Jingyi Lu,Guoxiang Xie,Weiping Jia, et al. Metabolomics in human type 2 diabetes research[J]. Front Med, 2013, 7(1): 4-13.
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http://journal.hep.com.cn/fmd/EN/10.1007/s11684-013-0248-4
http://journal.hep.com.cn/fmd/EN/Y2013/V7/I1/4
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PathwayMetaboliteChange of direction(vs. healthy control)SamplePlatformReference
Carbohydrate metabolism and TCA cycle1,5-AnhydrogluticolDownSerumNMR, UPLC-MS, GC-MS [38]
DownSerumGC-MS [21]
PyruvateUpSerumGC-MS [21]
LactateUpSerumGC-MS [21]
UpUrineNMR [22]
DownserumGC-MS [58]
DownSerumNMR [57]
CitrateUpUrineNMR [22]
UpUrineNMR [23]
DownSerumNMR [57]
MalateDownUrineNMR [23]
FumarateDownUrineNMR [23]
SuccinateDownUrineNMR [23]
Lipid metabolism3-HydroxybutyrateUpPlasmaGC-MS [36]
UPSerumNMR, UPLC-MS, GC-MS [38]
UpUrineNMR [23]
UpSerumGC-MS [21]
AcetoacetateUpUrineNMR [23]
Fatty acidsUpPlasmaGC-MS [36]
UpSerumGC-MS [21]
LysoPCsUpPlasmaUPLC-MS [46]
LysoPC (18:2)DownSerumLC-MS [50]
DownSerumLC-MS [49]
LysoPEsUp/DownPlasmaUPLC-MS [46]
PCsUp/DownSerumLC-MS [49]
AcetylcarnitinesUpPlasmaUPLC-MS [46]
UpPlasmaUPLC-MS [45]
Up/DownPlasmaUPLC-MS [42]
DownPlasmaUPLC-MS [47]
Amino acid metabolismValine (BCAA)UPSerumNMR, UPLC-MS, GC-MS [38]
UpserumGC-MS [58]
DownSerumNMR [57]
Leucine (BCAA)UPSerumNMR, UPLC-MS, GC-MS [38]
UpPlasmaUPLC-MS [46]
UpPlasmaGC-MS [36]
DownSerumNMR [57]
DownUrineNMR [23]
Isoleucine (BCAA)UPSerumNMR, UPLC-MS, GC-MS [38]
DownSerumNMR [57]
DownUrineNMR [23]
LysineDownPlasmaGC-MS [36]
DownSerumNMR [57]
DownserumGC-MS [58]
UpPlasmaUPLC-MS [46]
GlycineDownPlasmaGC-MS [36]
DownSerumLC-MS [50]
DownSerumLC-MS [49]
SerineUpPlasmaUPLC-MS [46]
TyrosineDownSerumNMR [57]
PhenylalanineUpPlasmaUPLC-MS [46]
UpSerumGC-MS [21]
PhenylalanineUpSerumLC-MS [49]
DownSerumNMR [57]
TryptophanDownUrineNMR [23]
AlanineUpUrineNMR [22]
DownSerumNMR [57]
GlutamineUpUrineNMR [23]
UpSerumGC-MS [21]
GlutamateUpserumGC-MS [58]
DownSerumGC-MS [21]
MethionineDownSerumNMR [57]
UpSerumGC-MS [21]
HistidineDownSerumNMR [57]
DownUrineNMR [23]
2-HydroxybutyrateUpPlasmaGC×GC-MS [62]
UpPlasmaGC-MS [36]
UpSerumGC-MS [21]
UpPlasmaGC-MS [36]
HippurateUpUrineNMR [22]
TaurineUpUrineNMR [23]
Choline metabolismBetaineUpUrineNMR [22]
DMAUpUrineNMR [22]
TMAOUpUrineNMR [22]
UpUrineNMR [23]
Bile acid metabolismCholateDownSerumNMR, UPLC-MS, GC-MS [38]
DeoxycholateUpSerumNMR, UPLC-MS, GC-MS [38]
Tab.1  List of altered metabolic pathways in T2DM patients
Fig.1  The intensity of serum pyruvate measured by GC-MS in different groups. NC, normal control; T2DM, type 2 diabetes; T1DM, type 1 diabetes; DKA, diabetic ketoacidosis; FT1DM, fulminant type 1 diabetes.
Fig.1  The intensity of serum pyruvate measured by GC-MS in different groups. NC, normal control; T2DM, type 2 diabetes; T1DM, type 1 diabetes; DKA, diabetic ketoacidosis; FT1DM, fulminant type 1 diabetes.
Fig.2  The comparison of 2-hydroxybutyrate between nondiabetic and diabetic subjects and the association of 2-hydroxybutyrate with HbA1c. (A) The intensity of serum 2-hydroxybutyrate measured by GC-MS in normal controls and diabetic patients. (B) The Pearson correlation between 2-hydroxybutyrate and HbA1c.
Fig.2  The comparison of 2-hydroxybutyrate between nondiabetic and diabetic subjects and the association of 2-hydroxybutyrate with HbA1c. (A) The intensity of serum 2-hydroxybutyrate measured by GC-MS in normal controls and diabetic patients. (B) The Pearson correlation between 2-hydroxybutyrate and HbA1c.
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