Metabolomics in human type 2 diabetes research

doi:10.1007/s11684-013-0248-4

Front Med

2013, Vol. 7

Issue (1) : 4-13 https://doi.org/10.1007/s11684-013-0248-4

REVIEW

Metabolomics in human type 2 diabetes research

Jingyi Lu¹, Guoxiang Xie^2,3, Weiping Jia¹, Wei Jia^2,3(

)

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 Author(s): 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.

URL:

https://academic.hep.com.cn/fmd/EN/10.1007/s11684-013-0248-4
https://academic.hep.com.cn/fmd/EN/Y2013/V7/I1/4

Pathway	Metabolite	Change of direction(vs. healthy control)	Sample	Platform	Reference
Carbohydrate metabolism and TCA cycle	1,5-Anhydrogluticol	Down	Serum	NMR, UPLC-MS, GC-MS	[38]
		Down	Serum	GC-MS	[21]
	Pyruvate	Up	Serum	GC-MS	[21]
	Lactate	Up	Serum	GC-MS	[21]
		Up	Urine	NMR	[22]
		Down	serum	GC-MS	[58]
		Down	Serum	NMR	[57]
	Citrate	Up	Urine	NMR	[22]
		Up	Urine	NMR	[23]
		Down	Serum	NMR	[57]
	Malate	Down	Urine	NMR	[23]
	Fumarate	Down	Urine	NMR	[23]
	Succinate	Down	Urine	NMR	[23]
Lipid metabolism	3-Hydroxybutyrate	Up	Plasma	GC-MS	[36]
		UP	Serum	NMR, UPLC-MS, GC-MS	[38]
		Up	Urine	NMR	[23]
		Up	Serum	GC-MS	[21]
	Acetoacetate	Up	Urine	NMR	[23]
	Fatty acids	Up	Plasma	GC-MS	[36]
		Up	Serum	GC-MS	[21]
	LysoPCs	Up	Plasma	UPLC-MS	[46]
	LysoPC (18:2)	Down	Serum	LC-MS	[50]
		Down	Serum	LC-MS	[49]
	LysoPEs	Up/Down	Plasma	UPLC-MS	[46]
	PCs	Up/Down	Serum	LC-MS	[49]
	Acetylcarnitines	Up	Plasma	UPLC-MS	[46]
		Up	Plasma	UPLC-MS	[45]
		Up/Down	Plasma	UPLC-MS	[42]
		Down	Plasma	UPLC-MS	[47]
Amino acid metabolism	Valine (BCAA)	UP	Serum	NMR, UPLC-MS, GC-MS	[38]
		Up	serum	GC-MS	[58]
		Down	Serum	NMR	[57]
	Leucine (BCAA)	UP	Serum	NMR, UPLC-MS, GC-MS	[38]
		Up	Plasma	UPLC-MS	[46]
		Up	Plasma	GC-MS	[36]
		Down	Serum	NMR	[57]
		Down	Urine	NMR	[23]
	Isoleucine (BCAA)	UP	Serum	NMR, UPLC-MS, GC-MS	[38]
		Down	Serum	NMR	[57]
		Down	Urine	NMR	[23]
	Lysine	Down	Plasma	GC-MS	[36]
		Down	Serum	NMR	[57]
		Down	serum	GC-MS	[58]
		Up	Plasma	UPLC-MS	[46]
	Glycine	Down	Plasma	GC-MS	[36]
		Down	Serum	LC-MS	[50]
		Down	Serum	LC-MS	[49]
	Serine	Up	Plasma	UPLC-MS	[46]
	Tyrosine	Down	Serum	NMR	[57]
	Phenylalanine	Up	Plasma	UPLC-MS	[46]
		Up	Serum	GC-MS	[21]
	Phenylalanine	Up	Serum	LC-MS	[49]
		Down	Serum	NMR	[57]
	Tryptophan	Down	Urine	NMR	[23]
	Alanine	Up	Urine	NMR	[22]
		Down	Serum	NMR	[57]
	Glutamine	Up	Urine	NMR	[23]
		Up	Serum	GC-MS	[21]
	Glutamate	Up	serum	GC-MS	[58]
		Down	Serum	GC-MS	[21]
	Methionine	Down	Serum	NMR	[57]
		Up	Serum	GC-MS	[21]
	Histidine	Down	Serum	NMR	[57]
		Down	Urine	NMR	[23]
	2-Hydroxybutyrate	Up	Plasma	GC×GC-MS	[62]
		Up	Plasma	GC-MS	[36]
		Up	Serum	GC-MS	[21]
		Up	Plasma	GC-MS	[36]
	Hippurate	Up	Urine	NMR	[22]
	Taurine	Up	Urine	NMR	[23]
Choline metabolism	Betaine	Up	Urine	NMR	[22]
	DMA	Up	Urine	NMR	[22]
	TMAO	Up	Urine	NMR	[22]
		Up	Urine	NMR	[23]
Bile acid metabolism	Cholate	Down	Serum	NMR, UPLC-MS, GC-MS	[38]
	Deoxycholate	Up	Serum	NMR, 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.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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