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Comprehensive Metabolomic Profiling of Type 2 Diabetes
Author(s) -
Yan Zheng,
Frank B. Hu
Publication year - 2015
Publication title -
clinical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.705
H-Index - 218
eISSN - 1530-8561
pISSN - 0009-9147
DOI - 10.1373/clinchem.2014.235986
Subject(s) - profiling (computer programming) , metabolomics , type 2 diabetes , computational biology , diabetes mellitus , medicine , chemistry , chromatography , computer science , biology , endocrinology , operating system
It is estimated that at least 29.1 million Americans, or 9.3% of the US population, currently have diabetes (1), a disease characterized by impaired insulin action and/or production. Although type 2 diabetes (T2D),4 which accounts for >90% of diagnosed diabetes, is largely predictable through anthropometric, lifestyle, and clinical factors, and is preventable through diet and lifestyle modifications, the metabolic pathways underlying its development and progression are incompletely understood. The rapidly developing area of metabolomics, which is designed to quantitatively profile a large number of small molecules in cells or biofluids, has emerged as a promising approach to elucidate altered metabolic pathways and discover novel biomarkers in T2D.The past several years have seen the initial success of metabolomics in identifying novel biomarkers for insulin resistance and T2D. In 2009, Newgard et al. (2) compared 131 targeted metabolites between 74 obese and 67 lean subjects and found that plasma concentrations of branched-chained amino acids (BCAAs) were strongly correlated with obesity and insulin resistance. In 2011, Wang et al. (3) measured a panel of 61 metabolites and found that 5 BCAAs and aromatic amino acids (i.e., isoleucine, leucine, valine, tyrosine, and phenylalanine) were predictive of developing diabetes in the Framingham Offspring Study and the Malmo Diet and Cancer Study. In 2012, Wang-Sattler et al. (4) quantified 140 metabolites in 4297 fasting serum samples in the Cooperative Health Research in the Region of Augsburg cohort and found that the concentrations of metabolites [i.e., lower values of glycine and lysophosphatidylcholine (18:2), as well as higher values of acetylcarnitine] predicted impaired glucose tolerance 7 years before disease onset. Meanwhile, Cheng et al. (5) observed significant associations of insulin resistance traits with metabolites glutamine and glutamate and the glutamine-to-glutamate ratio, from a panel of 45 metabolites in the Framingham Heart Study. Finally, Wang et al. …

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