Literature DB >> 28505362

Lipidomic profiling reveals distinct differences in plasma lipid composition in healthy, prediabetic, and type 2 diabetic individuals.

Huanzi Zhong1,2,3, Chao Fang1,2,4, Yanqun Fan1, Yan Lu5, Bo Wen1, Huahui Ren1,2,4, Guixue Hou6, Fangming Yang1,2,7, Hailiang Xie1,2, Zhuye Jie1,2, Ye Peng1,2,8, Zhiqiang Ye1,2, Jiegen Wu1,8, Jin Zi6, Guoqing Zhao2, Jiayu Chen2, Xiao Bao2, Yihe Hu5, Yan Gao5, Jun Zhang5, Huanming Yang1,2,9, Jian Wang1,2,9, Lise Madsen1,10,11, Karsten Kristiansen1,2,10, Chuanming Ni5, Junhua Li1,2,3, Siqi Liu1,6,12.   

Abstract

The relationship between dyslipidemia and type 2 diabetes mellitus (T2D) has been extensively reported, but the global lipid profiles, especially in the East Asia population, associated with the development of T2D remain to be characterized. Liquid chromatography coupled to tandem mass spectrometry was applied to detect the global lipidome in the fasting plasma of 293 Chinese individuals, including 114 T2D patients, 81 prediabetic subjects, and 98 individuals with normal glucose tolerance (NGT). Both qualitative and quantitative analyses revealed a gradual change in plasma lipid features with T2D patients exhibiting characteristics close to those of prediabetic individuals, whereas they differed significantly from individuals with NGT. We constructed and validated a random forest classifier with 28 lipidomic features that effectively discriminated T2D from NGT or prediabetes. Most of the selected features significantly correlated with diabetic clinical indices. Hydroxybutyrylcarnitine was positively correlated with fasting plasma glucose, 2-hour postprandial glucose, glycated hemoglobin, and insulin resistance index (HOMA-IR). Lysophosphatidylcholines such as lysophosphatidylcholine (18:0), lysophosphatidylcholine (18:1), and lysophosphatidylcholine (18:2) were all negatively correlated with HOMA-IR. The altered plasma lipidome in Chinese T2D and prediabetic subjects suggests that lipid features may play a role in the pathogenesis of T2D and that such features may provide a basis for evaluating risk and monitoring disease development.
© The Authors 2017. Published by Oxford University Press.

Entities:  

Keywords:  lipidomics; plasma; prediabetes; type 2 diabetes

Mesh:

Substances:

Year:  2017        PMID: 28505362      PMCID: PMC5502363          DOI: 10.1093/gigascience/gix036

Source DB:  PubMed          Journal:  Gigascience        ISSN: 2047-217X            Impact factor:   6.524


  46 in total

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