4.5 Article

Early second-trimester plasma protein profiling using multiplexed isobaric tandem mass tag (TMT) labeling predicts gestational diabetes mellitus

Journal

ACTA DIABETOLOGICA
Volume 52, Issue 6, Pages 1103-1112

Publisher

SPRINGER-VERLAG ITALIA SRL
DOI: 10.1007/s00592-015-0796-y

Keywords

Gestational diabetes mellitus; Plasma; TMT; LC-MS/MS

Funding

  1. National Natural Science Foundation of China [81000258, 81100436]
  2. Natural Science Foundation of Jiangsu Province [BK2010586]
  3. Bureau of Nanjing City Science and Technology Development Fund [201104014]
  4. Open topic of State Key Laboratory of Reproductive Medicine [SKLRM-KF-201109, SKLRM-B12]
  5. Nanjing Medical Technology Development Project [YKK14126, QRX11210, QRX11211]

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Gestational diabetes mellitus (GDM) is associated with an increased risk of serious complications for mother and child during pregnancy. The main option for diagnosis of GDM is 75 g oral glucose tolerance test (OGTT) at 24-28 gestation weeks, when harms to both mother and child have already potentially occurred. The aim of this study was to investigate new biomarkers for earlier detection and assessment of GDM at early second trimester (16-18 gestation weeks). We systematically used multiplexed isobaric tandem mass tag labeling combined with liquid chromatography mass spectrometry (LC-MS/MS) to screen differentially expressed proteins in plasma collected at 16-18 gestational weeks between pregnant women with and without GDM outcome. A total of 828 proteins were identified, of which 36 proteins implicated in immune response, inflammation, transport, platelet aggregation, catalyze and defense response were identified as differentially regulated proteins in GDM. To assess the validity of the results, four selected proteins including C-reactive protein, sex hormone-binding globulin, Ficolin 3 and pregnancy-specific beta-1-glycoprotein 4 were selected for subsequent Western blot analysis. This is the first comprehensive study that integrates multiple state-of-the-art proteomic technologies to discover the earlier potential plasma biomarkers for GDM.

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