, 12:3 | Cite as

Plasma metabolomic profiles in association with type 2 diabetes risk and prevalence in Chinese adults

  • Danxia Yu
  • Steven C. Moore
  • Charles E. Matthews
  • Yong-Bing Xiang
  • Xianglan Zhang
  • Yu-Tang Gao
  • Wei Zheng
  • Xiao-Ou ShuEmail author
Original Article


Metabolomic studies have identified several metabolites associated with type 2 diabetes (T2D) in populations of European ancestry. East Asians, a population of particular susceptibility to T2D, were generally not included in previous studies. We examined the associations of plasma metabolites with risk and prevalence of T2D in 976 Chinese men and women (40–74 years of age) who were participants of two prospective cohort studies and had no cardiovascular disease or cancer at baseline. Sixty-eight prevalent and 73 incident T2D cases were included. Non-targeted metabolomics was conducted that detected 689 metabolites with known identities and 690 unknown metabolites. Multivariable logistic and Cox regressions were used to evaluate the associations of standardized metabolites with diabetes risk and prevalence. We identified 36 known metabolites and 10 unknown metabolites associated with prevalent and/or incident T2D at false discovery rate <0.05. The known metabolites are involved in metabolic pathways of glycolysis/gluconeogenesis, branched-chain amino acids, other amino acids, fatty acids, glycerophospholipids, androgen, and bradykinin. Six metabolites showed independent associations with incident T2D: 1,5-anhydroglucitol, mannose, valine, 3-methoxytyrosine, docosapentaenoate (22:5n3), and bradykinin-hydroxy-pro(3). Each standard deviation increase in these metabolites was associated with a 40–150 % change in risk of developing diabetes (30–80 % after further adjustment for glucose). Risk prediction was significantly improved by adding these metabolites in addition to known T2D risk factors, including central obesity and glucose. These findings suggest that hexoses, branched-chain amino acids, and yet to be validated novel plasma metabolites may improve risk prediction and mechanistic understanding of T2D in Chinese populations.


Metabolomics Type 2 diabetes Epidemiology Prospective cohort study Chinese populations 



We thank Dr. Joshua Sampson for his comments on the paper. We thank Ms. Nancy Kennedy for her assistance on preparing the manuscript. We thank the research team and participants of the Shanghai Women’s Health Study and the Shanghai Men’s Health Study for their foundation work for this study.

Compliance with Ethical Standards

Conflict of interest

The authors, including Danxia Yu, Steven C. Moore, Charles E. Matthews, Yong-Bing Xiang, Xianglan Zhang, Yu-Tang Gao, Wei Zheng, and Xiao-Ou Shu, have no conflict of interest to declare. The study uses existing data and specimens that have already been collected by the parent studies, i.e., the Shanghai Women’s Health Study and the Shanghai Men’s Health Study. All study participants provided informed consent to the parent studies.


This work was supported, in part, by the US National Institutes of Health [R37 CA070867 and UM1 CA182910 to Dr. W. Zheng, UM1 CA173640, R01 HL079123 and NO2-CP11010-66 to Dr. X.O. Shu]. This work was also supported, in part, by the Breast Cancer Research Stamp Fund, awarded through competitive peer review and the Intramural Research Program of the National Cancer Institute, National Institutes of Health.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Danxia Yu
    • 1
  • Steven C. Moore
    • 2
  • Charles E. Matthews
    • 2
  • Yong-Bing Xiang
    • 3
  • Xianglan Zhang
    • 1
  • Yu-Tang Gao
    • 3
  • Wei Zheng
    • 1
  • Xiao-Ou Shu
    • 1
    Email author
  1. 1.Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology CenterVanderbilt University School of MedicineNashvilleUSA
  2. 2.Division of Cancer Epidemiology and GeneticsNational Cancer InstituteBethesdaUSA
  3. 3.Shanghai Cancer Institute, Renji HospitalShanghai Jiaotong University School of MedicineShanghaiChina

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