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Metabolomics

, 12:178 | Cite as

Identification of putative biomarkers for type 2 diabetes using metabolomics in the Korea Association REsource (KARE) cohort

  • Heun-Sik Lee
  • Tao Xu
  • Young Lee
  • Nam-Hee Kim
  • Yeon-Jung Kim
  • Jeong-Min Kim
  • Sang Yun Cho
  • Kwang-Youl Kim
  • Moonsuk Nam
  • Jerzy Adamski
  • Karsten Suhre
  • Wolfgang Rathmann
  • Annette Peters
  • Rui Wang-Sattler
  • Bok-Ghee HanEmail author
  • Bong-Jo KimEmail author
Original Article

Abstract

Introduction

Type 2 diabetes (T2D) is a multifactorial disease resulting from a complex interaction between environmental and genetic risk factors. Metabolomics provide a logical framework that reflects the functional endpoints of biological processes being triggered by genetic information and various external influences.

Objectives

Identification of metabolite biomarkers can shed insight into etiological pathways and improve the prediction of disease risk. Here, we aimed to identify serum metabolites as putative biomarkers for T2D and their association with genetic variants in the Korean population.

Methods

A targeted metabolomics approach was employed to quantify serum metabolites for 2240 participants in the Korea Association REsource (KARE) cohort. T2D-related metabolites were identified by statistical methods including multivariable linear and logistic regression, and were independently replicated in the Cooperative Health Research in the Region of Augsburg (KORA) cohort. Additionally, by combining a genome wide association study (GWAS) with metabolomics, genetic variants associated with the identified T2D-related metabolites were uncovered.

Results

123 metabolites were quantified from fasting serum samples and four metabolites, hexadecanoylcarnitine (C16), glycine, lysophosphatidylcholine acyl C18:2 (lysoPC a C18:2), and phosphatidylcholine acyl-alkyl C36:0 (PC ae C36:0), were significantly altered in T2D compared to non-T2D subjects (after the Bonferroni correction for multiple testing with P < 4.07E − 04, α = 0.05). Among them, C16, glycine, and lysoPC a C18:2 were independently replicated in the KORA cohort. Alterations of these metabolites were associated with ten genetic loci including six that were previously implicated in T2D or obesity.

Conclusion

Using a targeted-metabolomics and in combination with GWAS approach, we identified three serum metabolites associated with risk of T2D in both the KARE and KORA cohort and discovered ten genetic variants in relation to the identified metabolites. These findings provide a better understanding to develop novel preventive strategies for T2D in the Korean population.

Keywords

Targeted metabolomics Type 2 diabetes Serum metabolites Genetic variants Cohort study Korean population 

Notes

Acknowledgments

This work was supported by intramural grants from the Korea National Institute of Health (2013-NG73001-00). Biospecimens and data were provided from the Korean Genome Analysis Project (4845-301), the Korean Genome and Epidemiology Study (4845-302), and the Korea Biobank Project (4851-307, KBP-2014-012), which were supported by the Korea Center for Disease Control and Prevention in the Republic of Korea. Bok-Ghee Han and Bong-Jo Kim are the guarantors of this work, had full access to all the data in the study, and take responsibility for the integrity of the data and the accuracy of the data analysis.

Author Contributions

Heun-Sik Lee, Tao Xu, Young Lee, Nam-Hee Kim, Yeon-Jung Kim, and Rui Wang-Sattler analyzed the data and interpreted the results. Heun-Sik Lee, Tao Xu, Young Lee, Jeong-Min Kim, and Rui Wang-Sattler wrote the manuscript. Sang Yun Cho, Kwang-Youl Kim, Jerzy Adamski, Annette Peters, Run Wang-Sattler, and Bong-Jo Kim assisted in manuscript preparation and revision. Kwang-Youl Kim, Moonsuk Nam, Jerzy Adamski, and Karsten Suhre performed the metabolic profiling. Heun-Sik Lee, Jeong-Min Kim, and Bong-Jo Kim conceived and designed the current study. Wolfgang Rathmann and Annette Peters conceived the KORA study.

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflicts of interest.

Ethical approval

All procedures performed in the studies were in accordance with the ethical standards of the institution and/or practice at which the studies were conducted.

Supplementary material

11306_2016_1103_MOESM1_ESM.pdf (1.4 mb)
Supplementary material 1 (PDF 1457 kb)

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Heun-Sik Lee
    • 1
  • Tao Xu
    • 2
    • 3
  • Young Lee
    • 1
  • Nam-Hee Kim
    • 1
  • Yeon-Jung Kim
    • 1
  • Jeong-Min Kim
    • 1
  • Sang Yun Cho
    • 1
  • Kwang-Youl Kim
    • 4
  • Moonsuk Nam
    • 4
    • 5
  • Jerzy Adamski
    • 6
  • Karsten Suhre
    • 7
    • 8
  • Wolfgang Rathmann
    • 9
  • Annette Peters
    • 2
    • 3
  • Rui Wang-Sattler
    • 2
    • 3
  • Bok-Ghee Han
    • 1
    Email author
  • Bong-Jo Kim
    • 1
    Email author
  1. 1.Division of Structural and Functional Genomics, Center for Genome ScienceKorea National Institute of HealthCheongju-SiKorea
  2. 2.Research Unit of Molecular EpidemiologyHelmholtz Zentrum MünchenNeuherbergGermany
  3. 3.Institute of Epidemiology IIHelmholtz Zentrum MünchenNeuherbergGermany
  4. 4.Department of Clinical PharmacologyInha University HospitalIncheonKorea
  5. 5.Department of Internal MedicineInha University School of MedicineIncheonKorea
  6. 6.Institute of Experimental Genetics, Genome Analysis CenterHelmholtz Zentrum MünchenNeuherbergGermany
  7. 7.Institute of Bioinformatics and Systems BiologyHelmholtz Zentrum MünchenNeuherbergGermany
  8. 8.Department of Physiology and BiophysicsWeill Cornell Medical College in QatarDohaQatar
  9. 9.Institute for Biometrics and Epidemiology, German Diabetes CenterLeibniz Center for Diabetes Research at Heinrich Heine UniversityDüsseldorfGermany

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