Skip to main content

Metabolomic biomarkers and novel dietary factors associated with gestational diabetes in China

Metabolomics volume 14, Article number: 149 (2018) Cite this article

Abstract

Introduction

Gestational diabetes mellitus (GDM) is impaired glucose tolerance first recognised during pregnancy; its development is associated with many adverse outcomes. Mechanisms of GDM development are not fully elucidated and few studies have used Chinese participants.

Objectives

The aim of this study was to investigate the maternal metabolome associated with GDM in a Chinese population, and explore the relationship with maternal diet.

Methods

Ninety-three participants were recruited at 26–28 weeks’ gestation from Chongqing, China. Maternal urine, serum, and hair metabolomes were analysed using gas and liquid chromatography–mass spectrometry. Dietary intake was assessed using a 96-item food frequency questionnaire.

Results

Of the 1064 metabolites identified, 73 were significantly different between cases and controls (P < 0.05), but only 2-aminobutyric acid had both a p- and q-value < 0.05. A “snack-based-dietary-pattern” was associated with an increased likelihood of GDM (odds ratio 2·1; 95% confidence interval 1.1–3.9). The association remained significant after adjustment for calorie intake but not food volume.

Conclusion

This study provides a comprehensive characterization of the maternal metabolome. The snack-based dietary pattern associated with GDM suggests that timing and frequency of consumption are important factors in the relationship between maternal diet and GDM.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3

References

  • Bauersachs, T., Compaore, J., Hopmans, E. C., Stal, L. J., Schouten, S., & Sinninghe Damste, J. S. (2009). Distribution of heterocyst glycolipids in cyanobacteria. Phytochemistry, 70, 2034–2039.

    Article  CAS  Google Scholar 

  • Broadhurst, D. I., & Kell, D. B. (2006). Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics, 2, 171–196.

    Article  CAS  Google Scholar 

  • Broecker, S., Herre, S., & Pragst, F. 2012. General unknown screening in hair by liquid chromatography-hybrid quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Forensic Science International 218, 68–81.

    Article  CAS  Google Scholar 

  • Chen, X., Zhao, D., Mao, X., Xia, Y., Baker, P. N., & Zhang, H. 2016. Maternal dietary patterns and pregnancy outcome. Nutrients 8, 351

    Article  Google Scholar 

  • Chong, Y. S., Cai, S., Lin, H., Soh, S. E., Lee, Y. S., Leow, M. K., Chan, Y. H., Chen, L., Holbrook, J. D., Tan, K. H., Rajadurai, V. S., Yeo, G. S., Kramer, M. S., Saw, S. M., Gluckman, P. D., Godfrey, K. M., Kwek, K., & Group, G. S. (2014). Ethnic differences translate to inadequacy of high-risk screening for gestational diabetes mellitus in an asian population: A cohort study. BMC Pregnancy Childbirth, 14, 345.

    Article  Google Scholar 

  • Choong, E., Bondolfi, G., Etter, M., Jermann, F., Aubry, J. M., Bartolomei, J., Gholam-Rezaee, M., & Eap, C. B. (2012). Psychotropic drug-induced weight gain and other metabolic complications in a swiss psychiatric population. Journal of Psychiatric Research, 46, 540–548.

    Article  Google Scholar 

  • De Livera, A. M., Sysi-Aho, M., Jacob, L., Gagnon-Bartsch, J. A., Castillo, S., Simpson, J. A., & Speed, T. P. (2015). Statistical methods for handling unwanted variation in metabolomics data. Analytical Chemistry, 87, 3606–3615.

    Article  CAS  Google Scholar 

  • Federation, I. D. (2015). IDF Diabetes Atlas (7th Ed). Retrieved July 23, 2017, from http://www.Diabetesatlas.Org.

  • Ferrara, A. (2007). Increasing prevalence of gestational diabetes mellitus: A public health perspective. Diabetes Care, 30(Suppl 2), S141–S146.

    Article  Google Scholar 

  • Gall, W. E., Beebe, K., Lawton, K. A., Adam, K. P., Mitchell, M. W., Nakhle, P. J., Ryals, J. A., Milburn, M. V., Nannipieri, M., Camastra, S., Natali, A., Ferrannini, E., & Group, R. S. (2010). Alpha-hydroxybutyrate is an early biomarker of insulin resistance and glucose intolerance in a nondiabetic population. PLoS ONE. 5, E10883.

    Article  Google Scholar 

  • He, J. R., Yuan, M. Y., Chen, N. N., Lu, J. H., Hu, C. Y., Mai, W. B., Zhang, R. F., Pan, Y. H., Qiu, L., Wu, Y. F., Xiao, W. Q., Liu, Y., Xia, H. M., & Qiu, X. (2015). Maternal dietary patterns and gestational diabetes mellitus: A large prospective cohort study in China. British Journal of Nutrition, 113, 1292–1300.

    Article  CAS  Google Scholar 

  • Hong, G.-F. (1992). The nitrogen fixation and its research in China. Berlin: Springer-Verlag.

    Book  Google Scholar 

  • Karpievitch, Y. V., Dabney, A. R., & Smith, R. D. (2012). Normalization and missing value imputation for label-free LC-MS analysis. BMC Bioinformatics, 13(Suppl 16), S5.

    Article  CAS  Google Scholar 

  • Karpievitch, Y. V., Nikolic, S. B., Wilson, R., Sharman, J. E., & Edwards, L. M. (2014). Metabolomics data normalization with eigenms. PLoS ONE, 9, E116221.

    Article  Google Scholar 

  • Karpievitch, Y. V., Taverner, T., Adkins, J. N., Callister, S. J., Anderson, G. A., Smith, R. D., & Dabney, A. R. (2009). Normalization of peak intensities in bottom-up ms-based proteomics using singular value decomposition. Bioinformatics, 25, 2573–2580.

    Article  CAS  Google Scholar 

  • Kim, C., Newton, K. M., & Knopp, R. H. (2002). Gestational diabetes and the incidence of type 2 diabetes: A systematic review. Diabetes Care, 25, 862–868.

    Google Scholar 

  • Landaas, S. (1975). The formation of 2-hydroxybutyric acid in experimental animals. Clinica Chimica Acta, 58, 23–32.

    Article  CAS  Google Scholar 

  • Law, K. P., Han, T. L., Mao, X., & Zhang, H. (2017). Tryptophan and purine metabolites are consistently upregulated in the urinary metabolome of patients diagnosed with gestational diabetes mellitus throughout pregnancy: A longitudinal metabolomics study of Chinese pregnant women Part 2. Clinica Chimica Acta, 468, 126–139.

    Article  CAS  Google Scholar 

  • Law, K. P., Mao, X., Han, T. L., & Zhang, H. (2016). Unsaturated plasma phospholipids are consistently lower in the patients diagnosed with gestational diabetes mellitus throughout pregnancy: A longitudinal metabolomics study of chinese pregnant women part 1. Clinica Chimica Acta, 465, 53–71.

    Article  Google Scholar 

  • Lu, S. C. (2009). Regulation of glutathione synthesis. Molecular Aspects of Medicine, 30, 42–59.

    Article  CAS  Google Scholar 

  • Nicholson, J. K., Lindon, J. C., & Holmes, E. (1999). ‘Metabonomics’: Understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological nmr spectroscopic data. Xenobiotica, 29, 1181–1189.

    Article  CAS  Google Scholar 

  • Nordin, C., & Bertilsson, L. (1995). Active hydroxymetabolites of antidepressants. emphasis on E-10-hydroxy-nortriptyline. Clinical Pharmacokinetics, 28, 26–40.

    Article  CAS  Google Scholar 

  • O’gorman, A., Gibbons, H., & Brennan, L. (2013). Metabolomics in the identification of biomarkers of dietary intake. Computational and Structural Biotechnology Journal, 4, E201301004.

    Article  Google Scholar 

  • International Association of Diabetes, Pregnancy Study Groups Consensus, Metzger, B. E., Gabbe, S. G., Persson, B., Buchanan, T. A., Catalano, P. A., Damm, P., Dyer, A. R., Leiva, A., Hod, M., Kitzmiler, J. L., Lowe, L. P., Mcintyre, H. D., Oats, J. J., Omori, Y., & Schmidt, M. I. (2010). International Association of Diabetes And Pregnancy Study Groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care, 33, 676–682.

    Article  Google Scholar 

  • Reece, E. A., Leguizamon, G., & Wiznitzer, A. (2009). Gestational diabetes: The need for a common ground. Lancet, 373, 1789–1797.

    Article  Google Scholar 

  • Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J. C., & Muller, M. (2011). Proc: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, 77.

    Article  Google Scholar 

  • Smart, K. F., Aggio, R. B., Van Houtte, J. R., & Villas-Boas, S. G. (2010). Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography-mass spectrometry. Nature Protocols, 5, 1709–1729.

    Article  CAS  Google Scholar 

  • Storey, J. D., & Taylor J. E. (2004). Strong control, conservative point estimation, and simultaneous conservative consistency of false discovery rates: A unified approach. Journal of the Royal Statistical Society, Series B, 66, 187–205.

    Article  Google Scholar 

  • Storey, J. D., & Tibshirani, R. (2003). Statistical significance for genomewide studies. Proceedings of the National Academy of Sciences of the United States of America, 100, 9440–9445.

    Article  CAS  Google Scholar 

  • Sulek, K., Han, T. L., Villas-Boas, S. G., Wishart, D. S., Soh, S. E., Kwek, K., Gluckman, P. D., Chong, Y. S., Kenny, L. C., & Baker, P. N. (2014). Hair metabolomics: Identification of fetal compromise provides proof of concept for biomarker discovery. Theranostics, 4, 953–959.

    Article  CAS  Google Scholar 

  • United States Environmental Protection Agency. (2017). Health and ecological effects (Online). Retrieved July 23, 2017, from http://www.Epa.Gov/Nutrient-Policy-Data/Health-And-Ecological-Effects.

  • Wickham, H. (2009). Ggplot2: Elegant graphics for data analysis. New York: Springer-Verlag.

    Book  Google Scholar 

  • Willett, W., Manson, J., & Liu, S. (2002). Glycemic index, glycemic load, and risk of type 2 diabetes. The American Journal of Clinical Nutrition, 76, 274S–280S.

    Article  Google Scholar 

  • Wu, G., Fang, Y. Z., Yang, S., Lupton, J. R., & Turner, N. D. (2004). Glutathione metabolism and its implications for health. Journal of Nutrition, 134, 489–492.

    Article  CAS  Google Scholar 

  • Xia, J., & Wishart, D. S. (2016). Using metaboanalyst 3.0 for comprehensive metabolomics data analysis. Current Protocols in Bioinformatics, 55, 14.10.1–14.10.91.

    Article  Google Scholar 

  • Yang, Y. X., Wang, G. Y., & Pan, X. C. (2009). China food composition (Book 1). Beijing: Peking University Medical Press.

    Google Scholar 

  • Zhu, W. W., Yang, H. X., Wang, C., Su, R. N., Feng, H., & Kapur, A. (2017). High prevalence of gestational diabetes mellitus in beijing: Effect of maternal birth weight and other risk factors. Chinese Medical Journal (Engl), 130, 1019–1025.

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledge the LC–MS scientific support provided by Dr. Kai Law. We also thank Xun Mao and Diqi Zhao for the help of data and sample collection.

Funding

This work was supported by the National Natural Science Foundation of China [Grant Nos. 81571453, 81771607, 81701477, 81650110522] and The 111 Project [Grant No. Yuwaizhuan (2016)32]. The funders were not involved in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.

Author information

Author notes

  1. Xuyang Chen, Jamie V. de Seymour and Ting-Li Han are joint first authors.

Authors and Affiliations

  1. Department of Obstetrics and Gynaecology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China

    Xuyang Chen, Ting-Li Han, Chang Chen, Ting Zhang, Hua Zhang & Philip N. Baker

  2. State Key Laboratory of Maternal and Fetal Medicine of Chongqing Municipality, Chongqing Medical University, Chongqing, 400016, China

    Xuyang Chen, Ting Zhang & Hua Zhang

  3. Liggins Institute, The University of Auckland, Auckland, 1023, New Zealand

    Jamie V. de Seymour & Ting-Li Han

  4. School of Public Health and Management, Chongqing Medical University, Chongqing, 400016, China

    Yinyin Xia

  5. Institute of Life Sciences, Chongqing Medical University, Chongqing, 400016, China

    Chang Chen

  6. College of Medicine, Biological Sciences and Psychology, University of Leicester, Leicester, UK

    Philip N. Baker

Authors
  1. Xuyang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jamie V. de Seymour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ting-Li Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yinyin Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Chang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ting Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Philip N. Baker
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YX, PB and HZ conceived the idea and designed the study. TZ and XC collected the questionnaires and samples. TH, CC, and JdS analysed the data. XC, JdS, and TH contributed to the writing of the manuscript.

Corresponding author

Correspondence to Hua Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

Ethical approval was granted by the Ethics committee of the first affiliated hospital of Chongqing Medical University and written informed consent was obtained from all participants.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chen, X., de Seymour, J.V., Han, TL. et al. Metabolomic biomarkers and novel dietary factors associated with gestational diabetes in China. Metabolomics 14, 149 (2018). https://doi.org/10.1007/s11306-018-1445-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11306-018-1445-6

Keywords

  • Gestational diabetes
  • Metabolomics
  • Maternal diet
  • Biomarker