Diabetologia

pp 1–10 | Cite as

Metabolomics insights into early type 2 diabetes pathogenesis and detection in individuals with normal fasting glucose

  • Jordi Merino
  • Aaron Leong
  • Ching-Ti Liu
  • Bianca Porneala
  • Geoffrey A. Walford
  • Marcin von Grotthuss
  • Thomas J. Wang
  • Jason Flannick
  • Josée Dupuis
  • Daniel Levy
  • Robert E. Gerszten
  • Jose C. Florez
  • James B. Meigs
Article

Abstract

Aims/hypothesis

Identifying the metabolite profile of individuals with normal fasting glucose (NFG [<5.55 mmol/l]) who progressed to type 2 diabetes may give novel insights into early type 2 diabetes disease interception and detection.

Methods

We conducted a population-based prospective study among 1150 Framingham Heart Study Offspring cohort participants, age 40–65 years, with NFG. Plasma metabolites were profiled by LC-MS/MS. Penalised regression models were used to select measured metabolites for type 2 diabetes incidence classification (training dataset) and to internally validate the discriminatory capability of selected metabolites beyond conventional type 2 diabetes risk factors (testing dataset).

Results

Over a follow-up period of 20 years, 95 individuals with NFG developed type 2 diabetes. Nineteen metabolites were selected repeatedly in the training dataset for type 2 diabetes incidence classification and were found to improve type 2 diabetes risk prediction beyond conventional type 2 diabetes risk factors (AUC was 0.81 for risk factors vs 0.90 for risk factors + metabolites, p = 1.1 × 10−4). Using pathway enrichment analysis, the nitrogen metabolism pathway, which includes three prioritised metabolites (glycine, taurine and phenylalanine), was significantly enriched for association with type 2 diabetes risk at the false discovery rate of 5% (p = 0.047). In adjusted Cox proportional hazard models, the type 2 diabetes risk per 1 SD increase in glycine, taurine and phenylalanine was 0.65 (95% CI 0.54, 0.78), 0.73 (95% CI 0.59, 0.9) and 1.35 (95% CI 1.11, 1.65), respectively. Mendelian randomisation demonstrated a similar relationship for type 2 diabetes risk per 1 SD genetically increased glycine (OR 0.89 [95% CI 0.8, 0.99]) and phenylalanine (OR 1.6 [95% CI 1.08, 2.4]).

Conclusions/interpretation

In individuals with NFG, information from a discrete set of 19 metabolites improved prediction of type 2 diabetes beyond conventional risk factors. In addition, the nitrogen metabolism pathway and its components emerged as a potential effector of earliest stages of type 2 diabetes pathophysiology.

Keywords

Metabolomics Normoglycaemia Type 2 diabetes pathophysiology Type 2 diabetes prediction 

Abbreviations

FHS

Framingham Heart Study

HDLc

HDL-cholesterol

IFG

Impaired fasting glucose

IGT

Impaired glucose tolerance

IVW

Inverse-variance weighted

LASSO

Least absolute shrinkage and selection operator

LDLc

LDL-cholesterol

NFG

Normal fasting glucose

ROC

Receiver operator characteristic

TAG

Triacylglycerol

Notes

Acknowledgements

This research was conducted in part using data and resources from the FHS of the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. The analyses reflect intellectual input and resource development from the FHS investigators participating in the SNP Health Association Resource (SHARe) project. The authors wish to thank the GoT2D Consortium for access to their data.

Contribution statement

JM, GAW, CTL, JD, JCF and JBM participated in the design and conception of the study. JM, BP, MG and JF acquired and analysed the data. All authors participated in the interpretation of data, drafting of the manuscript and its revisions and approved the final version. JM and JBM are the guarantors of this work and, as such, 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.

Duality of interest

JCF has received consulting honoraria from Boehringer-Ingelheim, Merck and Intarcia Therapeutics. All other authors declare that there is no duality of interest associated with their contribution to this manuscript.

Supplementary material

125_2018_4599_MOESM1_ESM.pdf (303 kb)
ESM (PDF 303 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Jordi Merino
    • 1
    • 2
  • Aaron Leong
    • 2
    • 3
  • Ching-Ti Liu
    • 4
  • Bianca Porneala
    • 3
  • Geoffrey A. Walford
    • 1
    • 2
  • Marcin von Grotthuss
    • 2
  • Thomas J. Wang
    • 5
  • Jason Flannick
    • 1
    • 2
  • Josée Dupuis
    • 4
    • 6
  • Daniel Levy
    • 6
    • 7
  • Robert E. Gerszten
    • 8
    • 9
  • Jose C. Florez
    • 1
    • 2
    • 10
  • James B. Meigs
    • 2
    • 3
    • 10
  1. 1.Diabetes Unit and Center for Genomic MedicineMassachusetts General HospitalBostonUSA
  2. 2.Programs in Metabolism and Medical & Population GeneticsBroad Institute of MIT and HarvardCambridgeUSA
  3. 3.Division of General Internal MedicineMassachusetts General HospitalBostonUSA
  4. 4.Department of BiostatisticsBoston University School of Public HealthBostonUSA
  5. 5.Division of Cardiovascular MedicineVanderbilt UniversityNashvilleUSA
  6. 6.The Framingham Heart Study, National Heart, Lung and Blood InstituteNational Institutes of HealthFraminghamUSA
  7. 7.The Population Sciences Branch, Division of Intramural ResearchNational Heart, Lung, and Blood Institute, NIHBethesdaUSA
  8. 8.Division of Cardiovascular MedicineBeth Israel Deaconess Medical CenterBostonUSA
  9. 9.Broad Institute of MIT and Harvard Program in MetabolismCambridgeUSA
  10. 10.Department of MedicineHarvard Medical SchoolBostonUSA

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