, Volume 61, Issue 5, pp 1046–1054 | Cite as

Serum metabolomic profile of incident diabetes

  • Casey M. Rebholz
  • Bing Yu
  • Zihe Zheng
  • Patrick Chang
  • Adrienne Tin
  • Anna Köttgen
  • Lynne E. Wagenknecht
  • Josef Coresh
  • Eric Boerwinkle
  • Elizabeth Selvin



Metabolomic profiling offers the potential to reveal metabolic pathways relevant to the pathophysiology of diabetes and improve diabetes risk prediction.


We prospectively analysed known metabolites using an untargeted approach in serum specimens from baseline (1987–1989) and incident diabetes through to 31 December 2015 in a subset of 2939 Atherosclerosis Risk in Communities (ARIC) study participants with metabolomics data and without prevalent diabetes.


Among the 245 named compounds identified, seven metabolites were significantly associated with incident diabetes after Bonferroni correction and covariate adjustment; these included a food additive (erythritol) and compounds involved in amino acid metabolism [isoleucine, leucine, valine, asparagine, 3-(4-hydoxyphenyl)lactate] and glucose metabolism (trehalose). Higher levels of metabolites were associated with increased risk of incident diabetes (HR per 1 SD increase in isoleucine 2.96, 95% CI 2.02, 4.35, p = 3.18 × 10−8; HR per 1 SD increase in trehalose 1.16, 95% CI 1.09, 1.25, p = 1.87 × 10−5), with the exception of asparagine, which was associated with a lower risk of diabetes (HR per 1 SD increase in asparagine 0.78, 95% CI 0.71, 0.85, p = 4.19 × 10−8). The seven metabolites modestly improved prediction of incident diabetes beyond fasting glucose and established risk factors (C statistics 0.744 vs 0.735, p = 0.001 for the difference in C statistics).


Branched chain amino acids may play a role in diabetes development. Our study is the first to report asparagine as a protective biomarker of diabetes risk. The serum metabolome reflects known and novel metabolic disturbances that improve prediction of diabetes.


Amino acids Branched chain amino acids Diabetes Metabolic pathways Metabolomics 



Atherosclerosis Risk in Communities


Tricarboxylic acid



The authors thank the staff and participants of the ARIC study for their important contributions.

Contribution statement

CMR proposed the study, planned the statistical analysis and wrote the manuscript. EB obtained the metabolomics data. BY, ZZ, PC and AT contributed to the statistical analysis. AK, LEW, JC, EB and ES provided methodological and content-related expertise. All authors provided substantial contributions to the conception and design, acquisition of data, or analysis and interpretation of data; drafted the article or revised it critically for important intellectual content; and provided final approval of the version to be published. CMR is responsible for the integrity of the work as a whole.


The ARIC Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). CMR is supported by a mentored research scientist development award from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (K01 DK107782). ES is supported by NIH/NIDDK grants K24 DK106414 and R01 DK089174. JC is partially supported by the Chronic Kidney Disease Biomarkers Consortium from the NIDDK (U01 DK085689). AK is supported by Deutsche Forschungsgemeinschaft (DFG 3598/3–1 and DFG 3598/4–1).

Data availability

Selected data elements of the ARIC study are available upon request through the National Health, Lung, and Blood Institute (NHLBI) Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC).

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Supplementary material

125_2018_4573_MOESM1_ESM.pdf (113 kb)
ESM Tables (PDF 113 kb)


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Copyright information

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

Authors and Affiliations

  • Casey M. Rebholz
    • 1
    • 2
  • Bing Yu
    • 3
  • Zihe Zheng
    • 1
    • 2
  • Patrick Chang
    • 3
  • Adrienne Tin
    • 1
    • 2
  • Anna Köttgen
    • 1
    • 4
  • Lynne E. Wagenknecht
    • 5
  • Josef Coresh
    • 1
    • 2
  • Eric Boerwinkle
    • 3
  • Elizabeth Selvin
    • 1
    • 2
  1. 1.Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  2. 2.Welch Center for Prevention, Epidemiology and Clinical ResearchJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  3. 3.Department of Epidemiology, Human Genetics & Environmental SciencesUniversity of Texas Health Sciences Center at Houston School of Public HealthHoustonUSA
  4. 4.Institute of Genetic Epidemiology, Medical Center and Faculty of MedicineUniversity of FreiburgFreiburgGermany
  5. 5.Division of Public Health SciencesWake Forest School of MedicineWinston-SalemUSA

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