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. 2017;13(9):104.
doi: 10.1007/s11306-017-1239-2. Epub 2017 Jul 28.

Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study

Jun Liu  1 , Sabina Semiz  2   3 , Sven J van der Lee  1 , Ashley van der Spek  1 , Aswin Verhoeven  4 , Jan B van Klinken  5 , Eric Sijbrands  6 , Amy C Harms  7   8 , Thomas Hankemeier  1   7   8 , Ko Willems van Dijk  5   9 , Cornelia M van Duijn  1   8 , Ayşe Demirkan  1   5
Affiliations

Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study

Jun Liu et al. Metabolomics. 2017.

Abstract

Background: The growing field of metabolomics has opened up new opportunities for prediction of type 2 diabetes (T2D) going beyond the classical biochemistry assays.

Objectives: We aimed to identify markers from different pathways which represent early metabolic changes and test their predictive performance for T2D, as compared to the performance of traditional risk factors (TRF).

Methods: We analyzed 2776 participants from the Erasmus Rucphen Family study from which 1571 disease free individuals were followed up to 14-years. The targeted metabolomics measurements at baseline were performed by three different platforms using either nuclear magnetic resonance spectroscopy or mass spectrometry. We selected 24 T2D markers by using Least Absolute Shrinkage and Selection operator (LASSO) regression and tested their association to incidence of disease during follow-up.

Results: The 24 markers i.e. high-density, low-density and very low-density lipoprotein sub-fractions, certain triglycerides, amino acids, and small intermediate compounds predicted future T2D with an area under the curve (AUC) of 0.81. The performance of the metabolic markers compared to glucose was significantly higher among the young (age < 50 years) (0.86 vs. 0.77, p-value <0.0001), the female (0.88 vs. 0.84, p-value =0.009), and the lean (BMI < 25 kg/m2) (0.85 vs. 0.80, p-value =0.003). The full model with fasting glucose, TRFs, and metabolic markers yielded the best prediction model (AUC = 0.89).

Conclusions: Our novel prediction model increases the long-term prediction performance in combination with classical measurements, brings a higher resolution over the complexity of the lipoprotein component, increasing the specificity for individuals in the low risk group.

Keywords: Early biomarkers; Metabolites; Metabolomics; Prediction; Prospective study; Type 2 diabetes.

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Conflict of interest statement

Conflict of interest

All the authors report no financial or other conflict of interest relevant to the subject of this article.

Ethical approval

Informed consent has been obtained from patients where appropriate. The study protocol was approved by the medical ethics board of the Erasmus Medical Center Rotterdam, the Netherlands. This article does not contain any studies with animals performed by any of the authors.

Figures

Fig. 1
Fig. 1
Flow chart of the metabolite selection
Fig. 2
Fig. 2
AUC comparisons in different prediction models. Continuous Net Reclassification Improvement (NRI) indices were performed to compare different prediction models. FG fasting glucose, TRFs all traditional risk factors—age, sex, family history, BMI, waist circumference, hypertension, HDL-cholesterol, triglycerides
Fig. 3
Fig. 3
AUC comparisons in different subgroups. Continuous Net Reclassification Improvement (NRI) indices were performed to compare different prediction models. Black bars metabolite model; white bars fasting glucose model. (/): Number of controls and incident cases analyzed in the follow-up
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References

    1. Andrew G, Jennifer H. Data analysis using regression and multilevel/hierarchical models. Cambridge: Cambridge University Press; 2006. pp. 529–543.
    1. Aulchenko YS, de Koning DJ, Haley C. Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis. Genetics. 2007;177(1):577–585. doi: 10.1534/genetics.107.075614. - DOI - PMC - PubMed
    1. Carnethon MR, De Chavez PJ, Biggs ML, Lewis CE, Pankow JS, Bertoni AG, et al. Association of weight status with mortality in adults with incident diabetes. JAMA. 2012;308(6):581–590. doi: 10.1001/jama.2012.9282. - DOI - PMC - PubMed
    1. Demirkan A, Henneman P, Verhoeven A, Dharuri H, Amin N, van Klinken JB, et al. Insight in genome-wide association of metabolite quantitative traits by exome sequence analyses. PLoS Genetics. 2015;11(1):e1004835. doi: 10.1371/journal.pgen.1004835. - DOI - PMC - PubMed
    1. Demirkan A, van Duijn CM, Ugocsai P, Isaacs A, Pramstaller PP, Liebisch G, et al. Genome-wide association study identifies novel loci associated with circulating phospho- and sphingolipid concentrations. PLoS Genetics. 2012;8(2):e1002490. doi: 10.1371/journal.pgen.1002490. - DOI - PMC - PubMed

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