Abstract

BackgroundType 1 diabetes mellitus (T1DM) is one of the most common pediatric diseases and its incidence is rising in many countries. Recently, it has been shown that metabolites other than glucose play an important role in insulin deficiency and the development of diabetes. The aim of our study was to look for discriminating variation in the concentrations of small-molecule metabolites in the plasma of T1DM children as compared to non-diabetic matched controls using proton nuclear magnetic resonance (1H-NMR)-based metabolomics.MethodsA cross-sectional study was set-up to examine the metabolic profile in fasting plasma samples from seven children with poorly controlled T1DM and seven non-diabetic controls aged 8–18 years, and matched for gender, age and BMI-SDS. The obtained plasma 1H-NMR spectra were rationally divided into 110 integration regions, representing the metabolic phenotype. These integration regions reflect the relative metabolite concentrations and were used as statistical variables to construct (train) a classification model in discriminating between T1DM patients and controls.ResultsThe total amount of variation explained by the model between the groups is 81.0% [R2Y(cum)] and within the groups is 75.8% [R2X(cum)]. The predictive ability of the model [Q2(cum)] obtained by cross-validation is 50.7%, indicating that the discrimination between the groups on the basis of the metabolic phenotype is valid. Besides the expected higher concentration of glucose, the relative concentrations of lipids (triglycerides, phospholipids and cholinated phospholipids) are clearly lower in the plasma of T1DM patients as compared to controls. Also the concentrations of the amino acids serine, tryptophan and cysteine are slightly decreased.ConclusionsThe present study demonstrates that metabolic profiling of plasma by 1H-NMR spectroscopy allows to discriminate between T1DM patients and controls. The metabolites that significantly differ between both groups might point to disturbances in biochemical pathways including (1) choline deficiency, (2) increased gluconeogenesis, and (3) glomerular hyperfiltration. Although the sample size of this study is still somewhat limited and a validation should be performed, the proof of principle looks promising and justifies a deeper investigation of the diagnostic possibilities of 1H-NMR metabolomics in follow-up studies.Trial registration NCT03014908. Registered 06/01/2017. Retrospectively registered

Highlights

  • Type 1 diabetes mellitus (T1DM) is one of the most common pediatric diseases and its incidence is rising in many countries

  • Multivariate orthogonal partial least squares discriminant analysis (OPLS-DA) statistics was used to train a classification model in discriminating between T1DM patients and controls based on data input from their metabolic profile or phenotype

  • Staining the principal component analysis (PCA) score plots for gender, age and body mass index (BMI)-SDS clearly shows that none of these factors have a confounding effect on the discrimination between T1DM patients and controls, as was expected for matched groups

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Summary

Introduction

Type 1 diabetes mellitus (T1DM) is one of the most common pediatric diseases and its incidence is rising in many countries. The aim of our study was to look for discriminating variation in the concentrations of small-molecule metabolites in the plasma of T1DM children as compared to non-diabetic matched controls using proton nuclear magnetic resonance (1H-NMR)-based metabolomics. Only a few researchers investigated the plasma metabolic fingerprint of T1DM in children, using mass spectrometry as analytical tool [6,7,8]. The use of nuclear magnetic resonance (NMR)-based metabolomics to obtain a deeper knowledge of the plasma metabolic profile of T1DM has not been fully explored in the pediatric population. The objective of the current study was to investigate metabolic variations in the plasma of T1DM children and adolescents as compared to plasma of non-diabetic matched controls using 1H-NMR spectroscopy combined with multivariate statistics

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