Metabolic profiling of type 1 diabetes mellitus in children and adolescents: a case-control study

Abstract

Background: Type 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.

Methods: A 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.

Results: The 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.

Conclusions: The 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.

Keywords: 1H-NMR spectroscopy; Metabolomics; Pediatrics; Type 1 diabetes.

Fig. 1

PCA score plot obtained for T1DM patients (filled triangle) and healthy controls (circle). Each participant is represented by its metabolic profile and visualized as a single symbol of which the location is determined by the contributions of the 110 variables in the ¹H-NMR spectrum. The PCA score plot shows the first principal component (PC1: 69.4%), explaining the largest variance within the dataset, versus the second principal component (PC2: 12.6%) that explains the second largest variance

Fig. 2

OPLS-DA score plot (a) and S-line plot (b) obtained for T1DM patients (filled triangle) and healthy controls (circle). Each participant is represented by its metabolic profile and visualized as a single symbol of which the location is determined by the contributions of the 110 variables in the ¹H-NMR spectrum. The OPLS-DA score plot shows the first predictive component (t[1]P: 51.8%), explaining the variation between the groups, versus the first orthogonal component (t[1]O: 24.0%) that explains the variation within the groups. The OPLS-DA S-line plot visualizes differences between T1DM patients (negative) and controls (positive). The left y-axis represents p(ctr)[1], the covariance between a variable and the classification score. It indicates if an increase or decrease of a variable is correlated to the classification score. The magnitude of the covariance is however difficult to interpret since covariance is scale dependent. This means that a high value for the covariance does not necessary imply a strong correlation, as the covariance is also influenced by the intensity of the signal with respect to the noise level. Therefore this measure will likely indicate variables with large signal intensities. The right y-axis shows p(corr)[1], the correlation coefficient between a variable and the classification score (i.e. the normalized covariance). It gives a linear indication of the strength of the correlation. As the correlation is independent of the intensity of the variable, it will be a better measure for the reliability of the variable in the classification process. In  b, the red color stands for the highest absolute value of the correlation coefficient. Strongly discriminating variables have a large intensity and large reliability