Diabetes Metab J.  2021 Mar;45(2):195-208. 10.4093/dmj.2019.0209.

Plasma Targeted Metabolomics Analysis for Amino Acids and Acylcarnitines in Patients with Prediabetes, Type 2 Diabetes Mellitus, and Diabetic Vascular Complications

• Li X
• Li Y
• Liang Y
• Hu R
• Xu W
• Liu Y

• KMID: 2514196
• DOI: http://doi.org/10.4093/dmj.2019.0209

Abstract

Background
We hypothesized that specific amino acids or acylcarnitines would have benefits for the differential diagnosis of diabetes. Thus, a targeted metabolomics for amino acids and acylcarnitines in patients with diabetes and its complications was carried out.
Methods
A cohort of 54 normal individuals and 156 patients with type 2 diabetes mellitus and/or diabetic complications enrolled from the First Affiliated Hospital of Jinzhou Medical University was studied. The subjects were divided into five main groups: normal individuals, impaired fasting glucose, overt diabetes, diabetic microvascular complications, and diabetic peripheral vascular disease. The technique of tandem mass spectrometry was applied to obtain the plasma metabolite profiles. Metabolomics multivariate statistics were applied for the metabolic data analysis and the differential metabolites determination.
Results
A total of 10 cross-comparisons within diabetes and its complications were designed to explore the differential metabolites. The results demonstrated that eight comparisons existed and yielded significant metabolic differences. A total number of 24 differential metabolites were determined from six selected comparisons, including up-regulated amino acids, down-regulated medium-chain and long-chain acylcarnitines. Altered differential metabolites provided six panels of biomarkers, which were helpful in distinguishing diabetic patients.
Conclusion
Our results demonstrated that the biomarker panels consisted of specific amino acids and acylcarnitines which could reflect the metabolic variations among the different stages of diabetes and might be useful for the differential diagnosis of prediabetes, overt diabetes and diabetic complications.

Keyword

Amino acids; Carnitine; Diabetes mellitus, type 2; Metabolomics

Figure

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  Fig. 1. The orthogonal partial least squares discriminant analysis (OPLS-DA) score plots were generated from six comparisons. In these score plots, individuals from control groups and comparable disease groups were represented by different colors (“blue” for control groups, “red” for case groups). The parameters of OPLS-DA models (including R2X, R2Y, Q2Y, and RMSEE) were also plotted to represent the quality of these established models. t1 and to1 were the first principal component and the first orthogonal component of OPLS-DA models, respectively. IFG, impaired fasting glucose; NI, normal individual; SD, simple diabetes; DMVC, diabetic microvascular complication; DPVD, diabetic peripheral vascular disease.

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  Fig. 2. Differential metabolites were determined by six comparisons. (A) The heatmap represented the levels of all 58 metabolites in six groups and (B) the heatmap represented the level of 24 differential metabolites in six groups. NI, normal individual; IFG, impaired fasting glucose; SD, simple diabetes; DMVC, diabetic microvascular complication; DPVD, diabetic peripheral vascular disease.

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  Fig. 3. The enrichment pathways of each biomarker were provided by Metaboanalysis 4.0 platform. IFG, impaired fasting glucose; NI, normal individual; SD, simple diabetes; DMVC, diabetic microvascular complication; DPVD, diabetic peripheral vascular disease.

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  Fig. 4. The receiver operating characteristic (ROC) curves and prediction plots using the model were established by the six biomarker panels. These plots were plotted to represent the sensitivity, specify and predictive capacity of the partial least square (PLS) regression models established by each biomarker panel. In the ROC curves, the area under curve (AUC) and confidence interval (CI) were also given in the plots. In the prediction plots, individuals from control group and case group were also represented by different colors (blue and red). IFG, impaired fasting glucose; NI, normal individual; SD, simple diabetes; DMVC, diabetic microvascular complication; DPVD, diabetic peripheral vascular disease.

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  Fig. 5. The metabolic pathways of differential biomarkers from six comparison were plotted based on Kyoto Encyclopedia of Genes and Genomes (KEGG) database. In this plot, differential metabolites selected form six comparisons were mapped into several pathways, including tricarboxylic acid (TCA) cycle, urea cycle, fatty acid oxidation and etc. The levels of differential metabolites from six groups were represented in boxplot. Analysis of variance (ANOVA) was carried out to determine the significance of each differential metabolite among six groups. NI, normal individual; IFG, impaired fasting glucose; SD, simple diabetes; DMVC, diabetic microvascular complication; DPVD, diabetic peripheral vascular disease. aP<0.05, bP<0.01, cP<0.001, dP<0.0001.

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