Metabolomics of Type 2 Diabetes Mellitus in Sprague Dawley Rats-In Search of Potential Metabolic Biomarkers

Circulating glutamine/glutamate ratio is closely associated with type 2 diabetes and its associated complications

Objective

This study aims to conduct a comprehensive investigation of the serum amino acid profiles of individuals with type 2 diabetes (T2D) and its related complications.

Methods

Patients with T2D were enrolled in this study. Sixteen kinds of common amino acids in the fasting circulating were assessed through liquid chromatography-mass spectrometry (LC-MS). Subsequently, correlation, regression analyses, and receiver operating characteristic (ROC) curves were conducted to assess the associations between amino acids and clinical indicators.

Results

Thirteen different kinds of amino acids were identified in diabetic patients, as compared with normal controls. The Glutamine/Glutamate (Gln/Glu) ratio was negatively correlated with BMI, HbA1c, serum uric acid, and the triglyceride-glucose (TyG) index, while it was positively correlated with HDL-C. Logistic regression analyses indicated that Gln/Glu was a consistent protective factor for both T2D (OR = 0.65, 95% CI 0.50-0.86) and obesity (OR = 0.79, 95% CI 0.66-0.96). The ROC curves demonstrated that Gln/Glu, proline, valine, and leucine provided effective predictions for diabetes risk, with Gln/Glu exhibiting the highest AUC [0.767 (0.678-0.856)]. In patients with T2D, Gln was the only amino acid that displayed a negative correlation with HbA1c (r = -0.228, p = 0.017). Furthermore, HOMA-β exhibited a negative correlation with Glu (r = -0.301, p = 0.003) but a positive correlation with Gln/Glu (r = 0.245, p = 0.017). Notably, logistic regression analyses revealed an inverse correlation of Gln/Glu with the risk of diabetic kidney disease (OR = 0.74, 95% CI 0.55-0.98) and a positive association with the risk of diabetic retinopathy (OR = 1.53, 95% CI 1.08-2.15).

Conclusion

The Gln/Glu ratio exhibited a significant association with diabetes, common metabolic parameters, and diabetic complications. These findings shed light on the pivotal role of Gln metabolism in T2D and its associated complications.

Investigating the mechanism of cornel iridoid glycosides on type 2 diabetes mellitus using serum and urine metabolites in rats

ETHNOPHARMACOLOGICAL RELEVANCE

Cornel iridoid glycosides (CIG) are extracted from Corni fructus, a herbal medicine used in traditional Chinese medicine to treat diabetes. However, the antidiabetic effects of CIG and the underlying metabolic mechanisms require further exploration.

AIM OF THE STUDY

This study aimed to assess the antidiabetic effects and metabolic mechanism of CIG by performing metabolomic analyses of serum and urine samples of rats.

MATERIALS AND METHODS

A rat model of type 2 diabetes mellitus (T2DM) was established by administering a low dose of streptozotocin (30 mg/kg) intraperitoneally after 4 weeks of feeding a high-fat diet. The model was evaluated based on several parameters, including fasting blood glucose (FBG), random blood glucose (RBG), urine volume, liver index, body weight, histopathological sections, and serum biochemical parameters. Subsequently, serum and urine metabolomics were analyzed using ultra-high-pressure liquid chromatography coupled with linear ion trap-Orbitrap tandem mass spectrometry (UHPLC-LTQ-Orbitrap-MS). Data were analyzed using unsupervised principal component analysis (PCA) and supervised orthogonal partial least squares discriminant analysis (OPLS-DA). Differential metabolites were examined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways to explore the underlying mechanisms.

RESULTS

After 4 weeks of treatment with different doses of CIG, varying degrees of antidiabetic effects were observed, along with reduced liver and pancreatic injury, and improved oxidative stress levels. Compared with the T2DM group, 19 and 23 differential metabolites were detected in the serum and urine of the CIG treatment group, respectively. The key metabolites involved in pathway regulation include taurine, chenodeoxycholic acid, glycocholic acid, and L-tyrosine in the serum and glycine, hippuric acid, phenylacetylglycine, citric acid, and D-glucuronic acid in the urine, which are related to lipid, amino acid, energy, and carbohydrate metabolism.

CONCLUSIONS

This study confirmed the antidiabetic effects of CIG and revealed that CIG effectively controlled metabolic disorders in T2DM rats. This seems to be meaningful for the clinical application of CIG, and can benefit further studies on CIG mechanism.

A GC×GC-MS method based on solid-state modulator for non-targeted metabolomics: Comparison with traditional GC-MS method

The formidable challenge posed by the presence of extremely high amounts of compounds and large differences in concentrations in plasma significantly complicates non-targeted metabolomics analyses. In this study, a comprehensive two-dimensional gas chromatography-quadrupole mass spectrometry (GC×GC-qMS) method with a solid-state modulator (SSM) for non-targeted metabolomics in beagle plasma was first established based on a GC-MS method, and the qualitative and quantitative performance of the two platforms were compared. Identification of detected compounds was accomplished utilizing NIST database match scores, retention indices (RIs) and standards. Semi-quantification involved the calculation of peak area ratios to internal standards. Metabolite identification sheets were generated for plasma samples on both analytical platforms, featuring 22 representative metabolites chosen for validating qualitative accuracy, and for conducting comparisons of linearity, accuracy, precision, and sensitivity. The outcomes revealed a threefold increase in the number of identifiable metabolites on the GC×GC-MS platform, with lower limits of quantitation (LLOQs) reduced to 0.5-0.05 times those achieved on the GC-MS platform. Accuracy in quantification for both GC×GC-MS and GC-MS fell within the range of 85-115%, and the vast majority of intra- and inter-day precisions were within the range of 20%. These findings underscore that relative to the conventional GC-MS method, the GC×GC-MS method developed in this study, combined with SSM, exhibits enhanced qualitative capabilities, heightened sensitivity, and comparable accuracy and precision, rendering it more suitable for non-targeted metabolomics analyses.

A review of disease risk prediction methods and applications in the omics era

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

Metabolomics (Non-Targeted) of Induced Type 2 Diabetic Sprague Dawley Rats Comorbid with a Tissue-Dwelling Nematode Parasite

Type 2 diabetes is a non-communicable metabolic syndrome that is characterized by the dysfunction of pancreatic β-cells and insulin resistance. Both animal and human studies have been conducted, demonstrating that helminth infections are associated with a decreased prevalence of type 2 diabetes mellitus (T2DM). However, there is a paucity of information on the impact that helminths have on the metabolome of the host and how the infection ameliorates T2DM or its progression. Therefore, this study aimed at using a non-targeted metabolomics approach to systematically identify differentiating metabolites from serum samples of T2DM-induced Sprague Dawley (SD) rats infected with a tissue-dwelling nematode, Trichinella zimbabwensis, and determine the metabolic pathways impacted during comorbidity. Forty-five male SD rats with a body weight between 160 g and 180 g were used, and these were randomly selected into control (non-diabetic and not infected with T. zimbabwensis) (n = 15) and T2DM rats infected with T. zimbabwensis (TzDM) (n = 30). The results showed metabolic separation between the two groups, where d-mannitol, d-fructose, and glucose were upregulated in the TzDM group, when compared to the control group. L-tyrosine, glycine, diglycerol, L-lysine, and L-hydroxyproline were downregulated in the TzDM group when compared to the control group. Metabolic pathways which were highly impacted in the TzDM group include biotin metabolism, carnitine synthesis, and lactose degradation. We conclude from our study that infecting T2DM rats with a tissue-dwelling nematode, T. zimbabwensis, causes a shift in the metabolome, causing changes in different metabolic pathways. Additionally, the infection showed the potential to regulate or improve diabetes complications by causing a decrease in the amino acid concentration that results in metabolic syndrome.