Genetic contributions to Type 2 diabetes: recent insights

Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm

Background

Type 2 diabetes is a major health concern worldwide. The present study is aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes.

Methods

Differentially expressed genes (DEGs) between type 2 diabetes patients and normal controls were identified by analyses of integrated microarray data obtained from the Gene Expression Omnibus database using the Limma package. Functional analysis of genes was performed using the R software package clusterProfiler. Analyses of protein-protein interaction (PPI) performed using Cytoscape with the CytoHubba plugin were used to determine the most sensitive diagnostic gene biomarkers for type 2 diabetes in our study. The support vector machine (SVM) classification model was used to validate the gene biomarkers used for the diagnosis of type 2 diabetes.

Results

GSE164416 dataset analysis revealed 499 genes that were differentially expressed between type 2 diabetes patients and normal controls, and these DEGs were found to be enriched in the regulation of the immune effector pathway, type 1 diabetes mellitus, and fatty acid degradation. PPI analysis data showed that five MCODE clusters could be considered as clinically significant modules and that 10 genes (IL1B, ITGB2, ITGAX, COL1A1, CSF1, CXCL12, SPP1, FN1, C3, and MMP2) were identified as “real” hub genes in the PPI network using algorithms such as Degree, MNC, and Closeness. The sensitivity and specificity of the SVM model for identifying patients with type 2 diabetes were 100%, with an area under the curve of 1 in the training as well as the validation dataset.

Conclusion

Our results indicate that the SVM-based model developed by us can facilitate accurate diagnosis of type 2 diabetes.

Association of nesfatin-1 level with body composition, dietary intake and resting metabolic rate in obese and morbid obese subjects

AIMS

Nesfatin-1 identified neuroendocrine peptide is involved in regulation of homeostasis via modulation of metabolism, energy homeostasis and food intake. We aimed to investigate the associations of circulating nesfatin-1 level with food intake, body composition and resting metabolic rate (RMR) and also examine the correlation between circulating peroxisome proliferator-activated receptor gamma (PPARγ) and nesfatin-1 levels in obese and morbid obese subjects.

METHODS

A total of 96 obese subjects (including 18 morbid obese subjects) were participated in the current cross-sectional study. We assessed the body composition with the use of Body Composition Analyzer. RMR was measured by means of the MetaCheck™, an instrument designed to measure RMR using indirect calorimetry. All baseline blood samples were obtained following an overnight fasting. Plasma concentrations of nesfatin-1 and circulating PPARγ were measured with the use of an ELISA method. Statistical analyses were performed using SPSS.

RESULTS

We found significant associations between fat percent and circulating nesfatin-1 in obese and morbid obese subjects. There was main association between circulating nesfatin-1 and PPARγ concentration in obese subjects and it was more strong association in morbid obese participants. There was marginally significant differences between percent predicted RMR between different categorized nesfatin-1 levels. There were also higher intakes of calorie, carbohydrate and protein in obese group who had lower concentration of nesfatin-1.

CONCLUSIONS

Our data indicated the fat percent as main determinant factor in circulating nesfatin-1 level. It appears nesfatin-1 and PPARγ might be concurrently involved in adipogenesis pathway.

Crosstalk between circulating peroxisome proliferator-activated receptor gamma, adipokines and metabolic syndrome in obese subjects

BACKGROUND

Peroxisome proliferator-activated receptor gamma (PPARγ) has direct and indirect function in adipokines production process. We aimed to assess the possible influence of circulating PPARγ on relative risk of metabolic syndrome and also examine the association between circulating PPARγ and adipokines levels among obese subjects.

METHODS

A total of 96 obese subjects (body mass index (BMI) ≥30) were included in the current cross-sectional study. We assessed the body composition with the use of Body Composition Analyzer BC-418MA - Tanita. The MetS (metabolic syndrome) was defined based on the National Cholesterol Education Program Adult Treatment Panel III. All baseline blood samples were obtained following an overnight fasting. Serum concentrations of adipokines including Retinol binding protein 4 (RBP4), omentin-1, vaspin, progranulin, nesfatin-1 and circulating PPARγ was measured with the use of an enzyme-linked immunosorbent assay method. Statistical analyses were performed using software package used for statistical analysis (SPSS).

RESULTS

We found main association between circulating PPARγ and body composition in obese population. The risk of metabolic syndrome in subjects with higher concentration of PPARγ was 1.9 fold in compared with lower concentration of PPARγ after adjustment for age, sex and BMI. There was significant association between PPARγ and adipokines, specially nesfatin-1 and progranulin. Defined adipokines pattern among participants demonstrated the markedly higher concentration of vaspin, RBP4 and nesfatin-1 in participants with MetS compared to non-MetS subjects.

CONCLUSIONS

It appears all of studied adipokines might have association with PPARγ level and might simultaneously be involve in some common pathway to make susceptible obese subjects for MetS.