This study investigates the possible roles and potential prediction ability of metabolic parameters in the early development of T2D by detecting their serum levels at different fasting blood glucose (FBG) levels. Methods: The subjects were included and divided into normal glucose tolerance (NGT), prediabetes (PD), and T2Dsubgroups. Apart from detecting the levels of routine biochemical parameters, fasting serum insulin (FINS), 25(OH)D, thioredoxin-interacting protein (TXNIP), thioredoxin (TRX), and NOD-like receptor family, pyrin domain-containing 3 (NLRP3) were detected. β-cell dysfunction (HOMA-β) and insulin resistance (HOMA-IR) were assessed by homeostasis model assessment. Both univariate and multivariate logistic regression analyses were used to estimate the risk of metabolic parameters, and their optimal cut-off values were obtained in the receiver operating characteristic (ROC) curve analysis and the Youden index. Results: Among the 207 subjects, aged from 20 to 60 years (44.62+12.92) contain 118 males and 89 females. There was a significantly lower trend of TRX, HOMA-β, and 25(OH) D following the higher FBG level among these three subgroups, while a significantly higher trend of all the other metabolic parameters. The multivariate analysis showed that subjects with higher values of TRX, HOMA-β, and 25(OH)D had a significantly lower risk for patients to be diagnosed as PD (aOR: 0.945, 0.961, and 0.543) and T2D (aOR: 0.912, 0.947, 0.434). Under the reliable 95% CI, TXNIP with a cut-off value of 119.27 showed the highest AUC value, sensitivity, and specificity (AUC: 0.981, 95% CI: 0.8524-0.9839, 91.49%, and 83.33%) to diagnose PD. FINS with a cut-off value of 28.1 also showed the highest ones (AUC=0.9872, 95% CI: 0.9753-0.9992, 100%, and 92.91%) to diagnose T2D. Conclusion: Early prediction of T2D is vital for timely intervention. Based on the FBG ≥100.8 mg/dl, the results provide evidence that 25(OH)D might be the protective factor in the early development of T2D. Besides, TXNIP and FINS might be the predictor for PD and T2D, respectively.

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Vitamin D deficiency can damage the human immune system, and the complement system is a key component of the immune system. This study aimed to elucidate the mechanism by which vitamin D affects the immune system by analyzing the changes in the protein expression of the complement system under different vitamin D levels. We selected 40 participants and divided them into three groups according to their serum levels of 25-hydroxyvitamin D (25(OH)VD): group A, 25(OH)VD ≥ 40 ng/mL; group B, 30 ng/mL ≤ 25(OH)VD < 40 ng/mL; and group C, 25(OH)VD < 30 ng/mL. Serum samples were subjected to biochemical analysis, followed by proteomic analysis using high-throughput untargeted proteomic techniques. Vitamin D deficiency increased the levels of fasting blood sugar, fasting serum insulin, and homeostasis model assessment (HOMA) of insulin resistance and decreased the secretion of HOMA of β-cell function, which led to insulin resistance and glucose metabolism disorder. Moreover, vitamin D deficiency resulted in the abnormal expression of 56 differential proteins, among which the expression levels of complement factor B, complement component C9, inducible co-stimulator ligand, and peptidase inhibitor 16 significantly changed with the decrease in vitamin D content. Functional enrichment analysis of these differential proteins showed that they were mainly concentrated in functions and pathways related to insulin secretion and inflammation. In conclusion, vitamin D deficiency not only contributes to insulin resistance and glucose metabolism disorder but also causes abnormal protein expression, resulting in the abnormal activation of the complement system. This study provides a novel theoretical basis for further studies on the relationship between vitamin D and the immune system.

Rationale Proteomics and metabolomics are widely used in the study of diabetes, but rarely in prediabetes research. This study aimed to explore the mechanisms of early‐onset type 2 diabetes mellitus (T2DM) by analyzing proteomic changes at different stages of glucose metabolism. Methods A total of 40 individuals undergoing routine physical health examinations between December 2016 and April 2017 were enrolled. Subjects were divided into four groups based on fasting blood glucose (FPG) levels: FPG < 5.6 mmol/L (group A); FPG ≥ 5.6 mmol/L and <6.1 mmol/L (group B); FPG ≥ 6.1 mmol/L and <7.0 mmol/L (group C); and FPG ≥ 7.0 mmol/L (group D). Each group had 10 cases. Sera from these 40 subjects were analyzed by label‐free quantitative liquid chromatography coupled with tandem mass spectrometry (LC/MS/MS). LC/MS/MS with selected reaction monitoring mode was also performed for qualitative and quantitative metabolomics analysis. Differentially expressed proteins were identified. Partial least squares discriminant analysis (PLS‐DA) and orthogonal partial least squares discriminant analysis (OPLS‐DA) were used to analyze the differentially expressed metabolites. Results A total of 202 differentially expressed proteins were screened and were identified as mainly secreted proteins. Comparing group A with group B, 32 proteins were up‐regulated and 18 proteins were down‐regulated. Comparing group A with group C, 24 proteins were up‐regulated and 24 proteins were down‐regulated. Comparing group A with group D, 19 proteins were up‐regulated and 17 proteins were down‐regulated. The fold change for up‐regulated proteins was >1.2, p < 0.05, while the fold change for down‐regulated proteins was <−1.2, p < 0.05. PLS‐DA and OPLS‐DA revealed 113 differentially expressed metabolites. Correlation analysis of differentially expressed metabolites of group A versus group B revealed that among the down‐regulated differential proteins, transforming growth factor β‐induced protein ig‐h3 correlated negatively with metabolite L‐saccharin, while among the up‐regulated differential proteins, apolipoprotein C‐IV correlated negatively with metabolite 3‐methyloxindole. Among all differentially expressed proteins, 19 proteins were associated with early initiation of chronic inflammation, including CD14 and CSF‐1R, which were newly identified in the early onset of T2DM. Conclusions Many proteins are differentially expressed between prediabetes and after T2DM diagnosis, although the specific mechanism remains unclear. The expression level of CD14 was significantly up‐regulated and that of CSF‐1R was significantly down‐regulated when FPG was ≥5.6 mmol/L, suggesting that CD14 and CSF‐1R may be important markers for early‐onset T2DM and may serve as new targets for T2DM treatment.

Vitamin D is a fat-soluble vitamin with multiple functions. However, the metabolism of people with different vitamin D concentrations is still unclear. Herein, we collected clinical data and analysed the serum metabolome of people with 25-hydroxyvitamin D (25[OH]D) ≥40 ng/mL (A), 30 ng/mL ≤25(OH)D <40 ng/mL (B) and 25(OH)D <30 ng/mL (C) by the ultra-high-performance liquid chromatography-tandem mass spectrometry method. We found that haemoglobin A1c, fasting blood glucose, fasting insulin, homeostasis model assessment of insulin resistance and thioredoxin interaction protein were enhanced, while HOMA-β was reduced with the decrease of 25(OH)D concentration. In addition, people in the C group were diagnosed with prediabetes or diabetes. Metabolomics analysis showed that seven, thirty-four and nine differential metabolites were identified in the groups B vs A, C vs A and C vs B, respectively. Metabolites associated with cholesterol metabolism and bile acid biosynthesis, such as 7-ketolithocholic acid, 12-ketolithocholic acid, apocholic acid, N-arachidene glycine and d-mannose 6-phosphate, were significantly upregulated in the C group compared with the A or B groups. In conclusion, the disorder of vitamin D metabolism may be related to cholesterol metabolism and bile acid biosynthesis. This study provided a basis for exploring the possible mechanism leading to abnormal vitamin D metabolism.