Glycomics for Type 2 Diabetes Biomarker Discovery: Promise of Immunoglobulin G Subclass-Specific Fragment Crystallizable N-glycosylation in the Uyghur Population.

Aberrant immunoglobulin G (IgG) N-glycosylation offers new prospects to detect changes in cell metabolism and by extension, for biomarker discovery in type 2 diabetes mellitus (T2DM). However, past studies did not analyze the individual IgG subclasses in relation to T2DM pathophysiology. We report here original findings through a comparison of the IgG subclass-specific fragment crystallizable (Fc) glycan biosignatures in 115 T2DM patients with 122 healthy controls within the Uyghur population in China. IgG Fc glycosylation profiles were analyzed using nano-liquid chromatography-mass spectrometry to exclude changes attributed to fragment antigen binding N-glycosylation. After correction for clinical covariates, 27 directly measured and 4 derived glycan traits of the IgG subclass-specific N-glycopeptides were significantly associated with T2DM. Furthermore, we observed in T2DM a decrease in bisecting N-acetylglucosamine of IgG2 and agalactosylation of IgG4, and an increase in sialylation of IgG4 and digalactosylation of IgG2. Classification model based on IgG subclass-specific N-glycan traits was able to distinguish patients with T2DM from controls with an area under the receiver operating characteristic curve of 0.927 (95% confidence interval 0.894-0.960, p < 0.001). In conclusion, a robust association between the IgG subclass-specific Fc N-glycomes and T2DM was observed in the Uyghur population sample in China, suggesting a potential for the IgG Fc glycosylation as a biomarker candidate for type 2 diabetes. The integration of glycomics with other system science biomarkers might offer further hope for innovation in diagnosis and treatment of T2DM in the future. Finally, it is noteworthy that "Population Glycomics" is an emerging approach to biomarker discovery for common complex diseases.

Screening for potential serum-based proteomic biomarkers for human type 2 diabetes mellitus using MALDI-TOF MS.

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a complex, pandemic disease contributing towards the global burden of health issues. To date, there are no simple clinical tests for the early detection of T2DM. METHOD: To identify potential peptide biomarkers for such applications, 406 sera of T2DM patients (n = 206) and healthy controls (n = 200) are analyzed by using MALDI-TOF MS with a cross-sectional case-control design. RESULT: Six peptides (peaks m/z 1452.9, 1692.8, 1946.0, 2115.1, 2211.0 and 4053.6) are identified as candidate biomarkers for T2DM. A diagnostic model constructed with six peptides is able to discriminate T2DM patients from healthy controls, with an accuracy of 82.20%, sensitivity of 82.50%, and specificity of 77.80% in the validation set. Peptide peaks m/z 1452.9 and 1692.8 are identified as fragments of the complement C3f, while peptide peaks m/z 1946.0, 2115.1, and 2211.0 are identified as the fragments of kininogen 1 isoform 1 precursor. CONCLUSION: This study reinforces proteomic analyses as a potential technique for defining significant clinical peptide biomarkers, providing a simple and convenient diagnostic model for T2DM in clinical examination.

Contourlet textual features: improving the diagnosis of solitary pulmonary nodules in two dimensional CT images.

OBJECTIVE: To determine the value of contourlet textural features obtained from solitary pulmonary nodules in two dimensional CT images used in diagnoses of lung cancer. MATERIALS AND METHODS: A total of 6,299 CT images were acquired from 336 patients, with 1,454 benign pulmonary nodule images from 84 patients (50 male, 34 female) and 4,845 malignant from 252 patients (150 male, 102 female). Further to this, nineteen patient information categories, which included seven demographic parameters and twelve morphological features, were also collected. A contourlet was used to extract fourteen types of textural features. These were then used to establish three support vector machine models. One comprised a database constructed of nineteen collected patient information categories, another included contourlet textural features and the third one contained both sets of information. Ten-fold cross-validation was used to evaluate the diagnosis results for the three databases, with sensitivity, specificity, accuracy, the area under the curve (AUC), precision, Youden index, and F-measure were used as the assessment criteria. In addition, the synthetic minority over-sampling technique (SMOTE) was used to preprocess the unbalanced data. RESULTS: Using a database containing textural features and patient information, sensitivity, specificity, accuracy, AUC, precision, Youden index, and F-measure were: 0.95, 0.71, 0.89, 0.89, 0.92, 0.66, and 0.93 respectively. These results were higher than results derived using the database without textural features (0.82, 0.47, 0.74, 0.67, 0.84, 0.29, and 0.83 respectively) as well as the database comprising only textural features (0.81, 0.64, 0.67, 0.72, 0.88, 0.44, and 0.85 respectively). Using the SMOTE as a pre-processing procedure, new balanced database generated, including observations of 5,816 benign ROIs and 5,815 malignant ROIs, and accuracy was 0.93. CONCLUSION: Our results indicate that the combined contourlet textural features of solitary pulmonary nodules in CT images with patient profile information could potentially improve the diagnosis of lung cancer.