Type 2 diabetes and tooth loss are linked both epidemiologically and pathophysiologically. We applied label-free differential protein expression analysis using multidimensional liquid chromatography/tandem mass spectrometry (2D-LC-MS/MS) to explore the proteomic profile of saliva samples collected from selected type 2 diabetic edentulous patients and non-diabetic controls. Ninety-six peptides corresponding to 52 proteins were differentially expressed between the diabetic edentulous patients and controls (p < 0.05). Some diabetes-related inflammatory biomarkers including glyceraldehyde-3-phosphate dehydrogenase and serum amyloid A were detected with levels increased in diabetic samples. Other biomarkers including amylase, palate, lung and nasal epithelium associated protein (PLUNC), and serotransferrin levels were decreased in diabetic samples. In contrast with previous findings, salivary carbonic anhydrase 6 and alpha-2 macroglobulin levels, however, were decreased in this diabetic patient population. Cluster analysis and principle component analysis demonstrated a differential pattern of protein biomarker expression between diabetic and control subjects. Western blot analysis was completed to confirm the relatively lower expression level of two biomarkers, including PLUNC and amylase in the diabetic group compared to control subjects. The presence of salivary biomarkers specific for diabetes in edentulous subjects mimics those in serum, especially those related to inflammatory/lipid metabolism. While this exploratory study requires further validation with a larger population, it provides proof-of-principle for salivary proteomics for edentulous subjects with diabetes.

In a 2008 article on cone beam volumetric tomography (CBVT) and dentoalveolar applications, Tyndall and Rathore wrote: 'It is in the area of endodontic applications that the literature has proved most fruitful to date.' This statement is even truer today than in 2008. A review of the literature has demonstrated that, in many cases, CBVT is more efficacious than traditional forms of 2-D imaging. Endodontic applications of CBVT include the diagnosis of periapical lesions due to pulpal inflammation, identification and localization of internal and external resorption, the detection of vertical root fractures, the visualization of accessory canals, and elucidation of the causes of non-healing endodontically treated teeth. Prior to 2008, most published articles on CBVT applications in endodontics were either case reports or in vitro studies. Since that time more well designed clinically related scholarly activity has been published. This article attempts to survey the field of CBVT applications in endodontics and provide the readers with an overview of what has been found. The authors hope that this knowledge will form a foundation for appropriate clinical decision making with specific reference to selection criteria for the endodontic applications of CBVT.Keywords: Endodontics, radiography, cone beam volumetric tomography.Abbreviations and acronyms: ALARA = As Low As Reasonably Achievable; CBVT = cone beam volumetric tomography; FOV = field of view; MB2 = second mesiobuccal; MDCT = multidetector CT; VRF = vertical root fracture.

Effective monitoring of glucose levels is necessary for patients to achieve greater control over their diabetes. However, only about a quarter of subjects with diabetes who requires close serum glucose monitoring, regularly check their serum glucose daily. One of the potential barriers to patient compliance is the blood sampling requirement. Saliva and its protein contents can be altered in subjects with diabetes, possibly due to changes in glycemic control. We propose here that salivary proteomes of subjects with diabetes may be different based on their glycemic control as reflected in A1C levels. A total of 153 subjects with type 1 or 2 diabetes were recruited. Subjects in each type of diabetes were divided into 5 groups based on their A1C levels; <7, 7–8, 8–9, 9–10, >10. To examine the global proteomic changes associated with A1C, the proteomic profiling of pooled saliva samples from each group was created using label-free quantitative proteomics. Similar proteomic analysis for individual subjects (N=4, for each group) were then applied to examine proteins that may be less abundant in pooled samples. Principle component analysis (PCA) and cluster analysis (p<0.01 and p<0.001) were used to define the proteomic differences. We, therefore, defined the salivary proteomic changes associated with A1C changes. This study demonstrates that differences exist between salivary proteomic profiles in subjects with diabetes based on the A1C levels.

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