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Diabetes is a complex metabolic disorder affecting the glucose status of the human body. Chronic hyperglycaemia related to diabetes is associated with end organ failure. The clinical relationship between diabetes and atherosclerotic cardiovascular disease is well established. This makes therapeutic approaches that simultaneously target diabetes and atherosclerotic disease an attractive area for research. The majority of people with diabetes fall into two broad pathogenetic categories, type 1 or type 2 diabetes. The role of obesity, adipose tissue, gut microbiota and pancreatic beta cell function in diabetes are under intensive scrutiny with several clinical trials to have been completed while more are in development. The emerging role of inflammation in both type 1 and type 2 diabetes (T1D and T1D) pathophysiology and associated metabolic disorders, has generated increasing interest in targeting inflammation to improve prevention and control of the disease. After an extensive review of the possible mechanisms that drive the metabolic pattern in T1D and T2D and the inflammatory pathways that are involved, it becomes ever clearer that future research should focus on a model of combined suppression for various inflammatory response pathways.

The immense growth of MEDLINE coupled with the realization that a vast amount of biomedical knowledge is recorded in free-text format, has led to the appearance of a large number of literature mining techniques aiming to extract biomedical terms and their inter-relations from the scientific literature. Ontologies have been extensively utilized in the biomedical domain either as controlled vocabularies or to provide the framework for mapping relations between concepts in biology and medicine. Literature-based approaches and ontologies have been used in the past for the purpose of hypothesis generation in connection with drug discovery. Here, we review the application of literature mining and ontology modeling and traversal to the area of drug repurposing (DR). In recent years, DR has emerged as a noteworthy alternative to the traditional drug development process, in response to the decreased productivity of the biopharmaceutical industry. Thus, systematic approaches to DR have been developed, involving a variety of in silico, genomic and high-throughput screening technologies. Attempts to integrate literature mining with other types of data arising from the use of these technologies as well as visualization tools assisting in the discovery of novel associations between existing drugs and new indications will also be presented.

OBJECTIVETo identify risk factors for the development of statin-associated diabetes mellitus (DM). RESEARCH DESIGN AND METHODSThe study was conducted in two phases. Phase one involved high-throughput in silico processing of a large amount of biomedical data to identify risk factors for the development of statin-associated DM. In phase two, the most prominent risk factor identified was confirmed in an observational cohort study at Clalit, the largest health care organization in Israel. Time-dependent Poisson regression multivariable models were performed to assess rate ratios (RRs) with 95% CIs for DM occurrence. RESULTSA total of 39,263 statin nonusers were matched by propensity score to 20,334 highly compliant statin initiators in [2004][2005] CONCLUSIONSHypothyroidism is a risk factor for DM. Subclinical hypothyroidism-associated risk for DM is prominent only upon statin use. Identifying and treating hypothyroidism and subclinical hypothyroidism might reduce DM risk. Future clinical studies are needed to confirm the findings.Thyroid disease is common in the general population. Hypothyroidism and subclinical hypothyroidism are more prevalent in patients with type 2 diabetes mellitus (DM), and it is possible that hypothyroidism is a risk factor for the development of DM. Women with subclinical hypothyroidism are more likely to develop gestational diabetes (1). After restoration of thyroid function, reduction of glucose-stimulated insulin secretion has been shown in patients with hypothyroidism as well as in those with subclinical hypothyroidism (2).