Diabetes of the brain: computational approaches and interventional strategies

Diabetes mellitus (DM) is characterized by hyperglycemia either due to deficient insulin production (Type 1 Diabetes mellitus) or peripheral insulin resistance of the cells (Type 2 Diabetes mellitus). Both Type 1 Diabetes mellitus and Type 2 Diabetes mellitus are more prevalent and efforts are directed to actively control these metabolic syndromes. Currently, Alzheimer's disease (AD), is gaining popularity as 'Type 3 diabetes' or 'Diabetes of the brain' and it is now evident that this neurodegenerative disease has multiple shared pathology with DM. Alarming is the fact that the incidence of AD might double within the next two decades, and this is certain to cause devastating effects not only to the afflicted individual or the family, but also to the global economy. Methods to either delay the onset or inhibit the progression of AD are therefore necessary. Progressive dementia, increased deposition of amyloid- β protein, neurofibrillary tangles and neuritic plaques in the brain are some of the hallmarks of AD. More understanding of the disease at the cellular and molecular level will enable identifying the possible targets for intervention and pave way for either development of novel or modification of the existing therapeutic options. In this work we have performed semantic data mining analysis on a large collection of most recently published data and identified an updated list of common genes expressed in DM and AD. Functional analysis of these genes revealed both existing and missing links involved in a bigger network associated with both disease conditions. Thus we argue that computational analysis methods help not only in understanding the mechanistic links but also in narrowing down precise targets (genes, proteins, metabolites and signalling pathways) and provide the base for both disease intervention and development of therapeutic options.