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Alzheimer's disease and type 2 diabetes mellitus tend to occur together. We sought to identify protein(s) common to both conditions that could suggest a possible unifying pathogenic role. Using human neuronal butyrylcholinesterase (AAH08396

Abstract:We assessed the contribution of selected built environment factors to body weight in a pilot study in urban Visakhapatnam, South India. Participants were 123 men and 60 women (age 16 to 69 years; BMI 17.3-30.5) who had lived in the area for at least 3 years. Individuals with lower BMI tended to be (a) working people (non-home based-working away from home), (b) non-vegetarians, (c) physically active (activity mostly related to work), and (d) taking afternoon siestas. Psychological stress, quality of life and wellbeing data were used from an earlier study of individuals with diabetes mellitus. The measures included were depression, anxiety, energy, positive wellbeing, satisfaction, impact, and social worry and diabetes worry (Diabetes quality of life). Guttman's Smallest Space Analysis (SSA) suggested the relationships among the psychosocial measures can be accounted for by one facet with three axial sets of variables (a) positive wellbeing and energy, (b) satisfaction, impact, and social worry and diabetes worry, and (c) anxiety and depression. SSAs on male participants suggested that fasting blood glucose and weight were most closely associated with anxiety and energy levels. In female participants, weight and fasting glucose were most closely associated with energy and to a somewhat lesser extent with anxiety. In both sexes, age was closely associated with positive wellbeing. Also in both sexes, age, weight, and fasting glucose levels were closely associated with each other. The results support the importance of understanding the impact of built environment and psychosocial factors on body weight in diabetic individuals for designing prevention strategies.

Genomic Data is growing very rapidly with the sequencing of genomes of various forms of life. To understand the overwhelming data and to obtain meaningful information, Data Mining techniques such as Principal Component Analysis and Discriminant Analysis are used for the purpose. Data Mining is basically used when the data is vast and there is need to extract the hidden knowledge in the form of useful patterns. The data set taken into consideration is protein data pertaining to diabetes mellitus obtained from a database. The task at hand was to find out in which species most of the diabetes related proteins exist. It so happened that most of these proteins were prevalent in Human Beings, House Mice and Norway Rat as they are all mammals and Human Beings have orthologs as House Mice and Norway Rat. Both these techniques prove that human beings show a variation from those of House Mice and Norway Rat which are similar in terms of the variation of protein attributes. This can also be inferred from statistical analysis by using histograms and bivariate plots. Other Data Mining Techniques such as Regression and Clustering can be used to further explore the above inference.