Type 2 diabetes (T2D) causes an important and early increase reduction in the quality of life of patients and dead. Genetic component of T2D is over 50% but is largely unidentified. Study of variations present in the exome and their interactions with other factors will allow the identification of genetic variations and genes involved in this disease. Objectives and methodology: 1) Identification of all genetic variants present in the exome of participants of Di@bet.es study (about 3400 individuals). 2) Analysis of all variations found in relation to T2D and related parameters (BMI, insulin and glucose levels, metabolic syndrome, insulin resistance, response to glucose load, and lipid profile, and evolution after 9 years in all these parameters. 3) Applying of approximations based on conventional statistical procedures and in artificial intelligence to find genetic variants, environmental factors and their interactions, that are involved in T2D and related parameters. Identification of potential interactions among influential variables (genetic or not) in high dimensional spaces by machine learning approximations. 3a) Developing and testing the approximations with the 800 exomes that we have been already sequenced. 3b) Analysis of the Di@bet.es study using the initial data and at the follow-up. 3c) Analysis of about 13.000 exomes from public databases (Goto2 consortia) to verify results and identifying possible differences between samples. Results of current study can have an important role in T2D personalized medicine as they will facilitate identification of people at higher risk of T2D and other metabolic alterations.

DIABETES TIPO 2
EXOMA
INTELIGENCIA ARTIFICIAL
VARIACIONES GENÉTICAS
INTERACCIONES GENXGEN Y GENXAMBIENTE

Acción Estratégica en Salud (AES)