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EXOMAIR

Hacia una Medicina de Precisión: Análisis del Exposoma y Omicas usando Inteligencia Artificial Explicable para estudiar su impacto durante la vida en la Obesidad, Insulino Resistencia y salud metabólica

Photo: Canva
Duration
01/01/2024 - 31/12/2026
Coordinator
Augusto Anguita
Funded by
Proyecto PI23/01032

La obesidad adulta y sus complicaciones tienen su origen en acontecimientos tempranos del desarrollo; la obesidad infantil es un importante factor de riesgo para la salud metabólica a lo largo de la vida. El aumento de peso durante la infancia incrementa el riesgo a largo plazo de resistencia a la insulina (RA), que es el paso inicial del deterioro metábolico en la obesidad.

El objetivo general de este proyecto es evaluar el efecto de la exposición a riesgos ambientales en las trayectorias de salud cardiometabólica en cohortes prospectivas de niños con obesidadm con especial atención a la comprensión de las alteraciones metabolómicas genéticas y epigenéticas subyacentes al desarrollo de la RI asociada a la obesidad durante la maduración puberal y su impacto en la salud metabólica en la etapa puberal y la edad adulta temprana.

Se llevarán a cabo enfoques multiómicos y de exposoma a partir de dos cohortes ya existentes: PUBMEP (N=198) e IBEROMICs (N=753). Se realizará un estudio de asociación de genoma completo (GWAS), cambios globales en la metilación del ADN sanguíneo (EWAS) y metabolómica sérica.

Además, se caracterizarán los patrones de estilo de vida (dieta y actividad física), el exposoma urbano y la exposición  sustancias químicas. Se aplicarán técnicas de inteligencia artifical explicable (XAI) para realizar análisis masivos que integren múltiples datos ómicos, clínicos y de exposoma longitudinales, par abtener modelos descriptivos y predictivos que mejoren el diagnótico, el tratamiento y la predicción del riesgo de desarrollar RI y/u obesidad y su impacto en la salud metabólica posterior, facilitando el diseño de programas preventivos personalizados.

Financiación

139.375 €

Our Team

Coordinador

Equipo ISGlobal

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