Integrated Multi-omics and Machine Learning-driven Personalized Treatment of Obesity-associated Fatty Liver Disease
The investigators seek to analyze the samples provided by patients with obesity-associated fatty liver disease at the multi-omics level and to integrate the results with clinical information, genotypic variants, and factors influencing inter-organ crosstalk. The main aim is to improve the interpretation of fatty liver disease associated with obesity and diabetes by developing predictive models built with algorithms from artificial intelligence. The challenge is to decipher the flow of information by exploring contributing factors, proximate causes of regulatory defects, and maladaptive responses that may promote therapeutic approaches.
• Body mass index greater or equal to 40 kg/m\^2.
• Body mass index between 35 and 40 kg/m\^2 with high-risk comorbidities (diagnosis or treatment for hypertension, dyslipidemia, or type 2 diabetes mellitus).
• Positive psychiatric evaluation.
• Age greater or equal to 18 years old.