Integrated Multi-omics Data for Personalized Treatment of Obesity-associated Fatty Liver Disease
Integrated Multi-omics and Machine Learning-driven Personalized Treatment of Obesity-associated Fatty Liver Disease
Institut Investigacio Sanitaria Pere Virgili
1,104 participants
Jun 25, 2008
OBSERVATIONAL
Conditions
Summary
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.
Eligibility
Inclusion Criteria4
- 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.
Exclusion Criteria7
- Legal or illegal drug consumption, including alcohol.
- Diagnosis of Hepatitis.
- Current cancer diagnosis or treatment.
- Clinical or analytical evidence of severe illness.
- Clinical or analytical evidence of chronic or acute inflammation.
- Clinical or analytical evidence of infectious diseases.
- Clinical or analytical evidence of terminal illness.
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Interventions
Observational although patients are candidates for metabolic surgery.
Locations(1)
View Full Details on ClinicalTrials.gov
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NCT05554224