Clinical trial NCT05554224: Integrated Multi-omics Data for Personalized Treatment of Obesity-associated Fatty Liver Disease
Official title: Integrated Multi-omics and Machine Learning-driven Personalized Treatment of Obesity-associated Fatty Liver Disease
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: NAFLD
Obesity, Morbid
Comorbidities and Coexisting Conditions
Keywords: Weight loss
Bariatric surgery
Liver diseases
Mass spectrometry
Proteomics
Lipidomics
Metabolomics
Transcriptomics
Lead sponsor: Institut Investigacio Sanitaria Pere Virgili
Sponsor class: OTHER
Collaborators: name: Hospital Universitari Sant Joan
name: La Caixa Foundation
name: Instituto de Salud Carlos III
name: University of Barcelona
First posted: 2022-09-26
Enrollment: 1104
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Participants are consecutive patients with obesity type II or III attending the bariatric surgery unit for treatment. In this center, women are more frequent than men. The average age is 49 (interquartile range 41 - 58). Steatohepatitis (\~30 percent), type 2 diabetes mellitus (\~40 percent), hypertension (\~60 percent), and dyslipidemia (\~35 percent) are the most prevalent comorbidities. Other common ailments are obstructive sleep apnea (\~20 percent) and hypothyroidism (\~10 percent).

Brief 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.

Detailed description: The investigators study the most prevalent liver disease in the history of humankind, which is the leading cause of liver transplantation in its severe forms. It results from two silent pandemics with enormous health impacts: obesity and diabetes. Together or separately, they affect more than 30% of the world's population. The current term for the disease is MAFLD (metabolic (dysfunction)-associated fatty liver disease). This designation indicates that metabolic disorders related to obesity, diabetes, dyslipidemia, and hypertension are its primary cause. These disorders are related and lead to fat accumulation in the liver, the first step in a broad spectrum of chronic liver diseases. These diseases respond clinically in a very variable way and remain undiagnosed and untreated for a long time. There is no accepted pharmacological treatment, and lifestyle changes, although possibly effective, usually fail because they require particularly favorable conditions. Therefore, the identified problems that should be solve are:

(1) The diagnosis of MAFLD requires a liver biopsy, a costly and aggressive procedure. (2) Without examining the liver, clinicians can know little about the progression of the disease and the underlying causes. (3) The results in experimental models can be informative but difficult to translate to the clinic. Recent reports suggest the essential role of phospholipid biosynthesis and transport between the endoplasmic reticulum and mitochondria. (4) All of the above makes it difficult to obtain the necessary information to propose changes in clinical guidelines.

Considering these aspects, patients with morbid obesity can be an informative human model. Among other advantages, patients have surgical options that allow us to obtain portions of affected organs that facilitate specific diagnosis and that, because they require constant care, can be studied on an ongoing basis. The presented approach can improve patient care and essentially consists of identifying the most significant number of variables that can help. In particular, here are proposed the inclusion of variables that can already be obtained from recent advances in the laboratory, encompassed within the omics sciences (genomics, transcriptomics, proteomics, metabolomics, lipidomics, microbiomics). Each of these has its advantages and limitations. Predictive models can integrate these variables into clinical data to explore organ crosstalk.

Eligibility criteria: Inclusion Criteria:

* 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 Criteria:

* 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.

Primary outcomes:
measure: Weight change
timeFrame: 1 to 10 years
measure: Type 2 diabetes mellitus incidence
timeFrame: 1 to 10 years
measure: Hypertension incidence
timeFrame: 1 to 10 years
measure: Chronic liver diseases incidence
timeFrame: 1 to 10 years
measure: Dyslipidemia incidence
timeFrame: 1 to 10 years
Secondary outcomes:
Other outcomes:
Locations:
city: Reus
country: Spain
facility: Hospital Universitari Sant Joan
state: Tarragona
Study officials:
affiliation: Institut Investigacio Sanitaria Pere Virgili
name: Jorge Joven, Professor
role: PRINCIPAL_INVESTIGATOR
References: