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

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.

Study Overview

• Status: Recruiting
• Conditions: NAFLD; Obesity, Morbid; Comorbidities and Coexisting Conditions
• Intervention / Treatment: Diagnostic test: To propose diagnostic tests for liver diseases before surgical decisions.

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.

• Study Type: Observational
• Enrollment (Estimated): 1104

Contacts and Locations

• Study Contact: Name: Jorge Joven, Professor; Phone Number: 55409 +34977310300; Email: jorge.joven@urv.cat
• Study Contact Backup: Name: Helena Castañé, MSc; Phone Number: 55409 +34977310300; Email: helena.castane@iispv.cat

Study Locations

• Spain
  • Tarragona
    • Reus, Tarragona, Spain, 43204
      • Recruiting
      • Hospital Universitari Sant Joan
      • Contact: Jorge Joven, Professor; Phone Number: 55409 +34977310300; Email: jorge.joven@urv.cat
      • Principal Investigator: Jorge Joven, Professor
      • Principal Investigator: Daniel del Castillo, Professor
      • Sub-Investigator: Jordi Camps, PhD
      • Sub-Investigator: Isabel Fort-Gallifa, PhD
      • Sub-Investigator: Anna Hernández-Aguilera, PhD
      • Sub-Investigator: Marta Paris, PhD
      • Sub-Investigator: Gerard Baiges-Gaya, MSc
      • Sub-Investigator: Elisabet Rodríguez-Tomàs, MSc
      • Sub-Investigator: Jordi Riu, PhD
      • Sub-Investigator: Adria Cereto-Massague, PhD
      • Sub-Investigator: Helena Castañé, MSc
      • Sub-Investigator: Andrea Jiménez-Franco, MSc
      • Sub-Investigator: Alina-Iuliana Onoiu, MSc

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• 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).

Description

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.

Study Plan

How is the study designed?

Design Details

• Observational Models: Case-Control
• Time Perspectives: Prospective

Number of groups / cohorts

3

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Severe obesity without liver disease; Patients with severe obesity who did not meet the criteria described in Kleiner et al. (2005) for nonalcoholic steatohepatitis diagnosis (score 0-2).	Diagnostic test: To propose diagnostic tests for liver diseases before surgical decisions.; Observational although patients are candidates for metabolic surgery.; Other Names: External follow up monitoring liver diseases and weight loss.
Severe obesity with liver disease without criteria for steatohepatitis; Patients with severe obesity who did not meet the criteria described in Kleiner et al. (2005) for nonalcoholic steatohepatitis diagnosis, but their biopsies presented some liver severity (scores 3 and 4).	Diagnostic test: To propose diagnostic tests for liver diseases before surgical decisions.; Observational although patients are candidates for metabolic surgery.; Other Names: External follow up monitoring liver diseases and weight loss.
Severe obesity with well-defined steatohepatitis and/or cirrhosis; Patients with severe obesity who met the criteria described in Kleiner et al. (2005) for nonalcoholic steatohepatitis diagnosis (score 5-8).	Diagnostic test: To propose diagnostic tests for liver diseases before surgical decisions.; Observational although patients are candidates for metabolic surgery.; Other Names: External follow up monitoring liver diseases and weight loss.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Weight change	The effect of bariatric surgery on adiposity outcomes.	1 to 10 years
Type 2 diabetes mellitus incidence	The effect of bariatric surgery on metabolic outcomes.	1 to 10 years
Hypertension incidence	The effect of bariatric surgery on metabolic outcomes.	1 to 10 years
Chronic liver diseases incidence	The usefulness of imaging techniques on metabolic outcomes.	1 to 10 years
Dyslipidemia incidence	The effect of bariatric surgery on metabolic outcomes.	1 to 10 years

Collaborators and Investigators

• Sponsor: Institut Investigacio Sanitaria Pere Virgili
• Collaborators: Instituto de Salud Carlos III; University of Barcelona; La Caixa Foundation; Hospital Universitari Sant Joan
• Investigators: Principal Investigator: Jorge Joven, Professor, Institut Investigacio Sanitaria Pere Virgili

Study record dates

Study Major Dates

• Study Start (Actual): June 25, 2008
• Primary Completion (Estimated): December 31, 2028
• Study Completion (Estimated): December 31, 2028

Study Registration Dates

• First Submitted: September 21, 2022
• First Submitted That Met QC Criteria: September 23, 2022
• First Posted (Actual): September 26, 2022

Study Record Updates

• Last Update Posted (Actual): March 28, 2024
• Last Update Submitted That Met QC Criteria: March 27, 2024
• Last Verified: March 1, 2024

More Information

Terms related to this study

• Keywords: Metabolomics; Bariatric surgery; Weight loss; Proteomics; Mass spectrometry; Lipidomics; Transcriptomics; Liver diseases
• Additional Relevant MeSH Terms: Digestive System Diseases; Overnutrition; Nutrition Disorders; Overweight; Body Weight; Liver Diseases; Obesity; Fatty Liver; Obesity, Morbid; Hematinics; Liver Extracts
• Other Study ID Numbers: EOM study; EPIMET (Other Identifier: Hospital Universitari Sant Joan)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES

IPD Plan Description

The Data Management Plan makes data fully findable, accessible, interoperable, and reusable, following the indication of the Horizon 2020 initiative of the European Union. The clinical team identified sensitive data, including epidemiological, anthropometric, and medical information. It is the only responsibility of the principal investigator to ensure that sensitive data are de-identified, implementing technical safeguards to guarantee anonymity.

Most data will be experimental and obtained from the analysis of column value and data format description (.txt or .csv) and syntax scripts (.R).

The external collaborators, especially those involved in validation cohorts, may have access to data upon request.

With the acceptance of the principal investigator, Rovira i Virgili University's institutional service will guarantee digital access to repositories with raw data generated in research analyses.

• IPD Sharing Time Frame: Once decided the repository web address.
• IPD Sharing Access Criteria: Decided by the principal investigator.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ICF; ANALYTIC_CODE; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No