Neurophenotype Predicts CD Disease Progression

NCT ID: NCT06452550

Last Updated: 2024-06-11

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-05-01
• Study Completion Date: 2025-06-01

Brief Summary

The goal of this observational study is aimed to develop a novel multimodal neuroimaging-based model to characterize the neurophenotype of Crohn's Disease patients and assess its ability for predicting disease progression, using multiomics data to interpret the model.

Participants will be followed-up of at least six months for patients without disease progression to assess the relationship between neurophenotype and intestinal outcomes.

Detailed Description

Brain-gut axis plays a crucial role in the pathogenesis of Crohn's disease (CD); however, CD neurophenotype and its impact on intestinal disease progression remain unclear. We aimed to develop a novel multimodal neuroimaging-based model to characterize the neurophenotype of CD patients and assess its ability for predicting disease progression, using multiomics data to interpret the model. This study enrolled CD patients who underwent baseline testing (including neuroimaging, psychological scales, MR enterography, and ileocolonoscopy) and faecal/blood samples collection. The neurophenotypes of patients were characterized using a neuroimaging-based model. The predictive ability of neurophenotype model for disease progression was evaluated using Cox regression analysis. Multiomics data (including faecal microbiome, faecal/blood metabolomics, intestinal permeability, blood-brain-barrier permeability, and blood neurotransmitter levels) were used to elucidate how neurophenotypes reflect brain-gut interactions.

Conditions

Radiomics; Multi-omics

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: PROSPECTIVE

Study Groups

Crohn's disease patients with no-to-mild inflammation

On MR enterography, the Magnetic Resonance Index of Activity (MaRIA) and Magnetic Resonance Enterography Global Score (MEGS) were used to characterize intestinal inflammation by assessing intramural and extramural lesions in both the small and large intestines. In ileocolonoscopy, the Simplified Endoscopic Activity Score for Crohn's Disease (SES-CD) was used to quantify mucosal or luminal lesions from the terminal ileum to the colorectum. Both modalities were used to classify patients with no-to-mild or moderate-to-severe inflammation. We defined patients as having moderate-to-severe inflammation when at least two of the following criteria were met: (1) MaRIA≥11; (2) MEGS≥ 10 for the small bowel or MEGS≥12 for the colon; or (3) SES-CD≥11. Otherwise, patients were categorized as having no-to-mild inflammation.

Logistic regression (LR) prediction models

Intervention Type: DIAGNOSTIC_TEST

Logistic regression model was used to establish a model to distinguish different levels of intestinal inflammation.

Crohn's disease patients with moderate-to-severe inflammation

On MR enterography, the Magnetic Resonance Index of Activity (MaRIA) and Magnetic Resonance Enterography Global Score (MEGS) were used to characterize intestinal inflammation by assessing intramural and extramural lesions in both the small and large intestines. In ileocolonoscopy, the Simplified Endoscopic Activity Score for Crohn's Disease (SES-CD) was used to quantify mucosal or luminal lesions from the terminal ileum to the colorectum. Both modalities were used to classify patients with no-to-mild or moderate-to-severe inflammation. We defined patients as having moderate-to-severe inflammation when at least two of the following criteria were met: (1) MaRIA≥11; (2) MEGS≥ 10 for the small bowel or MEGS≥12 for the colon; or (3) SES-CD≥11. Otherwise, patients were categorized as having no-to-mild inflammation.

Logistic regression (LR) prediction models

Intervention Type: DIAGNOSTIC_TEST

Logistic regression model was used to establish a model to distinguish different levels of intestinal inflammation.

Interventions

Logistic regression (LR) prediction models

Logistic regression model was used to establish a model to distinguish different levels of intestinal inflammation.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• (a) CD patients aged 18-45 years; (b) the completion of multimodal brain MRI and administration of psychological questionnaires; (c) MR enterography (MRE), ileocolonoscopy, and blood or faecal samples, collection within one week of brain MRI; (d) a follow-up of at least six months for patients without disease progression; and (e) right-handedness.

Exclusion Criteria

• (a) recent use of antibiotics, probiotics, or prebiotics within three months prior to inclusion; (b) history of neurosurgery, cerebrovascular disease, or brain trauma; (c) use of central nervous system drugs or antidepressants within three months prior to inclusion; (d) identification of brain lesions on MR scan; (e) claustrophobia; or (f) presence of metal implants.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 45 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

First Affiliated Hospital, Sun Yat-Sen University

OTHER

Sponsor Role: lead

Responsible Party

Xuehua Li

associate chief physician

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Xuehua Li

Role: PRINCIPAL_INVESTIGATOR

Sun Yat-sen University First Affiliated Hospital Department of Radiology

Locations

XploreMET v3.0 system

Shanghai, , China

Site Status: RECRUITING

Countries

China

Central Contacts

Xuehua Li

Role: CONTACT

lxueh@mail.sysu.edu.cn

13580364103

Ruonan Zhang

Role: CONTACT

zhangrn25@mail2.sysu.edu.cn

15124748450

Facility Contacts

Xuehua Li

Role: primary

Other Identifiers

82070680

Identifier Type: -

Identifier Source: org_study_id