Metabolic Subtypes of Non-Alcoholic Fatty Liver Disease

NCT ID: NCT05560997

Last Updated: 2024-06-21

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 1000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2016-01-05
• Study Completion Date: 2025-06-01

Brief Summary

The purpose of this study was to use machine learning to explore a more precise classification of NAFLD subgroups towards informing individualized therapy.

Detailed Description

Clinical characteristics of NAFLD are heterogenous, but current classification for diagnosis is simply based on pathological examination. The conventional pathological classification is insufficient to reflect the complexity and heterogeneity of NAFLD and can not predict the prognosis. Towards precision treatment, a more refined metabolic classification of NAFLD phenotypes is highly demanded for a personalized diagnosis, aiming to identify patients at elevated risk of cardiovascular disease or cirrhosis. This kind of refined classification can provide a more precise diagnosis and enable more individualized preventive interventions and early treatments. In a cross-sectional cohort, unsupervised machine learning was used to cluster patients with biopsy-proved NAFLD from Drum Tower Hospital Affiliated to Nanjing University Medical School based on clinical variables. Verification of the clustering was performed in a longitudinal cohort.

Conditions

Non-Alcoholic Fatty Liver Disease; Machine Learning

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Study Groups

biopsy-proved NAFLD cohort

NAFLD is defined as the presence of at least 5% steatosis based on histological examination.

10-year ASCVD risk estimation

Intervention Type: DIAGNOSTIC_TEST

High CVD risk was defined as a history of CVD or a 10-year ASCVD risk ≥10%. The 10-year ASCVD risk estimation was carried out according to 2016 Chinese guidelines for the management of dyslipidemia in adults.

longitudinal physical examination cohort

The diagnosis of NAFLD was based on imaging evaluation.

10-year ASCVD risk estimation

Intervention Type: DIAGNOSTIC_TEST

High CVD risk was defined as a history of CVD or a 10-year ASCVD risk ≥10%. The 10-year ASCVD risk estimation was carried out according to 2016 Chinese guidelines for the management of dyslipidemia in adults.

Interventions

10-year ASCVD risk estimation

High CVD risk was defined as a history of CVD or a 10-year ASCVD risk ≥10%. The 10-year ASCVD risk estimation was carried out according to 2016 Chinese guidelines for the management of dyslipidemia in adults.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• biopsy-proved NALD cohort:

1. age 18 to 75 years

2. receiving liver biopsy at the time of metabolic surgery

3. relatively complete clinical information, including physical examination, biochemical and haematological assessments

• longitudinal cohort

1. age 18 to 75 years

2. receiving abdominal imaging examinations,

3. relatively complete clinical information, including physical examination, biochemical and haematological assessments (4）follow-up time at least more than 12 months

Exclusion Criteria

• (1)consumed excessive alcohol (≥140 g/week for males or ≥ 70 g/week for females) •
• (2) with history of other liver diseases including chronic hepatitis, biliary obstructive diseases or autoimmune hepatitis

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 75 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Yan Bi

OTHER

Sponsor Role: lead

Responsible Party

Yan Bi

Chief Physician

Responsibility Role: SPONSOR_INVESTIGATOR

Locations

Division of Endocrinology, the Affiliated Drum Tower Hospital of Nanjing University

Nanjing, Jiangsu, China

Site Status: RECRUITING

Countries

China

Facility Contacts

Dalong Zhu, MD,PhD

Role: primary

zhudldr@gmail.com

86-25-83-105302

Yan Bi, MD,PhD

Role: backup

biyan@nju.edu.cn

86-25-83-105302

Other Identifiers

NAFLDcluster

Identifier Type: -

Identifier Source: org_study_id