NCT05560997

Metabolic Subtypes of Non-Alcoholic Fatty Liver Disease

Machine Learning to Identify Metabolic Subtypes of Non-Alcoholic Fatty Liver Disease

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 1,000 (estimated)
• Sponsor: Yan Bi · Academic / Other
• Sex: All
• Age: 18 Years – 75 Years
• Healthy volunteers: Accepted

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

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	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.

Timeline

• Start date: 2016-01-05
• Primary completion: 2024-10-30
• Completion: 2025-06-01
• First posted: 2022-09-30
• Last updated: 2024-06-21

Locations

1 site across 1 country: China