Machine Learning to Identify Metabolic Subtypes of Non-Alcoholic Fatty Liver Disease
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Sponsor
- Enrollment
- 1,000
- Locations
- 1
- Primary Endpoint
- histological cirrhosis checklist
Study Overview
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.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Retrospective
Eligibility Criteria
- Ages
- 18 Years to 75 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
Inclusion Criteria
- •biopsy-proved NALD cohort:
- •age 18 to 75 years
- •receiving liver biopsy at the time of metabolic surgery
- •relatively complete clinical information, including physical examination, biochemical and haematological assessments
- •longitudinal cohort
- •age 18 to 75 years
- •receiving abdominal imaging examinations,
- •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
Outcomes
Primary Outcomes
histological cirrhosis checklist
Time Frame: up to 5 years
cirrhosis was defined as widespread disruption of normal liver structure by the formation of pseudolobules or Scheuer stage 4 fibrosis in pathological findings.
Ischemic heart disease
Time Frame: up to 5 years
Objective Findings of Coronary Stenosis (≥ 50%) in at least 2 coronary artery territories (ie, left anterior descending, ramus intermedius, left circumflex, right coronary artery) involving the vain vessel, a major branch, or a bypass graft
Documented heart disease checklist
Time Frame: up to 5 years
Documented Myocardial Infarction or Percutaneous Coronary Intervention or Coronary Artery Bypass Grafting
hepatocellular carcinoma
Time Frame: up to 5 years
the diagnosis of hepatocellular carcinoma was based on well-established diagnostic imaging criteria and/or histology.
Secondary Outcomes
No secondary outcomes reported
Investigators
Yan Bi
Chief Physician
The Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School