Metabolic Subtypes of Non-Alcoholic Fatty Liver Disease

Yan Bi1,000 enrolled

Overview

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

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 Type

OBSERVATIONAL

Enrollment

1,000

Conditions

Non-Alcoholic Fatty Liver Disease Machine Learning

Interventions

10-year ASCVD risk estimationDIAGNOSTIC_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.

Eligibility

Sex: ALLMin age: 18 YearsMax age: 75 YearsHealthy volunteers:

Medical Language ↔ Plain English

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

Locations (1)

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

Nanjing, Jiangsu, China

RECRUITING

Outcomes

Primary Outcomes

Ischemic heart disease

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

Time frame: up to 5 years

Documented heart disease checklist

Documented Myocardial Infarction or Percutaneous Coronary Intervention or Coronary Artery Bypass Grafting

Time frame: up to 5 years

histological cirrhosis checklist

cirrhosis was defined as widespread disruption of normal liver structure by the formation of pseudolobules or Scheuer stage 4 fibrosis in pathological findings.

Time frame: up to 5 years

hepatocellular carcinoma

the diagnosis of hepatocellular carcinoma was based on well-established diagnostic imaging criteria and/or histology.

Time frame: up to 5 years

Data from ClinicalTrials.gov

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