The Potential Value and Impact of Diagnostic Biomarkers for MAFLD Using Machine Learning Methods

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

500 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-06-01

Study Completion Date

2024-12-31

Brief Summary

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This is a case-control study that aims to build a predictive model for MAFLD based on machine learning.

Detailed Description

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Metabolic dysfunction-associated fatty liver disease (MAFLD) also known as non-alcoholic fatty liver disease (NAFLD), is one of the most prevalent liver diseases worldwide with high prevalence and economic burden, which affects 25% of global adult population. Despite extensive research on understanding the inner pathophysiology of MAFLD, it still keep growing with no approval therapy. Therefore, preventive measures are particularly important in diagnosing MAFLD. So far the liver biopsy is still the gold standard for diagnosis of MAFLD, however considering the invasive process and potential risks, it still has low acceptance for asymptomatic patients, thus non-invasive methods are necessary for this reason.

The purpose of this study is to establish a prediction model to identify MAFLD patients, which can accurately predict whether the participants have MAFLD according to the relevant metabolic indicators of the participants, without the need for invasive examinations such as tissue biopsy.

Conditions

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Metabolic Dysfunction-associated Fatty Liver Disease

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Study Groups

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case group

MAFLD patients

No interventions assigned to this group

control group

health people

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

1. aged 18 to 75 years;
2. meeting the diagnostic criteria of MAFLD;
3. no other organic lesions were found in imaging examination;
4. willing and able to sign informed consent.

Exclusion Criteria

1. significant drinking history (weekly alcohol consumption ≥ 140g for male, or weekly alcohol consumption ≥ 70g for female);
2. presence of evidence for having hepatic steatosis, viral hepatitis, history of hepatic cancer, drug-induced liver injury, liver cirrhosis and other liver and biliary tract diseases;
3. major organ malfunction, severe systemic illnesses, mental health issues, or inability to complete examination;
4. pregnant or pregnancy planning female;
5. missing of important clinical data.

Minimum Eligible Age

18 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Responsible Party

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Responsibility Role SPONSOR

Locations

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The First Clinical Medical College of Zhejiang Chinese Medical University

Hangzhou, Zhejiang, China

Site Status

Countries

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China

Other Identifiers

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2021-KL-202-01

Identifier Type: -

Identifier Source: org_study_id