Study Results
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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NOT_YET_RECRUITING
2000 participants
OBSERVATIONAL
2025-02-08
2038-12-31
Brief Summary
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The severe form of MAFLD, metabolic dysfunction-associated steatohepatitis (MASH), has been a hot and challenging area of research for non-invasive tests (NITs). However, serum markers, imaging examinations, and novel markers under development cannot replace liver biopsy for the diagnosis of MASH. Clinically, the disease outcomes of MAFLD mainly depend on metabolic cardiovascular risk factors and fibrosis staging. Both liver biopsy and NIT-diagnosed advanced fibrosis and cirrhosis can predict liver-related events and all-cause mortality risks in MAFLD patients. Artificial intelligence and machine learning methods can improve the consistency of pathologists in diagnosing MASH and fibrosis. The Agile score, which combines gender, T2DM status, AST/ALT ratio, platelet count, and liver stiffness measurement (LSM), can improve the diagnostic efficacy of advanced fibrosis and cirrhosis in MAFLD patients and the efficiency of predicting liver-related events. However, the predictive effect of fibrosis staging and its changes on liver cancer needs to be improved. There is a lack of high-quality research on early warning indicators for the incidence of CVD, chronic kidney disease, and non-liver malignancies in MAFLD patients. It is necessary to explore the role of conventional indicators such as low-density lipoprotein cholesterol, lipoprotein(a), uric acid, and high-sensitivity C-reactive protein, as well as multi-omics parameters, in the classification, staging, and risk prediction of MAFLD.
MAFLD is an increasingly serious public health issue associated with a higher risk of liver-related events, cardiovascular-renal-metabolic syndrome, and malignancies. The prevalence of MAFLD in China is high, but the rate of standardized management is low. Even patients with the same classification and staging often have different clinical characteristics and outcomes. There is currently a lack of a clinical classification and early warning system for MAFLD that combines metabolic cardiovascular risk factors and NITs for different outcome risks.
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Detailed Description
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On the basis of an existing cohort of 1,500 liver biopsy cases, recruit an additional 500 cases from a national multicenter liver biopsy follow-up cohort (totaling 2,000 cases). Collect demographic, anthropometric, laboratory, imaging, and liver biopsy results for these patients.
Concurrently, biological samples, including blood, urine, feces, and liver biopsy tissues, will be collected. Utilize these samples to perform quantitative metabolite information based on database matching. Employ techniques such as genomics, epigenomics, proteomics, metabolomics, immunomics, and microbiome metagenomics to screen for differential biomarkers across different subgroups.
Combine these findings with clinical and imaging parameters of MAFLD patients to analyze and explore key parameters and molecules at different stages and outcomes of MAFLD disease progression.
2. Development and Validation of a Diagnostic and Prognostic System:
Based on key molecules identified through multi-omics, in conjunction with characteristic parameters from clinical and imaging data of MAFLD patients, use machine learning methods (such as random forests neural networks) combined with logistic regression to establish a novel non-invasive diagnostic and prognostic assessment system for adverse outcomes (cardiovascular events, non-liver malignancies, and liver-related events).
Validate this new assessment system to ensure its reliability and accuracy in predicting disease outcomes.
Conditions
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Study Design
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COHORT
OTHER
Study Groups
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MAFLD diagnosed by liver-biopsy
retrospective and prospective cohort about MAFLD diagnosed by liver-biopsy
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
1. BMI ≥ 24 kg/m² or waist circumference ≥ 90 cm (men) and 85 cm (women) or excessive body fat content and body fat percentage.
2. Fasting blood glucose ≥ 6.1 mmol/L or 2-hour post-load blood glucose ≥ 7.8 mmol/L or HbA1c ≥ 5.7% or history of Type 2 Diabetes Mellitus (T2DM) or Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) ≥ 2.5.
3. Fasting serum triglycerides ≥ 1.70 mmol/L or currently receiving lipid-lowering drug therapy.
4. Serum high-density lipoprotein cholesterol (HDL-C) ≤ 1.0 mmol/L (men) and 1.3 mmol/L (women) or currently receiving lipid-lowering drug therapy.
5. Blood pressure ≥ 130/85 mmHg or currently receiving antihypertensive drug therapy.
* histology of liver-biopsy
Exclusion Criteria
* Viral Hepatitis Markers: Individuals who are positive for hepatitis B surface antigen (HBsAg), positive for hepatitis C virus (HCV) antibodies, or have missing information regarding these markers.
* History of Serious Medical Conditions: Individuals with a history of malignant tumors, cardiovascular diseases, chronic kidney disease, decompensated liver cirrhosis (manifested by ascites, gastrointestinal bleeding, hepatic encephalopathy, hepatorenal syndrome, etc.), or those who have undergone liver transplantation.
ALL
No
Sponsors
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Fudan University
OTHER
Tianjin Second People's Hospital
OTHER
Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
OTHER
The Affiliated Hospital of Hangzhou Normal University
OTHER
Ruijin Hospital
OTHER
Beijing Friendship Hospital
OTHER
Responsible Party
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Hong You
Vice-President
Principal Investigators
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Hong You, PhD. / M.D.
Role: PRINCIPAL_INVESTIGATOR
Beijing Friendship Hospital
Locations
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Beijing Friendship Hospital, Capital Medical University
Beijing, Xicheng, China
Hangzhou Normal University Affiliated Hospital
Hangzhou, Xicheng, China
Zhongshan Hospital, Fudan University
Shanghai, Xicheng, China
Shanghai Ruijin Hospital, Shanghai Jiaotong University School of Medicine
Shanghai, , China
Xinhua Hospital, Shanghai Jiaotong University School of Medicine
Shanghai, , China
Tianjin Second People's Hospital
Tianjin, , China
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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2023ZD0508702
Identifier Type: OTHER_GRANT
Identifier Source: secondary_id
2023ZD0508702
Identifier Type: -
Identifier Source: org_study_id
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