Human Models of Selective Insulin Resistance: Pancreatic Clamp
NCT06558422
Body Weight, Diabetes Mellitus
New York, United States
View Trial DetailsNCT Number: NCT06795646
Metabolic dysfunction-associated Fatty Liver Disease (MAFLD), also known as Non-Alcoholic Fatty Liver Disease (NAFLD), is the most common chronic progressive liver disease in China. It is closely related to the high incidence of cardiovascular-renal-metabolic syndrome and both liver and non-liver malignancies, posing a serious threat to public health. However, the diagnostic criteria for MAFLD are not unified globally, and the classification and staging still rely on liver biopsy for pathological assessment. The characteristics, mechanisms, and predictive indicators of liver and extrahepatic disease outcomes in MAFLD patients are not yet clear.
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.
Trial opening soon.
Get NotifiedAll sexes
Observational
Beijing Friendship Hospital, Capital Medical University, Beijing, Xicheng, China
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.
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.
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Time frame: 1-20 years
Number of participants with the composite endpoint, including
A. Liver-Related Events:
Cirrhosis Liver decompensation Hepatocellular carcinoma Liver transplantation
B. Metabolic Diseases:
Type 2 Diabetes Mellitus (T2DM) Hypertension Dyslipidemia Gout
C. Cardiovascular Diseases (CVD):
Coronary heart disease Stroke Heart failure Atrial fibrillation
D. Non-Liver Malignancies:
Colorectal adenoma/adenocarcinoma Gastric cancer Esophageal cancer Pancreatic cancer Gallbladder cancer Lung cancer Prostate cancer Hematological malignancies E. Chronic Kidney Disease
F. Mortality:
Liver disease-related deaths Cardiovascular disease-related deaths Other causes of death
Time frame: 1-20 years
Number of participants with liver-related events, including Cirrhosis, Liver decompensation, Hepatocellular carcinoma, Liver transplantation
Time frame: 1-20 years
Number of participants with metabolic disease, including Type 2 Diabetes Mellitus (T2DM), Hypertension, Dyslipidemia, Gout
Time frame: 1-20 years
Number of participants with CVD, including Coronary heart disease, Stroke, Heart failure, Atrial fibrillation
Time frame: 1-20 years
Number of participants with non-liver tumors, including colorectal adenoma/adenocarcinoma, gastric cancer, esophageal cancer, pancreatic cancer, gallbladder cancer, lung cancer, prostate cancer, hematological malignancies and so on
Time frame: 1-20 years
Number of participants with chronic kidney disease
Time frame: 1-20 years
Number of participants including liver disease-related deaths, cardiovascular disease-related deaths, and Other causes of death
Time frame: baseline
the clinical classification of MASLD by proteomics
Time frame: baseline
the clinical classification of MASLD by metabolomics
Time frame: baseline
the clinical classification of MASLD by immunomics
Time frame: baseline
the clinical classification of MASLD by microbial metagenomics
Contact information is provided by the study sponsor or research team.
Beijing Friendship Hospital
Other
Clinical Classification and Prediction System of MAFLD Based on Liver Biopsy
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
Published trials that share one or more normalized conditions with this study.
NCT06558422
Body Weight, Diabetes Mellitus
New York, United States
View Trial DetailsNCT07082751
Compensated Cirrhosis, Metabolic Dysfunction Associated Steatotic Liver Disease
Beijing, Beijing Municipality, China
View Trial DetailsNCT07707778
Acquired Immunodeficiency Syndrome, Blood-Borne Infections
Taipei, Taiwan
View Trial DetailsNCT06352697
Digestive System Diseases, Fatty Liver
Kyiv, Ukraine
View Trial Details