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Model Construction for Predicting MRI-PDFF Liver Fat Content Grading Based on Blood Indicators and Body Composition Indicators

Model Construction for Predicting MRI-PDFF Liver Fat Content Grading Based on Blood Indicators and Body Composition Indicators

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ITMCTR
Registry ID
ITMCTR2026001175
Enrollment
Unknown
Registered
2026-05-07
Start date
2026-05-09
Completion date
Unknown
Last updated
2026-06-01

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Metabolic dysfunction-associated fatty liver disease

Interventions

Training cohort:Retrospective observational study with no intervention
validation cohort:Retrospective observational study with no intervention

Sponsors

Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 98 Years

Inclusion criteria

Inclusion criteria: 1.Adult subjects aged = 18 years; 2.Subjects who underwent MRI-PDFF examination and obtained liver fat content measurement results at our center during the study period; 3.Subjects who completed blood biochemical tests within 3 months; 4.Subjects who completed body composition assessment or anthropometric measurements (e.g., BMI, body fat percentage, visceral fat, etc.) within 3 months; 5.Subjects with complete clinical data and laboratory data.

Exclusion criteria

Exclusion criteria: 1.Significant alcohol consumption: >40 g/day for men and >20 g/day for women; 2.Other definite liver diseases, including but not limited to: 3.Viral hepatitis (HBV, HCV, etc.); 4.Autoimmune liver disease; 5.Drug-induced liver injury (DILI); 6.Inherited metabolic liver diseases (e.g., Wilson's disease); 7.Previously diagnosed liver cirrhosis, hepatocellular carcinoma (HCC), or severe structural liver abnormalities; 8.Pregnant or lactating women; 9.Recent use of medications that may significantly affect hepatic steatosis (e.g., long-term glucocorticoids); 10.Subjects with missing clinical data or key indicators; 11.Poor-quality MRI images or those with unreliable PDFF measurement results.

Design outcomes

Secondary

MeasureTime frame
Hepatic and renal function;Fasting blood glucose;Blood lipids;Blood routine test;Weight;Body Mass Index (BMI);Fat mass;Body fat percentage;

Countries

China

Contacts

Public ContactLingying Huang

Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine

hly320@126.com137 6458 7034

Outcome results

None listed

Source: ITMCTR (via WHO ICTRP) · Data processed: Jun 11, 2026