Homogeneity of Liver Fat Distribution Serves as a Diagnostic Marker for Metabolic Dysfunction-Associated Steatohepatitis.
Authors
Affiliations (5)
Affiliations (5)
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Chinese Academy of Medical Science & Peking Union Medical College, China (D.Y., Y.B.); Branch of National Clinical Research Center for Metabolic Diseases, Nanjing, China (D.Y., D.F., T.G., W.F., Y.B., W.T.).
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China (D.F., T.G., W.F., Y.B., W.T.); Branch of National Clinical Research Center for Metabolic Diseases, Nanjing, China (D.Y., D.F., T.G., W.F., Y.B., W.T.).
- Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China (H.Z., J.C.).
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Chinese Academy of Medical Science & Peking Union Medical College, China (D.Y., Y.B.); Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China (D.F., T.G., W.F., Y.B., W.T.); Branch of National Clinical Research Center for Metabolic Diseases, Nanjing, China (D.Y., D.F., T.G., W.F., Y.B., W.T.).
- Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China (D.F., T.G., W.F., Y.B., W.T.); Branch of National Clinical Research Center for Metabolic Diseases, Nanjing, China (D.Y., D.F., T.G., W.F., Y.B., W.T.). Electronic address: [email protected].
Abstract
To characterize liver fat distribution in metabolic dysfunction-associated steatohepatitis (MASH) and propose a magnetic resonance imaging proton density fat fraction (MRI-PDFF)-based score for MASH identification in obesity. Individuals with obesity were recruited for liver biopsy and contemporaneous MR scanning. The variability in fat distribution was evaluated by calculating the standard deviation (SD), range, and coefficient of variation of MRI-PDFF across liver lobes. A machine learning-assisted strategy was employed to establish a diagnostic model for MASH. A total of 107 participants with biopsy-confirmed fatty liver were included, and 44 were diagnosed with MASH. The MASH group exhibited significantly higher fat content in all liver segments and more homogeneous fat distribution in the right liver lobe than those with non-MASH. A raw-MASH score, incorporating the mean value and SD of PDFF in the right lobe, alanine aminotransferase levels, and waist circumference, was then established to identify MASH with an area under the receiver operating characteristic curve (AUROC) of 0.90 (95%CI 0.84-0.95). It performed better than HAIR, ION, and acNASH (all p < 0.05), and exhibited higher AUROC than MR-MASH model (0.90 vs. 0.85, p = 0.066). Besides, the dual threshold strategy of raw-MASH improved the diagnostic performance with high sensitivity and specificity. In addition to the hepatic fat content, the homogeneity of fat distribution may represent another significant hallmark of MASH. The raw-MASH score, which is available from MR imaging and routine clinical collection, shows great potential for identifying MASH in obesity.