Liver fibrosis progression analyzed with AI predicts renal decline
Background & Aims: The relationship between biopsy-proven liver fibrosis progression and renal function decline in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) has not been fully elucidated. We used an automated quantitative liver fibrosis assessment (qFibro...
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Elsevier
2025-05-01
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| author | Dan-Qin Sun Jia-Qi Shen Xiao-Fei Tong Ya-Yun Ren Hai-Yang Yuan Yang-Yang Li Xin-Lei Wang Sui-Dan Chen Pei-Wu Zhu Xiao-Dong Wang Christopher D. Byrne Giovanni Targher Lai Wei Vincent W.S. Wong Dean Tai Arun J. Sanyal Hong You Ming-Hua Zheng |
| author_facet | Dan-Qin Sun Jia-Qi Shen Xiao-Fei Tong Ya-Yun Ren Hai-Yang Yuan Yang-Yang Li Xin-Lei Wang Sui-Dan Chen Pei-Wu Zhu Xiao-Dong Wang Christopher D. Byrne Giovanni Targher Lai Wei Vincent W.S. Wong Dean Tai Arun J. Sanyal Hong You Ming-Hua Zheng |
| author_sort | Dan-Qin Sun |
| collection | DOAJ |
| description | Background & Aims: The relationship between biopsy-proven liver fibrosis progression and renal function decline in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) has not been fully elucidated. We used an automated quantitative liver fibrosis assessment (qFibrosis) technique to investigate the temporal changes in regional liver fibrosis. Methods: This retrospective longitudinal study included 68 MASLD patients and their paired formalin-fixed sections of liver biopsies. One hundred eighty-four fibrosis parameters were quantified in five different hepatic regions, including portal tract, peri-portal, zone 2, peri-central and central vein regions, and qFibrosis continuous values were calculated for all samples based on 10 fibrosis parameters using qFibrosis assessment. Liver fibrosis progression (QLF+, n = 18) and regression (QLF-, n = 23) was defined as at least a 20% relative change in qFibrosis over a 23-month follow-up. Renal function decline was assessed by estimated glomerular filtration rate (eGFR) changes. Results: The eGFR decline was greater in the QLF+ group (106.53 ± 13.71 ml/min/1.73 m2 vs. 105.28 ± 12.46 ml/min/1.73 m2) than in the QLF- group (110.87 ± 14.58 ml/min/1.73 m2 vs. 114.18 ± 14.81 ml/min/1.73 m2). In addition, liver fibrosis changes in the central vein and pericentral regions were more strongly associated with eGFR decline than in periportal, zone 2 and portal tract regions. We combined these parameters to construct a prediction model, which better differentiated eGFR decline (a cut-off value of qFibrosis combined index = 0.52, p <0.001). Conclusions: A decline in renal function is significantly related to liver fibrosis progression in MASLD. Regional qFibrosis assessment may efficiently predict eGFR decline, thus highlighting the importance of assessing renal function in patients with MASLD with worsening liver fibrosis. Impact and implications: The study shows that liver fibrosis progression assessed by qFibrosis may be associated with renal function decline, which provides a new perspective for understanding complex pathological processes. A combination of artificial intelligence and digital pathology may earlier and more precisely quantify the progression of regional liver fibrosis, thus better identifying changes in renal function. This opens the possibility of early interventions, which are essential to improve patients’ outcomes. |
| format | Article |
| id | doaj-art-ec4ae58b5e0046f5a5964586759083b9 |
| institution | DOAJ |
| issn | 2589-5559 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
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| series | JHEP Reports |
| spelling | doaj-art-ec4ae58b5e0046f5a5964586759083b92025-08-20T03:11:06ZengElsevierJHEP Reports2589-55592025-05-017510135810.1016/j.jhepr.2025.101358Liver fibrosis progression analyzed with AI predicts renal declineDan-Qin Sun0Jia-Qi Shen1Xiao-Fei Tong2Ya-Yun Ren3Hai-Yang Yuan4Yang-Yang Li5Xin-Lei Wang6Sui-Dan Chen7Pei-Wu Zhu8Xiao-Dong Wang9Christopher D. Byrne10Giovanni Targher11Lai Wei12Vincent W.S. Wong13Dean Tai14Arun J. Sanyal15Hong You16Ming-Hua Zheng17Department of Nephrology, Jiangnan University Medical Center, Wuxi, China; Affiliated Wuxi Clinical College of Nantong University, Wuxi, China; Wuxi No. 2 People's Hospital, Wuxi, ChinaDepartment of Nephrology, Jiangnan University Medical Center, Wuxi, China; Affiliated Wuxi Clinical College of Nantong University, Wuxi, China; Wuxi No. 2 People's Hospital, Wuxi, ChinaLiver Research Center, Beijing Friendship Hospital, Beijing Key Laboratory of Translational Medicine on Liver Cirrhosis, National Clinical Research Center of Digestive Diseases, Capital Medical University, Beijing, ChinaHistoIndex Pte Ltd, SingaporeMAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China; Institute of Hepatology, Wenzhou Medical University, Wenzhou, ChinaDepartment of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaHistoIndex Pte Ltd, SingaporeDepartment of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaMAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaKey Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, ChinaSouthampton National Institute for Health and Care Research Biomedical Research Centre, University Hospital Southampton and University of Southampton, Southampton General Hospital, Southampton, UKDepartment of Medicine, University of Verona, Verona, Italy; Metabolic Diseases Research Unit, IRCCS Sacro Cuore-Don Calabria Hospital, Negrar di Valpolicella, ItalyHepatopancreatobiliary Center, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, ChinaDepartment of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong Special Administrative Region of ChinaHistoIndex Pte Ltd, SingaporeStravitz-Sanyal Institute for Liver Disease and Metabolic Health, Virginia Commonwealth University School of Medicine, Richmond, VA, USALiver Research Center, Beijing Friendship Hospital, Beijing Key Laboratory of Translational Medicine on Liver Cirrhosis, National Clinical Research Center of Digestive Diseases, Capital Medical University, Beijing, China; Corresponding authors. Addresses: MAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University; No. 2 Fuxue Lane, Wenzhou 325000, China, Tel.: +86-577-55579611; fax: +86-577-55578522 (M-H. Zheng); Liver Research Center, Beijing Friendship Hospital, Beijing Key Laboratory of Translational Medicine on Liver Cirrhosis, National Clinical Research Center of Digestive Diseases, Capital Medical University, Beijing 100050, China, Tel.: +86-10-63139019 (H. You).MAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China; Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China; Institute of Hepatology, Wenzhou Medical University, Wenzhou, China; Corresponding authors. Addresses: MAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University; No. 2 Fuxue Lane, Wenzhou 325000, China, Tel.: +86-577-55579611; fax: +86-577-55578522 (M-H. Zheng); Liver Research Center, Beijing Friendship Hospital, Beijing Key Laboratory of Translational Medicine on Liver Cirrhosis, National Clinical Research Center of Digestive Diseases, Capital Medical University, Beijing 100050, China, Tel.: +86-10-63139019 (H. You).Background & Aims: The relationship between biopsy-proven liver fibrosis progression and renal function decline in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) has not been fully elucidated. We used an automated quantitative liver fibrosis assessment (qFibrosis) technique to investigate the temporal changes in regional liver fibrosis. Methods: This retrospective longitudinal study included 68 MASLD patients and their paired formalin-fixed sections of liver biopsies. One hundred eighty-four fibrosis parameters were quantified in five different hepatic regions, including portal tract, peri-portal, zone 2, peri-central and central vein regions, and qFibrosis continuous values were calculated for all samples based on 10 fibrosis parameters using qFibrosis assessment. Liver fibrosis progression (QLF+, n = 18) and regression (QLF-, n = 23) was defined as at least a 20% relative change in qFibrosis over a 23-month follow-up. Renal function decline was assessed by estimated glomerular filtration rate (eGFR) changes. Results: The eGFR decline was greater in the QLF+ group (106.53 ± 13.71 ml/min/1.73 m2 vs. 105.28 ± 12.46 ml/min/1.73 m2) than in the QLF- group (110.87 ± 14.58 ml/min/1.73 m2 vs. 114.18 ± 14.81 ml/min/1.73 m2). In addition, liver fibrosis changes in the central vein and pericentral regions were more strongly associated with eGFR decline than in periportal, zone 2 and portal tract regions. We combined these parameters to construct a prediction model, which better differentiated eGFR decline (a cut-off value of qFibrosis combined index = 0.52, p <0.001). Conclusions: A decline in renal function is significantly related to liver fibrosis progression in MASLD. Regional qFibrosis assessment may efficiently predict eGFR decline, thus highlighting the importance of assessing renal function in patients with MASLD with worsening liver fibrosis. Impact and implications: The study shows that liver fibrosis progression assessed by qFibrosis may be associated with renal function decline, which provides a new perspective for understanding complex pathological processes. A combination of artificial intelligence and digital pathology may earlier and more precisely quantify the progression of regional liver fibrosis, thus better identifying changes in renal function. This opens the possibility of early interventions, which are essential to improve patients’ outcomes.http://www.sciencedirect.com/science/article/pii/S2589555925000345Metabolic dysfunction-associated steatotic liver diseaseRegional liver fibrosisKidney functionDigital pathologyMetabolic dysfunction-associated fatty liver disease |
| spellingShingle | Dan-Qin Sun Jia-Qi Shen Xiao-Fei Tong Ya-Yun Ren Hai-Yang Yuan Yang-Yang Li Xin-Lei Wang Sui-Dan Chen Pei-Wu Zhu Xiao-Dong Wang Christopher D. Byrne Giovanni Targher Lai Wei Vincent W.S. Wong Dean Tai Arun J. Sanyal Hong You Ming-Hua Zheng Liver fibrosis progression analyzed with AI predicts renal decline JHEP Reports Metabolic dysfunction-associated steatotic liver disease Regional liver fibrosis Kidney function Digital pathology Metabolic dysfunction-associated fatty liver disease |
| title | Liver fibrosis progression analyzed with AI predicts renal decline |
| title_full | Liver fibrosis progression analyzed with AI predicts renal decline |
| title_fullStr | Liver fibrosis progression analyzed with AI predicts renal decline |
| title_full_unstemmed | Liver fibrosis progression analyzed with AI predicts renal decline |
| title_short | Liver fibrosis progression analyzed with AI predicts renal decline |
| title_sort | liver fibrosis progression analyzed with ai predicts renal decline |
| topic | Metabolic dysfunction-associated steatotic liver disease Regional liver fibrosis Kidney function Digital pathology Metabolic dysfunction-associated fatty liver disease |
| url | http://www.sciencedirect.com/science/article/pii/S2589555925000345 |
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