Development and validation of a scoring system to predict MASLD patients with significant hepatic fibrosis
Abstract To address the need for a simple model to predict ≥ F2 fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD) patients, a study utilized data from 791 biopsy-proven MASLD patients from the NASH Clinical Research Network and Jinan University First Affiliated Hospital. T...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-91013-z |
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| author | Linjing Long Yue Wu Huijun Tang Yanhua Xiao Min Wang Lianli Shen Ying Shi Shufen Feng Chujing Li Jiaheng Lin Shaohui Tang Chutian Wu |
| author_facet | Linjing Long Yue Wu Huijun Tang Yanhua Xiao Min Wang Lianli Shen Ying Shi Shufen Feng Chujing Li Jiaheng Lin Shaohui Tang Chutian Wu |
| author_sort | Linjing Long |
| collection | DOAJ |
| description | Abstract To address the need for a simple model to predict ≥ F2 fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD) patients, a study utilized data from 791 biopsy-proven MASLD patients from the NASH Clinical Research Network and Jinan University First Affiliated Hospital. The data were divided into training and internal testing sets through randomized stratified sampling. A multivariable logistic regression model using key categorical variables was developed to identify ≥ F2 fibrosis. External validation was performed using data from the FLINT trial and multiple centers in China. The DA-GAG score, incorporating diabetes, age, GGT, aspartate aminotransferase/ platelet ratio, and globulin/ total protein ratio, demonstrated superior performance in distinguishing ≥ F2 fibrosis with an area under the receiver operating characteristic curve of 0.79 in training and over 0.80 in testing datasets. The DA-GAG score efficiently identifies MASLD patients with ≥ F2 fibrosis, significantly reducing the medical burden. |
| format | Article |
| id | doaj-art-c95fd4b9e9884d23a7776806f7b8f925 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c95fd4b9e9884d23a7776806f7b8f9252025-08-20T02:41:33ZengNature PortfolioScientific Reports2045-23222025-03-0115111210.1038/s41598-025-91013-zDevelopment and validation of a scoring system to predict MASLD patients with significant hepatic fibrosisLinjing Long0Yue Wu1Huijun Tang2Yanhua Xiao3Min Wang4Lianli Shen5Ying Shi6Shufen Feng7Chujing Li8Jiaheng Lin9Shaohui Tang10Chutian Wu11Department of Gastroenterology, the Fifth Affiliated Hospital, Guangzhou Medical UniversityDepartment of Hepatology, Guangzhou Eighth People’s Hospital, Guangzhou Medical UniversityDepartment of Gastroenterology, Shenzhen Integrated Traditional Chinese and Western Medicine HospitalDepartment of Pathology, Guangzhou Eighth People’s Hospital, Guangzhou Medical UniversityDepartment of Gastroenterology, the First Affiliated Hospital, Jinan UniversityDepartment of Gastroenterology, the First Affiliated Hospital, Jinan UniversityDepartment of Gastroenterology, the First Affiliated Hospital, Jinan UniversityDepartment of Gastroenterology, the First Affiliated Hospital, Jinan UniversityDepartment of Hepatology, Guangzhou Eighth People’s Hospital, Guangzhou Medical UniversityDepartment of Gastrointestinal Surgery, He Fifth Affiliated Hospital, Guangzhou Medical UniversityDepartment of Gastroenterology, the First Affiliated Hospital, Jinan UniversityDepartment of Gastroenterology, the Fifth Affiliated Hospital, Guangzhou Medical UniversityAbstract To address the need for a simple model to predict ≥ F2 fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD) patients, a study utilized data from 791 biopsy-proven MASLD patients from the NASH Clinical Research Network and Jinan University First Affiliated Hospital. The data were divided into training and internal testing sets through randomized stratified sampling. A multivariable logistic regression model using key categorical variables was developed to identify ≥ F2 fibrosis. External validation was performed using data from the FLINT trial and multiple centers in China. The DA-GAG score, incorporating diabetes, age, GGT, aspartate aminotransferase/ platelet ratio, and globulin/ total protein ratio, demonstrated superior performance in distinguishing ≥ F2 fibrosis with an area under the receiver operating characteristic curve of 0.79 in training and over 0.80 in testing datasets. The DA-GAG score efficiently identifies MASLD patients with ≥ F2 fibrosis, significantly reducing the medical burden.https://doi.org/10.1038/s41598-025-91013-zMASLDFibrosisDA-GAGPrediction |
| spellingShingle | Linjing Long Yue Wu Huijun Tang Yanhua Xiao Min Wang Lianli Shen Ying Shi Shufen Feng Chujing Li Jiaheng Lin Shaohui Tang Chutian Wu Development and validation of a scoring system to predict MASLD patients with significant hepatic fibrosis Scientific Reports MASLD Fibrosis DA-GAG Prediction |
| title | Development and validation of a scoring system to predict MASLD patients with significant hepatic fibrosis |
| title_full | Development and validation of a scoring system to predict MASLD patients with significant hepatic fibrosis |
| title_fullStr | Development and validation of a scoring system to predict MASLD patients with significant hepatic fibrosis |
| title_full_unstemmed | Development and validation of a scoring system to predict MASLD patients with significant hepatic fibrosis |
| title_short | Development and validation of a scoring system to predict MASLD patients with significant hepatic fibrosis |
| title_sort | development and validation of a scoring system to predict masld patients with significant hepatic fibrosis |
| topic | MASLD Fibrosis DA-GAG Prediction |
| url | https://doi.org/10.1038/s41598-025-91013-z |
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