Development and validation of a multimodal model integrating gut microbiota and metabolite for identifying sarcopenia in patients with MASLD: a study from two centers in China
Abstract Background and aims Metabolic dysfunction-associated steatotic liver disease (MASLD) is a common chronic liver disease worldwide, and identifying sarcopenia is critical since it is correlated with poor prognosis. Little is known about mechanistic alterations in the pathogenesis of this cond...
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2025-08-01
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| Online Access: | https://doi.org/10.1186/s12937-025-01198-2 |
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| author | Sizhe Wan Mingkai Li Wanjun Li Yuexiang Ren Yuankai Wu Qingtian Luo Wei Gong |
| author_facet | Sizhe Wan Mingkai Li Wanjun Li Yuexiang Ren Yuankai Wu Qingtian Luo Wei Gong |
| author_sort | Sizhe Wan |
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| description | Abstract Background and aims Metabolic dysfunction-associated steatotic liver disease (MASLD) is a common chronic liver disease worldwide, and identifying sarcopenia is critical since it is correlated with poor prognosis. Little is known about mechanistic alterations in the pathogenesis of this condition. This study aimed to explore the alterations in the gut microbiome and metabolome in patients with sarcopenia and develop a predictive model. Methods We performed shotgun metagenomic sequencing and untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomic profiling of fecal samples from the discovery cohort (70 patients without sarcopenia, 30 with sarcopenia). A microbiota-metabolite score (MM score) was developed using LASSO regression to identify key microbiome and metabolite features associated with sarcopenia. A multimodal prediction model incorporating the MM score and clinical parameters was then developed and validated in an independent cohort of 50 patients. Results Patients with sarcopenia exhibited altered gut microbiota and metabolomic profiles, with significantly elevated Enterococcus faecium and Bacteroides vulgatus species, and elevated bile acids. Integration of the MM score with clinical variables (age, BMI, AST, presence of diabetes) resulted in a multimodal model with an AUC of 0.911, outperforming existing models including FIB-4 (AUC 0.765), NFS (AUC 0.724), and using only MM score alone (AUC 0.818). In a prospective validation cohort, the multimodal model demonstrated superior diagnostic performance (AUC 0.897), with significant improvements in clinical utility as evidenced by calibration curves and decision curve analysis. Conclusions This study developed a novel multimodal model combining gut microbiome, metabolomics, and clinical data for accurate prediction of sarcopenia, offering a promising approach for early identification of high-risk MASLD patients with sarcopenia. |
| format | Article |
| id | doaj-art-e994aee796e64c669a3647bb40e25385 |
| institution | Kabale University |
| issn | 1475-2891 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Nutrition Journal |
| spelling | doaj-art-e994aee796e64c669a3647bb40e253852025-08-24T11:09:27ZengBMCNutrition Journal1475-28912025-08-0124111210.1186/s12937-025-01198-2Development and validation of a multimodal model integrating gut microbiota and metabolite for identifying sarcopenia in patients with MASLD: a study from two centers in ChinaSizhe Wan0Mingkai Li1Wanjun Li2Yuexiang Ren3Yuankai Wu4Qingtian Luo5Wei Gong6Department of Gastroenterology, Shenzhen Hospital, Southern Medical UniversityDepartment of Gastroenterology, Shenzhen Hospital, Southern Medical UniversityDepartment of Gastroenterology, Shenzhen Hospital, Southern Medical UniversityDepartment of Pathology, The Third Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Infection disease, The Third Affiliated Hospital of Sun Yat- Sen UniversityDepartment of Gastroenterology and Endoscopy Center, Affiliated Nanshan Hospital of Shenzhen UniversityDepartment of Gastroenterology, Shenzhen Hospital, Southern Medical UniversityAbstract Background and aims Metabolic dysfunction-associated steatotic liver disease (MASLD) is a common chronic liver disease worldwide, and identifying sarcopenia is critical since it is correlated with poor prognosis. Little is known about mechanistic alterations in the pathogenesis of this condition. This study aimed to explore the alterations in the gut microbiome and metabolome in patients with sarcopenia and develop a predictive model. Methods We performed shotgun metagenomic sequencing and untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomic profiling of fecal samples from the discovery cohort (70 patients without sarcopenia, 30 with sarcopenia). A microbiota-metabolite score (MM score) was developed using LASSO regression to identify key microbiome and metabolite features associated with sarcopenia. A multimodal prediction model incorporating the MM score and clinical parameters was then developed and validated in an independent cohort of 50 patients. Results Patients with sarcopenia exhibited altered gut microbiota and metabolomic profiles, with significantly elevated Enterococcus faecium and Bacteroides vulgatus species, and elevated bile acids. Integration of the MM score with clinical variables (age, BMI, AST, presence of diabetes) resulted in a multimodal model with an AUC of 0.911, outperforming existing models including FIB-4 (AUC 0.765), NFS (AUC 0.724), and using only MM score alone (AUC 0.818). In a prospective validation cohort, the multimodal model demonstrated superior diagnostic performance (AUC 0.897), with significant improvements in clinical utility as evidenced by calibration curves and decision curve analysis. Conclusions This study developed a novel multimodal model combining gut microbiome, metabolomics, and clinical data for accurate prediction of sarcopenia, offering a promising approach for early identification of high-risk MASLD patients with sarcopenia.https://doi.org/10.1186/s12937-025-01198-2Metabolic dysfunction-associated steatotic liver diseaseSarcopeniaGut microbiotaMetaboliteShotgun metagenomic sequencingUntargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics |
| spellingShingle | Sizhe Wan Mingkai Li Wanjun Li Yuexiang Ren Yuankai Wu Qingtian Luo Wei Gong Development and validation of a multimodal model integrating gut microbiota and metabolite for identifying sarcopenia in patients with MASLD: a study from two centers in China Nutrition Journal Metabolic dysfunction-associated steatotic liver disease Sarcopenia Gut microbiota Metabolite Shotgun metagenomic sequencing Untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics |
| title | Development and validation of a multimodal model integrating gut microbiota and metabolite for identifying sarcopenia in patients with MASLD: a study from two centers in China |
| title_full | Development and validation of a multimodal model integrating gut microbiota and metabolite for identifying sarcopenia in patients with MASLD: a study from two centers in China |
| title_fullStr | Development and validation of a multimodal model integrating gut microbiota and metabolite for identifying sarcopenia in patients with MASLD: a study from two centers in China |
| title_full_unstemmed | Development and validation of a multimodal model integrating gut microbiota and metabolite for identifying sarcopenia in patients with MASLD: a study from two centers in China |
| title_short | Development and validation of a multimodal model integrating gut microbiota and metabolite for identifying sarcopenia in patients with MASLD: a study from two centers in China |
| title_sort | development and validation of a multimodal model integrating gut microbiota and metabolite for identifying sarcopenia in patients with masld a study from two centers in china |
| topic | Metabolic dysfunction-associated steatotic liver disease Sarcopenia Gut microbiota Metabolite Shotgun metagenomic sequencing Untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics |
| url | https://doi.org/10.1186/s12937-025-01198-2 |
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