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

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. 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. 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. 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.

Authors

Institutions

Publication Details

Journal
Nutrition Journal
Published
2025-08-22
DOI
https://doi.org/10.1186/s12937-025-01198-2
Citations
1
Primary Topic
Liver Disease Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
1.35

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Wanjun Li, Mingkai Li, Qingtian Luo, Yuankai Wu et al.
1 citations
Nutrition Journal
Liver Disease Diagnosis and Treatment
1.35
article

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

Wanjun Li, Mingkai Li, Qingtian Luo, Yuankai Wu, Sizhe Wan, Yue Ren, Wei Gong
article en
1 citations

Abstract

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. 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. 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. 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.

Nutrition JournalVol. 24(1)
Sun Yat-sen University (CN), Shenzhen Sixth People's Hospital (CN), Southern Medical University Shenzhen Hospital (CN), Third Affiliated Hospital of Sun Yat-sen University (CN)
National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province
No poverty
Openalex Percentile: Top 16%
Liver Disease Diagnosis and Treatment
1.35
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.