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 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.