Bariatric surgery (BS) has several benefits, including resolution of non-alcoholic fatty liver disease (NAFLD) in many patients. However, a significant percentage of patients do not experience improvement in fatty liver after BS, and more than 10% develop new or worsening NAFLD features. Therefore, a question that remains unanswered is why some patients experience resolved NAFLD after BS and others do not. In this study, we investigated the fecal microbiota and plasma bile acids associated with NAFLD resolution in twelve morbidly obese patients undergoing BS, of whom six resolved their steatosis one year after surgery and another six did not. Results indicate that the hallmark of the gut microbiota in responder patients is a greater abundance of Bacteroides, Akkermansia, and several species of the Clostridia class (genera: Blautia, Faecalibacterium, Roseburia, Butyricicoccusa, and Clostridium), along with a decreased abundance of Actinomycetes/Bifidobacterium and Faecalicatena. NAFLD resolution was also associated with a sustained increase in primary bile acids (particularly non-conjugated), which likely results from a reduction in bacterial gut species capable of generating secondary bile acids. We conclude that there are specific changes in gut microbiota and plasma bile acids that could contribute to resolving NAFLD in BS patients. The knowledge acquired can help to design interventions with prebiotics and/or probiotics to promote a gut microbiome that favors NAFLD resolution.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is often asymptomatic and underdiagnosed; consequently, there is a demand for simple, non-invasive diagnostic tools. In this study, we developed a method to quantify liver steatosis based on miRNAs, present in liver and serum, that correlate with liver fat. The miRNAs were analyzed by miRNAseq in liver samples from two cohorts of patients with a precise quantification of liver steatosis. Common miRNAs showing correlation with liver steatosis were validated by RT-qPCR in paired liver and serum samples. Multivariate models were built using partial least squares (PLS) regression to predict the percentage of liver steatosis from serum miRNA levels. Leave-one-out cross validation and external validation were used for model selection and to estimate predictive performance. The miRNAseq results disclosed (a) 144 miRNAs correlating with triglycerides in a set of liver biobank samples (n = 20); and (b) 124 and 102 miRNAs correlating with steatosis by biopsy digital image and MRI analyses, respectively, in liver samples from morbidly obese patients (n = 24). However, only 35 miRNAs were common in both sets of samples. RT-qPCR allowed to validate the correlation of 10 miRNAs in paired liver and serum samples. The development of PLS models to quantitatively predict steatosis demonstrated that the combination of serum miR-145-3p, 122-5p, 143-3p, 500a-5p, and 182-5p provided the lowest root mean square error of cross validation (RMSECV = 1.1, p-value = 0.005). External validation of this model with a cohort of mixed MASLD patients (n = 25) showed a root mean squared error of prediction (RMSEP) of 5.3. In conclusion, it is possible to predict the percentage of hepatic steatosis with a low error rate by quantifying the serum level of five miRNAs using a cost-effective and easy-to-implement RT-qPCR method.