Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance

Distinct alterations of gut microbiota between viral- and non-viral-related hepatocellular carcinoma

Altered gut microbiota has been connected to hepatocellular carcinoma (HCC) occurrence and advancement. This study was conducted to identify a gut microbiota signature in differentiating between viral-related HCC (Viral-HCC) and non-hepatitis B-, non-hepatitis C-related HCC (NBNC-HCC). Fecal specimens were obtained from 16 healthy controls, 33 patients with viral-HCC (17 and 16 cases with hepatitis B virus (HBV) and hepatitis C virus (HCV) infection, respectively), and 18 patients with NBNC-HCC. Compositions of fecal microbiota were assessed by 16S rRNA sequencing. Bioinformatic analysis was performed by the DADA2 pipeline in the R program. Significantly different genera from the top 50 relative abundance were used to classify between subgroups of HCC by the Random Forest algorithm. Our data demonstrated that the HCC group had a significantly decreased alpha-diversity and changed microbial composition in comparison with healthy controls. Within the top 50 relative abundance, there were 11 genera including Faecalibacterium, Agathobacter, and Coprococcus that were significantly enhanced in Viral-HCC, while 5 genera such as Bacteroides, Streptococcus, Ruminococcus gnavus group, Parabacteroides, and Erysipelatoclostridium were enhanced in NBNC-HCC. Compared to Viral-HCC, the NBNC-HCC subgroup significantly reduced various short-chain fatty acid-producing bacteria, as well as declined fecal butyrate but elevated plasma surrogate markers of microbial translocation. Based on the machine learning algorithm, a high diagnostic accuracy to classify HCC subgroups was achieved with an area under the receiver-operating characteristic (ROC) curve (AUC) of 0.94. Collectively, these data revealed that gut dysbiosis was distinct according to etiological factors of HCC, which might play an essential role in hepatocarcinogenesis. These findings underscore the possible use of a gut microbiota signature for the diagnosis and therapeutic approaches regarding different subgroups of HCC. KEY POINTS: • Gut dysbiosis is connected to hepatocarcinogenesis and can be used as a novel biomarker. • Gut microbiota composition is significantly altered in different etiological factors of HCC. • Microbiota-based signature can accurately distinguish between Viral-HCC and NBNC-HCC.

Gut Microbiota and Sinusoidal Vasoregulation in MASLD: A Portal Perspective

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a common condition with heterogeneous outcomes difficult to predict at the individual level. Feared complications of advanced MASLD are linked to clinically significant portal hypertension and are initiated by functional and mechanical changes in the unique sinusoidal capillary network of the liver. Early sinusoidal vasoregulatory changes in MASLD lead to increased intrahepatic vascular resistance and represent the beginning of portal hypertension. In addition, the composition and function of gut microbiota in MASLD are distinctly different from the healthy state, and multiple lines of evidence demonstrate the association of dysbiosis with these vasoregulatory changes. The gut microbiota is involved in the biotransformation of nutrients, production of de novo metabolites, release of microbial structural components, and impairment of the intestinal barrier with impact on innate immune responses, metabolism, inflammation, fibrosis, and vasoregulation in the liver and beyond. The gut-liver axis is a conceptual framework in which portal circulation is the primary connection between gut microbiota and the liver. Accordingly, biochemical and hemodynamic attributes of portal circulation may hold the key to better understanding and predicting disease progression in MASLD. However, many specific details remain hidden due to limited access to the portal circulation, indicating a major unmet need for the development of innovative diagnostic tools to analyze portal metabolites and explore their effect on health and disease. We also need to safely and reliably monitor portal hemodynamics with the goal of providing preventive and curative interventions in all stages of MASLD. Here, we review recent advances that link portal metabolomics to altered sinusoidal vasoregulation and may allow for new insights into the development of portal hypertension in MASLD.

Links between gut microbiome, metabolome, clinical variables and non-alcoholic fatty liver disease severity in bariatric patients

BACKGROUND AND AIMS

Bacterial species and microbial pathways along with metabolites and clinical parameters may interact to contribute to non-alcoholic fatty liver disease (NAFLD) and disease severity. We used integrated machine learning models and a cross-validation approach to assess this interaction in bariatric patients.

METHODS

113 patients undergoing bariatric surgery had clinical and biochemical parameters, blood and stool metabolite measurements as well as faecal shotgun metagenome sequencing to profile the intestinal microbiome. Liver histology was classified as normal liver obese (NLO; n = 30), simple steatosis (SS; n = 41) or non-alcoholic steatohepatitis (NASH; n = 42); fibrosis was graded F0 to F4.

RESULTS

We found that those with NASH versus NLO had an increase in potentially harmful E. coli, a reduction of potentially beneficial Alistipes putredinis and an increase in ALT and AST. There was higher serum glucose, faecal 3-(3-hydroxyphenyl)-3-hydroxypropionic acid and faecal cholic acid and lower serum glycerophospholipids. In NAFLD, those with severe fibrosis (F3-F4) versus F0 had lower abundance of anti-inflammatory species (Eubacterium ventriosum, Alistipes finegoldii and Bacteroides dorei) and higher AST, serum glucose, faecal acylcarnitines, serum isoleucine and homocysteine as well as lower serum glycerophospholipids. Pathways involved with amino acid biosynthesis and degradation were significantly more represented in those with NASH compared to NLO, with severe fibrosis having an overall stronger significant association with Superpathway of menaquinol-10 biosynthesis and Peptidoglycan biosynthesis IV.

CONCLUSIONS

In bariatric patients, NASH and severe fibrosis were associated with specific bacterial species, metabolic pathways and metabolites that may contribute to NAFLD pathogenesis and disease severity.

Application of machine learning in affordable and accessible insulin management for type 1 and 2 diabetes: A comprehensive review

Proper insulin management is vital for maintaining stable blood sugar levels and preventing complications associated with diabetes. However, the soaring costs of insulin present significant challenges to ensuring affordable management. This paper conducts a comprehensive review of current literature on the application of machine learning (ML) in insulin management for diabetes patients, particularly focusing on enhancing affordability and accessibility within the United States. The review encompasses various facets of insulin management, including dosage calculation and response, prediction of blood glucose and insulin sensitivity, initial insulin estimation, resistance prediction, treatment adherence, complications, hypoglycemia prediction, and lifestyle modifications. Additionally, the study identifies key limitations in the utilization of ML within the insulin management literature and suggests future research directions aimed at furthering accessible and affordable insulin treatments. These proposed directions include exploring insurance coverage, optimizing insulin type selection, assessing the impact of biosimilar insulin and market competition, considering mental health factors, evaluating insulin delivery options, addressing cost-related issues affecting insulin usage and adherence, and selecting appropriate patient cost-sharing programs. By examining the potential of ML in addressing insulin management affordability and accessibility, this work aims to envision improved and cost-effective insulin management practices. It not only highlights existing research gaps but also offers insights into future directions, guiding the development of innovative solutions that have the potential to revolutionize insulin management and benefit patients reliant on this life-saving treatment.

Characterization of gut microbiome composition in Iranian patients with nonalcoholic fatty liver disease and nonalcoholic steatohepatitis

Gut microbiota dysbiosis is intimately associated with development of non-alcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH). Nevertheless, the gut microbial community during the course of NAFLD and NASH is yet to be comprehensively profiled. This study evaluated alterations in fecal microbiota composition in Iranian patients with NAFLD and NASH compared with healthy individuals. This cross-sectional study enrolled 15 NAFLD, 15 NASH patients, and 20 healthy controls, and their clinical parameters were examined. The taxonomic composition of the fecal microbiota was determined by sequencing the V3-V4 region of 16S rRNA genes of stool samples. Compared to the healthy controls, NAFLD and NASH patients presented reduced bacterial diversity and richness. We noticed a reduction in the relative abundance of Bacteroidota and a promotion in the relative abundance of Proteobacteria in NAFLD and NASH patients. L-histidine degradation I pathway, pyridoxal 5'-phosphate biosynthesis I pathway, and superpathway of pyridoxal 5'-phosphate biosynthesis and salvage were more abundant in NAFLD patients than in healthy individuals. This study examined fecal microbiota dysbiosis in NAFLD and NASH patients and presented consistent results to European countries. These condition- and ethnicity-specific data could provide different diagnostic signatures and therapeutic targets.

The impact of cancer cachexia on gut microbiota composition and short-chain fatty acid metabolism in a murine model

This study investigates the relationship between cancer cachexia and the gut microbiota, focusing on the influence of cancer on microbial composition. Lewis lung cancer cell allografts were used to induce cachexia in mice, and body and muscle weight changes were monitored. Fecal samples were collected for targeted metabolomic analysis for short chain fatty acids and microbiome analysis. The cachexia group exhibited lower alpha diversity and distinct beta diversity in gut microbiota, compared to the control group. Differential abundance analysis revealed higher Bifidobacterium and Romboutsia, but lower Streptococcus abundance in the cachexia group. Additionally, lower proportions of acetate and butyrate were observed in the cachexia group. The study observed that the impact of cancer cachexia on gut microbiota and their generated metabolites was significant, indicating a host-to-gut microbiota axis. [BMB Reports 2023; 56(7): 404-409].