Gut Microbiota as Biosensors in Patients With Cirrhosis

The Role of the Gut Microbiome in the Development and Progression of Type 2 Diabetes and Liver Disease

Type 2 diabetes mellitus (T2DM) and progressive liver disease are 2 of the most significant global health concerns, and they have alarming and ever-increasing prevalence. A growing body of literature has demonstrated a potential multilateral link between gut microbiome dysbiosis and the development and progression of the above-mentioned conditions. Modulation of gut microbial composition from the norm is due to changes in diet allied with external factors such as age, genetics, and environmental changes. In this comprehensive review, we recapitulate the research to date investigating the links between gut microbiome dysbiosis and T2DM or liver disease, with special attention to the importance of diet. Additionally, we review the most commonly used tools and methodologies of investigating changes in the gut microbiome, highlighting the advantages and limitations of each strategy, before introducing a novel in vitro approach to the problem. Finally, the review offers recommendations for future research in this field that will allow better understanding of how the gut microbiota affects disease progression and of the prospects for intestinal microbiota-based therapeutic options.

Microbiota and Long-Term Prognosis in Liver Cirrhosis

   Purpose. To compare the gut microbiota in patients with an anamnesis of liver cirrhosis of less than and more than 10 years.

   Materials and methods. A one-stage study and metagenomic fecal sequencing of 40 hospitalized patients with liver cirrhosis were conducted, of which 35 were with a history of cirrhosis of less than 10 years and 5 — more than 10 years. High-throughput sequencing was performed using a MiSeq genetic analyzer (Illumina, USA) and a protocol based on analysis of 16s rRNA gene variable regions. The study was registered in Clinicaltrials.gov (NCT05335213). Data analysis was performed using Kraken2 algorithm. The analysis of the difference in the proportional composition of the microbiome between the groups was carried out using polynomial Dirichlet modeling (Likelihood-Ratio-Test Statistics: Several Sample Dirichlet-Multinomial Test Comparison), the Mann-Whitney test with preliminary data transformation by CLR transformation (Centered log ratio transform), differential analysis of gene expression based on negative binomial distribution (DESeq2). The significance level α assumed to be 0.05.

   Results. In patients with liver cirrhosis, the dominant phylotypes of fecal microbiota are Firmicutes, Bacteroidetes, Proteobacteria, Actinobacteria, minor components include taxa Aquificae, Coprothermobacterota, Tenericutes, Verrucomicrobia, Chloroflexi, Deinococcus-Thermus, Thermotogae, Chlorobi. Significant differences have been established in the density of dominant and minor philotypes of gut bacteria, such as Actinobacteria, Proteobacteria, Tenericutes, Coprothermobacterota, as well as some classes, genera, bacterial species in patients with different disease duration (p < 0.05).

   Conclusion. There is no doubt about the effect of gut microbiota on compensation for liver function. The established differences in the composition of the microbiota in patients with liver cirrhosis depending on survival over 10 years are of scientific and practical importance for the formation of an evidence-based approach to the use of microbiome-associated interventions

Biosensors for point-of-care testing and personalized monitoring of gastrointestinal microbiota

The gastrointestinal (GI) microbiota is essential in maintaining human health. Alteration of the GI microbiota or gut microbiota (GM) from homeostasis (i.e., dysbiosis) is associated with several communicable and non-communicable diseases. Thus, it is crucial to constantly monitor the GM composition and host-microbe interactions in the GI tract since they could provide vital health information and indicate possible predispositions to various diseases. Pathogens in the GI tract must be detected early to prevent dysbiosis and related diseases. Similarly, the consumed beneficial microbial strains (i.e., probiotics) also require real-time monitoring to quantify the actual number of their colony-forming units within the GI tract. Unfortunately, due to the inherent limitations associated with the conventional methods, routine monitoring of one's GM health is not attainable till date. In this context, miniaturized diagnostic devices such as biosensors could provide alternative and rapid detection methods by offering robust, affordable, portable, convenient, and reliable technology. Though biosensors for GM are still at a relatively preliminary stage, they can potentially transform clinical diagnosis in the near future. In this mini-review, we have discussed the significance and recent advancements of biosensors in monitoring GM. Finally, the progresses on future biosensing techniques such as lab-on-chip, smart materials, ingestible capsules, wearable devices, and fusion of machine learning/artificial intelligence (ML/AI) have also been highlighted.

Stool microbiota are superior to saliva in distinguishing cirrhosis and hepatic encephalopathy using machine learning

BACKGROUND & AIMS

Saliva and stool microbiota are altered in cirrhosis. Since stool is logistically difficult to collect compared to saliva, it is important to determine their relative diagnostic and prognostic capabilities. We aimed to determine the ability of stool vs. saliva microbiota to differentiate between groups based on disease severity using machine learning (ML).

METHODS

Controls and outpatients with cirrhosis underwent saliva and stool microbiome analysis. Controls vs. cirrhosis and within cirrhosis (based on hepatic encephalopathy [HE], proton pump inhibitor [PPI] and rifaximin use) were classified using 4 ML techniques (random forest [RF], support vector machine, logistic regression, and gradient boosting) with AUC comparisons for stool, saliva or both sample types. Individual microbial contributions were computed using feature importance of RF and Shapley additive explanations. Finally, thresholds for including microbiota were varied between 2.5% and 10%, and core microbiome (DESeq2) analysis was performed.

RESULTS

Two hundred and sixty-nine participants, including 87 controls and 182 patients with cirrhosis, of whom 57 had HE, 78 were on PPIs and 29 on rifaximin were included. Regardless of the ML model, stool microbiota had a significantly higher AUC in differentiating groups vs. saliva. Regarding individual microbiota: autochthonous taxa drove the difference between controls vs. patients with cirrhosis, oral-origin microbiota the difference between PPI users/non-users, and pathobionts and autochthonous taxa the difference between rifaximin users/non-users and patients with/without HE. These were consistent with the core microbiome analysis results.

CONCLUSIONS

On ML analysis, stool microbiota composition is significantly more informative in differentiating between controls and patients with cirrhosis, and those with varying cirrhosis severity, compared to saliva. Despite logistic challenges, stool should be preferred over saliva for microbiome analysis.

LAY SUMMARY

Since it is harder to collect stool than saliva, we wanted to test whether microbes from saliva were better than stool in differentiating between healthy people and those with cirrhosis and, among those with cirrhosis, those with more severe disease. Using machine learning, we found that microbes in stool were more accurate than saliva alone or in combination, therefore, stool should be preferred for analysis and collection wherever possible.

Novel Insights Into Pathogenesis and Therapeutic Strategies of Hepatic Encephalopathy, From the Gut Microbiota Perspective

Since the 1950s, gradual changes in the gut microbiota of patients with hepatic encephalopathy have been observed. Previous research has indicated potential associations between the gut and brain, and the gut microbiota is becoming a hot topic in research on diseases of the nervous system. However, for the past few decades, studies of hepatic encephalopathy have been restricted to controlling the gut microbiota during macroscopic manipulation, such as probiotic intervention, while its clinical use remains controversial, and the cellular mechanisms underlying this condition are still poorly understood. This thesis seeks to comprehensively understand and explain the role of gut microbiota in hepatic encephalopathy as well as analyze the effects of intervention by regulating the gut microbiota. Evidence is presented that shows that dysbiosis of the gut microbiota is the primary pathological driver of hepatic encephalopathy and impacts pathologic progression via complex regulatory networks. As a result, suggestions were identified for future mechanistic research and improvements in therapeutic strategies for hepatic encephalopathy.

Microbiota changes and intestinal microbiota transplantation in liver diseases and cirrhosis

Patients with chronic liver disease and cirrhosis demonstrate a global mucosal immune impairment, which is associated with altered gut microbiota composition and functionality. These changes progress along with the advancing degree of cirrhosis and can be linked with hepatic encephalopathy, infections and even prognostication independent of clinical biomarkers. Along with compositional changes, functional alterations to the microbiota, related to short-chain fatty acids, bioenergetics and bile acid metabolism, are also associated with cirrhosis progression and outcomes. Altering the functional and structural profile of the microbiota is partly achieved by medications used in patients with cirrhosis such as rifaximin, lactulose, proton pump inhibitors and other antibiotics. However, the role of faecal or intestinal microbiota transplantation is increasingly being recognised. Herein, we review the challenges, opportunities and road ahead for the appropriate and safe use of intestinal microbiota transplantation in liver disease.

Cost-effectiveness of integrating gut microbiota analysis into hospitalisation prediction in cirrhosis

BACKGROUND

Admissions in cirrhosis are expensive and often unpredictable based on purely clinical variables. Admissions could be related to complications associated with gut microbial changes, which can improve prognostication. However, the cost-effectiveness is unclear.

AIMS

Determine cost-effectiveness of adding gut microbiota analysis to clinical parameters in prediction and subsequent reduction of admissions in cirrhosis.

METHODS

Using a Markov model of 1000 cirrhosis patients over 90 days, we modeled microbiota testing using 16srRNA ($250/sample), low-depth ($350/sample) and high-depth ($650/sample) metagenomics added to standard-of-care (SOC) for prevention of admissions using standard outcome costs and rates of development. We generated quality of life years (QALY) and Incremental cost-effectiveness ratios (ICER) for the base scenarios and performed sensitivity analyses by varying costs for outcomes (transplant, death, admission) and admission rates (40%, range 25%-60%).

RESULTS

Using fixed costs of outcomes and outcome rates, microbiota analysis was cost-saving ($47K-$97K) at $250 and $350/sample if admissions were reduced by 5%over SOC and >10% with $650/sample. When costs of LT, death and admissions were varied, these cost-savings remained robust provided there was >2.1% reduction (range 1.3%-3.2%) for $250/sample, >2.9% (range 1.8%-4.4%) for $350/sample and >5.4% (range 3.3%-8.2%) for $650/sample. These cost-savings remained robust even when the assumed admission rate was varied for all sample cost values.

CONCLUSIONS

Gut microbiota analysis is cost-effective for predicting and potentially preventing 90-day admissions in cirrhosis over current standard of care. This cost-saving remained robust even after sensitivity analyses that varied the background admission rates.