A Universal Gut-Microbiome-Derived Signature Predicts Cirrhosis.
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ABSTRACT:
SUBMITTER: Oh TG
PROVIDER: S-EPMC7822714 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Dysregulation of the gut microbiome has been implicated in the progression of nonalcoholic fatty liver disease (NAFLD) to advanced fibrosis and cirrhosis. To determine the diagnostic capacity of this association, stool microbiomes were compared across 163 well-characterized participants encompassing non-NAFLD controls, NAFLD-cirrhosis patients and their first-degree relatives. Interrogation of shotgun metagenomic and untargeted metabolomic profiles using the Random Forest machine learning algorithm and differential abundance analysis identified discrete metagenomic and metabolomic signatures that were similarly effective in detecting cirrhosis (diagnostic accuracy 0.91, AUC). Combining the metagenomic signature with age and serum albumin levels accurately distinguished cirrhosis in etiologically and genetically distinct cohorts from geographically separated regions. Additional inclusion of serum aspartate aminotransferase levels, which are increased in cirrhosis patients, enabled discrimination of cirrhosis from earlier stages of fibrosis. These findings demonstrate that a core set of gut microbiome species may offer universal utility as a non-invasive diagnostic test for cirrhosis.
| EGAS00001004600 | EGA
Project description:BackgroundThere are limited data on the diagnostic accuracy of gut microbial signatures for predicting hepatic decompensation in patients with cirrhosis.AimsTo determine whether a stool metagenome-derived signature accurately detects hepatic decompensation and mortality risk in cirrhosis secondary to non-alcoholic fatty liver disease (NAFLD) METHODS: Shotgun metagenomic sequencing was performed on faecal samples collected at study entry from a prospective cohort of adults with NAFLD-related cirrhosis. A Random Forest machine learning algorithm was utilised to identify a metagenomic signature of decompensated cirrhosis (defined by ascites, hepatic encephalopathy or variceal haemorrhage) and subsequently validated in an external cohort. A Cox proportional hazards regression model was used to examine predictors of all-cause mortality.ResultsIn all, 25 adults with NAFLD-related cirrhosis (training cohort) were included. Among the 16 participants with decompensated cirrhosis, 33% had ascites, 56% had hepatic encephalopathy and 22% had experienced a variceal haemorrhage (not mutually exclusive). We identified a stool metagenomic signature comprising 13 discriminatory species that reliably distinguished decompensated NAFLD-related cirrhosis (diagnostic accuracy, 0.97, 95% confidence interval [CI] 0.96-0.99). Diagnostic accuracy of the 13-species signature remained high after adjustment for lactulose (area under the curve [AUC] 0.99) and rifaximin use (AUC 0.93). The discriminative ability of 13-species metagenomic signature was robust in an independent test cohort (AUC 0.95, 95% CI 0.81-1.00). The 13-species metagenomic signature (hazard ratio [HR] 1.54, 95% CI 1.10-2.15, p = 0.01) was a stronger predictor of mortality than the Model for End-Stage Liver Disease score (HR 1.25, 95% CI 1.03-1.53, p = 0.03).ConclusionsThis study provides evidence for a gut metagenome-derived signature with high diagnostic accuracy for hepatic decompensation that predicts risk of mortality in NAFLD-related cirrhosis.
| S-EPMC9746351 | biostudies-literature
Project description:The presence of cirrhosis in nonalcoholic-fatty-liver-disease (NAFLD) is the most important predictor of liver-related mortality. Limited data exist concerning the diagnostic accuracy of gut-microbiome-derived signatures for detecting NAFLD-cirrhosis. Here we report 16S gut-microbiome compositions of 203 uniquely well-characterized participants from a prospective twin and family cohort, including 98 probands encompassing the entire spectrum of NAFLD and 105 of their first-degree relatives, assessed by advanced magnetic-resonance-imaging. We show strong familial correlation of gut-microbiome profiles, driven by shared housing. We report a panel of 30 features, including 27 bacterial features with discriminatory ability to detect NAFLD-cirrhosis using a Random Forest classifier model. In a derivation cohort of probands, the model has a robust diagnostic accuracy (AUROC of 0.92) for detecting NAFLD-cirrhosis, confirmed in a validation cohort of relatives of proband with NAFLD-cirrhosis (AUROC of 0.87). This study provides evidence for a fecal-microbiome-derived signature to detect NAFLD-cirrhosis.
| S-EPMC6440960 | biostudies-literature
Project description:Alterations of gut microbiota have been implicated in multiple diseases including cancer. However, the gut microbiota spectrum in lung cancer remains largely unknown. Here we profiled the gut microbiota composition in a discovery cohort containing 42 early-stage lung cancer patients and 65 healthy individuals through the 16S ribosomal RNA (rRNA) gene sequencing analysis. We found that lung cancer patients displayed a significant shift of microbiota composition in contrast to the healthy populations. To identify the optimal microbiota signature for noninvasive diagnosis purpose, we took advantage of Support-Vector Machine (SVM) and found that the predictive model with 13 operational taxonomic unit (OTU)-based biomarkers achieved a high accuracy in lung cancer prediction (area under curve, AUC = 97.6%). This signature performed reasonably well in the validation cohort (AUC = 76.4%), which contained 34 lung cancer patients and 40 healthy individuals. To facilitate potential clinical practice, we further constructed a 'patient discrimination index' (PDI), which largely retained the prediction efficiency in both the discovery cohort (AUC = 92.4%) and the validation cohort (AUC = 67.7%). Together, our study uncovered the microbiota spectrum of lung cancer patients and established the specific gut microbial signature for the potential prediction of the early-stage lung cancer.
| S-EPMC7524275 | biostudies-literature
Project description:Relatives have more similar gut microbiomes than nonrelatives, but the degree to which this similarity results from shared genotypes versus shared environments has been controversial. Here, we leveraged 16,234 gut microbiome profiles, collected over 14 years from 585 wild baboons, to reveal that host genetic effects on the gut microbiome are nearly universal. Controlling for diet, age, and socioecological variation, 97% of microbiome phenotypes were significantly heritable, including several reported as heritable in humans. Heritability was typically low (mean = 0.068) but was systematically greater in the dry season, with low diet diversity, and in older hosts. We show that longitudinal profiles and large sample sizes are crucial to quantifying microbiome heritability, and indicate scope for selection on microbiome characteristics as a host phenotype.
| S-EPMC8377764 | biostudies-literature
Project description:Cirrhosis is associated with gut microbial changes, but current 16S rDNA techniques sequence both dead and live bacteria. We aimed to determine the rRNA content compared with DNA from the same stool sample to evaluate cirrhosis progression and predict hospitalizations. Cirrhotics and controls provided stool for RNA and DNA analysis. Comparisons were made between cirrhotics/controls and within cirrhosis (compensated/decompensated, infected/uninfected, renal dysfunction/not, rifaximin use/not) with respect to DNA and RNA bacterial content using linear discriminant analysis. A separate group was treated with omeprazole for 14 days with longitudinal microbiota evaluation. Patients were followed for 90 days for hospitalizations. Multivariable models for hospitalizations with clinical data with and without DNA and RNA microbial data were created. Twenty-six controls and 154 cirrhotics (54 infected, 62 decompensated, 20 renal dysfunction, 18 rifaximin) were included. RNA and DNA analysis showed differing potentially pathogenic taxa but similar autochthonous taxa composition. Thirty subjects underwent the omeprazole study, which demonstrated differences between RNA and DNA changes. Thirty-six patients were hospitalized within 90 days. In the RNA model, MELD score and Enterococcus were independently predictive of hospitalizations, while in the DNA model MELD was predictive and Roseburia protective. In both models, adding microbiota significantly added to the MELD score in predicting hospitalizations. DNA and RNA analysis of the same stool sample demonstrated differing microbiota composition, which independently predicts the hospitalization risk in cirrhosis. RNA and DNA content of gut microbiota in cirrhosis are modulated differentially with disease severity, infections, and omeprazole use. NCT01458990. VA Merit I0CX001076.
| S-EPMC5922291 | biostudies-literature
Project description:There is an accumulation of evidence that the human gut microbiota plays a role in maintaining health, and that an altered gut microbiota (sometimes called dysbiosis) associates with risk for many noncommunicable diseases. However, the dynamics of disease-linked bacteria in the gut and other body sites remain unclear. If microbiome alterations prove causative in particular diseases, therapeutic intervention may be possible. Furthermore, microbial signature taxa have been established for the diagnosis of some diseases like colon cancer. We identified 163 disease-enriched and 98 disease-depleted gut microbiome signature taxa at species-level resolution (signature species) from 10 meta-analyses of multiple diseases such as colorectal cancer, ulcerative colitis, Crohn's disease, irritable bowel syndrome, pancreatic cancer, and COVID-19 infection. Eight signature species were enriched and nine were depleted across at least half of the diseases studied. Compared with signature species depleted in diseases, a significantly higher proportion of disease-enriched signature species were identified as extra-intestinal (primarily oral) inhabitants, had been reported in bacteremia cases from the literature, and were aerotolerant anaerobes. These findings highlight the potential involvement of oral microbes, bacteremia isolates, and aerotolerant anaerobes in disease-associated gut microbiome alterations, and they have implications for patient care and disease management.
| S-EPMC11370893 | biostudies-literature
Project description:BackgroundBacteremia, or bloodstream infection (BSI), is a leading cause of death among patients with certain types of cancer. A previous study reported that intestinal domination, defined as occupation of at least 30 % of the microbiota by a single bacterial taxon, is associated with BSI in patients undergoing allo-HSCT. However, the impact of the intestinal microbiome before treatment initiation on the risk of subsequent BSI remains unclear. Our objective was to characterize the fecal microbiome collected before treatment to identify microbes that predict the risk of BSI.MethodsWe sampled 28 patients with non-Hodgkin lymphoma undergoing allogeneic hematopoietic stem cell transplantation (HSCT) prior to administration of chemotherapy and characterized 16S ribosomal RNA genes using high-throughput DNA sequencing. We quantified bacterial taxa and used techniques from machine learning to identify microbial biomarkers that predicted subsequent BSI.ResultsWe found that patients who developed subsequent BSI exhibited decreased overall diversity and decreased abundance of taxa including Barnesiellaceae, Coriobacteriaceae, Faecalibacterium, Christensenella, Dehalobacterium, Desulfovibrio, and Sutterella. Using machine-learning methods, we developed a BSI risk index capable of predicting BSI incidence with a sensitivity of 90 % at a specificity of 90 % based only on the pretreatment fecal microbiome.ConclusionsThese results suggest that the gut microbiota can identify high-risk patients before HSCT and that manipulation of the gut microbiota for prevention of BSI in high-risk patients may be a useful direction for future research. This approach may inspire the development of similar microbiome-based diagnostic and prognostic models in other diseases.
| S-EPMC4848771 | biostudies-literature
Project description:Background and aimsCirrhosis is associated with changes in intestinal microbiota that can lead to hepatic encephalopathy (HE) and infections, especially with antibiotic-resistant organisms. However, the impact of gut microbial antibiotic resistance gene (ARG) burden on clinical outcomes is unclear. The aims of the study were to determine the impact of ARGs in cirrhosis-related gut metagenome on outcomes and disease progression, study the effect of rifaximin on ARG burden, and compare ARGs in cirrhosis with chronic kidney disease (CKD) and diabetes.MethodsIn outpatients with cirrhosis who underwent metagenomics, we evaluated change in ARG abundances with progression and their multivariable impact on 90-day hospitalizations and deaths over 1 year. We also studied ARGs pre- and 8 weeks post-rifaximin in patients with compensated cirrhosis in an open-label trial. Finally, ARGs from CKD and diabetes studies were compared with cirrhosis on machine learning.ResultsA total of 163 patients with cirrhosis (43 compensated, 20 ascites-only, 30 HE-only, 70 both) and 40 controls were included. ARG abundances were higher in cirrhosis versus controls and worsened with advancing cirrhosis severity; 44 patients were hospitalized and 14 died. ARG abundances were associated with hospitalizations and mortality while controlling for cirrhosis complications, medications, and demographics. Rifaximin trial: ARG abundance patterns were minimally affected in 19 patients post-rifaximin. CKD/diabetes comparison: ARG abundance patterns in cirrhosis are distinguishable on machine learning and include more gram-positive ARGs.ConclusionsCirrhosis is associated with high gut microbial ARG gene burden compared with controls, which worsens with disease progression and may be different from CKD and diabetes. ARGs are not affected by rifaximin and are associated with hospitalizations and death.
| S-EPMC9069394 | biostudies-literature
Project description:BACKGROUND & AIMS:The gut microbiome is altered in cirrhosis; however its evolution with disease progression is only partly understood. We aimed to study changes in the microbiome over cirrhosis severity, its stability over time and its longitudinal alterations with decompensation. METHODS:Controls and age-matched cirrhotics (compensated/decompensated/hospitalized) were included. Their stool microbiota was quantified using multi-tagged pyrosequencing. The ratio of autochthonous to non-autochthonous taxa was calculated as the cirrhosis dysbiosis ratio (CDR); a low number indicating dysbiosis. Firstly, the microbiome was compared between controls and cirrhotic sub-groups. Secondly, for stability assessment, stool collected twice within 6months in compensated outpatients was analyzed. Thirdly, changes after decompensation were assessed using (a) longitudinal comparison in patients before/after hepatic encephalopathy development (HE), (b) longitudinal cohort of hospitalized infected cirrhotics MELD-matched to uninfected cirrhotics followed for 30days. RESULTS:244 subjects [219 cirrhotics (121 compensated outpatients, 54 decompensated outpatients, 44 inpatients) and 25 age-matched controls] were included. CDR was highest in controls (2.05) followed by compensated (0.89), decompensated (0.66), and inpatients (0.32, p<0.0001) and negatively correlated with endotoxin. Microbiota and CDR remained unchanged in stable outpatient cirrhotics (0.91 vs. 0.86, p=0.45). In patients studied before/after HE development, dysbiosis occurred post-HE (CDR: 1.2 to 0.42, p=0.03). In the longitudinal matched-cohort, microbiota were significantly different between infected/uninfected cirrhotics at baseline and a low CDR was associated with death and organ failures within 30days. CONCLUSIONS:Progressive changes in the gut microbiome accompany cirrhosis and become more severe in the setting of decompensation. The cirrhosis dysbiosis ratio may be a useful quantitative index to describe microbiome alterations accompanying cirrhosis progression.
| S-EPMC3995845 | biostudies-literature