Gut metagenome‐derived signature predicts hepatic decompensation and mortality in NAFLD‐related cirrhosis

Abstract

Summary Background There are limited data on the diagnostic accuracy of gut microbial signatures for predicting hepatic decompensation in patients with cirrhosis. Aims To 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. Results In 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). Conclusions This 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.

De Novo Cleaning of Chimeric MS/MS Spectra for LC-MS/MS-Based Metabolomics

“…Using data resampling, we also investigated the impact of sample size in a metabolomics study and found that having 5 samples in a metabolomics study is enough to purify half of the MS/MS spectra and effectively improve spectra quality (Text S7). 21,29 If a feature table is provided, DNMS2Purifier can start with the second step, assigning the corresponding MS/MS spectra to each metabolic feature provided. Next, DNMS2Purifier cleans the MS/MS spectra.…”

“…Using data resampling, we also investigated the impact of sample size in a metabolomics study and found that having 5 samples in a metabolomics study is enough to purify half of the MS/MS spectra and effectively improve spectra quality (Text S7). , It should also be noted that the prediction performance of the trained XGBoost model might be reduced for metabolomics data generated in different LC/MS instruments. In particular, the intensity ratio RSDs used for prediction is instrument-dependent.…”

“…The urine was donated by a volunteer, and the serum samples were collected from patients with hepatitis disease, as described in a previous study. 21 LC-MS/MS Analysis. LC-MS/MS analysis was performed using an Agilent 1290 series ultrahigh-performance liquid chromatography system (Agilent Technologies, Palo Alto, CA) coupled to a Bruker Impact II quadrupole time-of-flight mass spectrometer (Bruker Daltonics Billerica, MA).…”

“…DNMS2Purifier was also demonstrated using a real metabolomics-based hepatitis study of 22 serum samples from 6 decompensated and 16 compensated patients. 21 The hepatitis study data set has been uploaded to the Metabolomics Workbench with the Project ID PR001688. 25 Following the prediction of the identity of the fragments using the DNMS2Purifier, the false fragment rate was calculated using the number of recognized false fragments divided by the total number of fragments in the MS/MS spectra.…”

Global epidemiology of cirrhosis — aetiology, trends and predictions

“…Disadvantaged and under-served communities are often disproportionately affected by liver disease but often lack timely access to appropriate care 91 , 101 , 102 . Despite advances in the diagnosis, treatment and management of cirrhosis, medical interventions often have limited benefit on long-term survival, necessitating the use of resource-heavy treatments such as liver transplantation 7 , 103 – 106 . Therefore, the focus of care should be shifted upstream, from the management of complications to prevention and early treatment 74 .…”