Microbiome used to diagnose liver disease
Scientists from the Salk Institute for Biological Studies have created a microbiome-based diagnostic tool that accurately, quickly and inexpensively identifies liver fibrosis and cirrhosis over 90% of the time in human patients.
Described in the journal Cell Metabolism, the non-invasive method relies on an algorithm to analyse patient stool samples — which contain traces of what lives in the gut — and could lead to improved patient care and treatment outcomes for liver disease.
Non-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease globally and can progress to liver fibrosis and cirrhosis and potentially cancer, as the liver starts to experience scarring and cell death. Moreover, diagnostic tools for liver fibrosis and cirrhosis are currently lacking — biopsies are invasive and can miss injured regions of the liver, and MRIs are expensive and are often not available in rural areas. To address these challenges, Salk researchers explored the microbiome as a way to meet the urgent need for a new test to identify patients at risk.
“We sought to develop a universal, non-invasive test for liver fibrosis and cirrhosis based on a ‘microbiome signature’ of the disease,” said Salk scientist Michael Downes, a co-author on the new study.
In collaboration with scientists from the University of California, San Diego, the team optimised a computational method called machine learning to uncover a complex disease signature based on 19 bacterial species present in the stool samples of a patient group. The signature is made up of the different quantities of bacteria, creating a universal fingerprint for identifying liver fibrosis and cirrhosis. The study included 163 clinical samples from both healthy as well as sick family members to identify variables that were indicative of liver disease.
Using data from microbiome genetic profiling and from metabolites from the stool samples, the researchers discovered a microbiome signature that was associated with a cirrhosis diagnosis with 94% accuracy. The microbiome signature could also determine the stage of liver fibrosis, which could allow doctors to grade patients based on their stage of the disease and improve treatment strategies.
“These findings demonstrate that it is possible to use machine learning to identify a universal signature that can be used for accurate diagnosis of a disease, such as liver cirrhosis,” said Salk’s Tae Gyu Oh, first author of the paper. “The patterns we found reflect the complexity of the microbiome and how gut health likely affects disease.”
The researchers then applied their microbiome signature to two independent populations of patients from China and Italy. The team’s signature could accurately identify cirrhosis in over 90% of patients, which validates the power and accuracy of the algorithm across different genetics and diets.
“Because this diagnostic is fast and low cost, it could be something that becomes widely used, especially in the many areas that lack specialty clinics and physicians,” said Salk Professor Ronald Evans, co-corresponding author on the study. “Simply said, it could be a real game changer, with worldwide implications.”
In the future, the scientists will examine the causal link between the microbiome and liver disease by testing whether restoring parts of the microbiome leads to regression of the disease or removing certain bacteria makes it worse. The team also hopes this approach can be used to characterise additional diseases, such as inflammatory bowel disease, colon cancer, Alzheimer’s and other diseases shown to be likely affected by a dysregulated microbiome.
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