"It has already been proven that the microorganisms in the human gut contribute to the development of NAFLD. We wanted to find out if the microbiome of a healthy person could predict whether or not they would develop NAFLD in the future," Panagiotou explains. When the subjects were re-examined four years later, it was revealed that 90 of them had since developed NAFLD. Samples from those affected were compared to a control group of 90 people who did not have NAFLD at baseline or at the follow-up visit.
"Using different methods, we were able to find very subtle differences in the samples we took four years prior," explains first author Howell Leung from Panagiotou's group at Leibniz HKI. "With this data, we were able to develop a model that can predict who will develop NAFLD in the future based on the microbiome with 80 percent certainty." Currently, there are clinical models that use biochemical parameters in the blood to make a prediction with 60 percent accuracy. "The model we developed combines easily measurable information from the blood with data from the microbiome and can thus increase the reliability enormously," says Panagiotou.
The research team developed a so-called machine learning model - a computer model that is trained to recognize certain patterns in a set of data. The model can then use these patterns to analyze new datasets and, in this case, predict possible non-alcoholic fatty liver disease. "The whole process of developing our model took over three years due to the complexity of the data. However, in the end we were successful and were able to create a useful tool for predicting NAFLD," says Panagiotou.
Late stage non-alcoholic fatty liver disease is irreversible and in the worst cases can even lead to liver cancer. People who already suffer from a precursor or are particularly at risk must therefore be identified early on in order to be able to counteract the disease. "NAFLD is a silent disease. This means that in most cases it is asymptomatic and is usually only detected by chance," explains Gianni Panagiotou.
MEDICA-tradefair.com; Source: Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute
