Innovative Computational Tool Analyzes Gut Microbiome for Early Health Insights

A team of Mayo Clinic researchers has developed a unique computational tool that analyzes the gut microbiome, a complex ecosystem of trillions of bacteria, fungi, viruses, and other microorganisms within the digestive system, to provide insights about general well-being.

A recent study that was published in Nature Communications showed that the tool could distinguish between people who were healthy and those who had any kind of sickness with at least 80% accuracy. To create the tool, stool gut microbiome profiles from over 8,000 samples that represented a range of illnesses, geographical locations, and demographic groupings were analyzed.

The Gut Microbiome Wellness Index 2 is a tool that can assess whether a person is recovering from or heading toward an illness by detecting even minute changes in gut health. The researchers analyzed stool samples from 54 published studies covering 26 nations and six continents for gut microbiome profiles using bioinformatics and machine learning techniques, yielding a rich and diversified dataset.

This capability tackles long-standing problems in the study of the human microbiome, such as defining the parameters of a “healthy” microbiome and locating early warning signs of possible health problems. Additionally, it closes a large gap in the current wellness and health measurement tools.

Researchers are discovering that an imbalance in the gut microbiome can be connected to several chronic diseases. The gut microbiome is essential for the immune system, metabolism, and digestion.

Finally, we have a standardized index to quantitatively measure how 'healthy' a person's gut microbiome is.”

Jaeyun Sung, Ph.D., Senior Author and Computational Biologist, Mayo Clinic Center

Sung said, “Our tool is not intended to diagnose specific diseases but rather to serve as a proactive health indicator. By identifying adverse changes in gut health before serious symptoms arise, the tool could potentially inform dietary or lifestyle modifications to prevent mild issues from escalating into more severe health conditions, or prompt further diagnostic testing.”

By being able to answer whether a person’s gut is healthy or trending toward a diseased state, we ultimately aim to empower individuals to take proactive steps in managing their own health.”

Jaeyun Sung, Ph.D., Senior Author and Computational Biologist, Mayo Clinic Center

The tool creation process included the identification of microbial species, careful selection of the most pertinent features, and optimization of the machine learning model.

A gut microbiome sample is screened, and the resulting index indicates how much the sample resembles a healthy (disease-free) or non-healthy (diseased) individual.

After validating the index's performance on a fresh cohort of 1,140 samples, the research team tested it first on a training set of over 8,000 microbiome samples.

To show that their technique could identify changes in gut health, the team also tested it in a variety of clinical settings, such as those who had received fecal microbiota transplantation, altered their dietary fiber consumption, or were exposed to antibiotics.

The team's initial tool is improved upon by the Gut Microbiome Wellness Index 2, which uses more sophisticated computational techniques and incorporates a larger variety of data. With this updated version, the team intends to improve accuracy when evaluating gut health and tracking alterations in the gut flora.

To improve the Gut Microbiome Health Index 2, Dr. Sung and his colleagues intend to add more sophisticated artificial intelligence techniques to improve the tool's predictive accuracy and adaptability, as well as broaden the dataset to include microbiome samples from a wider range of populations.