AI-Guided Gut Microbiome Profiling for the Diagnostics of Neurological Disorders

Abstract

The gut microbiome is a complex ecosystem of microorganisms that reside in the gastrointestinal tract (GIT). The interaction between the gut microbiota and the brain is often called the microbiota/gut-brain axis, which is a bidirectional relationship. Any imbalance in the gut microbiome through dietary changes, medication use, lifestyle choices, environmental factors, and aging has a potential pathophysiological impact on the body in general and CNS in particular. Early studies linking alterations in the gut microbiome with neurobehavioral phenotypes launched the concept of a microbiota-gut-brain axis whereby intestinal microbiota can influence brain function and behavior. This suggests that targeting the gut microbiome could be a potential strategy for developing new therapies for neurological and neurodegenerative diseases. Previously, various studies implicated the microbiome as one of the key susceptibility factors for neurological disorders. As a result, recent research has focused on identifying potential therapeutic interventions for neurological and neurodegenerative diseases by exploiting the gut microbiome profiles. However, there is a high level of overlap between neurological disease state and normal condition microbiome profiles, which makes it difficult to develop specialized treatments. In addition to this, for now, only a symptomatic cure exists. Therefore, this study aims to leverage machine learning (ML) modeling techniques to identify potential targets within the gut microbiome associated with a neurological disease. In this study, predictive modeling techniques were employed, including ensemble methods such as XGBoost, alongside other models like Artificial Neural Networks (ANN), Support Vector Machines (SVM) with a non-linear polynomial kernel up to the fifth degree, K-Nearest Neighbors (KNN), and Random Forest. The results indicated that all models converged around 60% accuracy, 17 with specificity and sensitivity metrics following similar trends. These models demonstrated the potential to analyze differentially expressed biomarkers, highlighting areas for future research. Future work should focus on developing models specifically designed for addressing biological problems rather than merely predictive modeling. Additionally, it is essential to examine the species level and the mechanistic aspects of the microbial profile to understand the underlying factors that transition conditions from normal to diseased states.