Modeling the relationship between the gut microbiome and progressive neurodegenerative diseases: case study Alzheimer’s disease

Abstract

Alzheimer’s Disease (AD) has been known since 1906 and many of the symptoms and signs from the first case continue in the conceptualization of AD, such as memory loss, visuospatial disorders, impaired verbal communication, delirium, impotence and personality changes, such as depression and irritability, is the most common cause of dementia and neurodegenerative disease. It is expected to see an increment of up to 225% in the number of patients during a 40-year time frame (2010 - 2050). Clinically, the hallmark pathology of AD is the accumulation of amyloid-β (Aβ) protein fragments outside the neurons and accumulation of abnormal tau tangles within neurons. However, the microbiome composition is unique to a patient and, current studies have also proven the existence of a correlation with the microbiota that results in inflammation patterns and the accumulation of proteins related to AD. Nevertheless, no study so far has presented a model representing the interaction between the microbiota and the current tests to diagnose AD. In this study for the master’s program in Computer Science, we will approach a novel characterization of AD integrating clinical data, gut microbial metabolites and serum lipids metabolites. From a systems biology perspective, we intend to explain these covariates through machine learning and feature selection algorithms that would serve to find biomarkers between those who advance to the disease and those who does not. Data has been collected from various sources, the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and the Alzheimer’s Disease Metabolomics Consortium (ADMC). Our findings suggest that the combination of gut microbial metabolites with the well-known neuropsychological tests could enhance the diagnosis and prediction of AD. This research project invite the researcher to carry out more experiments about the microbiome since we realized is becoming the key to better comprehend AD and probably other neurodegenerative diseases.