Machine learning determines the incidence of Alzheimer's disease based on population gut microbiome profile.

The human microbiome is a complex and dynamic community of microbes, thought to have symbiotic benefit to its host. Influences of the gut microbiome on brain microglia have been identified as a potential mechanism contributing to neurodegenerative diseases, such as Alzheimer's disease, motor neurone disease and Parkinson's disease (Boddy SL, Giovannelli I, Sassani M, et al. The gut microbiome: A key player in the complexity of amyotrophic lateral sclerosis (ALS). BMC Med. 2021;19(1):13). We hypothesize that population level differences in the gut microbiome will predict the incidence of Alzheimer's disease using machine learning methods. Cross-sectional analyses were performed in R, using two large, open-access microbiome datasets (n = 959 and n = 2012). Countries in these datasets were grouped based on Alzheimer's disease incidence and the gut microbiome profiles compared. In countries with a high incidence of Alzheimer's disease, there is a significantly lower diversity of the gut microbiome (P < 0.05). A permutational analysis of variance test (P < 0.05) revealed significant differences in the microbiome profile between countries with high versus low incidence of Alzheimer's disease with several contributing taxa identified: at a species level Escherichia coli, and at a genus level Haemophilus and Akkermansia were found to be reproducibly protective in both datasets. Additionally, using machine learning, we were able to predict the incidence of Alzheimer's disease within a country based on the microbiome profile (mean area under the curve 0.889 and 0.927). We conclude that differences in the microbiome can predict the varying incidence of Alzheimer's disease between countries. Our results support a key role of the gut microbiome in neurodegeneration at a population level.