Stratification of the Gut Microbiota Composition Landscape Across the Alzheimer's Disease Continuum in a Turkish Cohort
Alzheimer's disease (AD) is a heterogeneous neurodegenerative disorder that spans over a continuum with multiple phases including preclinical, mild cognitive impairment, and dementia. Unlike most other chronic diseases there are limited number of human studies reporting on AD gut microbiota in the literature. These published studies suggest that the gut microbiota of AD continuum patients varies considerably throughout the disease stages, raising expectations for existence of multiple microbiota community types. However, the community types of AD gut microbiota were not systematically investigated before, leaving important research gap for diet-based intervention studies and recently initiated precision nutrition approaches aiming at stratifying patients into distinct dietary subgroups. Here, we comprehensively assessed the community types of gut microbiota across the AD continuum. We analyze 16S rRNA amplicon sequencing of stool samples from 27 mild cognitive patients, 47 AD, and 51 non-demented control subjects using tools compatible with compositional nature of microbiota. To characterize gut microbiota community types, we applied multiple machine learning techniques including partitioning around the medoid clustering, fitting probabilistic Dirichlet mixture model, Latent Dirichlet Allocation model, and performed topological data analysis for population scale microbiome stratification based on Mapper algorithm. These four distinct techniques all converge on Prevotella and Bacteroides partitioning of the gut microbiota across AD continuum while some methods provided fine scale resolution in partitioning the community landscape. The Signature taxa and neuropsychometric parameters together robustly classify the heterogenous groups within the cohort. Our results provide a framework for precision nutrition approaches and diet-based intervention studies targeting AD cohorts.