The prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease-modifying treatments and drug trials have failed, due partly to the heterogeneous and complex nature of the disease.

No abstract

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.IMPORTANCEThe prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease modifying treatments and drug trials have failed due partly to the heterogeneous and complex nature of the disease. Unlike other neurodegenerative diseases gut microbiota of AD patients is poorly studied. Recently initiated ambitious precision nutrition initiative or other diet-based interventions can potentially be more effective if the heterogeneous disease such as AD is deconstructed into multiple strata allowing for better identification of biomarkers across narrower patient population for improved results. Because gut microbiota is inherently integral part of the nutritional interventions there is unmet need for microbiota-informed stratification of AD clinical cohorts in nutritional studies. Our study fills in this gap and draws attention to the need for microbiota stratification as one of the essential steps for precision nutrition interventions. We demonstrate that while Prevotella and Bacteroides clusters are the consensus partitions the newly developed probabilistic methods can provide fine scale resolution in partitioning the AD gut microbiome landscape.