eScholarship@UMassChan
eScholarship@UMassChan is a digital archive for UMass Chan Medical School's research and scholarship, including journal articles, theses, datasets and more. We welcome submissions from our faculty, staff, and students. eScholarship@UMassChan is a service of the Lamar Soutter Library, Worcester, MA, USA. See also our open access journal publishing services.
Questions? See the Help menu or contact escholarship@umassmed.edu.
Communities in eScholarship@UMassChan
Select a community to browse its collections.
Recent Publications
Publication UMCCTS Newsletter, November 2024(UMass Chan Medical School, 2025-01-08)This is the Novembr 2024 issue of the UMass Center for Clinical and Translational Science Newsletter containing news and events of interest.Publication UMCCTS Newsletter, January 2025(UMass Chan Medical School, 2025-01-08)This is the January 2025 issue of the UMass Center for Clinical and Translational Science Newsletter containing news and events of interest.Publication UMCCTS Newsletter, December 2024(UMass Chan Medical School, 2025-01-08)This is the December 2024 issue of the UMass Center for Clinical and Translational Science Newsletter containing news and events of interest.Publication Longitudinal cognitive outcomes are strongly correlated with the Alzheimer’s Disease microbiome(Wiley, 2025-1-3)Background: It has been shown that dysbiosis, or dysfunction of the gastrointestinal (gut) microbiome is associated with Alzheimer’s disease (AD). Here, we aimed to expand on beyond our previously reported findings of the gut microbiome associating with AD and explore if the gut microbiome is predictive of cognitive performance in individuals with AD. We sought to identity what cognitive domains are associated with the microbiome in our cohort of AD patients and healthy controls without dementia. Method: Older individuals residing in the general community of central Massachusetts were enrolled in our study. At each visit, fecal samples and clinical variables were collected in addition to cognitive testing using the ADAS-Cog-13 tool, such as delayed memory, word recall, recognition etc. Metagenomic profiling was performed on longitudinal fecal samples. Z-scores for different cognitive domains, including memory, executive function and language were generated for the study population. Mixed-effect random forest regression (MERFR) models were created to identify metagenomic features informative of cognitive performance across these different cognitive tests and domains. Result: Replicating our previous work, among AD diagnosed individuals, MERFR models predicted performance on ADAS-Cog 13 from microbial abundance and pathways with a strong accuracy. The ADAS-Cog 13 was not well predicted by the microbiome in the healthy controls. Additionally, in our new analysis across different cognitive domains, Z-Scores were well predicted by MERFR models using microbial abundance and encoded pathways. Conclusion: Not only is the gut microbiome composition highly predictive of AD diagnosis, but there is also a strong correlation of the gut microbiome and cognitive functioning. This is true across the multiple domains of cognition including memory, executive function and language, however different bacterial species were significant in associating with each domain. This work highlights the complexity of the microbiome-gut-brain axis and how the microbiome community makeup might play a role in cognitive decline.Publication Utilizing Latent Dirichlet Allocation and Differential Abundance to Identify Microbial Communities in both the Oral and Fecal Microbiome Associated with Alzheimer’s Disease(Wiley, 2025-1-3)Background: Several studies have found that oral and gut microbiome and their byproducts can impact Alzheimer’s Disease (AD). The objective of our study is to analyze metagenomic sequencing data from paired oral and fecal microbiomes, along with clinical variables, to identify communities of bacteria associated with AD. This research aims to improve our understanding of the microbiome community matrix, and how these communities interact and correlate with AD status compared to healthy controls (HC) through an oral-gut microbial axis. Method: The study includes 223 HC and 43 individuals with AD. During each visit, paired oral and fecal samples were collected, along with clinical variables. Metagenomic profiling was done on all samples. Latent Dirichlet Allocation (LDA) was applied to identify differences in microbial species groups between these two body sites in realtion to AD status. LDA is used as a topic modeling method to uncover the complex structure and function of microbial communities. Subsequently, differential abundance (DA) analysis was performed to identify species with differential abundance at each body site. Result: We identified microbiotal communities sharing similar characteristics and pinpointed representative bacteria within these communities that are highly relevant to AD. Within the oral microbiome, we have identified 27 topics, including several bacteria that are highly relevant to AD. These included Alistipes (beta = 3.919232e-01), Paraprevotella xylaniphila (beta = 1.227791e-01), Desulfovibrio (beta = 6.013213e- 02), and Lachnospiraceae (beta = 2.304369e-02). In the gut, we have identified 50 topics, reflecting the gut is more complex the oral microbiome. Notable bacteria in the gut microbiome include Actinomyces oricola (beta = 6.959554e-01), Roseburia (beta = 8.861444e-02), Bacteroidetes (beta = 5.010610e-01), and Actinomyces gerencseriae (beta = 3.048668e-02). Conclusion: Our study has identified a variety of bacteria that exhibit novel community patterns that associate with AD. In the gut, A. gerencseriae and other oral microbiomes were observed in AD patients. Also, the microbial communities differ between AD and HC. Thereforth, we conclude that translocation of oral and gut microbiota may contribute to AD through an oral-gut-microbiome axis.