BACKGROUND: Interactions between the gut microbiota, microglia, and aging may modulate Alzheimer's disease (AD) pathogenesis but the precise nature of such interactions is not known. METHODS: We developed an integrated multi-dimensional, knowledge-driven, systems approach to identify interactions among microbial metabolites, microglia, and AD. Publicly available datasets were repurposed to create a multi-dimensional knowledge-driven pipeline consisting of an integrated network of microbial metabolite-gene-pathway-phenotype (MGPPN) consisting of 34,509 nodes (216 microbial metabolites, 22,982 genes, 1329 pathways, 9982 mouse phenotypes) and 1,032,942 edges. RESULTS: We evaluated the network-based ranking algorithm by showing that abnormal microglia function and physiology are significantly associated with AD pathology at both genetic and phenotypic levels: AD risk genes were ranked at the top 6.4% among 22,982 genes, P < 0.001. AD phenotypes were ranked at the top 11.5% among 9982 phenotypes, P < 0.001. A total of 8094 microglia-microbial metabolite-gene-pathway-phenotype-AD interactions were identified for top-ranked AD-associated microbial metabolites. Short-chain fatty acids (SCFAs) were ranked at the top among prioritized AD-associated microbial metabolites. Through data-driven analyses, we provided evidence that SCFAs are involved in microglia-mediated gut-microbiota-brain interactions in AD at both genetic, functional, and phenotypic levels. CONCLUSION: Our analysis produces a novel framework to offer insights into the mechanistic links between gut microbial metabolites, microglia, and AD, with the overall goal to facilitate disease mechanism understanding, therapeutic target identification, and designing confirmatory experimental studies.