ReviewFrontiers in molecular biosciences2026
The gut-brain axis in Alzheimer's disease: early detection, microbial metabolites, mechanisms, and therapeutic opportunities.
Review in Frontiers in molecular biosciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Alzheimer's disease (AD), the leading cause of dementia worldwide, imposes a growing clinical and societal burden, yet no therapies have been proven to alter its progression despite decades of intensive research. As traditional targets have yielded limited success, attention has shifted to modifiable upstream pathways, notably the gut-brain axis, a bidirectional system linking gut microbiota with CNS function. Emerging evidence indicates that microbial dysbiosis may influence key processes leading to AD, including neuroinflammation, amyloid and tau pathology, and cognitive decline. While microbiome composition is associated with AD, it remains unclear at which stage-preclinical, mild cognitive impairment (MCI), or AD dementia-these differences first arise, or how specific risk bacteria and metabolites contribute to progression. The precise roles of these microbes and metabolites in AD pathology or brain resilience also remain poorly understood, and few microbiome-targeted treatments have been validated in humans. Existing reviews often overlook host-specific factors that influence microbiome composition and confound associations with AD. To bridge these gaps, we summarize human studies published in the past 5 years. The literature suggests that gut microbial changes may precede clinical symptoms, with consistent dysbiosis observed in AD patients. We adopt a microbiome-centered perspective emphasizing bacteria-driven and metabolite-driven mechanisms, each playing distinct yet complementary roles in neural and bloodstream pathways. These pathways offer potential targets for microbiome-based prevention and treatment but require more human validation. Future studies should leverage longitudinal, multi-omics approaches and artificial intelligence (AI) tools while rigorously accounting for confounders to improve early detection and develop personalized therapies for AD.
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