<![CDATA[Background:. Alzheimer’s disease (AD) is a progressively deteriorating neurodegenerative condition that poses a substantial burden on society. Current diagnostic methods of AD are often considered complex and costly. Mild Cognitive Impairment (MCI) is defined as the transitional stage before AD. Alterations in the gut microbiota (dysbiosis) are observed in MCI and preclinical AD and are hypothesized to contribute to disease pathogenesis. Aim: The objective of this review was to evaluate gut microbiota profiles as non-invasive, low-cost indicators and tailor diagnostic biomarkers. The diagnostic potential and the efficacy of nutritional modulation strategies across the AD continuum were assessed. Methods: The mechanisms by which dysbiosis promotes neurodegeneration were analyzed, including increased intestinal permeability, Lipopolysaccharide (LPS) leakage, and reduced production of short-chain fatty acids (SCFAs). Taxonomic shifts were summarized, documenting the depletion of SCFA producers (e.g., Firmicutes) and the enrichment of pro-inflammatory genera. The quantitative performance of machine learning models utilizing microbial data for disease classification was reviewed. Results: High predictive accuracy for AD incidence was achieved by machine learning classifiers based solely on gut microbiome profiles, with Area Under the Curve (AU-ROC) values reaching up to 0.927. Nutritional interventions, including probiotics (Lactobacillus, Bifidobacterium) and prebiotics, were found to modulate the gut effectively by enhancing SCFA production, reinforcing intestinal barrier integrity, and suppressing systemic neuroinflammation. Conclusion: Robust potential is demonstrated by gut microbial profiles as high-performance, non-invasive screening tools for early detection in the AD continuum. However, future validation is required through large-scale, long-term randomized controlled trials (RCTs) focusing on establishing causation and developing personalized gut-based therapies.]]>
