Gut-brain communication in Alzheimer's disease: early signs, microbial chemicals, processes, and treatment possibilities

Alzheimer’s disease (AD) affects over 50 million people globally and represents the leading cause of dementia, with prevalence expected to double over the next 3 decades due to population aging (Javaid et al., 2021; Association, 2024). This neurodegenerative disorder is characterized by progressive memory loss, cognitive decline, and behavioral changes that severely impair functioning and quality of life. Early symptoms often include deficits in episodic memory, followed by impairments in language, visuospatial skills, and executive function. These cognitive impairments distinguish AD from normal aging and eventually render individuals unable to live independently (Testo et al., 2024). As the disease advances, patients may also experience behavioral and mood disturbances such as apathy, depression, aggression, and sleep or eating disorders that burden caregivers and healthcare systems (Selles et al., 2018).

Despite increased awareness and scientific advancements, existing strategies for managing AD continue to face considerable limitations. Available FDA-approved treatments offer only modest symptomatic relief and do not significantly alter disease progression or target underlying pathological mechanisms (Pardo-Moreno et al., 2022; Self and Holtzman, 2023). This underscores the importance of early detection, when intervention may be most beneficial. Cerebrospinal fluid (CSF) analyses and PET neuroimaging are informative but invasive, costly, and not widely accessible during the preclinical stage, when pathological changes may precede symptom onset by years (Teunissen et al., 2022; Gonzalez-Ortiz et al., 2023). MRI and EEG are more scalable and may support early risk stratification, but they are better viewed as complementary screening tools rather than as substitutes for amyloid and tau specific staging (Khan, 2018; Rossini et al., 2020; Farina et al., 2020). Likewise, genome-wide association studies have advanced understanding of AD genetics but often fail to resolve precise biological pathways and remain disproportionately based on populations of European descent, limiting broader applicability (Pimenova et al., 2018; Reitz et al., 2023). Together, these limitations present a significant barrier to meaningful advancements in AD prevention and management, motivating the search for novel, human-centered approaches.

Consequently, research efforts have increasingly shifted to exploring strategies that integrate disease-modifying therapies with lifestyle interventions (Baker et al., 2025). This shift has brought particular attention to the gut microbiome, as microbial composition and function are tightly linked to lifestyle exposures (Hitch et al., 2022; Zeng et al., 2025). The gut-brain axis (GBA) offers a particularly relevant framework through which such exposures may influence neuroinflammation, barrier dysfunction, amyloid-beta (Aβ) aggregation, and cognitive decline (Liu et al., 2020; Qu et al., 2024; Leblhuber et al., 2021; Conn et al., 2024). Critically, unlike relatively fixed genetic risk factors such as APOE-ε4 or established proteinopathies, the gut microbiome is dynamic and potentially modifiable through diet, probiotics, and fecal microbiota transplantation (FMT) (Ullah et al., 2025). These features make the GBA especially attractive for AD research, as microbiome-related signatures may serve not only as non-invasive biomarkers for identifying individuals at high risk, but also as modifiable targets for early intervention (Varesi et al., 2022; Zhao et al., 2023; Pan et al., 2024).

Despite its promise, critical knowledge gaps in microbiome research limit progress. Most fundamentally, mechanistic understanding of gut microbiota involvement, especially during the preclinical stages of AD, remains incomplete. Although recent studies have demonstrated significant gut bacterial and metabolite changes during this period (Jung et al., 2022; Ferreiro et al., 2023), this critical temporal window remains understudied, partly because complex trial designs required to capture this stage (Rafii and Aisen, 2023). In addition, existing studies often focus on broad metabolite classes such as short-chain fatty acids (SCFAs), with limited attention to other disease-relevant metabolites and how specific bacterial species influence their production and downstream signaling (Mann et al., 2024; Bishop et al., 2022). At the translational level, progress toward clinically meaningful AD treatments remains hampered by limited human intervention evidence and continued reliance on transgenic mouse models that do not fully recapitulate the long pathological timeline of human AD or the complexity of human microbiome–brain interaction (Loh et al., 2024; Zhong et al., 2024).

To better define these gaps, we conducted a comprehensive review of human-based studies mostly published between 1 January 2020, and 31 May 2025 (search strategy and study selection detailed in Supplementary Material). Using the microbiota-gut-brain axis as the central framework, we first systematically characterize gut microbiota dysbiosis at different stages of the AD progression. We then provide in-depth mechanistic analysis of gut-derived metabolites, including fatty acids, amino acids, and bile acids (BAs). To improve mechanistic clarity, we distinguish between bacteria-driven and metabolite-driven pathways, with particular attention to vagal signaling, blood-brain barrier (BBB) disruption, and immune amplification. We further critically evaluate therapeutic interventions targeting both the entire gut microbiome community and specific bacterial species or strains. Finally, we comprehensively address population-level confounders, including APOE genotype, age, sex, and environmental influences that significantly shape microbiome composition. Through this human-centered and mechanistically focused approach, our review seeks to advance early detection, prevention, and management strategies for AD.

Understanding microbiome changes across the AD continuum is important for identifying early biomarkers and clarifying when gut dysbiosis becomes biologically relevant to disease progression. AD is commonly described across three stages: the preclinical phase, MCI, and AD dementia. Each stage reflects distinct pathological and clinical changes, with the preclinical phase offering a critical window for early detection before irreversible neurodegeneration sets in. In this section, we evaluate how gut microbial alterations are reported at these stages. Overall, current human evidence supports stage-associated gut microbiome differences, but most studies remain cross-sectional (Table 1), limiting the extent to which observed associations can be interpreted causally.

During preclinical AD, individuals are typically cognitively normal but already show AD-related features, such as Aβ accumulation and early tau-related changes (Jack Jr et al., 2018; Tahami Monfared et al., 2022). Recent studies suggest that gut dysbiosis is already detectable at this stage. Alpha diversity (a measure of within-sample species richness and evenness) is generally preserved, whereas evidence for beta diversity (which captures differences among individual samples) differences is inconsistent. Smaller 16S-based cohorts often detect taxon-specific shifts without clear global between-group separation (Sheng et al., 2022; Jung et al., 2022), whereas a larger metagenomic study with stricter dietary and clinical covariate control reported clearer compositional differences (Ferreiro et al., 2023). Apparent differences across studies may reflect variation in cohort size, sequencing resolution, and study designs as much as variation in biology. Age and recruitment setting may be especially important at this stage. Preclinical AD cohorts are often older yet cognitively normal, and many are drawn from research settings in which participants are preselected based on AD-related biomarkers, making disease-related microbial shifts difficult to separate from aging-related microbiome remodeling and cohort-selection effects. At the taxonomic level, broader patterns recur across preclinical AD cohorts, such as enrichment of Bacteroidetes-related taxa and reduction of Firmicutes-related or SCFA-associated taxa, whereas the specific genera or species identified vary substantially by study (Ferreiro et al., 2023; Sheng et al., 2022; Jung et al., 2022). Such early signals may prove useful as part of multimodal, non-invasive screening frameworks once consistent validation is established.

Following the preclinical phase, individuals often progress to MCI, a transitional phase between normal cognition and dementia. MCI is characterized by measurable deficits in memory and other cognitive functions that do not yet interfere substantially with daily living (Arevalo-Rodriguez et al., 2021; Chen et al., 2022c; Anderson, 2019). At this stage, studies often report preserved alpha diversity alongside selective taxonomic differences, while evidence for beta-diversity dissimilarities remains mixed. As with preclinical AD, similarities across MCI studies are more consistent at the level of broader functional patterns, particularly reduced SCFA-related or potentially protective microbial capacity, than at the level of any single genus. Varying taxa include Lachnospira, Ruminococcus, and Butyricimonas, but the exact genera differ across cohorts and analytical pipelines (Abdugheni et al., 2022; Fan et al., 2023). Prevotella has received particular attention, with one medication-naive study reporting a negative correlation between its abundance and cognitive performance (Guo et al., 2021). Meta-analytic evidence additionally suggests that Phascolarctobacterium may be increased in MCI, whereas global diversity measures such as Shannon and Chao do not show consistent significant differences across studies (Jemimah et al., 2023). The frequent mismatch between diversity-level and taxon-level findings indicates that MCI-related dysbiosis may reflect partial community reconfiguration rather than wholesale loss of diversity. Drawing conclusions remains challenging because MCI is etiologically heterogeneous, and differences in biomarker definition and analytical pipelines likely contribute to inconsistent findings across cohorts.

In AD dementia, gut microbiome variation generally appears more pronounced than in earlier stages. Studies more commonly report an imbalance between protective and pro-inflammatory taxa, consistent with convergence on inflammatory and metabolic pathways relevant to neurodegeneration. Several studies report decreases in Firmicutes and increases in Bacteroidetes, together with reduced Bifidobacterium and enrichment of Proteobacteria- or Enterobacteriaceae-related taxa (Vogt et al., 2017; Liu et al., 2019). These alterations have also been associated with AD-related biomarkers, including phosphorylated tau (p-tau) levels and p-tau to Aβ42 ratio (Verhaar et al., 2022). However, interpretation of these results is especially challenging because aging-related and advanced-disease factors accumulate together, and recruitment setting differs substantially across community-based, clinic-based, and nursing-home cohorts (Vogt et al., 2017; Haran et al., 2019). Some studies included substantial exposure to acetylcholinesterase inhibitors or memantine (Vogt et al., 2017), whereas others differed in frailty, malnutrition, and antipsychotic use and adjusted for these variables analytically (Haran et al., 2019). Notably, even newly diagnosed AD patients without prior treatment still support dysbiosis as a factor, but show only partial overlap at the taxon level with other studies with different recruitment strategies (Guo et al., 2021). Thus, the most robust conclusion is that AD cases have more pro-inflammatory and reduced butyrate- or SCFA-related microbial capacity, but not a specific taxonomic signature in AD dementia.

Across all three stages, current human evidence supports stage-associated gut microbiome alteration, with findings remaining suggestive in preclinical and MCI stages but showing stronger functional convergence in AD dementia, yet without a single taxonomically unified signature across any stage. A decrease in Firmicutes and increase in Bacteroidetes are among the more frequently reported trends (Hung et al., 2022; Bostanciklioğlu, 2019), although reproducibility remains inconsistent across all cohorts. Varying findings across studies likely reflect a combination of biological complexity, population heterogeneity, and methodological differences, rather than simply conflicting underlying biology. In particular, 16S-based studies may collapse divergent species-level signals within the same genus, whereas shotgun studies better resolve species-level and functional differences. Geographic variation adds an additional layer of complexity, as meta-analysis indicates region-specific patterns for taxa such as Bacteroides (Jemimah et al., 2023). Figure 1 summarizes gut microbial alterations across AD stages at the genus and species levels.

Gut-derived metabolites offer a functional layer through which the microbiota-gut-brain axis can influence inflammation, barrier biology, neural signaling, and cognition in AD. Key metabolite classes include SCFAs—the most extensively studied in this context—alongside other fatty acids, amino acid-related metabolites, and BAs (Figure 2), although these are not equally informative as readouts of microbial activity.

As shown on the left side of Figure 2, fatty acids are a key group of microbial metabolites linked to gut-brain signaling in AD. Fatty acids are commonly divided into short-chain, medium-chain, and long-chain fatty acids. Among these, SCFAs such as butyrate, propionate, and acetate are most clearly linked to microbial fermentation of dietary fibers by gut microbiota. They contribute to gut homeostasis by reducing local inflammation, preventing pathogen infiltration, and preserving gut barrier integrity (Di Tommaso et al., 2021), through mechanisms including the activation of G protein-coupled receptors (GPR41, GPR43 and GPR84) and the inhibition of histone deacetylase activity (Yoo et al., 2020; Samuel et al., 2008; Qiu et al., 2024). Butyrate-related pathways appear more consistently protective than SCFA-related signals as a whole; the effects of other SCFAs, especially propionate, appear more context- and pathway-dependent (Chen H. et al., 2022; Alpino et al., 2024). Dysbiosis—typically featuring an overgrowth of Proteobacteria alongside reduced Bifidobacteria—has been linked to lower SCFA-related capacity, but this inference remains indirect because SCFA levels depend not only on microbial composition but also on substrate availability, especially dietary fiber intake (Vogt et al., 2017; Liu et al., 2019; Yoo et al., 2020). Accordingly, SCFAs may collectively serve as indicators of microbial fermentation capacity and barrier-supportive physiology, although how butyrate findings extend to other SCFAs in AD contexts remains to be established.

Unlike SCFAs, medium-chain fatty acids (MCFAs) and long-chain fatty acids (LCFAs) are primarily obtained from the diet rather than being produced by gut bacteria. MCFAs typically contain 6–12 carbon atoms and are absorbed more rapidly than LCFAs, transported mainly via the portal vein, and readily converted in the liver into ketone bodies that can serve as an alternative energy source for the brain (Castro et al., 2023; Watanabe and Tsujino, 2022). In human studies, MCFA- or medium-chain triglycerides (MCT)-based interventions consistently increase circulating ketone bodies, but cognitive findings remain mixed (Castro et al., 2023). Some trials report cognitive improvement in MCI or AD, including possible subgroup effects by APOE genotype, whereas others have shown little or no significant benefit, particularly in small or short-duration studies (Xu Q. et al., 2020; Watanabe and Tsujino, 2022). MCFAs and MCTs may also influence gut and metabolic homeostasis, but this literature is less directly informative for AD. For example, MCT supplementation may shift the balance between Gram-positive and Gram-negative bacteria, potentially reducing harmful bacterial overgrowth (Rial et al., 2016); however, whether such effects translate into AD-specific microbiome risk in humans remains unclear.

LCFAs present even greater challenges in establishing their origin as microbiome-derived signals. They contain 13 or more carbons and reflect a complex mixture of dietary intake, endogenous metabolism, and host-microbiome interactions. This complexity likely contributes to the comparatively limited clinical research in humans on LCFAs versus SCFAs and MCFAs (Schö”nfeld and Wojtczak, 2016). Although many mouse studies report anti-inflammatory and neuroprotective effects of specific LCFAs, particularly omega-3 fatty acids (Joffre et al., 2019; Giacobbe et al., 2020), those findings should not be generalized to the LCFA category as a whole. Similarly, although Escherichia coli Nissle 1917 can produce hydroxylated LCFAs, such as 3-hydroxyoctadecaenoic acid (C18-3OH), which may contribute to anti-inflammatory effects in the gut (Pujo et al., 2021), this concerns a specific bacterial lipid product rather than a general feature of LCFAs.

Taken together, fatty-acid findings in AD are most interpretable when framed by degree of microbial specificity. SCFAs represent the most direct microbial readouts among fatty acids, MCFAs and MCTs are primarily diet-linked interventions with possible microbiome effects, and LCFAs mainly reflect broader diet-host-microbiome interplay rather than direct microbial metabolism. The field may benefit more from identifying reproducible cross-class patterns than from searching for a single fatty-acid marker of AD.

As illustrated in the top right of Figure 2, amino acids represent another important class of microbial metabolites that play a key role in gut-brain communication. Specific gut bacteria, including Bacteroides, Lactobacilli, and Bifidobacteria, contribute to the production of gamma-aminobutyric acid (GABA), a key inhibitory neurotransmitter in the central nervous system (CNS) (Strandwitz et al., 2019). Specifically, Lactobacilli and Bifidobacteria can convert glutamic acid into GABA via the enzyme glutamate decarboxylase (GAD) (Yin et al., 2023). However, gut-derived GABA does not readily cross the BBB, and its potential CNS effects are therefore thought to be mediated indirectly through vagal afferent pathways, among other routes (Dicks, 2022; Belelli et al., 2025). Critically, direct evidence linking microbiome-derived GABA to vagal signaling and AD-related outcomes in humans remains limited. Current evidence more consistently supports altered GABAergic signaling within the AD brain than a clearly defined gut-derived GABA signature in AD patients. In particular, AD-related GABAergic dysfunction has been associated with interneuron deficits, altered tonic inhibition, and impaired excitatory-inhibitory balance (Xu Y. et al., 2020; Wu et al., 2014). At the same time, reactive astrocytes may accumulate and release GABA in AD brains, further complicating attempts to distinguish gut-derived, neuronal, and astrocytic sources of GABA (Li et al., 2019; Ishibashi et al., 2019). For this reason, amino acid-related metabolite research in AD should be framed less as evidence that gut bacteria directly determine brain GABA levels, and more as a still-open question about whether microbiome-related amino acid metabolism contributes meaningfully to GABAergic dysfunction in humans. Resolving this question will require paired human studies that integrate microbial composition, amino acid metabolomics, AD biomarkers, and cognition.

As shown in the bottom right of Figure 2, BAs play a significant role in modulating CNS function. Synthesized from cholesterol in the liver, primary BAs are transformed by gut microbes such as those in the Lachnospiraceae and Ruminococcaceae families into secondary BAs, such as deoxycholic acid (DCA) (Kakiyama et al., 2013; Hylemon et al., 2009). Compared with SCFAs, BA profiles reflect joint regulation by the liver, gut microbiota, enterohepatic circulation, and host transporters rather than microbial activity alone (Hylemon et al., 2009; Evangelakos et al., 2021; Wang S. et al., 2023). Across human observational cohorts, the more reproducible signal lies at the level of broader BA profile shifts rather than any single BA species. In particular, AD-related cognitive impairment has been associated with lower primary BAs and higher bacterially derived secondary and conjugated BAs, as well as with increased secondary-to-primary BA ratios (Nho et al., 2019; MahmoudianDehkordi et al., 2019). Thus, BA alterations may appear more reproducible at the level of broad profile shifts than SCFA-related findings, yet they are also harder to attribute specifically to microbial causation.

DCA illustrates this interpretive challenge particularly well. It is a biologically plausible candidate mediator because it is a prominent microbially derived secondary BA, can inhibit the growth of certain beneficial taxa such as Bifidobacteria and Lactobacilli, and has been linked to dysbiosis, impaired enterohepatic signaling, and intestinal inflammation in experimental settings (Yang and Qian, 2022; Xu et al., 2021). However, experimental studies do not support a single uniform role for DCA. Animal and cell studies using defined DCA exposures (often unconjugated, at supraphysiological concentrations, via hippocampal or systemic administration) reveal dose- and context-dependent effects that vary by BA class, conjugation state, hydrophobicity, exposure route, concentration, and duration (Chen et al., 2024; Lirong et al., 2022; Li et al., 2024; Evangelakos et al., 2021; Liu et al., 2025; Zhang et al., 2025) (Table 2). Paradoxically, despite its cytotoxic potential, DCA has been observed to confer neuroprotective effects in AD mouse models through the farnesoid X receptor (FXR) pathway, which plays a role in regulating cholesterol and inflammation (Lirong et al., 2022; Chen et al., 2024). An additional challenge is that fecal, circulating, and brain BA pools are not directly interchangeable, and future studies need to clarify whether observed BA changes primarily reflect microbial conversion, host transport, or central accumulation.

Understanding such nuanced and sometimes contradictory roles of microbial metabolites underscores the critical need to move beyond taxonomic profiling and directly assess microbial function. Most studies in Table 1 infer microbial contributions from DNA-based abundance or prior literature, without validating whether the identified taxa actively produce disease-relevant metabolites or proteins in situ. However, if only DNA sequencing data are available, researchers can still perform functional analysis to predict microbial metabolic potential. For instance, PICRUSt2 infers functional potential by inserting 16S rRNA sequences into a reference phylogeny to generate KEGG or MetaCyc pathway profiles from amplicon data (Douglas et al., 2020). HUMAnN processes metagenomic or metatranscriptomic reads to quantify species-resolved gene families and reconstruct metabolic pathways (Franzosa et al., 2018). These frameworks can validate whether compositional changes in the microbiome translate into biochemical activities relevant to host health, especially neurotoxin production and inflammatory signaling. Such integration is critical for uncovering causal mechanisms and establishing the microbiome’s functional relevance in AD.

Beyond methodological considerations, it is important to recognize that microbial metabolites do not act alone. Their relevance to AD depends on the host biology through which gut-derived signals are sensed, amplified, or filtered before influencing the brain. We therefore next turn to the microbiota-gut-brain mechanisms that may translate microbial and metabolic variation into disease-relevant host responses.

Having outlined stage-related microbial alterations and gut-derived metabolite patterns, we next unpack the host pathways through which these signals may become biologically relevant to the brain. Rather than relying primarily on a human-centered dichotomy between “direct” and “indirect” effects—such as assigning vagus nerve and BBB pathways as “direct” pathways, and peripheral immunity as “indirect” ones (Lista et al., 2025)—we adopt a microbiome-centered framework that distinguishes between cell-associated (bacteria-driven) and diffusible (metabolite-driven) mechanisms. Cell-associated mechanisms include structural bacterial components such as lipopolysaccharides (LPS), peptidoglycan, outer membrane vesicles, and bacterial amyloids, which may interact directly with host receptors through enteroendocrine cells and vagal afferent neurons (Tursi et al., 2017; Kieser and Kagan, 2017). Diffusible metabolite-driven mechanisms involve small microbial-derived molecules, including SCFAs, BAs, and tryptophan metabolites, which may influence peripheral immunity, BBB integrity, and directly affect neuroinflammation and neuronal health (Salminen, 2023; Wang J. et al., 2023; Mann et al., 2024). Clearly differentiating between these cell-associated and metabolite-driven interactions enhances mechanistic clarity and helps pinpoint therapeutic opportunities within the multifaceted progression of AD.

To illustrate these distinct yet interacting pathways, we design a dual-pathways schematic (Figure 3), with Panel A illustrating the neural (vagus nerve) pathway and Panel B depicting the bloodstream route impacting the BBB. Rather than acting independently, these pathways likely operate synergistically: gut-derived signals may activate neural pathways via the vagus nerve, while systemic inflammation and microbial translocation compromise BBB integrity, allowing harmful molecules to enter the brain and exacerbate neurodegeneration.

Grounded in these GBA-related pathological mechanisms, strategies that restore a balanced microbiome, boost beneficial metabolite production, modulate immune responses, and protect both the gut and BBB hold potential to prevent or slow AD progression. To translate these insights into clinical practice, the remainder of this review is organized around a pathway-guided therapeutic roadmap: we map each therapeutic or study design choice back to the microbiota-gut-brain pathways introduced.

Numerous human studies report benefits of these interventions on cognition, microbiome composition, and inflammatory markers in AD and related populations (Table 3). Outcomes were assessed primarily using standardized cognitive tests (MMSE, MoCA, ADAS-Cog/ADAS-Jcog, RBANS, CERAD) and, in some studies, supported by microbiome profiling and blood-based inflammatory, oxidative stress, or neurotrophic markers. However, evidence that microbiome-based interventions reliably slow AD progression remains limited because most human studies are small, heterogeneous, and short-term, and they typically track cognitive scores rather than validated disease-modifying endpoints or longitudinal AT(N) trajectories. Nevertheless, this remains an active area of research, and larger, longer-duration trials with harmonized cognitive batteries and AT(N) biomarkers are needed to determine whether microbiome modulation can alter disease course. Despite the preliminary nature of many trials, several convergent biological signatures emerge, including enrichment of metabolite-producing taxa, reduction of pro-inflammatory taxa, restoration of gut and BBB integrity, and modulation of immune and neurotrophic signaling. Together, these shared endpoints offer a translational axis for preventive interventions linking molecular changes to clinical outcomes.

Importantly, the degree to which an intervention succeeds is shaped by individual-specific factors—such as APOE genotype, age, sex, and environmental background—which modulate baseline microbiome composition and responsiveness. These dimensions are addressed in detail later.

While prior findings underscore the therapeutic potential of microbiome-based interventions in AD, translating these strategies into effective clinical applications requires careful attention to host-specific and contextual factors that shape both microbiome composition and responsiveness. This section explores critical confounding factors—APOE genotype, age and sex differences, and population or environmental variability—that complicate gut microbiome data interpretation and may obscure true gut-brain interactions in AD studies. Understanding and accounting for these influences is essential for both designing robust microbiome research and successfully translating gut microbiota-targeting strategies into effective treatments (see Table 4).

The main challenge in AD microbiome research is no longer to show that microbial differences can be observed, but to determine which of those differences are biologically meaningful and clinically relevant. Answering that question requires testable mechanistic chains rather than further associative surveys, for instance by pairing fecal metabolomics with peripheral inflammatory markers and AT(N) biomarkers to assess whether gut-derived signals systematically precede or track measurable pathological change. Concretely, this means asking whether specific gut-derived metabolites or bacterial products are linked to defined peripheral immune states, whether those states relate to BBB or glial changes, and whether those changes in turn predict biomarker progression or cognitive decline. Framing the problem in this way makes mechanism experimentally tractable and raises the standard for what constitutes a meaningful intervention study.

However, defining that intervention standard requires more than mechanistic clarity alone. The relevance of microbiome-targeted interventions will depend not only on whether they produce short-term cognitive or biochemical improvement, but also on whether they can durably maintain beneficial microbial and metabolite states, remain feasible under real-world conditions, and translate into sustained effects on biomarkers or disease progression rather than transient symptomatic change. Future intervention studies should therefore prioritize stage-specific enrollment, repeated longitudinal sampling, integrated microbiome-metabolite-host biomarker readouts, and durability-oriented follow-up, so that transient compositional shifts can be distinguished from biologically meaningful modification of disease-relevant trajectories.

A more complete and comparable picture of the AD microbiome will require addressing several persistent methodological constraints. Body-site specificity remains unresolved, as it is unclear whether AD-related microbial signals are gut-specific or partly shared across oral and blood compartments; while expanding beyond the gut may prove informative, low biomass, contamination risk, and poor cross-compartment comparability remain major obstacles (Issilbayeva et al., 2024; Chen et al., 2022b; Alonso et al., 2018; Hu et al., 2023; Link, 2021; Bedarf et al., 2021). At the analytical level, variation in reference databases, sequencing platforms, functional annotation pipelines, and analytic workflows continues to limit cross-study interpretation (Huttenhower et al., 2023; Chetty and Blekhman, 2024). Artificial intelligence and advanced bioinformatics are most valuable not as substitutes for biological reasoning, but as tools for integrating multi-omics data, longitudinal measurements, and host metadata in ways that improve stratification and sharpen mechanistic hypotheses (D’Urso and Broccolo, 2024; Cheng et al., 2024; Probul et al., 2024).

Underlying all of these challenges is a conceptual one. AD and the gut microbiome are both complex, dynamic systems, and the biologically realistic problem is likely one of state transition rather than one-microbe or one-metabolite causation. If transitions between microbial states, rather than the presence or absence of individual taxa, are what matter most, then studying the conditions that support resilient states becomes as important as cataloguing pathological ones. Features such as anti-inflammatory metabolite production and preserved barrier integrity have already been associated with better cognitive outcomes in both human and experimental work (Neuner et al., 2022; Ahangari et al., 2023; de Vries et al., 2024). Therefore, the goal is not merely to identify microbes associated with AD, but to define reproducible taxon-function-host patterns that distinguish vulnerability from resilience and that can meaningfully inform prediction, interpretation, and intervention (Figure 4).

In conclusion, current human evidence does not support a single universal microbiome signature for AD. Instead, it supports a stage-sensitive and host-dependent framework in which microbiome-related signals appear more reproducible at the level of function, host response, and pathway engagement than at the level of any one taxonomic pattern. The central task for the field is therefore to identify which microbiome-related signals are biologically consequential, when they emerge along the AD continuum, and which are robust enough to guide clinical translation. In this sense, the value of the microbiota-gut-brain axis lies not in providing a new descriptive layer of association, but in offering a framework for connecting microbial variation to modifiable host pathways relevant to early detection, mechanistic understanding, and intervention.