Altered Microbial Cargo in Gut Microbiota-Derived Outer Membrane Vesicles as Novel Biomarkers for Vascular Dementia

Altered Microbial Cargo in Gut Microbiota-Derived Outer Membrane Vesicles as Novel Biomarkers for Vascular Dementia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Altered Microbial Cargo in Gut Microbiota-Derived Outer Membrane Vesicles as Novel Biomarkers for Vascular Dementia Xin Li, Wei Wei, Shouchao Wei, Wenwei Xu, Lang Mo, Junjun Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8523404/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Apr, 2026 Read the published version in BMC Microbiology → Version 1 posted 11 You are reading this latest preprint version Abstract Background This study aims to analyze the composition, diversity, and metabolic functions of gut microbiota (GM)-derived outer membrane vesicles (OMVs) in patients with vascular dementia (VaD), to identify potential biomarkers for VaD diagnosis. Methods GM-derived OMVs were isolated from 29 VaD patients and 28 matched controls via ultracentrifugation and characterized using transmission electron microscopy and nanoparticle tracking analysis. PKH26-labeled OMVs were used for in vivo tracking in mouse brains. Microbial composition was profiled by 16S rRNA sequencing, combined with diversity analysis and machine learning. Results VaD-OMVs were widely distributed in multiple cognitive function-related regions of mouse brains. The VaD group showed a decreased Chao1 index and increased coverage. β-diversity (PCoA/PLS-DA) revealed significant structural differences. Conditional pathogens (e.g., Pseudomonas , Acinetobacter ) were enriched, while beneficial bacteria (e.g., Bifidobacterium ) were reduced. Correlation analysis indicated promoting effects of Pseudomonadaceae and inhibitory effects of Faecalibacterium. Metabolic pathways including amino acid, carbohydrate, and nucleotide metabolism were enriched. A random forest model achieved an AUC of 0.74 (95% CI: 0.59–0.88) in classifying VaD. Conclusion VaD is associated with distinct OMV microbial and functional profiles. OMV-based biomarkers show potential for VaD diagnosis. Vascular dementia Outer membrane vesicles Gut microbiota High-throughput sequencing Machine learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Vascular dementia (VaD) is an acquired cognitive impairment syndrome resulting from cerebrovascular pathologies and is characterized by progressive cognitive decline and significant functional deterioration due to cerebral tissue damage ( 1 ). Accounting for 15%–30% of dementia cases worldwide, VaD is the second most common type after Alzheimer's disease ( 2 , 3 ). Its prevalence in developing Asian countries, such as China, reaches 30%, markedly higher than that in Western regions ( 4 ), suggesting significant influences from environmental and lifestyle factors. Established vascular risk factors, including hypertension, hyperlipidemia, and type 2 diabetes, are strongly associated with VaD pathogenesis ( 5 – 7 ). Nonetheless, the exact mechanisms remain unclear, impeding early diagnosis and effective treatment. A growing body of evidence indicates that the gut microbiota (GM) can mediate central nervous system functions through multiple pathways, providing a new dimension for in-depth exploration of neurological disease mechanisms ( 8 – 11 ). As the largest symbiotic microbial ecosystem in the human body, the GM has a biomass of up to 10 14 ( 12 ). These microorganisms perform vital physiological functions through mechanisms such as immune regulation, maintenance of intestinal barrier integrity, and participation in nutrient metabolism, resulting in their designation as the host’s "second genome" ( 13 , 14 ). Several investigations have revealed characteristic alterations in the GM composition of VaD patients ( 15 – 18 ). Nevertheless, the specific mechanisms through which the GM influences VaD onset and progression remain to be fully elucidated. GM-derived outer membrane vesicles (OMVs) are increasingly recognized as novel mediators of bacterium‒host interactions. These nanosized membrane structures (20–350 nm in diameter), which are actively secreted by gram-negative bacteria, play a significant role in the pathogenesis of cerebrovascular diseases ( 18 ). OMVs can activate immune responses and promote the release of inflammatory factors, thereby disrupting blood‒brain barrier integrity and increasing permeability, which facilitates the entry of pathological substances into the brain parenchyma( 19 – 21 ). Notably, the RNA, proteins, and lipids carried by OMVs may modulate neuronal and vascular endothelial functions, potentially amplifying the extent of damage or accelerating disease progression ( 22 – 26 ). While current research has identified molecular and microbial distinctions between VaD patients and healthy controls, the multiomics features and functional roles of VaD-derived OMVs remain largely unexplored. Elucidating the composition and function of intestinal OMVs is critical for understanding VaD pathogenesis, yet this field remains underinvestigated. Here, we apply an integrated approach, including 16S rRNA sequencing, machine learning, and functional prediction, to systematically profile the OMV-associated microbiota, metabolic activity, and diagnostic utility ( Fig. 1 ) . Our work aims to gain insight into host‒microbe interactions in VaD and support the development of OMV-based diagnostics and microbiome-targeted therapies. Materials and methods Patient selection and sample collection This study was conducted at Central People's Hospital of Zhanjiang (CPHZ) from February 2024 to July 2025. Eligible VaD patients and age-/sex-matched controls were recruited on the basis of predefined inclusion and exclusion criteria. All participants provided qualified fecal samples and completed questionnaire surveys. This study received ethical approval from the CPHZ Ethics Committee (Approval No. IIT-2024046-01). For details, please refer to the Supplementary Materials . Fresh morning stool samples (≥ 200 g) were collected in sterile containers, immediately sealed in anaerobic bags, and stored at − 80°C to preserve microbial integrity. OMV purification and characterization On the basis of the literature reports and previous research methods from our research group, we further optimized the extraction and purification procedures of OMVs, with the specific steps as follows( 68 ): thawed fecal samples were homogenized in ice-cold 0.9% saline via low-frequency pulse homogenization (3–5 s intervals); sequential filtration (70 µm nylon mesh) and differential centrifugation (4°C) were performed as follows: 500 ×g, 10 min (6 cycles); 1,000 ×g, 15 min (5 cycles); 3,000 ×g, 30 min (3 cycles); 5,000 ×g, 60 min (2 cycles); and the supernatants were filtered (0.45 µm, 0.22 µm, 3 cycles) and ultracentrifuged (100,000 ×g, 2 h, SW32Ti/SW41Ti rotors). OMVs (10 µL) were adsorbed onto ethanol-cleaned copper grids (25°C, RH 45%, 15 min), negatively stained with 2% phosphotungstic acid (pH 6.8, 5 min), and imaged under 120 kV acceleration (Hitachi HT7800). The size distribution of the OMVs was quantified via a Malvern NS300 (532 nm laser, sCMOS camera, NTA 3.4 software) at 25°C. 16S rRNA sequencing The purified OMV samples were stored at − 80°C. The genomic DNA of the GM from the fecal samples was extracted via the E.Z.N.A.® Soil DNA Kit (Omega Biotek, USA). The V3-V4 region of the bacterial 16S rRNA gene was subsequently amplified via the ABI GeneAmp® 9700 PCR Thermal Cycler (ABI, California, USA) with the primers 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'). Following the standard protocol established by Meiji Biomedical Technology Co., Ltd. (Shanghai, China), equimolar purified amplicon pools were prepared for paired-end sequencing on the Illumina MiSeq PE300 platform/NovaSeq PE250 platform (Illumina, San Diego, USA), and raw 16S rRNA gene sequencing reads were demultiplexed. Analysis of 16S rRNA sequencing data Operational taxonomic units (OTUs) were clustered via the UPARSE algorithm (version 7.1; http://drive5.com/uparse/ ) with a 97% similarity cutoff. Taxonomic classification of representative sequences from each OTU was performed via the RDP classifier (version 2.2) on the basis of the 16S rRNA database (Release 138; http://www.arb-silva.de ). Rank-abundance curves were constructed by sorting the OTUs in descending order on the basis of sequence count and plotted via R language tools. Pan/core species analysis was conducted at the taxonomic level of the OTUs, with analytical visualization performed via the vegan package (version 2.4.3) in R (version 3.3.1). α-Diversity analysis, which is based on the Chao1, Sobs, Shannon, ACE, and coverage indices, was employed to assess the species richness and structural heterogeneity of the microbial communities derived from OMVs. β-Diversity analyses, including principal coordinate analysis (PCoA) and partial least squares discriminant analysis (PLS-DA), were performed. The Gut Microbiome Health Index (GMHI) was used to evaluate host health status. At the genus level, we identified the core community of VaD-derived bacteria and compared the environmental sensitivity of the OMV-associated microbial communities. This comparison included the mean relative abundance (abundance) and the detection frequency (number) of three persistence types across samples: transient, intermediate, and persistent taxa( 69 , 70 ). A linear regression analysis (with reported R² values and P values) was performed to correlate the species detection frequency with the mean relative abundance across all samples. Furthermore, we employed keystone species analysis to screen the top 10 taxa ranked by the median of the structural keystone index. This approach effectively identified the keystone species within the microbial community( 71 ). The bacterial community composition of each sample was quantified at the phylum, class, order, family, and genus levels. To visually represent unique and shared operational taxonomic units (OTUs) present across multiple samples, Venn diagrams were generated to calculate the number of species present in each sample. On the basis of parametric/nonparametric testing strategies, intergroup significance difference tests were employed to resolve structural heterogeneity in the microbiota between groups. We also utilized linear discriminant analysis effect size (LEfSe) to perform hierarchical differential analysis of microbiome data through biomarker screening across taxonomic hierarchies (phylum to species). Linear discriminant analysis (LDA) was applied to estimate the effect size of each component (species) on differential effects. This study employed random forest analysis, an ensemble learning model, to jointly interpret the multidimensional feature spaces of samples by constructing multidecision tree classifiers. The receiver operating characteristic (ROC) curve was used to reveal the trade-off between sensitivity and specificity through dynamic threshold adjustments. Single-factor correlation networks were constructed to analyze species‒species correlations, with network attributes enabling the identification of key species implicated in disease progression. PICRUSt2 was utilized as a microbiome functional prediction tool to infer the functional composition of microbial communities. The distributions of distinct bacterial taxa identified in sample populations were visualized via R software (version 3.3.1) and the mixOmics package. Analyses were performed via R (version 3.3.1), the ade4 package, and the cluster package. Data processing was conducted on the Majorbio Cloud Platform ( www.majorbio.com ). Animals The experimental animals used in this study were two-week-old male C57BL/6J mice (specific pathogen-free [SPF] grade, body weight 20 ± 3 g) provided by Hangzhou Ziyuan Biotechnology Co., Ltd. (License No. SCXK [Zhe] 2019-0004). Adaptive housing was conducted at the SPF-level animal experimental center of Guangdong Medical University. Anesthesia Method To ensure animals were in a state of deep analgesia and unconsciousness prior to brain tissue perfusion and collection, thereby eliminating pain and stress during surgical procedures. Experimental animals were all subjected to deep anesthesia prior to euthanasia, ensuring they remained completely unconscious throughout the critical operative steps. 1.25% tribromoethanol (Avertin) solution (M2920, Nanjing AlBi Bio-Technology Co..Ltd, China) was used. This agent is a commonly used short-acting anesthetic characterized by rapid onset, moderate duration of anesthesia, and stable anesthetic depth, making it suitable for acute surgical procedures in rodent models. M2920 is a ready-to-use sterile solution containing 1.25% (v/v) tribromoethanol (Avertin), tertiary amyl alcohol, and 0.9% physiological saline, with a final tribromoethanol concentration of 20 mg/ml. Administration was via intraperitoneal injection at a dose of 0.2 ml/10 g body weight for mice. This dosage ensures that animals enter a stable surgical plane of anesthesia within minutes, as indicated by the loss of corneal and toe-pinch reflexes. Euthanasia/Sacrifice Method Following deep anesthesia (unconsciousness), transcardiac perfusion fixation was employed as the terminal procedure. This method enables both humane euthanasia and optimal preservation of tissue architecture for high-quality neurohistological analysis. Detailed Procedure: the deeply anesthetized mouse was positioned supine and secured on a surgical board; the thoracic cavity was quickly opened to expose the heart; a perfusion needle was inserted into the ascending aorta via the left ventricle, and the right atrial appendage was incised to serve as an outflow tract; approximately 20 mL of ice-cold 0.9% physiological saline was rapidly perfused first, until the liver and lungs turned pale and the effluent from the right atrial appendage ran clear, ensuring thorough removal of blood from the vasculature; this was followed by perfusion with approximately 50 mL of ice-cold 4% paraformaldehyde (PFA) in phosphate buffer, continuing until rigidity and tremors were observed in the limbs, tail, and torso of the animal, after which perfusion was stopped. All animal experimental protocols and procedures were reviewed and approved by the Institutional Animal Ethics Committee of Guangdong Medical University (Ethics Approval No. GDY2403466). For details, please refer to the Supplementary Materials . Fluorescent Labeling of OMVs To clarify the distribution of VaD-OMVs in the brains of C57BL/6J mice, PKH26 fluorescent dye was used to label VaD-OMVs. OMVs were incubated with PKH26 dye at 4°C for 2 hours. Unbound PKH26 was removed via ultracentrifugation using an SW 60 Ti rotor at 100,000 × g and 4°C for 1 hour. The pelleted material was resuspended in PBS to obtain the labeled product. Fasted C57BL/6J mice (after 24 hours of fasting) were orally administered labeled OMVs at a dose of 20 µg/200 µL via oral gavage. Mouse Brain Tissue Harvesting and Immunofluorescence The mice were anesthetized via intraperitoneal injection. After complete anesthesia, the brains were harvested and fixed. Brain tissue sections (35 µm thick) were prepared via a cryostat microtome via the sectioning-mounting method. The distribution of VaD-OMVs in various brain regions was observed via a 10x objective lens under an inverted laser confocal microscope (A1-SHR-LFOV, Nikon, Japan). Results Clinical baseline characteristics of VaD patients and controls Baseline demographic and clinical characteristics demonstrated no significant intergroup differences in age, sex distribution, or comorbidities (hypertension, diabetes, hyperlipidemia, valvular heart disease, or COPD; all P > 0.05; Table 1). However, VaD patients exhibited markedly impaired neurocognitive function compared with controls, as evidenced by lower MMSE ( t = 17.454, P < 0.001) and MoCA ( t = 20.798, P < 0.001) scores and higher HIS scores ( t = -18.783, P < 0.001). These findings align with the diagnostic criteria for vascular cognitive impairment. Table 1 Demographic and clinical comparisons between the VaD and control groups. Variable VaD Group (n=29) Control Group (n=28) Statistical Value P Value Age (years) 68.59±10.50 67.65±8.78 t =-0.818 0.417 Male 13/29 9/28 χ² =0.967 0.325 Hypertension 18/29 13/28 χ² =1.405 0.236 Diabetes mellitus 6/29 4/28 Fisher’s exact test 0.740 Hyperlipidemia 5/29 5/28 χ² =0.004 0.951 Valvular heart disease 8/29 4/28 Fisher’s exact test 0.331 COPD 0/29 1/28 Fisher’s exact test 0.491 MoCA 10.59 ±4.23 27.46 ±0.74 t =20.798 <0.001 MMSE 12.55 ± 4.66 28.50 ± 1.32 t =17.454 <0.001 HIS 11.66 ±2.60 1.89 ± 1.03 t =-18.783 <0.001 Abbreviations : VaD, vascular dementia; Con, control; COPD, chronic obstructive pulmonary disease; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; HIS, Hachinski Ischemic Scale. Morphological characteristics of OMVs This study investigated the morphological and dimensional features of OMVs isolated from the GM of individuals with VaD compared with those from control subjects. Visualization by transmission electron microscopy (TEM) revealed that both VaD- and control-derived OMVs displayed smooth-edged, well-defined, and nearly spherical nanostructures ( Fig. 2B and 2C) . Particle size analysis indicated that the OMVs from both groups predominantly fell within a size range of 0–200 nm. Consistent with these findings, nanoparticle tracking analysis (NTA) revealed no statistically significant differences in vesicle size distribution between the two groups ( Fig. 2D and 2E) . Cerebral distribution of VaD-derived OMVs PKH26-labeled OMVs were administered via oral gavage, and their biodistribution in the mouse brain was evaluated via immunofluorescence. Widespread dispersion of OMVs was observed across multiple brain regions implicated in cognitive function, including the frontal association cortex (FrA) ( Fig. 3A ), the anterior olfactory nuclei (dorsal and ventral parts, AOD and AOV) ( Fig. 3B ), the secondary motor cortex (M2) and cingulate cortex (Cg) ( Fig. 3C ), the lateral shell of the nucleus accumbens (LAcbSh) ( Fig. 3D ), the triangular septum (TS) ( Fig. 3E ), the caudate putamen (CPu) and lateral globus pallidus (LGP) ( Fig. 3F ), the hippocampal CA1 region, molecular layer (Mol), and dentate gyrus (DG; Fig. 3G ), as well as the posterior hypothalamus (PH) ( Fig. 3H ). Reduced Microbial Diversity and Significant Dysbiosis in VaD-OMV-Associated Microbiota Diversity analysis of 57 samples revealed 4,942,454 high-quality sequences (mean length: 415 bp). Operational taxonomic unit (OTU) clustering and taxonomic alignment were performed to characterize the bacterial composition and abundance. Rank‒abundance curves revealed differences in community structure between VaD-OMVs and Con-OMVs. VaD-OMVs presented greater species abundance but lower evenness (Fig. 4A) . Rarefaction analysis based on the Sobs index indicated that sequencing saturation was achieved, with sufficient depth and biological reproducibility (Fig. 4B) . Core/Pan species curves confirmed adequate sequencing depth and sample size (Fig. 4C, Supplementary Fig. 1A) . α-Diversity analysis revealed significantly lower bacterial richness in the VaD-OMV group than in the Con-OMV group, as indicated by reductions in the Ace, Chao, and Sobs indices (all P < 0.01) ( Fig. 4D and Supplementary Fig.s 1B-C ). Coverage was greater in VaD-OMVs ( P < 0.01) (Fig. 4E) , whereas the Shannon index was not significantly different ( P = 0.086) ( Supplementary Fig. 1D ). β-Diversity analysis revealed clear separation between groups via PCoA ( P < 0.01) ( Supplementary Fig. 1E ) and PLS-DA (Fig. 4F) , which was supported by significant intergroup dissimilarity ( P < 0.01) ( Supplementary Fig. 1F ). VaD-OMVs also presented a significantly lower gut microbiome health index (GMHI) ( P < 0.01) (Fig. 4G) and a higher microbial dysbiosis index (MD index) ( P < 0.01) ( Fig. 4H ), indicating a compromised microbiota structure compared with those of the controls. Microbial Community Assembly: Prevalence‒Abundance Relationships and Identification of Core Keystone Taxa To elucidate microbial community assembly mechanisms, we assessed the relationship between GM prevalence and mean relative abundance. A strong positive correlation was observed ( R ² = 0.750, P < 0.001) (Fig. 5A) , indicating that widely distributed species tend to be more abundant. Microorganisms were classified into persistent, intermediate, and transient taxa on the basis of their prevalence. Although intermediate (46.28%–52.32%) and transient taxa (16.46%–16.74%) contributed more to taxonomic richness, persistent taxa constituted a smaller proportion (31.22%–36.98%) (Fig. 5B, “Number”) . In contrast, persistent taxa dominated in terms of relative abundance (79.24%–83.8%), far exceeding intermediate (14.23%–19.3%) and transient taxa (1.46%–1.97%) (Fig. 5B, “Abundance”) , suggesting that while diversity is sustained by rare and transitional species, ecosystem function is driven primarily by a persistent core. We further identified keystone taxa via a structural keystone index (Fig. 5C) . The top 10 keystone species included Acinetobacter and Enterococcus , which presented the highest indices, indicating central roles in the microbial network. Other key taxa, such as Pseudomonas , Ruminococcus gnavus , and Dorea , are also likely critical for maintaining community stability and function. Significant Shifts in GM Taxonomy Across Multiple Levels in VaD-OMVs GM composition analysis across taxonomic levels revealed significant shifts in VaD-OMV-associated GM compared with those in controls (Supplementary Fig. 2 A-D, Fig. 6A) . At the phylum level, VaD-OMVs increased Proteobacteria (55.73% vs. 33.18%) and Bacteroidota (5.97% vs. 3.41%) but decreased Firmicutes (27.22% vs. 41.74%) and Actinobacteriota (4.21% vs. 11.26%). Similarly, genera such as Pseudomonas (16.34% vs. 7.01%), Acinetobacter (11.26% vs. 8.54%), and Brevundimonas (4.52% vs. 2.05%) were enriched in VaD-OMVs, whereas Bifidobacterium (2.64% vs. 8.00%), Faecalibacterium (2.60% vs. 7.17%), and Subdoligranulum (1.61% vs. 7.29%) were reduced. Venn analysis revealed 359 unique genera in VaD-OMVs, 396 in controls, and 576 in common (Fig. 6B) . Significant enrichment of specific GMs in VaD-OMVs Differential abundance analysis revealed significant taxonomic shifts between the VaD- and Con-OMV groups (Supplementary Table 1 and Fig. 7A-E) . VaD-OMVs presented increased abundances of Proteobacteria (55.73 ± 9.83 vs. 33.18 ± 18.57) and Bacteroidota (5.97 ± 2.11 vs. 3.41 ± 2.85) at the phylum level (Fig. 7A). At the class level (Fig. 7B), Gammaproteobacteria (37.72 ± 9.20 vs. 25.15 ± 16.54), Alphaproteobacteria (18.01 ± 3.58 vs. 8.02 ± 5.37), and Bacteroidia (5.97 ± 2.11 vs. 3.41 ± 2.85) were significantly enriched. At the order level (Fig. 7C), Pseudomonadales (27.74 ± 5.69 vs. 15.60 ± 10.75), Rhizobiales (9.37 ± 2.06 vs. 4.30 ± 3.10), Burkholderiales (6.02 ± 1.45 vs. 4.80 ± 4.20), and Caulobacterales (5.65 ± 1.36 vs. 2.65 ±1.70) increased. At the family level (Fig. 7D), Pseudomonadaceae (16.34 ± 3.69 vs. 7.01 ± 4.88), Moraxellaceae (11.40 ± 4.87 vs. 8.60 ± 9.25), and Caulobacteracea e (5.59 ± 1.33 vs. 2.63 ± 1.67) were enriched. At the genus level (Fig. 7E), Pseudomonas (16.34 ± 3.69 vs. 7.01 ± 4.88), Acinetobacter (11.26 ± 4.87 vs. 8.54 ± 9.24), and Brevundimonas (4.52 ± 1.19 vs. 2.05 ± 1.27) were significantly enriched in VaD-OMVs (all P < 0.05). LEfSe analysis confirmed significant enrichment of Proteobacteria and related taxa (e.g., Pseudomonadales ) in VaD-OMVs, whereas Firmicutes , Actinobacteria , and Bifidobacterium were enriched in Con-OMVs (LDA > 3.5, P < 0.05) (Fig.s 8A, 8B) , indicating their potential as group-specific biomarkers. A Random Forest Model for Differentiating VaD Based on OMV-Derived GM with Diagnostic Potential In this study, a random forest algorithm was applied to construct a machine learning model for assessing the classification performance and taxonomic composition differences between the VaD-OMV and Con-OMV groups. A two-dimensional scatterplot generated from the random forest proximity matrix revealed clear spatial separation between the two groups, reflecting significant intergroup differences in microbial composition (Fig. 9A) . To identify the GM taxa derived from the OMVs that contribute most to the diagnosis of VaD-OMVs, the top 10 discriminant genera were selected on the basis of the mean decrease in accuracy (or Gini importance) from the random forest regression (Fig. 9B) . At the genus level, the following taxa were ranked by their contribution values: Holdemanella (6.24), norank-f-Saccharimonadaceae (5.93), Pseudomonas (5.84), norank-f-Eubacterium_coprostanoligenes_group (5.21), Libanicoccus (5.04), Brevundimonas (4.90), Bacteroides (4.86), Sediminibacterium (4.82), Solobacterium (4.67), and Allorhizobium-N-P-R (4.65). For diagnostic evaluation, receiver operating characteristic (ROC) analysis revealed an area under the curve (AUC) of 0.74 (95% CI: 0.59--0.88) (Fig. 9C) , indicating that the random forest model based on the GM structure can effectively differentiate VaD patients from non-VaD controls and supporting its potential clinical utility. We constructed a correlation network based on Spearman's rank coefficients to investigate GM sample relationships through the visualization of abundance correlations across common GM genera. Network analysis revealed decreased complexity in the VaD-OMV group compared with the Con-OMV group (Fig. 9D) . Notably, Pseudomonas showed positive interactions with multiple taxa, whereas Faecalibacterium appeared to exhibit inhibitory relationships. Functional prediction of the OMV-derived microbiota in VaD In this study, PICRUSt2 was employed to predict the functional profiles of GM-derived OMVs on the basis of 16S rRNA sequencing data. Through analysis of functional composition and abundance, we investigated the potential mechanistic contributions of OMV-associated bacterial communities to the pathogenesis of VaD. Integrated annotation with the COG database indicated that amino acid metabolism represented the predominant functional category, with its relative abundance significantly surpassing that of other metabolic modules (Fig. 10A). Further functional prediction via the KEGG database revealed that, at the primary functional level, the microbiota was predominantly enriched in carbon and nitrogen metabolic networks, encompassing pathways involved in carbohydrate, amino acid, and nucleotide metabolism (Fig. 10B) . At the tertiary KEGG level, OMV-associated functions clustered into three major modules: (1) metabolic regulation, including secondary metabolite biosynthesis, amino acid biosynthesis, and purine metabolism; (2) environmental adaptation mechanisms, such as ABC transporters, two-component systems, and quorum sensing; and (3) fundamental cellular functions, exemplified by ribosome assembly and central carbon metabolic pathways (Fig. 10C) . Analysis of Microbiota-Derived OMVs Reveals Key Microbial Taxa Associated with Clinical Factors in VaD Spearman correlation heatmap analysis was used to identify which OMV-associated GM exhibited relatively strong correlations with clinical factors. The results demonstrated that the MMSE and MoCA scores were highly positively correlated with the Family-XIII-AD3011 group , Peptococcus , Sellimonas , norank- o- Clostridia - UCG - 014 , Comamonas , Dorea , TM7x, Blautia , norank - f - Saccharimonadaceae , Holdemanella , and Subdoligranulum . Conversely, these clinical scores were highly negatively correlated with Acinetobacter , unclassified - f - Lachnospiraceae , Sediminibacterium , Pelomonas , Phyllobacterium , Chryseobacterium , Veillonella , Ruminococcus , Brevundimonas , Pseudomonas , Acidovorax , Bradyrhizobium , Aquabacterium , Sphingomonas , Methylobacterium - Methylorubrum , Bacteroides , norank - f - Eubacterium - coprostanoligenes - group , and Novosphingobium (Fig. 11A) . These microorganisms may represent key species influencing VaD-related clinical factors and could thus serve as one approach for screening microbial biomarkers distinguishing the VaD group from the healthy control group. Clinical information from patients was subsequently collected to investigate the relationships between the microbial abundance of OMVs and clinical factors. Considering the correlations among clinical risk factors, a preliminary screening of the clinical data was performed prior to the clinical risk factor association analysis. The clinical information selected for this study included age, diabetes status, COPD status, etc. RDA/CAA revealed that diabetes exerted the most prominent influence on the GM derived from OMVs (Fig. 11B) . Further analysis via MaAsLin revealed that the diabetic status of VaD patients was significantly associated with the enrichment of Aquabacterium , Blautia, Veillonella , Methylobacterium-Methylorubrum and Holdemanella (all P < 0.01) (Fig. 11C-G) . Discussion This study reveals for the first time that OMVs derived from the GM of VaD patients can migrate from the intestinal tract into the brain tissue of mice and demonstrates significant alterations in the microbial composition represented by OMVs from VaD patients. In the VaD group, the abundances of potentially pathogenic bacteria such as Pseudomonas , Acinetobacter , and Brevundimonas increased, whereas those of beneficial bacteria, including Bifidobacterium and Faecalibacterium , significantly decreased. These findings provide a basis for subsequent multiomics analysis and mechanistic investigations and establish a theoretical foundation for developing OMV-based precision delivery systems, regional VaD prevention and control strategies, and localized microbial interventions. Accumulating evidence in recent years has highlighted the significant role of GM-derived OMVs in the pathogenesis of cerebrovascular diseases, such as stroke and cerebral aneurysms. OMVs can activate immune responses and promote the release of inflammatory factors, which constitute a central mechanism underlying cerebrovascular pathology (19, 20). Moreover, by triggering inflammatory reactions in cells of the blood‒brain barrier, OMVs increase their permeability, thereby facilitating the entry of pathological substances into the brain parenchyma (21). Once within the brain, OMVs can further activate immune cells, including microglia, exacerbating neuroinflammatory responses and contributing to vascular injury (22, 23). The RNA, proteins, and lipids carried by OMVs are capable of modulating neuronal and vascular endothelial functions and may even propagate across different brain regions, amplifying the extent of damage or accelerating disease progression (24-26). As important mediators of intercellular communication, OMVs are likely to exert pleiotropic effects on the initiation and development of cerebrovascular disorders. Given that VaD is a form of cognitive impairment closely linked to cerebrovascular damage, the composition and functional characteristics of intestinal OMVs in VaD patients remain poorly investigated and merit further in-depth exploration. In the present study, we employed, for the first time, 16S rRNA V3–V4 sequencing to analyze the microbiota associated with intestinal OMVs in patients with VaD. Our results demonstrate that OMVs derived from VaD patients can translocate from the gastrointestinal tract into the brain and be widely distributed in mice. Specifically, these OMVs have been detected in multiple brain regions critically involved in cognitive function, including the frontal association cortex (FrA)/secondary motor cortex (M2)(27, 28), cingulate cortex (Cg)(29), lateral part of the accumbens shell (LAcbSh) (30), caudate putamen (CPu) (31), and dentate gyrus (DG)/Cornu Ammonis area 1 (CA1) (32, 33). Furthermore, OMVs are also present in regions with more indirect or specialized cognitive roles, such as the lateral globus pallidus (LGP)(34), triangular septal nucleus (TS) (35), and anterior olfactory nucleus (AOD/AOV) (36). The specific localization of OMVs within key nodes of cognitive circuits—including the hippocampus (DG/CA1) for memory (37), frontal cortex for executive function (38, 39), and striatal regions (CPu, AcbSh) for motivation (40, 41) and reward—strongly suggests their potential to directly disrupt the neural processes underlying cognition. These findings support the proposed mechanism whereby gut-derived OMVs may enter the circulation and cross the blood‒brain barrier, thereby contributing to neuropathological processes (42, 43). While the presence of bacterial components in the bloodstream is well documented, their cellular origin remains debated: some researchers hypothesize gastrointestinal leakage, whereas others suggest derivation from the skin or oral cavity, particularly under conditions of barrier compromise (44-46). Our results provide experimental evidence that OMVs are a potential source of such components. Overall, we propose that OMVs produced by the disturbed gut microbiota in VaD may represent a previously underappreciated mechanism contributing to disease progression via the gut‒brain axis. α-Diversity analysis revealed that the Ace, Chao, and Sobs indices were lower in the VaD-OMV group than in the control group, which is consistent with several previous studies on the VaD microbiota (17, 18). This reduction may reflect greater community similarity among samples within the VaD group. Although the Shannon index showed an increasing trend, the difference was not statistically significant, suggesting that further studies with larger sample sizes are needed to confirm this observation. This study elucidates the assembly principles and core structure of the microbial community within GM-derived OMVs from VaD patients. We identified a strong positive correlation between species prevalence and relative abundance ( R² = 0.750, P < 0.001), indicating that the assembly of the OMV microbial community adheres to the classic ecological "abundance-distribution" law and is nonrandom (47) . Further community structure analysis revealed that while microbial diversity is maintained by many rare and transient taxa, ecological function (measured by total biomass) is highly concentrated in a small number of persistent taxa (accounting for 79.24%-83.8%). These findings suggest that the function of VaD-associated OMVs is driven primarily by a small, core set of persistent species. Crucially, among the identified core keystone taxa, genera such as Acinetobacter and Pseudomonas were identified as network hubs, and these same taxa were significantly enriched in VaD-OMVs. These findings indicate that these conditionally pathogenic bacteria, which proliferate in the VaD environment, may exert a disproportionate core effect via their OMVs in gut‒brain axis communication; their dynamics could amplify detrimental impacts on the entire community and the host. We conclude that the microbial community in OMVs from VaD patients is dominated by a core keystone taxon enriched with conditionally pathogenic bacteria, providing a novel structural foundation for understanding the specific role of OMVs in the pathological mechanisms of VaD. We further analyzed the taxonomic origins of OMVs. Firmicutes and Bacteroidota constitute more than 80% of the GM community(48). The Firmicutes / Bacteroidetes (F/B) ratio is crucial for gut homeostasis, and its dysregulation is linked to various diseases (49, 50). For example, an elevated F/B ratio is associated with diabetes and obesity (51, 52), whereas a decreased ratio is observed in patients with inflammatory bowel disease and nonalcoholic fatty liver disease/steatohepatitis (NAFLD/NASH) (53, 54). Thus, the F/B ratio may predict inflammation-related changes, as Firmicutes exert anti-inflammatory effects that may alleviate IBD progression, whereas Bacteroidota may promote cytokine-driven intestinal inflammation (55). In this study, VaD patients presented a reduced abundance of Firmicutes and increased abundance of Bacteroidota, resulting in a lower F/B ratio, suggesting its potential as a biomarker for VaD activity. However, owing to the limited sample size and heterogeneous treatment responses, it remains unclear whether the F/B ratio changes stem from pharmacological intervention or disease remission. Although confounding factors were partially controlled, dietary and medication variables may still influence the results. Limitations in species-level resolution and functional profiling also warrant consideration. At the genus level, VaD patients presented increased abundances of Pseudomonas , Acinetobacter , and Brevundimonas , along with a decrease in beneficial genera such as Bifidobacterium and Faecalibacterium , compared with those in the control group. Pseudomonas enrichment has been associated with cognitive impairment, potentially via amyloid production stimulation (56), and may affect the central nervous system through gut–brain axis metabolites (57). Acinetobacter , an opportunistic pathogen, can trigger infections in immunocompromised hosts (58). These findings suggest that these bacterial groups not only are "bystanders" in the gut microenvironment but also may actively contribute to neuroinflammation and cognitive impairment in VaD. The reduction in Faecalibacterium abundance in VaD patients is notable, as its depletion is also observed in IBD, diabetes, and chronic kidney disease (59-61). Takuji et al. compared the GM among healthy individuals, those with mild cognitive impairment (MCI), and Alzheimer’s disease (AD) patients and identified Faecalibacterium as a potentially beneficial genus for preventing MCI. In an Aβ-injected mouse model, strains Fp14 and Fp360 of Faecalibacterium were shown to improve cognitive function, potentially via the mitigation of cerebral oxidative stress and the regulation of mitochondrial function (61). Moreover, this study revealed a marked decrease in the abundance of Bifidobacterium within OMVs from VaD patients compared with controls. Clinical studies indicate that Bifidobacterium supplementation improves neurological function, cognitive performance, and immune regulation in elderly stroke patients, as demonstrated by improved NIHSS and MoCA scores alongside GM alterations (62). The mechanism may involve enhanced intestinal barrier function, increased short-chain fatty acid production, and antioxidant/antineuroinflammatory effects mediated by Bifidobacterium -derived OMVs (63). Animal studies have confirmed that Bifidobacterium BGN4 promotes neuronal regeneration in aging models (64), whereas its immunomodulatory role, characterized by reduced proinflammatory cytokines (IL-6, IL-1β, TNF-α) and elevated immunoglobulins, is particularly relevant to VaD pathophysiology (62). In summary, Bifidobacterium has significant cognitive regulatory functions, suggesting that its supplementation or OMV-based delivery represents a promising microbe-targeted strategy for VaD treatment. In the diagnostic model, OMVs derived from Holdemanella had the highest feature importance (contribution value = 6.2427). As an important gut commensal bacterium, Holdemanella produces metabolites that activate intestinal L cells to secrete GLP-1, thereby improving glucose metabolism and enhancing neural signaling (65-67). We speculate that OMVs may serve as key mediators linking metabolic disorders to cerebrovascular impairment and hold potential as probiotic candidates for VaD intervention. Moreover, the significantly reduced abundance of Bifidobacterium in the OMVs of VaD patients further underscores the crucial role of protective microbiota loss in cognitive impairment. Correlation analysis revealed that the MMSE and MoCA scores were significantly associated with several OMV-related microorganisms. For example, genera such as Holdemanella and Sabacter are positively correlated with cognitive function, potentially exerting neuroprotective effects through mechanisms such as short-chain fatty acid production, maintenance of intestinal barrier integrity, and immune regulation. In contrast, conditional pathogens such as Acinetobacter , Pseudomonas , and Brevundimonas were negatively correlated with cognitive scores. Their enrichment may exacerbate systemic neuroinflammation via pathogen-associated molecular patterns (PAMPs) carried by OMVs, disrupting the blood‒brain barrier and promoting VaD progression. In summary, this study establishes the value of diabetes-related OMV microbial markers in VaD diagnosis, with Holdemanella and Bifidobacterium serving as potential positive and negative regulators, respectively, together forming key targets for microbiota-based intervention in VaD. Future research should focus on validating the specific mechanisms of these key OMVs and exploring their applications in precise diagnosis and targeted therapy. Furthermore, in functional mechanism exploration, we identified differences in microbial signatures between VaD-OMVs and Con-OMVs through microbiome data analysis and predicted their associations with amino acid metabolism pathways. Subsequent metabolomic analysis was conducted to infer how VaD-OMVs may influence VaD progression via biological processes related to amino acid metabolism. This study is positioned at the forefront of international GM research and presents the first high-throughput analysis of GM-derived OMVs in a VaD population from a tropical region of China. A series of differentially abundant bacteria were identified, and a diagnostically promising model was constructed. However, several limitations should be cautiously considered, informing future research directions. Although this study adjusted for basic variables such as age and sex, the independent effects of key comorbidities such as hypertension and diabetes on VaD-OMVs remain insufficiently evaluated. These metabolic diseases may interact with VaD through pathways such as oxidative stress and endothelial dysfunction, potentially confounding the interpretation of the results. Future studies should adopt prospective cohort designs, incorporate multivariate Cox regression models for further adjustment, and perform risk-stratified subgroup analyses to increase robustness. At the molecular level, while integrated multiomics analyses suggest potential roles of genera such as Holdemanella in VaD-OMVs, the specific mechanisms through which key metabolites (e.g., 3-hydroxyoctadecenoic acid) cross the blood‒brain barrier and act on VaD remain unclear. Future investigations employing spatial metabolomics (DESI-MSI), organoid and blood‒brain barrier chip coculture models, and CRISPRi metabolic pathway modulation will help validate the causality and targets of their neuroprotective effects. The core innovation of this study lies in the use of OMVs as biomarker carriers, providing a new perspective to overcome the "black box" limitations of traditional microbiota research. Conclusion Our findings establish gut microbiota-derived outer membrane vesicles (OMVs) as a critical conduit in the gut-brain axis of vascular dementia (VaD), linking intestinal microbial dysbiosis to central neurological pathology. This study identifies and functionally characterizes a distinct OMV-associated microbial signature in VaD patients, marked by the enrichment of conditional pathogens and the depletion of beneficial taxa. The integration of high-throughput sequencing, machine learning, and functional prediction supports a disease model centred on OMV-mediated dissemination of pro-inflammatory and metabolic effectors to the brain. A random forest model derived from this OMV signature demonstrates promising diagnostic potential, highlighting its translational value for early detection and risk stratification. With further validation, this OMV-centric framework may advance the development of precision diagnostics and microbiome-targeted therapies for VaD. Abbreviations Ace: Abundance-based Coverage Estimator AD: Alzheimer's Disease AOD: Anterior Olfactory nucleus, dorsal part AOV: Anterior Olfactory nucleus, ventral part ASV: Amplicon Sequence Variant AUC: Area Under the Curve CA1: Cornu Ammonis area 1 Cgl: Cingulate cortex, lateral part CI: Confidence Interval COG: Clusters of Orthologous Groups Con-OMVs: Control-derived Outer Membrane Vesicles COPD: Chronic Obstructive Pulmonary Disease CPHZ: Central People's Hospital of Zhanjiang CPu: Caudate Putamen DG: Dentate Gyrus F/B ratio: Firmicutes/Bacteroidetes ratio FDR: False Discovery Rate FrA: Frontal Association cortex GM: Gut Microbiota GMHI: Gut Microbiome Health Index HIS: Hachinski Ischemic Scale IBD: Inflammatory Bowel Disease KEGG: Kyoto Encyclopedia of Genes and Genomes LAcbSh: lateral part of the Accumbens Shell LDA: Linear Discriminant Analysis LEfSe: Linear Discriminant Analysis Effect Size LGP: Lateral Globus Pallidus MaAsLin: Multivariate Association with Linear Models MCI: Mild Cognitive Impairment MDI: Microbial Dysbiosis Index M2: secondary Motor cortex MMSE: Mini-Mental State Examination MoCA: Montreal Cognitive Assessment Mol: Molecular layer of the cerebellum NAFLD: Nonalcoholic Fatty Liver Disease NASH: Nonalcoholic Steatohepatitis NTA: Nanoparticle Tracking Analysis OMVs: Outer Membrane Vesicles OTU: Operational Taxonomic Unit PAMPs: Pathogen-Associated Molecular Patterns PCoA: Principal Coordinate Analysis PH: Posterior Hypothalamic area PICRUSt: Phylogenetic Investigation of Communities by Reconstruction of Unobserved States PLS-DA: Partial Least Squares-Discriminant Analysis RDA: Redundancy Analysis ROC: Receiver Operating Characteristic SPF: Specific Pathogen-Free TEM: Transmission Electron Microscopy TS: Triangular Septal nucleus VaD: Vascular Dementia VaD-OMVs: Vascular Dementia-derived Outer Membrane Vesicles Declarations Acknowledgements The authors thank the study participants for their contribution and consent to this study. Authors’ contributions XL,FRJ , WFX, LM and SCW collected the clinical specimens; WW and XL performed the experiments; XL, FRJ, WW, HZ and ZL analyzed the data, and prepared the manuscript; JWM and SCW supervised data collection. SCW conceptualized the study, acquired the funding, and reviewed and edited the manuscript. Funding The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was funded by the Doctoral Research Startup Project of Central People’s Hospital of Zhanjiang (No. 2022A10, 2022A21, 2022A22, 2022A14), the Natural Science Foundation of Guangdong Province (2022A1515010749), Weifang Municipal Health Commission Scientific Research Project (2021X091662) and Zhanjiang City Science and Technology Plan Project (2024B01238). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. Data availability statement The data supporting the conclusions of this study have been publicly stored in Figshare, and the website address is http://doi.org/10.6084/m9.figshare.31066648 Ethics statement The studies involving humans were approved by the Ethics Committee of Zhanjiang Central People’s Hospital. The studies were conducted in accordance with local legislation and institutional requirements. The participants provided written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article. The clinical component of this study was conducted after approval by the Ethics Committee of Zhanjiang Central People's Hospital and strictly adhered to the principles of the Declaration of Helsinki. The animal experiments were approved by the Animal Ethics Committee of Guangdong Medical University. Clinical Trial Number: This study received ethical approval from the CPHZ Ethics Committee (Approval No. IIT-2024046-01). Consent for publication Not applicable. Competing interests The authors have declared no conflicts of interest. Disclosure statement The authors declare no conflicts of interest that pertain to this work. References Dang C, Wang Q, Zhuang Y, Li Q, Feng L, Xiong Y, et al. Pharmacological treatments for vascular dementia: a systematic review and Bayesian network meta-analysis. Front Pharmacol. 2024;15:1451032. Fortinsky RH, Shugrue N, Robison JT, Gitlin LN. The Case for Conducting Pragmatic Dementia Care Trials in Medicaid Home and Community-Based Service Settings. 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Supplementary Files Supplementarymaterials.docx SupFigure1.jpg SupFigure2.jpg Cite Share Download PDF Status: Published Journal Publication published 21 Apr, 2026 Read the published version in BMC Microbiology → Version 1 posted Editorial decision: Revision requested 03 Mar, 2026 Reviews received at journal 03 Mar, 2026 Reviewers agreed at journal 02 Mar, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviews received at journal 21 Feb, 2026 Reviewers agreed at journal 14 Feb, 2026 Reviewers invited by journal 08 Feb, 2026 Editor invited by journal 28 Jan, 2026 Editor assigned by journal 22 Jan, 2026 Submission checks completed at journal 21 Jan, 2026 First submitted to journal 21 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8523404","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587793981,"identity":"005c2f0e-852f-476d-9055-86f2983c5ff9","order_by":0,"name":"Xin Li","email":"","orcid":"","institution":"Central People’s Hospital of Zhanjiang","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Li","suffix":""},{"id":587793982,"identity":"2e6078de-7374-4f27-b373-c5850382857f","order_by":1,"name":"Wei Wei","email":"","orcid":"","institution":"Central People’s Hospital of Zhanjiang","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wei","suffix":""},{"id":587793984,"identity":"93f1db55-7225-4059-bbb2-7a4c0ee638dc","order_by":2,"name":"Shouchao Wei","email":"","orcid":"","institution":"Central People’s Hospital of Zhanjiang","correspondingAuthor":false,"prefix":"","firstName":"Shouchao","middleName":"","lastName":"Wei","suffix":""},{"id":587793986,"identity":"b37aa958-aba3-4169-adbf-f09dd2651627","order_by":3,"name":"Wenwei Xu","email":"","orcid":"","institution":"Central People’s Hospital of Zhanjiang","correspondingAuthor":false,"prefix":"","firstName":"Wenwei","middleName":"","lastName":"Xu","suffix":""},{"id":587793987,"identity":"b1bf26d5-575a-4e5b-b649-5e6be1d5fd2d","order_by":4,"name":"Lang Mo","email":"","orcid":"","institution":"Central People’s Hospital of Zhanjiang","correspondingAuthor":false,"prefix":"","firstName":"Lang","middleName":"","lastName":"Mo","suffix":""},{"id":587793989,"identity":"4bb50adc-af47-4e5c-a332-224dbea61853","order_by":5,"name":"Junjun Wang","email":"","orcid":"","institution":"Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Junjun","middleName":"","lastName":"Wang","suffix":""},{"id":587793990,"identity":"6de704a4-4aa1-4cb8-af1c-3462924b52d5","order_by":6,"name":"He Zhu","email":"","orcid":"","institution":"Central People’s Hospital of Zhanjiang","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Zhu","suffix":""},{"id":587793992,"identity":"d20bcbe2-988d-4cfb-92eb-1b2eb60809c6","order_by":7,"name":"Zhou Liu","email":"","orcid":"","institution":"The Affiliated Hospital of Guangdong Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Liu","suffix":""},{"id":587793993,"identity":"88306dd4-5697-45eb-8c1d-bc0f90a89b8d","order_by":8,"name":"Fengri Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBAC9gYQaWDDw8/MfODAhx9EaOE5ANaSJifZzpZ4cGYP0VoYDhsbnOcxPszBRowW9t7DrysKmBMbDvN8OMzAwyDPL3aAgBaec2mWZwzYEhubeTccLrBgMJw5OwG/FnuJHDPDBgOexGZmoJYZPAwJBrcJaOGRfwPSIpHYxszz4DAPGzFaJHiMHzYYGBjzMPMwEKmFJ8eMscEgQU6Cmc0AGMgShP3Cw37G+GPDn/889ucPP/7w4YeNPL80AS1AwCaBxJHAqQwZMH8gStkoGAWjYBSMXAAAth5A6zGUMIYAAAAASUVORK5CYII=","orcid":"","institution":"Central People’s Hospital of Zhanjiang","correspondingAuthor":true,"prefix":"","firstName":"Fengri","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2026-01-05 16:23:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8523404/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8523404/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12866-026-05040-5","type":"published","date":"2026-04-21T15:59:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102621025,"identity":"9064417c-b5a1-484e-9ffc-4a36d8047afb","added_by":"auto","created_at":"2026-02-13 16:46:30","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1228600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall flowchart of this study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523404/v1/02d2c07765e46c5e5a79ba28.jpg"},{"id":102748170,"identity":"7f77115d-c7d4-4b75-b5d7-93eaa16243bf","added_by":"auto","created_at":"2026-02-16 09:06:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4118736,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIsolation and characterization of OMVs.\u003c/strong\u003e (\u003cstrong\u003eA\u003c/strong\u003e) Workflow for the extraction and purification of OMVs. (\u003cstrong\u003eB–C\u003c/strong\u003e) Representative TEM images of OMVs isolated from VaD patients (\u003cstrong\u003eB\u003c/strong\u003e) and control subjects (C). (D–E) NTA of OMVs derived from the VaD and control groups, showing the particle concentration (\u003cstrong\u003eD\u003c/strong\u003e) and size distribution by intensity (E). \u003cstrong\u003eAbbreviations\u003c/strong\u003e: OMVs, outer membrane vesicles; VaD, vascular dementia; VaD-OMVs, vascular dementia-derived outer membrane vesicles; Con-OMVs, control-derived outer membrane vesicles; TEM, transmission electron microscopy; NTA, nanoparticle tracking analysis.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523404/v1/c7186901b70472ea22e1c95a.jpg"},{"id":102747005,"identity":"6862ec34-99da-4917-8105-06d4f959ac99","added_by":"auto","created_at":"2026-02-16 09:03:31","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3729711,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegional distribution of VaD-OMVs in the mouse brain. \u003c/strong\u003e(\u003cstrong\u003eA–I\u003c/strong\u003e) Fluorescence imaging showing the presence of VaD-OMVs in distinct brain regions: (\u003cstrong\u003eA\u003c/strong\u003e) FrA; (\u003cstrong\u003eB\u003c/strong\u003e) AOD and AOV; (\u003cstrong\u003eC\u003c/strong\u003e) M2 and Cgl; (\u003cstrong\u003eD\u003c/strong\u003e) LAcbSh; (\u003cstrong\u003eE\u003c/strong\u003e) TS; (\u003cstrong\u003eF\u003c/strong\u003e) CPu and LGP; (\u003cstrong\u003eG\u003c/strong\u003e) CA1, Mol and DG; (\u003cstrong\u003eH\u003c/strong\u003e) PH.\u003cbr\u003e\n \u003cstrong\u003eAbbreviations\u003c/strong\u003e: VaD-OMVs, vascular dementia-derived outer membrane vesicles; FrA, frontal association cortex; AOD, anterior olfactory nucleus; AOV, anterior olfactory nucleus, ventral part; M2, secondary motor cortex; Cgl, cingulate cortex, lateral part; LAcbSh, lateral part of the Accumbens Shell; TS, triangular septal nucleus; LGP, lateral globus pallidus; CPu, caudate putamen; Mol, molecular layer of the cerebellum; DG, dentate gyrus; CA1, cornu Ammonis area 1; PH, posterior hypothalamic area.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523404/v1/61b1375ae5aafd798387a2b8.jpg"},{"id":102621030,"identity":"42f3ce7c-1780-43df-93d2-b3e4f4b8c369","added_by":"auto","created_at":"2026-02-13 16:46:30","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3191425,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial diversity and dysbiosis analysis of VaD-OMV-associated microbiota\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) Rank–abundance curves depicting species richness and evenness. (\u003cstrong\u003eB\u003c/strong\u003e) Rarefaction curves based on the Sobs index. (C) Core species accumulation curves. (\u003cstrong\u003eD-E\u003c/strong\u003e) α diversity indices at the OTU level: Ace (D) and Coverage (\u003cstrong\u003eE\u003c/strong\u003e). (\u003cstrong\u003eF\u003c/strong\u003e) β diversity analyses: PCoA. (\u003cstrong\u003eG\u003c/strong\u003e) Gut microbiome health index GMHI comparison between VaD-OMVs and Con-OMVs groups. (\u003cstrong\u003eH\u003c/strong\u003e) MDI comparison between VaD-OMVs and Con-OMVs groups. \u003cstrong\u003eAbbreviations\u003c/strong\u003e: VaD-OMVs, vascular dementia-derived outer membrane vesicles; Con-OMVs, control-derived outer membrane vesicles; OTU, operational taxonomic unit; GMHI, Gut Microbiome Health Index; MDI, Microbial Dysbiosis Index; PLS-DA, partial least squares-discriminant analysis.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523404/v1/67b3cee2d5ea518dbffbd710.jpg"},{"id":102621027,"identity":"402989b7-59bb-4416-be04-05b9872b524e","added_by":"auto","created_at":"2026-02-13 16:46:30","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2171152,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial community assembly: prevalence–abundance relationships and identification of keystone taxa. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Correlation between the average abundance and occurrence frequency of ASVs in the OMV-derived microbiota. (\u003cstrong\u003eB\u003c/strong\u003e) Number and relative abundance of ASVs across different occurrence frequency categories. (\u003cstrong\u003eC\u003c/strong\u003e) Keystone species analysis showing the top 10 taxa ranked by median centrality. \u003cstrong\u003eAbbreviations\u003c/strong\u003e: VaD-OMVs, vascular dementia-derived outer membrane vesicles; Con-OMVs, control-derived outer membrane vesicles; ASV, amplicon sequence variant.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523404/v1/9278e611f7763afddc0b31d6.jpg"},{"id":102621028,"identity":"6256290a-2913-4609-9474-dae7a14cb2a6","added_by":"auto","created_at":"2026-02-13 16:46:30","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1722114,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGut microbial composition of VaD-derived and control-derived OMVs across taxonomic levels.\u003c/strong\u003e Relative abundance of bacterial communities at the phylum (A), class (B), order (C), family (D), and genus (E) levels. (F) Venn diagram illustrating shared and unique genera between the two groups. Abbreviations: VaD-OMVs, vascular dementia-derived outer membrane vesicles; Con-OMVs, control-derived outer membrane vesicles.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523404/v1/383b7e09e28e535e73276fdb.jpg"},{"id":102747557,"identity":"92de3459-ae4f-4b15-a8ed-e01bf4f53974","added_by":"auto","created_at":"2026-02-16 09:04:57","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2423710,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential abundance analysis of OMV-derived GM between the VaD and control groups. \u003c/strong\u003eSignificantly altered taxa at the phylum (\u003cstrong\u003eA\u003c/strong\u003e), class (\u003cstrong\u003eB\u003c/strong\u003e), order (\u003cstrong\u003eC\u003c/strong\u003e), family (\u003cstrong\u003eD\u003c/strong\u003e), and genus (\u003cstrong\u003eE\u003c/strong\u003e) levels. \u003cstrong\u003eStatistical methods:\u003c/strong\u003e Wilcoxon rank-sum test; false discovery rate (FDR) correction; bootstrap confidence interval = 0.95. \u003cstrong\u003eAbbreviations\u003c/strong\u003e: VaD-OMVs, vascular dementia-derived outer membrane vesicles; Con-OMVs, control-derived outer membrane vesicles; GM, Gut Microbiota.\u003c/p\u003e","description":"","filename":"Figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523404/v1/94aebd7915b764d2874445db.jpg"},{"id":102962290,"identity":"f87208d3-3dca-4e18-9d8c-e172d93faf45","added_by":"auto","created_at":"2026-02-19 04:07:06","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2612384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLEfSe analysis identifying differentially abundant taxa across multiple taxonomic ranks\u003c/strong\u003e. (\u003cstrong\u003eA\u003c/strong\u003e) Cladogram generated from LDA effect size (LEfSe) showing enriched taxa in each group. (\u003cstrong\u003eB\u003c/strong\u003e) Histogram of LDA scores indicating effect sizes of significantly discriminative taxa. \u003cstrong\u003eAnalysis parameters\u003c/strong\u003e: all-against-all multigroup comparison; taxonomic levels: phylum to genus. \u003cstrong\u003eAbbreviations\u003c/strong\u003e: VaD-OMVs, vascular dementia-derived outer membrane vesicles; Con-OMVs, control-derived outer membrane vesicles; LDA, linear discriminant analysis; LEfSe, linear discriminant analysis effect size.\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523404/v1/b9e46f84ab48a0434f624a60.jpg"},{"id":102621036,"identity":"51aeef66-215d-4d85-95ac-e50ae949e6fd","added_by":"auto","created_at":"2026-02-13 16:46:31","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4427029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntegrated diagnostic model and functional predictions of the OMV microbiome in VaD. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Random forest classification plot based on microbiota composition. (\u003cstrong\u003eB\u003c/strong\u003e) The top 10 bacterial features contributing to VaD diagnosis identified by random forest regression. (\u003cstrong\u003eC\u003c/strong\u003e) Spearman correlation network of prevalent genera; node color indicates abundance, and edge color represents the correlation coefficient (red: positive, blue: negative). (\u003cstrong\u003eD\u003c/strong\u003e) ROC curve analysis of the OMV-derived microbiota at the genus level.\u003cstrong\u003e Abbreviations\u003c/strong\u003e: VaD-OMVs, vascular dementia-derived outer membrane vesicles; Con-OMVs, control-derived outer membrane vesicles; ROC, Receiver Operating Characteristic.\u003c/p\u003e","description":"","filename":"Figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523404/v1/361e02e5e8de7aa2188d9a71.jpg"},{"id":102621032,"identity":"4719c9e1-cc5c-4996-8c98-8bc162a6bb41","added_by":"auto","created_at":"2026-02-13 16:46:30","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":2411169,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional prediction of the OMV-derived microbiota in VaD patients. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Functional prediction of the OMV-derived microbiota in VaD patients was performed via PICRUSt2 in conjunction with the KEGG database. \u003cstrong\u003e(B-C\u003c/strong\u003e) Results of KEGG pathway analysis at level 2 (\u003cstrong\u003eB\u003c/strong\u003e) and level 3 (\u003cstrong\u003eC\u003c/strong\u003e). \u003cstrong\u003eAbbreviations\u003c/strong\u003e: VaD-OMVs, vascular dementia-derived outer membrane vesicles; Con-OMVs, control-derived outer membrane vesicles; COG, Clusters of Orthologous Groups.\u003c/p\u003e","description":"","filename":"Figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523404/v1/df28c6b767836a0160276eaf.jpg"},{"id":102621038,"identity":"c7c2c75e-745a-4a49-a508-e8b27989a044","added_by":"auto","created_at":"2026-02-13 16:46:31","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":3790855,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between VaD-OMV-derived microbiota and clinical parameters. \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Heatmap of Spearman correlations between bacterial abundance and clinical factors. (\u003cstrong\u003eB\u003c/strong\u003e) Redundancy analysis (RDA) ordination plot showing the relationships between microbial composition and clinical variables. (\u003cstrong\u003eC–G\u003c/strong\u003e) MaAsLin2-identified genera significantly associated with diabetes: \u003cem\u003eAquabacterium\u003c/em\u003e (\u003cstrong\u003eC\u003c/strong\u003e), \u003cem\u003eBlautia\u003c/em\u003e (\u003cstrong\u003eD\u003c/strong\u003e), \u003cem\u003eVeillonella\u003c/em\u003e (\u003cstrong\u003eE\u003c/strong\u003e), \u003cem\u003eAnaeroglobus\u003c/em\u003e (\u003cstrong\u003eF\u003c/strong\u003e), and \u003cem\u003eMethylobacterium-Methylorubrum\u003c/em\u003e (G). \u003cstrong\u003eAbbreviations\u003c/strong\u003e: VaD-OMVs, vascular dementia-derived outer membrane vesicles; Con-OMVs, control-derived outer membrane vesicles; MaAsLin, multivariate association with linear models.\u003c/p\u003e","description":"","filename":"Figure11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523404/v1/9ed09d6ca6762ec496f4484d.jpg"},{"id":107928623,"identity":"883b94eb-a414-4905-8524-0eee0dfe9a1d","added_by":"auto","created_at":"2026-04-27 16:11:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27841765,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8523404/v1/d78aaaa9-1f78-4fed-916a-c4a60dba470d.pdf"},{"id":102748269,"identity":"0f14b3b5-dc23-44da-b0e5-c0f247514c66","added_by":"auto","created_at":"2026-02-16 09:06:37","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":10130993,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8523404/v1/f3e3faf4ba9e9ed1a9e345e3.docx"},{"id":102621039,"identity":"f028d684-20b3-4366-bf0c-d399aa8e9d47","added_by":"auto","created_at":"2026-02-13 16:46:31","extension":"jpg","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":2213121,"visible":true,"origin":"","legend":"","description":"","filename":"SupFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523404/v1/fe0444315745b1189d48ac72.jpg"},{"id":102621033,"identity":"a2602bb0-02b9-4f6e-95ab-7b9584683cb1","added_by":"auto","created_at":"2026-02-13 16:46:31","extension":"jpg","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":3757877,"visible":true,"origin":"","legend":"","description":"","filename":"SupFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8523404/v1/34c88b86ab8c1c49b8a82545.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Altered Microbial Cargo in Gut Microbiota-Derived Outer Membrane Vesicles as Novel Biomarkers for Vascular Dementia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVascular dementia (VaD) is an acquired cognitive impairment syndrome resulting from cerebrovascular pathologies and is characterized by progressive cognitive decline and significant functional deterioration due to cerebral tissue damage (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Accounting for 15%\u0026ndash;30% of dementia cases worldwide, VaD is the second most common type after Alzheimer's disease (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Its prevalence in developing Asian countries, such as China, reaches 30%, markedly higher than that in Western regions (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), suggesting significant influences from environmental and lifestyle factors. Established vascular risk factors, including hypertension, hyperlipidemia, and type 2 diabetes, are strongly associated with VaD pathogenesis (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Nonetheless, the exact mechanisms remain unclear, impeding early diagnosis and effective treatment.\u003c/p\u003e \u003cp\u003eA growing body of evidence indicates that the gut microbiota (GM) can mediate central nervous system functions through multiple pathways, providing a new dimension for in-depth exploration of neurological disease mechanisms (\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs the largest symbiotic microbial ecosystem in the human body, the GM has a biomass of up to 10\u003csup\u003e14\u003c/sup\u003e (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These microorganisms perform vital physiological functions through mechanisms such as immune regulation, maintenance of intestinal barrier integrity, and participation in nutrient metabolism, resulting in their designation as the host\u0026rsquo;s \"second genome\" (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Several investigations have revealed characteristic alterations in the GM composition of VaD patients (\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Nevertheless, the specific mechanisms through which the GM influences VaD onset and progression remain to be fully elucidated.\u003c/p\u003e \u003cp\u003eGM-derived outer membrane vesicles (OMVs) are increasingly recognized as novel mediators of bacterium‒host interactions. These nanosized membrane structures (20\u0026ndash;350 nm in diameter), which are actively secreted by gram-negative bacteria, play a significant role in the pathogenesis of cerebrovascular diseases (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). OMVs can activate immune responses and promote the release of inflammatory factors, thereby disrupting blood‒brain barrier integrity and increasing permeability, which facilitates the entry of pathological substances into the brain parenchyma(\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Notably, the RNA, proteins, and lipids carried by OMVs may modulate neuronal and vascular endothelial functions, potentially amplifying the extent of damage or accelerating disease progression (\u003cspan additionalcitationids=\"CR23 CR24 CR25\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile current research has identified molecular and microbial distinctions between VaD patients and healthy controls, the multiomics features and functional roles of VaD-derived OMVs remain largely unexplored. Elucidating the composition and function of intestinal OMVs is critical for understanding VaD pathogenesis, yet this field remains underinvestigated. Here, we apply an integrated approach, including 16S rRNA sequencing, machine learning, and functional prediction, to systematically profile the OMV-associated microbiota, metabolic activity, and diagnostic utility \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Our work aims to gain insight into host‒microbe interactions in VaD and support the development of OMV-based diagnostics and microbiome-targeted therapies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection and sample collection\u003c/h2\u003e \u003cp\u003eThis study was conducted at Central People's Hospital of Zhanjiang (CPHZ) from February 2024 to July 2025. Eligible VaD patients and age-/sex-matched controls were recruited on the basis of predefined inclusion and exclusion criteria. All participants provided qualified fecal samples and completed questionnaire surveys. This study received ethical approval from the CPHZ Ethics Committee (Approval No. IIT-2024046-01). For details, please refer to the \u003cb\u003eSupplementary Materials\u003c/b\u003e. Fresh morning stool samples (\u0026ge;\u0026thinsp;200 g) were collected in sterile containers, immediately sealed in anaerobic bags, and stored at \u0026minus;\u0026thinsp;80\u0026deg;C to preserve microbial integrity.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eOMV purification and characterization\u003c/h3\u003e\n\u003cp\u003eOn the basis of the literature reports and previous research methods from our research group, we further optimized the extraction and purification procedures of OMVs, with the specific steps as follows(\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e): thawed fecal samples were homogenized in ice-cold 0.9% saline via low-frequency pulse homogenization (3\u0026ndash;5 s intervals); sequential filtration (70 \u0026micro;m nylon mesh) and differential centrifugation (4\u0026deg;C) were performed as follows: 500 \u0026times;g, 10 min (6 cycles); 1,000 \u0026times;g, 15 min (5 cycles); 3,000 \u0026times;g, 30 min (3 cycles); 5,000 \u0026times;g, 60 min (2 cycles); and the supernatants were filtered (0.45 \u0026micro;m, 0.22 \u0026micro;m, 3 cycles) and ultracentrifuged (100,000 \u0026times;g, 2 h, SW32Ti/SW41Ti rotors). OMVs (10 \u0026micro;L) were adsorbed onto ethanol-cleaned copper grids (25\u0026deg;C, RH 45%, 15 min), negatively stained with 2% phosphotungstic acid (pH 6.8, 5 min), and imaged under 120 kV acceleration (Hitachi HT7800). The size distribution of the OMVs was quantified via a Malvern NS300 (532 nm laser, sCMOS camera, NTA 3.4 software) at 25\u0026deg;C.\u003c/p\u003e \u003cp\u003e \u003cb\u003e16S rRNA sequencing\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe purified OMV samples were stored at \u0026minus;\u0026thinsp;80\u0026deg;C. The genomic DNA of the GM from the fecal samples was extracted via the E.Z.N.A.\u0026reg; Soil DNA Kit (Omega Biotek, USA). The V3-V4 region of the bacterial 16S rRNA gene was subsequently amplified via the ABI GeneAmp\u0026reg; 9700 PCR Thermal Cycler (ABI, California, USA) with the primers 338F (5'-ACTCCTACGGGAGGCAGCAG-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3'). Following the standard protocol established by Meiji Biomedical Technology Co., Ltd. (Shanghai, China), equimolar purified amplicon pools were prepared for paired-end sequencing on the Illumina MiSeq PE300 platform/NovaSeq PE250 platform (Illumina, San Diego, USA), and raw 16S rRNA gene sequencing reads were demultiplexed.\u003c/p\u003e\n\u003ch3\u003eAnalysis of 16S rRNA sequencing data\u003c/h3\u003e\n\u003cp\u003eOperational taxonomic units (OTUs) were clustered via the UPARSE algorithm (version 7.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://drive5.com/uparse/\u003c/span\u003e\u003cspan address=\"http://drive5.com/uparse/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a 97% similarity cutoff. Taxonomic classification of representative sequences from each OTU was performed via the RDP classifier (version 2.2) on the basis of the 16S rRNA database (Release 138; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.arb-silva.de\u003c/span\u003e\u003cspan address=\"http://www.arb-silva.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Rank-abundance curves were constructed by sorting the OTUs in descending order on the basis of sequence count and plotted via R language tools. Pan/core species analysis was conducted at the taxonomic level of the OTUs, with analytical visualization performed via the vegan package (version 2.4.3) in R (version 3.3.1). α-Diversity analysis, which is based on the Chao1, Sobs, Shannon, ACE, and coverage indices, was employed to assess the species richness and structural heterogeneity of the microbial communities derived from OMVs. β-Diversity analyses, including principal coordinate analysis (PCoA) and partial least squares discriminant analysis (PLS-DA), were performed. The Gut Microbiome Health Index (GMHI) was used to evaluate host health status.\u003c/p\u003e \u003cp\u003eAt the genus level, we identified the core community of VaD-derived bacteria and compared the environmental sensitivity of the OMV-associated microbial communities. This comparison included the mean relative abundance (abundance) and the detection frequency (number) of three persistence types across samples: transient, intermediate, and persistent taxa(\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). A linear regression analysis (with reported \u003cem\u003eR\u0026sup2;\u003c/em\u003e values and \u003cem\u003eP\u003c/em\u003e values) was performed to correlate the species detection frequency with the mean relative abundance across all samples. Furthermore, we employed keystone species analysis to screen the top 10 taxa ranked by the median of the structural keystone index. This approach effectively identified the keystone species within the microbial community(\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe bacterial community composition of each sample was quantified at the phylum, class, order, family, and genus levels. To visually represent unique and shared operational taxonomic units (OTUs) present across multiple samples, Venn diagrams were generated to calculate the number of species present in each sample. On the basis of parametric/nonparametric testing strategies, intergroup significance difference tests were employed to resolve structural heterogeneity in the microbiota between groups. We also utilized linear discriminant analysis effect size (LEfSe) to perform hierarchical differential analysis of microbiome data through biomarker screening across taxonomic hierarchies (phylum to species). Linear discriminant analysis (LDA) was applied to estimate the effect size of each component (species) on differential effects.\u003c/p\u003e \u003cp\u003eThis study employed random forest analysis, an ensemble learning model, to jointly interpret the multidimensional feature spaces of samples by constructing multidecision tree classifiers. The receiver operating characteristic (ROC) curve was used to reveal the trade-off between sensitivity and specificity through dynamic threshold adjustments. Single-factor correlation networks were constructed to analyze species‒species correlations, with network attributes enabling the identification of key species implicated in disease progression. PICRUSt2 was utilized as a microbiome functional prediction tool to infer the functional composition of microbial communities. The distributions of distinct bacterial taxa identified in sample populations were visualized via R software (version 3.3.1) and the mixOmics package. Analyses were performed via R (version 3.3.1), the ade4 package, and the cluster package. Data processing was conducted on the Majorbio Cloud Platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://drive5.com/uparse/\" target=\"_blank\"\u003ewww.majorbio.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.majorbio.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAnimals\u003c/h3\u003e\n\u003cp\u003eThe experimental animals used in this study were two-week-old male C57BL/6J mice (specific pathogen-free [SPF] grade, body weight 20\u0026thinsp;\u0026plusmn;\u0026thinsp;3 g) provided by Hangzhou Ziyuan Biotechnology Co., Ltd. (License No. SCXK [Zhe] 2019-0004). Adaptive housing was conducted at the SPF-level animal experimental center of Guangdong Medical University.\u003c/p\u003e\n\u003ch3\u003eAnesthesia Method\u003c/h3\u003e\n\u003cp\u003eTo ensure animals were in a state of deep analgesia and unconsciousness prior to brain tissue perfusion and collection, thereby eliminating pain and stress during surgical procedures. Experimental animals were all subjected to deep anesthesia prior to euthanasia, ensuring they remained completely unconscious throughout the critical operative steps. 1.25% tribromoethanol (Avertin) solution (M2920, Nanjing AlBi Bio-Technology Co..Ltd, China) was used. This agent is a commonly used short-acting anesthetic characterized by rapid onset, moderate duration of anesthesia, and stable anesthetic depth, making it suitable for acute surgical procedures in rodent models. M2920 is a ready-to-use sterile solution containing 1.25% (v/v) tribromoethanol (Avertin), tertiary amyl alcohol, and 0.9% physiological saline, with a final tribromoethanol concentration of 20 mg/ml. Administration was via intraperitoneal injection at a dose of 0.2 ml/10 g body weight for mice. This dosage ensures that animals enter a stable surgical plane of anesthesia within minutes, as indicated by the loss of corneal and toe-pinch reflexes.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEuthanasia/Sacrifice Method\u003c/h2\u003e \u003cp\u003eFollowing deep anesthesia (unconsciousness), transcardiac perfusion fixation was employed as the terminal procedure. This method enables both humane euthanasia and optimal preservation of tissue architecture for high-quality neurohistological analysis. Detailed Procedure: the deeply anesthetized mouse was positioned supine and secured on a surgical board; the thoracic cavity was quickly opened to expose the heart; a perfusion needle was inserted into the ascending aorta via the left ventricle, and the right atrial appendage was incised to serve as an outflow tract; approximately 20 mL of ice-cold 0.9% physiological saline was rapidly perfused first, until the liver and lungs turned pale and the effluent from the right atrial appendage ran clear, ensuring thorough removal of blood from the vasculature; this was followed by perfusion with approximately 50 mL of ice-cold 4% paraformaldehyde (PFA) in phosphate buffer, continuing until rigidity and tremors were observed in the limbs, tail, and torso of the animal, after which perfusion was stopped.\u003c/p\u003e \u003cp\u003e All animal experimental protocols and procedures were reviewed and approved by the Institutional Animal Ethics Committee of Guangdong Medical University (Ethics Approval No. GDY2403466). For details, please refer to the \u003cb\u003eSupplementary Materials\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFluorescent Labeling of OMVs\u003c/h3\u003e\n\u003cp\u003eTo clarify the distribution of VaD-OMVs in the brains of C57BL/6J mice, PKH26 fluorescent dye was used to label VaD-OMVs. OMVs were incubated with PKH26 dye at 4\u0026deg;C for 2 hours. Unbound PKH26 was removed via ultracentrifugation using an SW 60 Ti rotor at 100,000 \u0026times; g and 4\u0026deg;C for 1 hour. The pelleted material was resuspended in PBS to obtain the labeled product. Fasted C57BL/6J mice (after 24 hours of fasting) were orally administered labeled OMVs at a dose of 20 \u0026micro;g/200 \u0026micro;L via oral gavage.\u003c/p\u003e\n\u003ch3\u003eMouse Brain Tissue Harvesting and Immunofluorescence\u003c/h3\u003e\n\u003cp\u003eThe mice were anesthetized via intraperitoneal injection. After complete anesthesia, the brains were harvested and fixed. Brain tissue sections (35 \u0026micro;m thick) were prepared via a cryostat microtome via the sectioning-mounting method. The distribution of VaD-OMVs in various brain regions was observed via a 10x objective lens under an inverted laser confocal microscope (A1-SHR-LFOV, Nikon, Japan).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eClinical\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ebaseline characteristics of VaD patients and controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline demographic and clinical characteristics demonstrated no significant intergroup differences in age, sex distribution, or comorbidities (hypertension, diabetes, hyperlipidemia, valvular heart disease, or COPD; all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05; Table 1). However, VaD patients exhibited markedly impaired neurocognitive function compared with controls, as evidenced by lower MMSE (\u003cem\u003et\u003c/em\u003e = 17.454, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) and MoCA (\u003cem\u003et\u003c/em\u003e = 20.798, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) scores and higher HIS scores (\u003cem\u003et\u003c/em\u003e = -18.783, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). These findings align with the diagnostic criteria for vascular cognitive impairment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Demographic and clinical comparisons between the VaD and control groups.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5124%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9752%;\"\u003e\n \u003cp\u003eVaD Group (n=29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6364%;\"\u003e\n \u003cp\u003eControl Group (n=28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1736%;\"\u003e\n \u003cp\u003eStatistical Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.7025%;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u0026nbsp;\u003c/em\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5124%;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9752%;\"\u003e\n \u003cp\u003e68.59\u0026plusmn;10.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6364%;\"\u003e\n \u003cp\u003e67.65\u0026plusmn;8.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1736%;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e=-0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.7025%;\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18.5124%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.9752%;\"\u003e\n \u003cp\u003e13/29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6364%;\"\u003e\n \u003cp\u003e9/28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1736%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e=0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.7025%;\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.5124%;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.9752%;\"\u003e\n \u003cp\u003e18/29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6364%;\"\u003e\n \u003cp\u003e13/28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.1736%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e=1.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.7025%;\"\u003e\n \u003cp\u003e0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.5124%;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.9752%;\"\u003e\n \u003cp\u003e6/29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6364%;\"\u003e\n \u003cp\u003e4/28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.1736%;\"\u003e\n \u003cp\u003eFisher\u0026rsquo;s exact test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.7025%;\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.5124%;\"\u003e\n \u003cp\u003eHyperlipidemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.9752%;\"\u003e\n \u003cp\u003e5/29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6364%;\"\u003e\n \u003cp\u003e5/28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.1736%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e=0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.7025%;\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.5124%;\"\u003e\n \u003cp\u003eValvular heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.9752%;\"\u003e\n \u003cp\u003e8/29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6364%;\"\u003e\n \u003cp\u003e4/28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.1736%;\"\u003e\n \u003cp\u003eFisher\u0026rsquo;s exact test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.7025%;\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.5124%;\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.9752%;\"\u003e\n \u003cp\u003e0/29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6364%;\"\u003e\n \u003cp\u003e1/28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.1736%;\"\u003e\n \u003cp\u003eFisher\u0026rsquo;s exact test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.7025%;\"\u003e\n \u003cp\u003e0.491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.5124%;\"\u003e\n \u003cp\u003eMoCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.9752%;\"\u003e\n \u003cp\u003e10.59 \u0026plusmn;4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6364%;\"\u003e\n \u003cp\u003e27.46 \u0026plusmn;0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.1736%;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e=20.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.7025%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.5124%;\"\u003e\n \u003cp\u003eMMSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.9752%;\"\u003e\n \u003cp\u003e12.55 \u0026plusmn; 4.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6364%;\"\u003e\n \u003cp\u003e28.50 \u0026plusmn; 1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.1736%;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e=17.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.7025%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 18.5124%;\"\u003e\n \u003cp\u003eHIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.9752%;\"\u003e\n \u003cp\u003e11.66 \u0026plusmn;2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.6364%;\"\u003e\n \u003cp\u003e1.89 \u0026plusmn; 1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.1736%;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e=-18.783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.7025%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: VaD,\u0026nbsp;vascular dementia; Con,\u0026nbsp;control; COPD,\u0026nbsp;chronic obstructive pulmonary disease; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; HIS, Hachinski\u0026nbsp;Ischemic\u0026nbsp;Scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMorphological\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003echaracteristics\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of OMVs\u003c/strong\u003e\u003cbr\u003e This study investigated the morphological and dimensional features of OMVs isolated from the GM of individuals with VaD compared with those from control subjects. Visualization by transmission electron microscopy (TEM) revealed that both VaD- and control-derived OMVs displayed smooth-edged, well-defined, and nearly spherical nanostructures \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eFig.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2B and 2C)\u003c/strong\u003e. Particle size analysis indicated that the OMVs from both groups predominantly fell within a size range of 0\u0026ndash;200 nm. Consistent with these findings, nanoparticle tracking analysis (NTA) revealed no statistically significant differences in vesicle size distribution between the two groups \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eFig.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2D and 2E)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCerebral distribution of VaD-derived OMVs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePKH26-labeled OMVs were administered via oral gavage,\u0026nbsp;and\u0026nbsp;their biodistribution in\u0026nbsp;the\u0026nbsp;mouse brain\u0026nbsp;was evaluated via\u0026nbsp;immunofluorescence. Widespread dispersion of OMVs was observed across multiple brain regions implicated in cognitive function, including the frontal association cortex (FrA) (\u003cstrong\u003eFig. 3A\u003c/strong\u003e), the anterior olfactory nuclei (dorsal and ventral parts, AOD and AOV) (\u003cstrong\u003eFig. 3B\u003c/strong\u003e), the secondary motor cortex (M2) and cingulate cortex (Cg) (\u003cstrong\u003eFig. 3C\u003c/strong\u003e), the lateral shell of the nucleus accumbens (LAcbSh) (\u003cstrong\u003eFig. 3D\u003c/strong\u003e), the triangular septum (TS) (\u003cstrong\u003eFig. 3E\u003c/strong\u003e), the caudate putamen (CPu) and lateral globus pallidus (LGP) ( \u003cstrong\u003eFig. 3F\u003c/strong\u003e), the hippocampal CA1 region, molecular layer (Mol), and dentate gyrus (DG; \u003cstrong\u003eFig. 3G\u003c/strong\u003e), as well as the posterior hypothalamus (PH) (\u003cstrong\u003eFig. 3H\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReduced Microbial Diversity and Significant Dysbiosis in VaD-OMV-Associated Microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDiversity analysis of 57 samples revealed 4,942,454 high-quality sequences (mean length: 415 bp). Operational taxonomic unit (OTU) clustering and taxonomic alignment were performed to characterize the bacterial composition and abundance. Rank‒abundance curves revealed differences in community structure between VaD-OMVs and Con-OMVs. VaD-OMVs presented greater species abundance but lower evenness \u003cstrong\u003e(Fig. 4A)\u003c/strong\u003e. Rarefaction analysis based on\u0026nbsp;the\u0026nbsp;Sobs index indicated that sequencing saturation was achieved, with sufficient depth and biological reproducibility\u003cstrong\u003e\u0026nbsp;(Fig. 4B)\u003c/strong\u003e. Core/Pan species curves confirmed adequate sequencing depth and sample size\u003cstrong\u003e\u0026nbsp;(Fig. 4C, Supplementary\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFig.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;1A)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026alpha;-Diversity analysis revealed significantly lower bacterial richness in the VaD-OMV group than in the Con-OMV group, as indicated by reductions in the Ace, Chao, and Sobs indices (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01)\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFig.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;4D and\u003c/strong\u003e \u003cstrong\u003eSupplementary\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFig.s\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;1B-C\u003c/strong\u003e). Coverage was\u0026nbsp;greater\u0026nbsp;in VaD-OMVs (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) \u003cstrong\u003e(Fig. 4E)\u003c/strong\u003e,\u0026nbsp;whereas\u0026nbsp;the Shannon index\u0026nbsp;was not significantly different\u0026nbsp;(\u003cem\u003eP\u003c/em\u003e = 0.086) (\u003cstrong\u003eSupplementary\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFig.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;1D\u003c/strong\u003e). \u0026beta;-Diversity\u0026nbsp;analysis\u0026nbsp;revealed\u0026nbsp;clear separation between groups\u0026nbsp;via\u0026nbsp;PCoA (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) (\u003cstrong\u003eSupplementary\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFig.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;1E\u003c/strong\u003e) and PLS-DA\u003cstrong\u003e\u0026nbsp;(Fig. 4F)\u003c/strong\u003e,\u0026nbsp;which was\u0026nbsp;supported by significant intergroup dissimilarity (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) (\u003cstrong\u003eSupplementary\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFig.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;1F\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eVaD-OMVs also\u0026nbsp;presented\u0026nbsp;a significantly lower\u0026nbsp;gut microbiome health index\u0026nbsp;(GMHI) (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) \u003cstrong\u003e(Fig. 4G)\u003c/strong\u003e and a higher microbial dysbiosis index (MD index) (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01)\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(\u003cstrong\u003eFig. 4H\u003c/strong\u003e), indicating a compromised microbiota structure compared with those of the controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMicrobial Community Assembly:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePrevalence‒Abundance Relationships\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and Identification of Core Keystone Taxa\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate microbial community assembly mechanisms, we assessed the relationship between GM prevalence and mean relative abundance. A strong positive correlation was observed (\u003cem\u003eR\u003c/em\u003e\u0026sup2; = 0.750, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) \u003cstrong\u003e(Fig. 5A)\u003c/strong\u003e, indicating that widely distributed species tend to be more abundant.\u003c/p\u003e\n\u003cp\u003eMicroorganisms were classified into persistent, intermediate, and transient taxa on the basis of their prevalence. Although intermediate (46.28%\u0026ndash;52.32%) and transient taxa (16.46%\u0026ndash;16.74%) contributed more to taxonomic richness, persistent taxa constituted a smaller proportion (31.22%\u0026ndash;36.98%)\u003cstrong\u003e\u0026nbsp;(Fig. 5B, \u0026ldquo;Number\u0026rdquo;)\u003c/strong\u003e. In contrast, persistent taxa dominated in terms of relative abundance (79.24%\u0026ndash;83.8%), far exceeding intermediate (14.23%\u0026ndash;19.3%) and transient taxa (1.46%\u0026ndash;1.97%) \u003cstrong\u003e(Fig. 5B, \u0026ldquo;Abundance\u0026rdquo;)\u003c/strong\u003e, suggesting that while diversity is sustained by rare and transitional species, ecosystem function is driven primarily by a persistent core.\u003c/p\u003e\n\u003cp\u003eWe further identified keystone taxa via a structural keystone index \u003cstrong\u003e(Fig. 5C)\u003c/strong\u003e. The top 10 keystone species included \u003cem\u003eAcinetobacter\u003c/em\u003e and \u003cem\u003eEnterococcus\u003c/em\u003e, which presented the highest indices, indicating central roles in the microbial network. Other key taxa, such as \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e \u003cem\u003egnavus\u003c/em\u003e, and \u003cem\u003eDorea\u003c/em\u003e, are also likely critical for maintaining community stability and function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignificant Shifts in GM Taxonomy Across Multiple Levels in VaD-OMVs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGM composition analysis across taxonomic levels revealed significant shifts in VaD-OMV-associated GM compared\u0026nbsp;with those in\u0026nbsp;controls\u003cstrong\u003e\u0026nbsp;(Supplementary\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFig.\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;2 A-D, Fig. 6A)\u003c/strong\u003e. At the phylum level, VaD-OMVs increased \u003cem\u003eProteobacteria\u003c/em\u003e (55.73% vs. 33.18%) and \u003cem\u003eBacteroidota\u003c/em\u003e (5.97% vs. 3.41%) but decreased \u003cem\u003eFirmicutes\u003c/em\u003e (27.22% vs. 41.74%) and \u003cem\u003eActinobacteriota\u003c/em\u003e (4.21% vs. 11.26%). Similarly, genera such as \u003cem\u003ePseudomonas\u003c/em\u003e (16.34% vs. 7.01%), \u003cem\u003eAcinetobacter\u003c/em\u003e (11.26% vs. 8.54%), and \u003cem\u003eBrevundimonas\u003c/em\u003e (4.52% vs. 2.05%) were enriched in VaD-OMVs, whereas \u003cem\u003eBifidobacterium\u003c/em\u003e (2.64% vs. 8.00%), \u003cem\u003eFaecalibacterium\u003c/em\u003e (2.60% vs. 7.17%), and \u003cem\u003eSubdoligranulum\u003c/em\u003e (1.61% vs. 7.29%) were reduced. Venn analysis revealed 359 unique genera in VaD-OMVs, 396 in controls, and 576 in common \u003cstrong\u003e(Fig. 6B)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignificant enrichment of specific GMs in VaD-OMVs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential abundance analysis revealed significant taxonomic shifts between the VaD- and Con-OMV groups \u003cstrong\u003e(Supplementary\u0026nbsp;Table 1 and\u0026nbsp;Fig. 7A-E)\u003c/strong\u003e. VaD-OMVs presented increased abundances of \u003cem\u003eProteobacteria\u003c/em\u003e (55.73 \u0026plusmn; 9.83 vs. 33.18 \u0026plusmn; 18.57) and \u003cem\u003eBacteroidota\u003c/em\u003e (5.97 \u0026plusmn; 2.11 vs. 3.41 \u0026plusmn; 2.85) at the phylum level (Fig. 7A). At the class level (Fig. 7B), \u003cem\u003eGammaproteobacteria\u003c/em\u003e (37.72 \u0026plusmn; 9.20 vs. 25.15 \u0026plusmn; 16.54), \u003cem\u003eAlphaproteobacteria\u003c/em\u003e (18.01 \u0026plusmn; 3.58 vs. 8.02 \u0026plusmn; 5.37), and \u003cem\u003eBacteroidia\u003c/em\u003e (5.97 \u0026plusmn; 2.11 vs. 3.41 \u0026plusmn; 2.85) were significantly enriched. At the order level (Fig. 7C), \u003cem\u003ePseudomonadales\u003c/em\u003e (27.74 \u0026plusmn; 5.69 vs. 15.60 \u0026plusmn; 10.75), \u003cem\u003eRhizobiales\u003c/em\u003e (9.37 \u0026plusmn; 2.06 vs. 4.30 \u0026plusmn; 3.10), \u003cem\u003eBurkholderiales\u003c/em\u003e (6.02 \u0026plusmn; 1.45 vs. 4.80 \u0026plusmn; 4.20), and \u003cem\u003eCaulobacterales\u003c/em\u003e (5.65 \u0026plusmn; 1.36 vs. 2.65 \u0026plusmn;1.70) increased. At the family level (Fig. 7D), \u003cem\u003ePseudomonadaceae\u003c/em\u003e (16.34 \u0026plusmn; 3.69 vs. 7.01 \u0026plusmn; 4.88), \u003cem\u003eMoraxellaceae\u003c/em\u003e (11.40 \u0026plusmn; 4.87 vs. 8.60 \u0026plusmn; 9.25), and \u003cem\u003eCaulobacteracea\u003c/em\u003ee (5.59 \u0026plusmn; 1.33 vs. 2.63 \u0026plusmn; 1.67) were enriched. At the genus level (Fig. 7E), \u003cem\u003ePseudomonas\u003c/em\u003e (16.34 \u0026plusmn; 3.69 vs. 7.01 \u0026plusmn; 4.88), \u003cem\u003eAcinetobacter\u003c/em\u003e (11.26 \u0026plusmn; 4.87 vs. 8.54 \u0026plusmn; 9.24), and \u003cem\u003eBrevundimonas\u003c/em\u003e (4.52 \u0026plusmn; 1.19 vs. 2.05 \u0026plusmn; 1.27) were significantly enriched in VaD-OMVs (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). LEfSe analysis confirmed significant enrichment of \u003cem\u003eProteobacteria\u003c/em\u003e and related taxa (e.g., \u003cem\u003ePseudomonadales\u003c/em\u003e) in VaD-OMVs, whereas \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u003c/em\u003e were enriched in Con-OMVs (LDA \u0026gt; 3.5, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05)\u003cstrong\u003e\u0026nbsp;(Fig.s 8A, 8B)\u003c/strong\u003e, indicating their potential as group-specific biomarkers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA Random Forest Model for Differentiating VaD Based on OMV-Derived GM with Diagnostic Potential\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, a random forest algorithm was applied to construct a machine learning model for assessing the classification performance and taxonomic composition differences between the VaD-OMV and Con-OMV groups. A two-dimensional scatterplot generated from the random forest proximity matrix revealed clear spatial separation between the two groups, reflecting significant intergroup differences in microbial composition \u003cstrong\u003e(Fig. 9A)\u003c/strong\u003e. To identify\u0026nbsp;the\u0026nbsp;GM taxa derived from\u0026nbsp;the\u0026nbsp;OMVs that contribute most to the diagnosis of VaD-OMVs, the top 10 discriminant genera were selected on\u0026nbsp;the basis of the\u0026nbsp;mean decrease\u0026nbsp;in\u0026nbsp;accuracy (or Gini importance) from the\u0026nbsp;random forest\u0026nbsp;regression\u003cstrong\u003e\u0026nbsp;(Fig. 9B)\u003c/strong\u003e. At the genus level, the following taxa were ranked by their contribution values: \u003cem\u003eHoldemanella\u003c/em\u003e (6.24), \u003cem\u003enorank-f-Saccharimonadaceae\u003c/em\u003e (5.93), \u003cem\u003ePseudomonas\u003c/em\u003e (5.84), \u003cem\u003enorank-f-Eubacterium_coprostanoligenes_group\u0026nbsp;\u003c/em\u003e(5.21), \u003cem\u003eLibanicoccus\u003c/em\u003e (5.04), \u003cem\u003eBrevundimonas\u003c/em\u003e (4.90), \u003cem\u003eBacteroides\u003c/em\u003e (4.86), \u003cem\u003eSediminibacterium\u003c/em\u003e (4.82), \u003cem\u003eSolobacterium\u003c/em\u003e (4.67), and \u003cem\u003eAllorhizobium-N-P-R\u003c/em\u003e (4.65).\u003c/p\u003e\n\u003cp\u003eFor diagnostic evaluation, receiver operating characteristic (ROC) analysis revealed an area under the curve (AUC) of 0.74 (95% CI: 0.59--0.88)\u003cstrong\u003e\u0026nbsp;(Fig. 9C)\u003c/strong\u003e, indicating that the\u0026nbsp;random forest\u0026nbsp;model based on\u0026nbsp;the\u0026nbsp;GM structure can effectively differentiate VaD patients from non-VaD controls and supporting its potential clinical utility.\u003c/p\u003e\n\u003cp\u003eWe constructed a correlation network based on Spearman\u0026apos;s rank coefficients to investigate GM sample relationships through the visualization of abundance correlations across common GM genera. Network analysis revealed decreased complexity in the VaD-OMV group compared with the Con-OMV group \u003cstrong\u003e(Fig. 9D)\u003c/strong\u003e. Notably, \u003cem\u003ePseudomonas\u003c/em\u003e showed positive interactions with multiple taxa, whereas \u003cem\u003eFaecalibacterium\u003c/em\u003e appeared to exhibit inhibitory relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eprediction\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe OMV-derived microbiota\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;in VaD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, PICRUSt2 was employed to predict the functional profiles of GM-derived OMVs on the basis of 16S rRNA sequencing data. Through analysis of functional composition and abundance, we investigated the potential mechanistic contributions of OMV-associated bacterial communities to the pathogenesis of VaD. Integrated annotation with the COG database indicated that amino acid metabolism represented the predominant functional category, with its relative abundance significantly surpassing that of other metabolic modules \u003cstrong\u003e(Fig. 10A).\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;Further functional prediction via the KEGG database revealed that, at the primary functional level, the microbiota was predominantly enriched in carbon and nitrogen metabolic networks, encompassing pathways involved in carbohydrate, amino acid, and nucleotide metabolism\u003cstrong\u003e\u0026nbsp;(Fig. 10B)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;At the tertiary KEGG level, OMV-associated functions clustered into three major modules: (1) metabolic regulation, including secondary metabolite biosynthesis, amino acid biosynthesis, and purine metabolism; (2) environmental adaptation mechanisms, such as ABC transporters, two-component systems, and quorum sensing; and (3) fundamental cellular functions, exemplified by ribosome assembly and central carbon metabolic pathways\u003cstrong\u003e\u0026nbsp;(Fig. 10C)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of Microbiota-Derived OMVs Reveals Key Microbial Taxa Associated with Clinical Factors in VaD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpearman correlation heatmap analysis was used to identify which OMV-associated GM exhibited relatively strong correlations with clinical factors. The results demonstrated that the MMSE and MoCA scores were highly positively correlated with the \u003cem\u003eFamily-XIII-AD3011 group\u003c/em\u003e, \u003cem\u003ePeptococcus\u003c/em\u003e, \u003cem\u003eSellimonas\u003c/em\u003e, \u003cem\u003enorank-\u003c/em\u003eo-\u003cem\u003eClostridia\u003c/em\u003e-\u003cem\u003eUCG\u003c/em\u003e-\u003cem\u003e014\u003c/em\u003e, \u003cem\u003eComamonas\u003c/em\u003e, \u003cem\u003eDorea\u003c/em\u003e, TM7x, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003enorank\u003c/em\u003e-\u003cem\u003ef\u003c/em\u003e-\u003cem\u003eSaccharimonadaceae\u003c/em\u003e, \u003cem\u003eHoldemanella\u003c/em\u003e, and \u003cem\u003eSubdoligranulum\u003c/em\u003e. Conversely, these clinical scores were highly negatively correlated with \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003eunclassified\u003c/em\u003e-\u003cem\u003ef\u003c/em\u003e-\u003cem\u003eLachnospiraceae\u003c/em\u003e, \u003cem\u003eSediminibacterium\u003c/em\u003e, \u003cem\u003ePelomonas\u003c/em\u003e, \u003cem\u003ePhyllobacterium\u003c/em\u003e, \u003cem\u003eChryseobacterium\u003c/em\u003e, \u003cem\u003eVeillonella\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eBrevundimonas\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eAcidovorax\u003c/em\u003e, \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eAquabacterium\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eMethylobacterium\u003c/em\u003e-\u003cem\u003eMethylorubrum\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003enorank\u003c/em\u003e-\u003cem\u003ef\u003c/em\u003e-\u003cem\u003eEubacterium\u003c/em\u003e-\u003cem\u003ecoprostanoligenes\u003c/em\u003e-\u003cem\u003egroup\u003c/em\u003e, and \u003cem\u003eNovosphingobium\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e(Fig. 11A)\u003c/strong\u003e. These microorganisms may represent key species influencing VaD-related clinical factors and could thus serve as one approach for screening microbial biomarkers distinguishing the VaD group from the healthy control group.\u003c/p\u003e\n\u003cp\u003eClinical information from patients was subsequently collected to investigate the relationships between the microbial abundance of OMVs and clinical factors. Considering the correlations among clinical risk factors, a preliminary screening of the clinical data was performed prior to the clinical risk factor association analysis. The clinical information selected for this study included age, diabetes status, COPD status, etc. RDA/CAA revealed that diabetes exerted the most prominent influence on the GM derived from OMVs \u003cstrong\u003e(Fig. 11B)\u003c/strong\u003e. Further analysis via MaAsLin revealed that the diabetic status of VaD patients was significantly associated with the enrichment of \u003cem\u003eAquabacterium\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eBlautia,\u003c/em\u003e \u003cem\u003eVeillonella\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eMethylobacterium-Methylorubrum\u003c/em\u003e and \u003cem\u003eHoldemanella\u0026nbsp;\u003c/em\u003e(all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) \u003cstrong\u003e(Fig. 11C-G)\u003c/strong\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study reveals for the first time that OMVs derived from the GM of VaD patients can migrate from the intestinal tract into the brain tissue of mice and demonstrates significant alterations in the microbial composition represented by OMVs from VaD patients. In the VaD group, the abundances of potentially pathogenic bacteria such as \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, and \u003cem\u003eBrevundimonas\u0026nbsp;\u003c/em\u003eincreased, whereas those of beneficial bacteria, including \u003cem\u003eBifidobacterium\u0026nbsp;\u003c/em\u003eand \u003cem\u003eFaecalibacterium\u003c/em\u003e\u003cem\u003e,\u003c/em\u003esignificantly\u0026nbsp;decreased. These findings provide a basis for subsequent\u0026nbsp;multiomics\u0026nbsp;analysis and mechanistic investigations and establish a theoretical foundation for developing OMV-based precision delivery systems, regional VaD prevention and control strategies, and localized microbial interventions.\u003c/p\u003e\n\u003cp\u003eAccumulating evidence in recent years has highlighted the significant role of GM-derived OMVs in the pathogenesis of cerebrovascular diseases, such as stroke and cerebral aneurysms. OMVs can activate immune responses and promote the release of inflammatory factors, which constitute a central mechanism underlying cerebrovascular pathology (19, 20). Moreover, by triggering inflammatory reactions in cells of the blood‒brain barrier, OMVs increase their permeability, thereby facilitating the entry of pathological substances into the brain parenchyma (21). Once within the brain, OMVs can further activate immune cells, including microglia, exacerbating neuroinflammatory responses and contributing to vascular injury (22, 23). The RNA, proteins, and lipids carried by OMVs are capable of modulating neuronal and vascular endothelial functions and may even propagate across different brain regions, amplifying the extent of damage or accelerating disease progression (24-26). As important mediators of intercellular communication, OMVs are likely to exert pleiotropic effects on the initiation and development of cerebrovascular disorders. Given that VaD is a form of cognitive impairment closely linked to cerebrovascular damage, the composition and functional characteristics of intestinal OMVs in VaD patients remain poorly investigated and merit further in-depth exploration.\u003c/p\u003e\n\u003cp\u003eIn the present study, we employed, for the first time, 16S rRNA V3\u0026ndash;V4 sequencing to analyze the microbiota associated with intestinal OMVs in patients with VaD. Our results demonstrate that OMVs derived from VaD patients can translocate from the gastrointestinal tract into the brain and be widely distributed in mice. Specifically, these OMVs have been detected in multiple brain regions critically involved in cognitive function, including the frontal association cortex (FrA)/secondary motor cortex (M2)(27, 28), cingulate cortex (Cg)(29), lateral part of the accumbens shell (LAcbSh) (30), caudate putamen (CPu) (31), and dentate gyrus (DG)/Cornu Ammonis area 1 (CA1) (32, 33). Furthermore, OMVs are also present in regions with more indirect or specialized cognitive roles, such as the lateral globus pallidus (LGP)(34), triangular septal nucleus (TS) (35), and anterior olfactory nucleus (AOD/AOV) (36). The specific localization of OMVs within key nodes of cognitive circuits\u0026mdash;including the hippocampus (DG/CA1) for memory (37), frontal cortex for executive function (38, 39), and striatal regions (CPu, AcbSh) for motivation (40, 41) and reward\u0026mdash;strongly suggests their potential to directly disrupt the neural processes underlying cognition. These findings support the proposed mechanism whereby gut-derived OMVs may enter the circulation and cross the blood‒brain barrier, thereby contributing to neuropathological processes (42, 43). While the presence of bacterial components in the bloodstream is well documented, their cellular origin remains debated: some researchers hypothesize gastrointestinal leakage, whereas others suggest derivation from the skin or oral cavity, particularly under conditions of barrier compromise (44-46). Our results provide experimental evidence that OMVs are a potential source of such components. Overall, we propose that OMVs produced by the disturbed gut microbiota in VaD may represent a previously underappreciated mechanism contributing to disease progression via the gut‒brain axis.\u003c/p\u003e\n\u003cp\u003e\u0026alpha;-Diversity analysis revealed that the Ace, Chao, and Sobs indices were lower in the VaD-OMV group than in the control group, which is consistent with several previous studies on the VaD microbiota (17, 18). This reduction may reflect greater community similarity among samples within the VaD group. Although the Shannon index showed an increasing trend, the difference was not statistically significant, suggesting that further studies with larger sample sizes are needed to confirm this observation.\u003c/p\u003e\n\u003cp\u003eThis study elucidates the assembly principles and core structure of the microbial community within GM-derived OMVs from VaD patients. We identified a strong positive correlation between species prevalence and relative abundance (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.750, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), indicating that the assembly of the OMV microbial community adheres to the classic ecological \u0026quot;abundance-distribution\u0026quot; law and is nonrandom\u003csup\u003e(47)\u003c/sup\u003e. Further community structure analysis revealed that while microbial diversity is maintained by many rare and transient taxa, ecological function (measured by total biomass) is highly concentrated in a small number of persistent taxa (accounting for 79.24%-83.8%). These findings suggest that the function of VaD-associated OMVs is driven primarily by a small, core set of persistent species. Crucially, among the identified core keystone taxa, genera such as \u003cem\u003eAcinetobacter\u003c/em\u003e and \u003cem\u003ePseudomonas\u0026nbsp;\u003c/em\u003ewere identified as network hubs, and these same taxa were significantly enriched in VaD-OMVs.\u0026nbsp;These findings indicate\u0026nbsp;that these conditionally pathogenic bacteria, which proliferate in the VaD environment, may exert a disproportionate core effect via their OMVs in\u0026nbsp;gut‒brain\u0026nbsp;axis communication; their dynamics could amplify detrimental impacts on the entire community and the host. We conclude that the microbial community in OMVs from VaD patients is dominated by a core keystone\u0026nbsp;taxon\u0026nbsp;enriched\u0026nbsp;with\u0026nbsp;conditionally pathogenic bacteria, providing a novel structural foundation for understanding the specific role of OMVs in the pathological mechanisms of VaD.\u003c/p\u003e\n\u003cp\u003eWe further analyzed the taxonomic origins of OMVs. \u003cem\u003eFirmicutes\u0026nbsp;\u003c/em\u003eand \u003cem\u003eBacteroidota\u0026nbsp;\u003c/em\u003econstitute more than 80% of the GM community(48). The \u003cem\u003eFirmicutes\u003c/em\u003e/\u003cem\u003eBacteroidetes\u0026nbsp;\u003c/em\u003e(F/B) ratio is crucial for gut homeostasis, and its dysregulation is linked to various diseases (49, 50). For example, an elevated F/B ratio is associated with diabetes and obesity (51, 52), whereas a decreased ratio is observed in patients with inflammatory bowel disease and nonalcoholic fatty liver disease/steatohepatitis (NAFLD/NASH) (53, 54). Thus, the F/B ratio may predict inflammation-related changes, as \u003cem\u003eFirmicutes\u0026nbsp;\u003c/em\u003eexert anti-inflammatory effects that may alleviate IBD progression, whereas Bacteroidota may promote cytokine-driven intestinal inflammation (55). In this study, VaD patients presented a reduced abundance of \u003cem\u003eFirmicutes\u0026nbsp;\u003c/em\u003eand increased abundance of Bacteroidota, resulting in a lower F/B ratio, suggesting its potential as a biomarker for VaD activity. However, owing to the limited sample size and heterogeneous treatment responses, it remains unclear whether the F/B ratio changes stem from pharmacological intervention or disease remission. Although confounding factors were partially controlled, dietary and medication variables may still influence the results. Limitations in species-level resolution and functional profiling also warrant consideration.\u003c/p\u003e\n\u003cp\u003eAt the genus level, VaD patients presented increased abundances of \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, and \u003cem\u003eBrevundimonas\u003c/em\u003e, along with a decrease in beneficial genera such as \u003cem\u003eBifidobacterium\u0026nbsp;\u003c/em\u003eand \u003cem\u003eFaecalibacterium\u003c/em\u003e\u003cem\u003e,\u003c/em\u003ecompared with those in the control group. \u003cem\u003ePseudomonas\u0026nbsp;\u003c/em\u003eenrichment has been associated with cognitive impairment, potentially via amyloid production stimulation (56), and may affect the central nervous system through gut\u0026ndash;brain axis metabolites (57). \u003cem\u003eAcinetobacter\u003c/em\u003e, an opportunistic pathogen, can trigger infections in immunocompromised hosts\u0026nbsp;(58).\u0026nbsp;These findings suggest\u0026nbsp;that these bacterial groups not\u0026nbsp;only are\u0026nbsp;\u0026quot;bystanders\u0026quot; in the gut microenvironment but\u0026nbsp;also\u0026nbsp;may actively contribute to neuroinflammation and cognitive impairment in VaD.\u003c/p\u003e\n\u003cp\u003eThe reduction in \u003cem\u003eFaecalibacterium\u0026nbsp;\u003c/em\u003e\u003cem\u003eabundance\u0026nbsp;\u003c/em\u003ein VaD patients is notable, as its depletion is also observed in IBD, diabetes, and chronic kidney disease (59-61). Takuji et al. compared the GM among healthy individuals, those with mild cognitive impairment (MCI), and Alzheimer\u0026rsquo;s disease (AD) patients and identified \u003cem\u003eFaecalibacterium\u0026nbsp;\u003c/em\u003eas a potentially beneficial genus for preventing MCI. In an A\u0026beta;-injected mouse model, strains Fp14 and Fp360 of \u003cem\u003eFaecalibacterium\u003c/em\u003e were shown to improve cognitive function, potentially via the mitigation of cerebral oxidative stress and the regulation of mitochondrial function (61).\u003c/p\u003e\n\u003cp\u003eMoreover, this study revealed a marked decrease in the abundance of \u003cem\u003eBifidobacterium\u0026nbsp;\u003c/em\u003ewithin OMVs from VaD patients compared with controls. Clinical studies indicate that Bifidobacterium supplementation improves neurological function, cognitive performance, and immune regulation in elderly stroke patients, as demonstrated by improved NIHSS and MoCA scores alongside GM alterations (62). The mechanism may involve enhanced intestinal barrier function, increased short-chain fatty acid production, and antioxidant/antineuroinflammatory effects mediated by \u003cem\u003eBifidobacterium\u003c/em\u003e-derived OMVs (63). Animal studies have confirmed that \u003cem\u003eBifidobacterium BGN4\u0026nbsp;\u003c/em\u003epromotes neuronal regeneration in aging models\u0026nbsp;(64),\u0026nbsp;whereas\u0026nbsp;its immunomodulatory role, characterized by reduced\u0026nbsp;proinflammatory\u0026nbsp;cytokines (IL-6, IL-1\u0026beta;, TNF-\u0026alpha;) and elevated immunoglobulins, is particularly relevant to VaD pathophysiology\u0026nbsp;(62). In summary, Bifidobacterium\u0026nbsp;has\u0026nbsp;significant cognitive regulatory functions, suggesting\u0026nbsp;that\u0026nbsp;its supplementation or OMV-based delivery represents a promising microbe-targeted strategy for VaD treatment.\u003c/p\u003e\n\u003cp\u003eIn the diagnostic model, OMVs derived from \u003cem\u003eHoldemanella\u0026nbsp;\u003c/em\u003ehad the highest feature importance (contribution value = 6.2427). As an important gut commensal bacterium, \u003cem\u003eHoldemanella\u003c/em\u003e produces metabolites that activate intestinal L cells to secrete GLP-1, thereby improving glucose metabolism and enhancing neural signaling (65-67). We speculate that OMVs may serve as key mediators linking metabolic disorders to cerebrovascular impairment and hold potential as probiotic candidates for VaD intervention. Moreover, the significantly reduced abundance of \u003cem\u003eBifidobacterium\u003c/em\u003e in the OMVs of VaD patients further underscores the crucial role of protective microbiota loss in cognitive impairment.\u003c/p\u003e\n\u003cp\u003eCorrelation analysis revealed that the MMSE and MoCA scores were significantly associated with several OMV-related microorganisms. For example, genera such as \u003cem\u003eHoldemanella\u003c/em\u003e and \u003cem\u003eSabacter\u003c/em\u003e are positively correlated with cognitive function, potentially exerting neuroprotective effects through mechanisms such as short-chain fatty acid production, maintenance of intestinal barrier integrity, and immune regulation. In contrast, conditional pathogens such as \u003cem\u003eAcinetobacter\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, and \u003cem\u003eBrevundimonas\u0026nbsp;\u003c/em\u003ewere negatively correlated with cognitive scores. Their enrichment may exacerbate systemic neuroinflammation via pathogen-associated molecular patterns (PAMPs) carried by OMVs, disrupting the blood‒brain barrier and promoting VaD progression.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; In summary, this study establishes the value of diabetes-related OMV microbial markers in VaD diagnosis, with \u003cem\u003eHoldemanella\u0026nbsp;\u003c/em\u003eand \u003cem\u003eBifidobacterium\u0026nbsp;\u003c/em\u003eserving as potential positive and negative regulators, respectively, together forming key targets for microbiota-based intervention in VaD. Future research should focus on validating the specific mechanisms of these key OMVs and exploring their applications in precise diagnosis and targeted therapy.\u0026nbsp;Furthermore, in functional mechanism exploration, we identified differences in microbial signatures between VaD-OMVs and Con-OMVs through microbiome data analysis and predicted their associations with amino acid metabolism pathways. Subsequent metabolomic analysis was conducted to infer how VaD-OMVs may influence VaD progression via biological processes related to amino acid metabolism.\u003c/p\u003e\n\u003cp\u003eThis study is positioned at the forefront of international GM research and presents the first high-throughput analysis of GM-derived OMVs in a VaD population from a tropical region of China. A series of differentially abundant bacteria were identified, and a diagnostically promising model was constructed. However, several limitations should be cautiously considered, informing future research directions.\u003c/p\u003e\n\u003cp\u003eAlthough this study adjusted for basic variables such as age and sex, the independent effects of key comorbidities such as hypertension and diabetes on VaD-OMVs remain insufficiently evaluated. These metabolic diseases may interact with VaD through pathways such as oxidative stress and endothelial dysfunction, potentially confounding the interpretation of the results. Future studies should adopt prospective cohort designs, incorporate multivariate Cox regression models for further adjustment, and perform risk-stratified subgroup analyses to increase robustness.\u003c/p\u003e\n\u003cp\u003eAt the molecular level, while integrated multiomics analyses suggest potential roles of genera such as \u003cem\u003eHoldemanella\u003c/em\u003e in VaD-OMVs, the specific mechanisms through which key metabolites (e.g., 3-hydroxyoctadecenoic acid) cross the blood‒brain barrier and act on VaD remain unclear. Future investigations employing spatial metabolomics (DESI-MSI), organoid and blood‒brain barrier chip coculture models, and CRISPRi metabolic pathway modulation will help validate the causality and targets of their neuroprotective effects. The core innovation of this study lies in the use of OMVs as biomarker carriers, providing a new perspective to overcome the \u0026quot;black box\u0026quot; limitations of traditional microbiota research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings establish gut microbiota-derived outer membrane vesicles (OMVs) as a critical conduit in the gut-brain axis of vascular dementia (VaD), linking intestinal microbial dysbiosis to central neurological pathology. This study identifies and functionally characterizes a distinct OMV-associated microbial signature in VaD patients, marked by the enrichment of conditional pathogens and the depletion of beneficial taxa. The integration of high-throughput sequencing, machine learning, and functional prediction supports a disease model centred on OMV-mediated dissemination of pro-inflammatory and metabolic effectors to the brain. A random forest model derived from this OMV signature demonstrates promising diagnostic potential, highlighting its translational value for early detection and risk stratification. With further validation, this OMV-centric framework may advance the development of precision diagnostics and microbiome-targeted therapies for VaD.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAce: Abundance-based Coverage Estimator\u003c/p\u003e\n\u003cp\u003eAD: Alzheimer's Disease\u003c/p\u003e\n\u003cp\u003eAOD: Anterior Olfactory nucleus, dorsal part\u003c/p\u003e\n\u003cp\u003eAOV: Anterior Olfactory nucleus, ventral part\u003c/p\u003e\n\u003cp\u003eASV: Amplicon Sequence Variant\u003c/p\u003e\n\u003cp\u003eAUC: Area Under the Curve\u003c/p\u003e\n\u003cp\u003eCA1: Cornu Ammonis area 1\u003c/p\u003e\n\u003cp\u003eCgl: Cingulate cortex, lateral part\u003c/p\u003e\n\u003cp\u003eCI: Confidence Interval\u003c/p\u003e\n\u003cp\u003eCOG: Clusters of Orthologous Groups\u003c/p\u003e\n\u003cp\u003eCon-OMVs: Control-derived Outer Membrane Vesicles\u003c/p\u003e\n\u003cp\u003eCOPD: Chronic Obstructive Pulmonary Disease\u003c/p\u003e\n\u003cp\u003eCPHZ: Central People's Hospital of Zhanjiang\u003c/p\u003e\n\u003cp\u003eCPu: Caudate Putamen\u003c/p\u003e\n\u003cp\u003eDG: Dentate Gyrus\u003c/p\u003e\n\u003cp\u003eF/B ratio: Firmicutes/Bacteroidetes ratio\u003c/p\u003e\n\u003cp\u003eFDR: False Discovery Rate\u003c/p\u003e\n\u003cp\u003eFrA: Frontal Association cortex\u003c/p\u003e\n\u003cp\u003eGM: Gut Microbiota\u003c/p\u003e\n\u003cp\u003eGMHI: Gut Microbiome Health Index\u003c/p\u003e\n\u003cp\u003eHIS: Hachinski Ischemic Scale\u003c/p\u003e\n\u003cp\u003eIBD: Inflammatory Bowel Disease\u003c/p\u003e\n\u003cp\u003eKEGG: Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eLAcbSh: lateral part of the Accumbens Shell\u003c/p\u003e\n\u003cp\u003eLDA: Linear Discriminant Analysis\u003c/p\u003e\n\u003cp\u003eLEfSe: Linear Discriminant Analysis Effect Size\u003c/p\u003e\n\u003cp\u003eLGP: Lateral Globus Pallidus\u003c/p\u003e\n\u003cp\u003eMaAsLin: Multivariate Association with Linear Models\u003c/p\u003e\n\u003cp\u003eMCI: Mild Cognitive Impairment\u003c/p\u003e\n\u003cp\u003eMDI: Microbial Dysbiosis Index\u003c/p\u003e\n\u003cp\u003eM2: secondary Motor cortex\u003c/p\u003e\n\u003cp\u003eMMSE: Mini-Mental State Examination\u003c/p\u003e\n\u003cp\u003eMoCA: Montreal Cognitive Assessment\u003c/p\u003e\n\u003cp\u003eMol: Molecular layer of the cerebellum\u003c/p\u003e\n\u003cp\u003eNAFLD: Nonalcoholic Fatty Liver Disease\u003c/p\u003e\n\u003cp\u003eNASH: Nonalcoholic Steatohepatitis\u003c/p\u003e\n\u003cp\u003eNTA: Nanoparticle Tracking Analysis\u003c/p\u003e\n\u003cp\u003eOMVs: Outer Membrane Vesicles\u003c/p\u003e\n\u003cp\u003eOTU: Operational Taxonomic Unit\u003c/p\u003e\n\u003cp\u003ePAMPs: Pathogen-Associated Molecular Patterns\u003c/p\u003e\n\u003cp\u003ePCoA: Principal Coordinate Analysis\u003c/p\u003e\n\u003cp\u003ePH: Posterior Hypothalamic area\u003c/p\u003e\n\u003cp\u003ePICRUSt: Phylogenetic Investigation of Communities by Reconstruction of Unobserved States\u003c/p\u003e\n\u003cp\u003ePLS-DA: Partial Least Squares-Discriminant Analysis\u003c/p\u003e\n\u003cp\u003eRDA: Redundancy Analysis\u003c/p\u003e\n\u003cp\u003eROC: Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003eSPF: Specific Pathogen-Free\u003c/p\u003e\n\u003cp\u003eTEM: Transmission Electron Microscopy\u003c/p\u003e\n\u003cp\u003eTS: Triangular Septal nucleus\u003c/p\u003e\n\u003cp\u003eVaD: Vascular Dementia\u003c/p\u003e\n\u003cp\u003eVaD-OMVs: Vascular Dementia-derived Outer Membrane Vesicles\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the study participants for their contribution and consent to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXL,FRJ , WFX, LM and SCW collected the clinical specimens; WW and XL performed the experiments; XL, FRJ, WW, HZ and ZL analyzed the data, and prepared the manuscript; JWM and SCW supervised data collection. SCW conceptualized the study, acquired the funding, and reviewed and edited the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study was funded by the Doctoral Research Startup Project of Central People’s Hospital of Zhanjiang (No. 2022A10, 2022A21, 2022A22, 2022A14), the Natural Science Foundation of Guangdong Province (2022A1515010749), Weifang Municipal Health Commission Scientific Research Project (2021X091662) and Zhanjiang City Science and Technology Plan Project (2024B01238). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the conclusions of this study have been publicly stored in Figshare, and the website address is http://doi.org/10.6084/m9.figshare.31066648\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving humans were approved by the Ethics Committee of Zhanjiang Central People’s Hospital. The studies were conducted in accordance with local legislation and institutional requirements. The participants provided written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.\u003c/p\u003e\n\n\u003cp\u003eThe clinical component of this study was conducted after approval by the Ethics Committee of Zhanjiang Central People's Hospital and strictly adhered to the principles of the Declaration of Helsinki. The animal experiments were approved by the Animal Ethics Committee of Guangdong Medical University. Clinical Trial Number: This study received ethical approval from the CPHZ Ethics Committee (Approval No. IIT-2024046-01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have declared no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest that pertain to this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDang C, Wang Q, Zhuang Y, Li Q, Feng L, Xiong Y, et al. Pharmacological treatments for vascular dementia: a systematic review and Bayesian network meta-analysis. 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Nat Ecol Evol. 2024;8(1):22\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Vascular dementia, Outer membrane vesicles, Gut microbiota, High-throughput sequencing, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-8523404/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8523404/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aims to analyze the composition, diversity, and metabolic functions of gut microbiota (GM)-derived outer membrane vesicles (OMVs) in patients with vascular dementia (VaD), to identify potential biomarkers for VaD diagnosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGM-derived OMVs were isolated from 29 VaD patients and 28 matched controls via ultracentrifugation and characterized using transmission electron microscopy and nanoparticle tracking analysis. PKH26-labeled OMVs were used for in vivo tracking in mouse brains. Microbial composition was profiled by 16S rRNA sequencing, combined with diversity analysis and machine learning.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eVaD-OMVs were widely distributed in multiple cognitive function-related regions of mouse brains. The VaD group showed a decreased Chao1 index and increased coverage. β-diversity (PCoA/PLS-DA) revealed significant structural differences. Conditional pathogens (e.g., \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e) were enriched, while beneficial bacteria (e.g., \u003cem\u003eBifidobacterium\u003c/em\u003e) were reduced. Correlation analysis indicated promoting effects of Pseudomonadaceae and inhibitory effects of Faecalibacterium. Metabolic pathways including amino acid, carbohydrate, and nucleotide metabolism were enriched. A random forest model achieved an AUC of 0.74 (95% CI: 0.59\u0026ndash;0.88) in classifying VaD.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eVaD is associated with distinct OMV microbial and functional profiles. OMV-based biomarkers show potential for VaD diagnosis.\u003c/p\u003e","manuscriptTitle":"Altered Microbial Cargo in Gut Microbiota-Derived Outer Membrane Vesicles as Novel Biomarkers for Vascular Dementia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 16:46:24","doi":"10.21203/rs.3.rs-8523404/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-04T04:40:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T05:33:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123436944725578059126277280383115803693","date":"2026-03-03T04:09:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146835827495841959827755306445479133272","date":"2026-02-26T15:19:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-21T13:50:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332146111051888631857761222338692433438","date":"2026-02-14T06:13:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-08T10:47:04+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-28T06:56:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-22T06:39:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-21T15:28:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2026-01-21T15:19:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"60f24f70-e4d4-48ff-aebe-cfb25fb803a3","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:09:07+00:00","versionOfRecord":{"articleIdentity":"rs-8523404","link":"https://doi.org/10.1186/s12866-026-05040-5","journal":{"identity":"bmc-microbiology","isVorOnly":false,"title":"BMC Microbiology"},"publishedOn":"2026-04-21 15:59:12","publishedOnDateReadable":"April 21st, 2026"},"versionCreatedAt":"2026-02-13 16:46:24","video":"","vorDoi":"10.1186/s12866-026-05040-5","vorDoiUrl":"https://doi.org/10.1186/s12866-026-05040-5","workflowStages":[]},"version":"v1","identity":"rs-8523404","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8523404","identity":"rs-8523404","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}