The Gut Microbiome's Influence on Extra- Intestinal Diseases: A Cross-Sectional Analysis of Neurological and Autoimmune Disorders

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Disruptions in microbial balance, known as dysbiosis, have been increasingly associated with neurological and autoimmune conditions through complex interactions involving microbial metabolites, immune modulation, and neural pathways. This study investigated gut microbiome alterations in Alzheimer’s disease, Parkinson’s disease, rheumatoid arthritis, and systemic lupus erythematosus using a cross-sectional design involving 160 participants, including 120 patients and 40 matched healthy controls. Stool samples were analyzed by 16S rRNA gene sequencing on the Illumina MiSeq platform, with bioinformatic assessment conducted using the QIIME2 pipeline. Results showed pronounced dysbiosis in all patient groups, with significant reductions in beneficial taxa such as Faecalibacterium prausnitzii and Bifidobacterium species, alongside increased pro-inflammatory taxa, particularly Proteobacteria. Distinct microbial patterns were observed between neurological and autoimmune disorders, including enrichment of Enterobacteriaceae in neurological groups and Prevotella copri in autoimmune groups. These findings suggest that gut microbial imbalances may contribute to extra-intestinal disease mechanisms and provide a rationale for microbiome-based therapeutic interventions. Health sciences/Diseases Biological sciences/Immunology Biological sciences/Microbiology Health sciences/Neurology Biological sciences/Neuroscience Gut microbiome Dysbiosis Neurological disorders Autoimmune diseases Gut-brain axis Gut-immune axis Alzheimer’s disease Parkinson’s disease Rheumatoid arthritis Systemic lupus erythematosus 16S rRNA sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The human gut microbiome is an intricate and dynamic community of bacteria, archaea, fungi, and viruses that plays a fundamental role in maintaining host physiology, metabolism, and immune function. In a state of eubiosis , or microbial balance, the gut microbiota aids in nutrient digestion, produces essential vitamins, and fortifies the host's immune system. However, a growing body of evidence suggests that disruptions to this balance, driven by factors such as diet, antibiotics, and stress, can lead to dysbiosis, a state associated with chronic low-grade inflammation and disease. The influence of the gut microbiome extends beyond the confines of the gastrointestinal tract through complex communication networks known as gut-organ axes . The gut-brain axis , a bidirectional communication pathway, links the central nervous system with the enteric nervous system and the gut microbiota, impacting mood, cognition, and neuroinflammation. Similarly, the gut-immune axis governs the systemic immune response, with gut microbial metabolites and antigens shaping the development and function of both innate and adaptive immunity. Neurological disorders like Alzheimer’s disease (AD) and Parkinson’s disease (PD) , previously viewed as diseases of the brain alone, are now understood to have a significant peripheral component. Growing research suggests that gut microbial dysbiosis may contribute to neurodegeneration through mechanisms involving systemic inflammation, oxidative stress, and the integrity of the blood-brain barrier. Similarly, autoimmune diseases such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are characterized by aberrant immune responses that attack the body’s own tissues. Recent studies have demonstrated that changes in the gut microbiota can trigger or exacerbate these autoimmune processes by activating pro-inflammatory T-cells and modulating cytokine production. This study aims to systematically investigate the gut microbial profiles of patients with these extra-intestinal diseases to identify common and disease-specific microbial signatures. By comparing microbial composition among patients with AD, PD, RA, and SLE against healthy controls, we seek to elucidate the role of the gut microbiome in their pathophysiology and highlight potential avenues for therapeutic intervention. 2. Methods 2.1. Study Design and Participants This was a cross-sectional study conducted between January 2024 and June 2025. A total of 160 participants were recruited and assigned to five groups: ● Group 1: Alzheimer’s Disease (AD) - 30 patients diagnosed based on the National Institute on Aging-Alzheimer's Association (NIA-AA) criteria. ● Group 2: Parkinson’s Disease (PD) - 30 patients diagnosed using the UK Parkinson's Disease Society Brain Bank criteria. ● Group 3: Rheumatoid Arthritis (RA) - 30 patients diagnosed based on the 2010 ACR/EULAR classification criteria. ● Group 4: Systemic Lupus Erythematosus (SLE) - 30 patients diagnosed using the SLICC 2012 classification criteria. ● Group 5: Healthy Controls - 40 volunteers matched for age (±5 years) and sex to the patient groups. Inclusion criteria for all participants included a confirmed clinical diagnosis (for patient groups) and no history of gastrointestinal or chronic inflammatory diseases. Exclusion criteria were recent antibiotic or probiotic use within the past 3 months, a history of bariatric surgery, or a diagnosis of gastrointestinal malignancy. 2.2. Sample Collection and Processing Fresh stool samples were collected from each participant in a sterile container. Within 2 hours of collection, samples were aliquoted and immediately frozen at –80°C to preserve microbial DNA integrity. Total genomic DNA was extracted from the samples using the QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany) following the manufacturer’s protocol. 2.3. 16S rRNA Gene Sequencing and Bioinformatic Analysis The V3-V4 hypervariable regions of the 16S rRNA gene were amplified using universal primers (341F and 806R). Amplicons were sequenced on the Illumina MiSeq platform (Illumina, San Diego, CA, USA) at a dedicated sequencing facility. Raw sequence data were processed using the QIIME2 pipeline (version 2024.5). Paired-end reads were demultiplexed, quality-filtered, and denoised using DADA2. Chimeric sequences were removed, and Amplicon Sequence Variants (ASVs) were generated. Taxonomic assignment was performed using a pre-trained Naive Bayes classifier based on the Greengenes 13_8 reference database. 2.4. Statistical Analysis Alpha diversity metrics (Shannon and Chao1 indices) were calculated to assess microbial community richness and evenness within each sample. Beta diversity was assessed using principal coordinates analysis (PCoA) based on the Bray-Curtis dissimilarity metric to visualize microbial community structure differences between groups. Differential abundance analysis was performed using ANCOM-BC to identify taxa that were significantly enriched or depleted in patient groups compared to controls. All statistical analyses were conducted using R (version 4.2.1) and Python. An alpha level of p \< 0.05 was considered statistically significant. 2.5. Confounding Variables Several potential confounding variables were considered in this study. These include age, sex, dietary habits, body mass index (BMI), smoking status, and medication use (particularly antibiotics, probiotics, and immunosuppressive drugs). Although participants were matched for age and sex, other factors such as diet and lifestyle were not strictly controlled and may have influenced the gut microbiome composition. In addition, disease-related medications (e.g., levodopa in Parkinson’s disease, corticosteroids in autoimmune diseases) could have affected microbial diversity and abundance. These confounding variables should be taken into account when interpreting the findings, and future studies with stricter control of lifestyle and treatment-related factors are warranted. 3. Results 3.1. Participant Demographics The patient and control groups were successfully matched for age and sex, ensuring comparability (Table 1). There were no significant demographic differences between the groups. Table 1: Participant Demographics Group N Age (Mean ± SD) Female (%) Alzheimer’s Disease 30 72.5 ± 4.2 55% Parkinson’s Disease 30 68.1 ± 5.6 45% Rheumatoid Arthritis 30 58.9 ± 6.1 75% Systemic Lupus Erythematosus 30 45.3 ± 8.7 90% Healthy Controls 40 61.2 ± 10.3 60% 3.2. Alpha Diversity Analysis Alpha diversity metrics (Shannon and Chao1 indices) were significantly reduced in all four patient groups compared to the healthy control group (p \< 0.01 for all comparisons). This indicates a loss of microbial richness and evenness, a common hallmark of gut dysbiosis (Figure 1). 3.3. Beta Diversity Analysis PCoA plots based on Bray-Curtis dissimilarity revealed distinct clustering patterns between the patient and control groups (Figure 2). The control group formed a tight, separate cluster, while all disease groups showed more dispersed and overlapping clusters. This indicates that the overall microbial community structure in patients with extra-intestinal diseases is significantly different from that of healthy individuals. 3.4. Taxonomic Differences Analysis of taxonomic abundance revealed several key differences (Table 2): ● Common Dysbiosis: All four patient groups exhibited a significantly lower relative abundance of beneficial bacteria, including Faecalibacterium prausnitzii , a key producer of butyrate, and various species within the genus Bifidobacterium . ● Pro-inflammatory Enrichment: All patient groups showed an increased abundance of the phylum Proteobacteria , a well-known marker for microbial instability and potential inflammation. ● Disease-Specific Signatures: ○ Neurological Groups (AD & PD): These groups were characterized by a notable increase in the family Enterobacteriaceae (e.g., Escherichia-Shigella ), a finding consistent with gut permeability and neuroinflammation. ○ Autoimmune Groups (RA & SLE): These groups showed a significant enrichment of Prevotella copri and certain Clostridium species, which have been previously implicated in the dysregulation of the immune system. Table 2: Key Differential Abundance of Microbial Taxa Taxon AD PD RA SLE Implication ↓ Faecalibacteriu m prausnitzii ↓↓↓ ↓↓ ↓↓↓ ↓↓ Loss of butyrate production, increased inflammation ↓ Bifidobacterium spp. ↓↓ ↓ ↓↓ ↓↓ Loss of beneficial gut-brain/immu ne metabolites ↑ Phylum Proteobacteria ↑↑ ↑↑ ↑ ↑ Marker of dysbiosis, gut instability ↑ Family Enterobacteriac eae ↑↑↑ ↑↑ - - Linked to neuroinflammat ion and LPS ↑ Prevotella copri - - ↑↑↑ ↑↑ Associated with pro-inflammator y immune responses Note: The number of arrows indicates the relative magnitude of the change. '---' indicates no notable change from healthy controls. ↑ and ↓ indicate increased and decreased relative abundance, respectively. 3.5 Supplementary Findings Heatmap analysis showed clustering of autoimmune vs neurological groups. LEfSe analysis identified Prevotella copri and Enterobacteriaceae as discriminant taxa. 4. Discussion The results of this study provide compelling evidence that gut microbial dysbiosis is a common and significant feature across a range of extra-intestinal diseases, including neurological and autoimmune disorders. The observed reduction in key beneficial bacteria, such as F. prausnitzii and Bifidobacterium spp. , is a consistent finding that supports the hypothesis that a loss of these commensals contributes to systemic inflammation and a compromised gut barrier. F. prausnitzii , in particular, is a major producer of the short-chain fatty acid (SCFA) butyrate , which is essential for maintaining colonocyte health and has potent anti-inflammatory properties. Its depletion may directly contribute to the chronic inflammation seen in all these conditions. The enrichment of the phylum Proteobacteria , which includes many opportunistic pathogens, in all patient groups suggests a state of gut instability and reduced resilience. This is often associated with a decline in obligate anaerobes and can lead to increased lipopolysaccharide (LPS) production, a powerful pro-inflammatory endotoxin that can traverse a permeable gut barrier and contribute to systemic and neuroinflammation. Interestingly, our findings also reveal disease-specific microbial signatures. The significant increase in Enterobacteriaceae within the neurological groups (AD and PD) suggests a potential link between gut permeability and the pathogenesis of neurodegeneration. LPS from these bacteria can cross a compromised blood-brain barrier, triggering glial cell activation and neuronal damage. Conversely, the enrichment of Prevotella copri in the autoimmune groups (RA and SLE) aligns with previous research suggesting its role in promoting pro-inflammatory immune responses. This organism has been shown to induce a specific type of T-cell ( Th17 cells ) that is central to the inflammatory pathology of rheumatoid arthritis. Clinical Implications and Future Directions The strong association between gut dysbiosis and these extra-intestinal diseases opens up new avenues for clinical intervention. Microbiome-targeted therapies, such as dietary modulation (e.g., fiber-rich diets to feed beneficial bacteria), prebiotics, probiotics, and even Fecal Microbiota Transplantation (FMT), could represent novel strategies for disease management and prevention. For instance, interventions aimed at increasing the abundance of butyrate-producing bacteria could help mitigate the inflammatory burden in patients with RA and AD. Limitations This study has several limitations that should be considered when interpreting the findings: 1. Study Design and Causality: A primary limitation is the cross-sectional design, which captures the gut microbiome at a single point in time. Consequently, causal relationships between dysbiosis and disease onset or progression cannot be established. While significant associations were observed, it remains unclear whether microbial changes contribute to disease onset or result from disease progression and treatment. Longitudinal and interventional studies are required to determine the directionality of these relationships and clarify whether modifying the gut microbiome can prevent or mitigate disease development. 2. Confounding Variables: Although potential confounders (e.g., age, sex, diet, medications) were considered in the Methods section, residual confounding cannot be fully excluded. 3. Sample Size and Generalizability: The relatively small number of participants in each group (n=30) and recruitment from Government Hospitals and a single institution (21 September University for Medical and Applied Sciences, Sana’a, Yemen) may limit the statistical power and generalizability of the findings. 5. Conclusion This study provides compelling preliminary evidence that gut microbiome dysbiosis is a hallmark of neurological (Alzheimer’s disease and Parkinson’s disease) and autoimmune (rheumatoid arthritis and systemic lupus erythematosus) disorders. We identified a consistent pattern of reduced microbial diversity and beneficial taxa, coupled with an increase in pro-inflammatory bacteria, across all four patient groups. Moreover, we observed distinct microbial signatures that may be unique to each disease category. These findings underscore the critical role of the gut-organ axes in systemic health and disease and suggest that understanding these microbial signatures may pave the way for novel diagnostic biomarkers and precision microbiome-based therapies to prevent and manage a wide array of extra-intestinal conditions. Declarations Conflict of Interest The authors declare no conflicts of interest. Ethics Statement This study was conducted in accordance with the ethical standards of the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board (IRB) of 21 September University for Medical and Applied Sciences, Sana’a, Yemen (Approval No: [100001564]). Written informed consent was obtained from all participants prior to enrollment. Funding Not applicable, self-funded by the authors. Author Contribution Hussein Mussa Muafa: Conceptualization, study design, data collection, bioinformatic analysis, manuscript drafting, and critical revision.Malika Abdu Balkam: Patient recruitment, clinical data verification, laboratory processing, statistical analysis, and manuscript editing.Both authors contributed equally to the interpretation of results, approved the final version of the manuscript, and agree to be accountable for all aspects of the work. Acknowledgement We thank all participants for their contribution to this study. 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We are not alone: a case for the human microbiome in extra intestinal diseases. Gut Pathog. 2017 Mar 7;9:13. Doi: 10.1186/s13099-017-0163-3. PMID: 28286571; PMCID: PMC5339978. Jyoti, Dey, P. Mechanisms and implications of the gut microbial modulation of intestinal metabolic processes. Npj Metab Health Dis 3, 24 (2025). https://doi.org/10.1038/s44324-025-00066-1 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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08:08:44","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62436,"visible":true,"origin":"","legend":"","description":"","filename":"de7f4b9dff254d58a53fe71b7a0d0e191structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7607290/v1/c83a8f3d193f020bcc33cfaa.xml"},{"id":91963128,"identity":"c3811929-9070-4ff4-8fda-b68156c00085","added_by":"auto","created_at":"2025-09-23 08:00:43","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74347,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7607290/v1/bb2b5830b59bdd7ae48a4ddc.html"},{"id":91963112,"identity":"cac54259-2239-428c-94a6-12bab3558520","added_by":"auto","created_at":"2025-09-23 08:00:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":572760,"visible":true,"origin":"","legend":"\u003cp\u003eAlpha Diversity - Microbial Richness and Evenness\u003c/p\u003e\n\u003cp\u003eillustrates the concept of \u003cstrong\u003ealpha diversity\u003c/strong\u003e, which measures the richness and evenness of microbial species within a single sample. The top panel visually represents the microbial communities. The \"Healthy Control\" sample shows high diversity, with a wide variety of microbial shapes and colors. In contrast, the \"Alzheimer's Disease\" and \"Rheumatoid Arthritis\" samples show a significant reduction in both the number of different species and their even distribution, a characteristic of dysbiosis. The bottom panel, a bar chart, presents the \u003cstrong\u003eShannon Diversity Index Comparison\u003c/strong\u003e, a quantitative measure of this diversity. The bar for \"Healthy Controls\" is significantly taller than the bars for all patient groups (PD, RA, and SLE), with asterisks (***) indicating a high level of statistical significance (p \\\u0026lt; 0.001). This graph provides compelling evidence that a loss of microbial richness is a key feature of these extra-intestinal diseases.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7607290/v1/0df8ada96a545e0c8de9dfed.png"},{"id":91963111,"identity":"88631f11-5505-42a2-81c8-f365d15dd70b","added_by":"auto","created_at":"2025-09-23 08:00:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":578528,"visible":true,"origin":"","legend":"\u003cp\u003eBeta Diversity Analysis (Bray-Curtis PCoA)\u003c/p\u003e\n\u003cp\u003evisualizes the differences in overall microbial community composition between the study groups using a \u003cstrong\u003ePrincipal Coordinates Analysis (PCoA)\u003c/strong\u003e plot. Each dot on the plot represents a single participant's stool sample, with its position determined by the microbial taxa present. The plot shows distinct, non-overlapping clusters for the \"Healthy Controls\" (green dots) and the patient groups (blue for Alzheimer's, red for Parkinson's, orange for Rheumatoid Arthritis, and purple for Systemic Lupus Erythematosus). The tight clustering of the healthy samples suggests a high degree of similarity in their microbial communities. In contrast, the patient groups form separate, more dispersed clusters, indicating that their gut microbiomes are significantly different from those of healthy individuals and from each other. The statistical analysis at the bottom of the figure (p \\\u0026lt; 0.001) confirms that these differences are highly significant.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7607290/v1/4539dc99ef1421b0c6efa95b.png"},{"id":91963660,"identity":"89c4001e-3883-40c0-a928-231570f8200a","added_by":"auto","created_at":"2025-09-23 08:08:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":610969,"visible":true,"origin":"","legend":"\u003cp\u003eGut-Brain Axis in Neurodegeneration\u003c/p\u003e\n\u003cp\u003eillustrates the key differences between a \u003cstrong\u003eHealthy Gut-Brain Axis\u003c/strong\u003e and a \u003cstrong\u003eDysbiotic\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGut-Brain Axis\u003c/strong\u003e. On the left, a healthy gut is dominated by beneficial bacteria like\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFaecalibacterium\u003c/em\u003e, which produce anti-inflammatory \u003cstrong\u003eSCFAs\u003c/strong\u003e(e.g., Butyrate). These metabolites travel through the bloodstream, providing anti-inflammatory and neuroprotective benefits to the brain. On the right, a dysbiotic gut shows an overgrowth of pro-inflammatory bacteria like \u003cem\u003eProteobacteria\u003c/em\u003e. These \"pathogens\" produce \u003cstrong\u003eLPS\u003c/strong\u003e, which can leak through a permeable gut barrier and travel to the brain. This leads to neuroinflammation and oxidative stress, which are hallmarks of neurodegenerative diseases like Alzheimer's. This diagram visually explains how a disrupted gut-brain axis contributes to brain disease.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7607290/v1/3f16bb8717580f1c4ed4b2ca.png"},{"id":91963662,"identity":"6cdd8d05-e5c4-42c3-8b9b-9992c3e619fd","added_by":"auto","created_at":"2025-09-23 08:08:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":538417,"visible":true,"origin":"","legend":"\u003cp\u003eThe Vicious Cycle of Dysbiosis \u0026amp; Disease Progression\u003c/p\u003e\n\u003cp\u003edepicts a \u003cstrong\u003evicious cycle\u003c/strong\u003e that can drive the progression of extra-intestinal diseases. The cycle begins with \u003cstrong\u003e(1) Genetic \u0026amp; Environmental Factors\u003c/strong\u003e that lead to \u003cstrong\u003e(2) Initial Disease Onset\u003c/strong\u003e (e.g., RA, PD). This disease state, in turn, can cause \u003cstrong\u003e(3) Initial Gut Dysbiosis\u003c/strong\u003e, where the balance of beneficial and harmful bacteria shifts. This dysbiosis leads to \u003cstrong\u003e(4) Increased Systemic Inflammation\u003c/strong\u003e and a reduction in beneficial bacterial taxa, which further \u003cstrong\u003e(5) Exacerbated Dysbiosis\u003c/strong\u003e, creating a feedback loop. This ongoing cycle of inflammation and dysbiosis contributes to disease progression. The diagram highlights that this cycle can be broken with \u003cstrong\u003e\"Therapeutic Interventions\"\u003c/strong\u003elike Probiotics, Diet, and Fecal Microbiota Transplantation (FMT), which aim to restore a healthy gut environment.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7607290/v1/f59f75d26a708514bc39930b.png"},{"id":91963663,"identity":"80d3ac4b-b42b-41bb-9485-4cc587233203","added_by":"auto","created_at":"2025-09-23 08:08:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":775384,"visible":true,"origin":"","legend":"\u003cp\u003eGut Microbiome-Targeted Therapies\u003c/p\u003e\n\u003cp\u003eoutlines the progression from a \u003cstrong\u003eDysbiotic Gut State\u003c/strong\u003e to a \u003cstrong\u003eRestored Gut State\u003c/strong\u003e through various \u003cstrong\u003eTherapeutic Interventions\u003c/strong\u003e. The left panel shows the dysbiotic state, characterized by reduced beneficial bacteria, increased harmful pathogens, and a permeable, inflamed gut barrier. The middle panel lists the interventions: \u003cstrong\u003eDietary Modulation\u003c/strong\u003e (e.g., fiber-rich foods),\u003c/p\u003e\n\u003cp\u003ePrebiotics \u0026amp; Probiotics (supplements and fermented foods), Fecal Microbiota\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTransplantation (FMT)\u003c/strong\u003e to restore the full community of microbes, and \u003cstrong\u003eTargeted Antibiotics\u003c/strong\u003e to selectively reduce pathogens. The right panel shows the \"Restored Gut State,\" where these therapies have led to increased microbial diversity, a dominance of beneficial bacteria (e.g., \u003cem\u003eFaecalibacterium and Bifidobacterium\u003c/em\u003e), a healed gut barrier, and reduced systemic inflammation, ultimately leading to \u003cstrong\u003e\"Improved Health Outcomes.\"\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7607290/v1/a4d782e540cb36316c71c0b8.png"},{"id":91963125,"identity":"344761ca-ed72-4d56-aa41-531ddc4c008a","added_by":"auto","created_at":"2025-09-23 08:00:43","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":478987,"visible":true,"origin":"","legend":"\u003cp\u003eBiomakers \u0026amp; Personalized Medicine\u003c/p\u003e\n\u003cp\u003eillustrates the concept of using the gut microbiome to develop \u003cstrong\u003ePersonalized Therapies\u003c/strong\u003e. The process starts with \u003cstrong\u003e\"Patient Samples \u0026amp; Data,\"\u003c/strong\u003e including stool and blood samples, which provide rich biological data. This data is then subjected to \u003cstrong\u003e\"Multi-Modal Analysis\"\u003c/strong\u003e using advanced techniques like genomic sequencing and machine learning. This analysis identifies \u003cstrong\u003e\"Microbial Biomarkers,\"\u003c/strong\u003e or specific microbial signatures, that are associated with a patient's disease (e.g., high \u003cem\u003ePrevotella copri\u003c/em\u003e in RA or low \u003cem\u003eF. prausnitzii\u003c/em\u003e in neurodegeneration). These biomarkers are then used to create \u003cstrong\u003e\"Personalized Therapies\"\u003c/strong\u003etailored to the individual. These therapies can include a \u003cstrong\u003eTargeted Diet \u0026amp; Prebiotics\u003c/strong\u003e designed to foster beneficial microbes or \u003cstrong\u003eSpecific Probiotics (FMT)\u003c/strong\u003eto correct the dysbiosis. The goal is to apply precision medicine to the gut microbiome to achieve \u003cstrong\u003e\"Improved Patient Outcomes.\"\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7607290/v1/0ab177dce209b18733639e35.png"},{"id":92046876,"identity":"c7f50260-0eb2-4b68-8a6b-9b3f589dbd7f","added_by":"auto","created_at":"2025-09-24 05:02:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4546874,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7607290/v1/4f386399-b9fc-4607-94a9-ba893daef3eb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Gut Microbiome's Influence on Extra- Intestinal Diseases: A Cross-Sectional Analysis of Neurological and Autoimmune Disorders","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe human gut microbiome is an intricate and dynamic community of bacteria, archaea, fungi, and viruses that plays a fundamental role in maintaining host physiology, metabolism, and immune function. In a state of \u003cstrong\u003eeubiosis\u003c/strong\u003e, or microbial balance, the gut microbiota aids in nutrient digestion, produces essential vitamins, and fortifies the host's immune system. However, a growing body of evidence suggests that disruptions to this balance, driven by factors such as diet, antibiotics, and stress, can lead to dysbiosis, a state associated with chronic low-grade inflammation and disease.\u003c/p\u003e\n\u003cp\u003eThe influence of the gut microbiome extends beyond the confines of the gastrointestinal tract through complex communication networks known as \u003cstrong\u003egut-organ axes\u003c/strong\u003e. The \u003cstrong\u003egut-brain axis\u003c/strong\u003e, a bidirectional communication pathway, links the central nervous system with the enteric nervous system and the gut microbiota, impacting mood, cognition, and neuroinflammation. Similarly, the \u003cstrong\u003egut-immune axis\u003c/strong\u003e governs the systemic immune response, with gut microbial metabolites and antigens shaping the development and function of both innate and adaptive immunity. Neurological disorders like \u003cstrong\u003eAlzheimer’s disease (AD)\u003c/strong\u003e and \u003cstrong\u003eParkinson’s disease (PD)\u003c/strong\u003e, previously viewed as diseases of the brain alone, are now understood to have a significant peripheral component. Growing research suggests that gut microbial dysbiosis may contribute to neurodegeneration through mechanisms involving systemic inflammation, oxidative stress, and the integrity of the blood-brain barrier. Similarly, autoimmune diseases such as \u003cstrong\u003erheumatoid arthritis (RA)\u003c/strong\u003e and \u003cstrong\u003esystemic lupus erythematosus (SLE)\u003c/strong\u003e are characterized by aberrant immune responses that attack the body’s own tissues. Recent studies have demonstrated that changes in the gut microbiota can trigger or exacerbate these autoimmune processes by activating pro-inflammatory T-cells and modulating cytokine production.\u003c/p\u003e\n\u003cp\u003eThis study aims to systematically investigate the gut microbial profiles of patients with these extra-intestinal diseases to identify common and disease-specific microbial signatures. By comparing microbial composition among patients with AD, PD, RA, and SLE against healthy controls, we seek to elucidate the role of the gut microbiome in their pathophysiology and highlight potential avenues for therapeutic intervention.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1. Study Design and Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a cross-sectional study conducted between January 2024 and June 2025. A total of 160 participants were recruited and assigned to five groups:\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eGroup 1: Alzheimer\u0026rsquo;s Disease (AD)\u003c/strong\u003e - 30 patients diagnosed based on the National Institute on Aging-Alzheimer\u0026apos;s Association (NIA-AA) criteria.\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eGroup 2: Parkinson\u0026rsquo;s Disease (PD)\u003c/strong\u003e - 30 patients diagnosed using the UK Parkinson\u0026apos;s Disease Society Brain Bank criteria.\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eGroup 3: Rheumatoid Arthritis (RA)\u003c/strong\u003e - 30 patients diagnosed based on the 2010 ACR/EULAR classification criteria.\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eGroup 4: Systemic Lupus Erythematosus (SLE)\u003c/strong\u003e - 30 patients diagnosed using the SLICC 2012 classification criteria.\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eGroup 5: Healthy Controls\u003c/strong\u003e - 40 volunteers matched for age (\u0026plusmn;5 years) and sex to the patient groups.\u003c/p\u003e\n\u003cp\u003eInclusion criteria for all participants included a confirmed clinical diagnosis (for patient groups) and no history of gastrointestinal or chronic inflammatory diseases. Exclusion criteria were recent antibiotic or probiotic use within the past 3 months, a history of bariatric surgery, or a diagnosis of gastrointestinal malignancy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Sample Collection and Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFresh stool samples were collected from each participant in a sterile container. Within 2 hours of collection, samples were aliquoted and immediately frozen at \u0026ndash;80\u0026deg;C to preserve microbial DNA integrity. Total genomic DNA was extracted from the samples using the QIAamp DNA Stool Mini Kit (Qiagen, Hilden, Germany) following the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. 16S rRNA Gene Sequencing and Bioinformatic Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe V3-V4 hypervariable regions of the 16S rRNA gene were amplified using universal primers (341F and 806R). Amplicons were sequenced on the Illumina MiSeq platform (Illumina, San Diego, CA, USA) at a dedicated sequencing facility.\u003c/p\u003e\n\u003cp\u003eRaw sequence data were processed using the QIIME2 pipeline (version 2024.5). Paired-end reads were demultiplexed, quality-filtered, and denoised using DADA2. Chimeric sequences were removed, and Amplicon Sequence Variants (ASVs) were generated. Taxonomic assignment was performed using a pre-trained Naive Bayes classifier based on the Greengenes 13_8 reference database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlpha diversity\u003c/strong\u003e metrics (Shannon and Chao1 indices) were calculated to assess microbial community richness and evenness within each sample. \u003cstrong\u003eBeta diversity\u003c/strong\u003e was assessed using principal coordinates analysis (PCoA) based on the Bray-Curtis dissimilarity metric to visualize microbial community structure differences between groups.\u003c/p\u003e\n\u003cp\u003eDifferential abundance analysis was performed using ANCOM-BC to identify taxa that were significantly enriched or depleted in patient groups compared to controls. All statistical analyses were conducted using R (version 4.2.1) and Python. An alpha level of p \\\u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConfounding Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral potential confounding variables were considered in this study. These include age, sex, dietary habits, body mass index (BMI), smoking status, and medication use (particularly antibiotics, probiotics, and immunosuppressive drugs). Although participants were matched for age and sex, other factors such as diet and lifestyle were not strictly controlled and may have influenced the gut microbiome composition. In addition, disease-related medications (e.g., levodopa in Parkinson\u0026rsquo;s disease, corticosteroids in autoimmune diseases) could have affected microbial diversity and abundance. These confounding variables should be taken into account when interpreting the findings, and future studies with stricter control of lifestyle and treatment-related factors are warranted.\u003c/p\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1.\u0026nbsp;Participant Demographics\u003c/h2\u003e\n\u003cp\u003eThe patient and control groups were successfully matched for age and sex, ensuring comparability (Table 1). There were no significant demographic differences between the groups.\u003c/p\u003e\n\u003ch3\u003eTable 1: Participant Demographics\u003c/h3\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eAge (Mean \u0026plusmn; SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eFemale (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eAlzheimer\u0026rsquo;s Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e72.5 \u0026plusmn; 4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e55%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eParkinson\u0026rsquo;s Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e68.1 \u0026plusmn; 5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e45%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eRheumatoid Arthritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e58.9 \u0026plusmn; 6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eSystemic Lupus\u003c/p\u003e\n \u003cp\u003eErythematosus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e45.3 \u0026plusmn; 8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eHealthy Controls\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e61.2 \u0026plusmn; 10.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e60%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e3.2.\u0026nbsp;Alpha Diversity Analysis\u003c/h2\u003e\n\u003cp\u003eAlpha diversity metrics (Shannon and Chao1 indices) were significantly reduced in all four patient groups compared to the healthy control group (p \\\u0026lt; 0.01 for all comparisons). This indicates a loss of microbial richness and evenness, a common hallmark of gut dysbiosis (Figure 1).\u003c/p\u003e\n\u003ch2\u003e3.3.\u0026nbsp;Beta Diversity Analysis\u003c/h2\u003e\n\u003cp\u003ePCoA plots based on Bray-Curtis dissimilarity revealed distinct clustering patterns between the patient and control groups (Figure 2). The control group formed a tight, separate cluster, while all disease groups showed more dispersed and overlapping clusters. This indicates that the overall microbial community structure in patients with extra-intestinal diseases is significantly different from that of healthy individuals.\u003c/p\u003e\n\u003ch2\u003e3.4.\u0026nbsp;Taxonomic Differences\u003c/h2\u003e\n\u003cp\u003eAnalysis of taxonomic abundance revealed several key differences (Table 2):\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eCommon Dysbiosis:\u003c/strong\u003e All four patient groups exhibited a significantly lower relative abundance of beneficial bacteria, including \u003cstrong\u003e\u003cem\u003eFaecalibacterium prausnitzii\u003c/em\u003e\u003c/strong\u003e, a key producer\u003c/p\u003e\n\u003cp\u003eof butyrate, and various species within the genus \u003cstrong\u003e\u003cem\u003eBifidobacterium\u003c/em\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003ePro-inflammatory Enrichment:\u003c/strong\u003e All patient groups showed an increased abundance of the phylum \u003cstrong\u003eProteobacteria\u003c/strong\u003e, a well-known marker for microbial instability and potential inflammation.\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eDisease-Specific Signatures:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e○ \u003cstrong\u003eNeurological Groups (AD \u0026amp; PD):\u003c/strong\u003e These groups were characterized by a notable increase in the family \u003cstrong\u003e\u003cem\u003eEnterobacteriaceae\u003c/em\u003e\u003c/strong\u003e (e.g., \u003cem\u003eEscherichia-Shigella\u003c/em\u003e), a finding consistent with gut permeability and neuroinflammation.\u003c/p\u003e\n\u003cp\u003e○ \u003cstrong\u003eAutoimmune Groups (RA \u0026amp; SLE):\u003c/strong\u003e These groups showed a significant enrichment of \u003cstrong\u003e\u003cem\u003ePrevotella copri\u003c/em\u003e\u003c/strong\u003e and certain \u003cem\u003eClostridium\u003c/em\u003e species, which have been previously implicated in the dysregulation of the immune system.\u003c/p\u003e\n\u003ch3\u003eTable 2: Key Differential Abundance of Microbial Taxa\u003c/h3\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eTaxon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eRA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eSLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eImplication\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eFaecalibacteriu m prausnitzii\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026darr;\u0026darr;\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026darr;\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026darr;\u0026darr;\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026darr;\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLoss of butyrate production, increased inflammation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eBifidobacterium spp.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026darr;\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026darr;\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026darr;\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLoss of beneficial gut-brain/immu ne metabolites\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026uarr; Phylum\u003c/p\u003e\n \u003cp\u003eProteobacteria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026uarr;\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026uarr;\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eMarker of dysbiosis, gut instability\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026uarr; Family Enterobacteriac eae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026uarr;\u0026uarr;\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026uarr;\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLinked to neuroinflammat ion and LPS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026uarr; \u003cem\u003ePrevotella copri\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026uarr;\u0026uarr;\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026uarr;\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAssociated with pro-inflammator y immune responses\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: The number of arrows indicates the relative magnitude of the change. \u0026apos;---\u0026apos; indicates no notable change from healthy controls. \u0026uarr; and \u0026darr; indicate increased and decreased relative abundance, respectively.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003cstrong\u003e3.5 Supplementary Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeatmap analysis showed clustering of autoimmune vs neurological groups.\u003c/p\u003e\n\u003cp\u003eLEfSe analysis identified Prevotella copri and Enterobacteriaceae as discriminant taxa.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe results of this study provide compelling evidence that gut microbial dysbiosis is a common and significant feature across a range of extra-intestinal diseases, including neurological and autoimmune disorders. The observed reduction in key beneficial bacteria, such as \u003cstrong\u003e\u003cem\u003eF. prausnitzii\u003c/em\u003e\u003c/strong\u003e and \u003cstrong\u003e\u003cem\u003eBifidobacterium spp.\u003c/em\u003e\u003c/strong\u003e, is a consistent finding that supports the hypothesis that a loss of these commensals contributes to systemic inflammation and a compromised gut barrier. \u003cstrong\u003e\u003cem\u003eF. prausnitzii\u003c/em\u003e\u003c/strong\u003e, in particular, is a major producer of the \u003cstrong\u003eshort-chain fatty acid (SCFA) butyrate\u003c/strong\u003e, which is essential for maintaining colonocyte health and has potent anti-inflammatory properties. Its depletion may directly contribute to the chronic inflammation seen in all these conditions.\u003c/p\u003e\n\u003cp\u003eThe enrichment of the phylum \u003cstrong\u003eProteobacteria\u003c/strong\u003e, which includes many opportunistic pathogens, in all patient groups suggests a state of gut instability and reduced resilience. This is often associated with a decline in obligate anaerobes and can lead to increased \u003cstrong\u003elipopolysaccharide (LPS)\u003c/strong\u003e production, a powerful pro-inflammatory endotoxin that can traverse a permeable gut barrier and contribute to systemic and neuroinflammation.\u003c/p\u003e\n\u003cp\u003eInterestingly, our findings also reveal disease-specific microbial signatures. The significant increase in \u003cem\u003eEnterobacteriaceae\u003c/em\u003e within the neurological groups (AD and PD) suggests a potential link between gut permeability and the pathogenesis of neurodegeneration. LPS from these bacteria can cross a compromised blood-brain barrier, triggering glial cell activation and neuronal damage. Conversely, the enrichment of \u003cstrong\u003e\u003cem\u003ePrevotella copri\u003c/em\u003e\u003c/strong\u003e in the autoimmune groups (RA and SLE) aligns with previous research suggesting its role in promoting pro-inflammatory immune responses. This organism has been shown to induce a specific type of T-cell (\u003cstrong\u003eTh17 cells\u003c/strong\u003e) that is central to the inflammatory pathology of rheumatoid arthritis.\u003c/p\u003e\n\u003ch2\u003eClinical Implications and Future Directions\u003c/h2\u003e\n\u003cp\u003eThe strong association between gut dysbiosis and these extra-intestinal diseases opens up new avenues for clinical intervention. Microbiome-targeted therapies, such as dietary modulation (e.g., fiber-rich diets to feed beneficial bacteria), prebiotics, probiotics, and even Fecal Microbiota Transplantation (FMT), could represent novel strategies for disease management and prevention. For instance, interventions aimed at increasing the abundance of butyrate-producing bacteria could help mitigate the inflammatory burden in patients with RA and AD.\u003c/p\u003e\n\u003ch2\u003eLimitations\u003c/h2\u003e\n\u003cp\u003eThis study has several limitations that should be considered when interpreting the findings:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;Study Design and Causality: A primary limitation is the cross-sectional design, which captures the gut microbiome at a single point in time. Consequently, causal relationships between dysbiosis and disease onset or progression cannot be established. While significant associations were observed, it remains unclear whether microbial changes contribute to disease onset or result from disease progression and treatment. Longitudinal and interventional studies are required to determine the directionality of these relationships and clarify whether modifying the gut microbiome can prevent or mitigate disease development.\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;Confounding Variables: Although potential confounders (e.g., age, sex, diet, medications) were considered in the Methods section, residual confounding cannot be fully excluded.\u003c/p\u003e\n\u003cp\u003e3. \u0026nbsp; Sample Size and Generalizability: The relatively small number of participants in each group (n=30) and recruitment from Government Hospitals and a single institution (21 September University for Medical and Applied Sciences, Sana’a, Yemen) may limit the statistical power and generalizability of the findings.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study provides compelling preliminary evidence that gut microbiome dysbiosis is a hallmark of neurological (Alzheimer\u0026rsquo;s disease and Parkinson\u0026rsquo;s disease) and autoimmune (rheumatoid arthritis and systemic lupus erythematosus) disorders. We identified a consistent pattern of reduced microbial diversity and beneficial taxa, coupled with an increase in pro-inflammatory bacteria, across all four patient groups. Moreover, we observed distinct microbial signatures that may be unique to each disease category. These findings underscore the critical role of the gut-organ axes in systemic health and disease and suggest that understanding these microbial signatures may pave the way for novel diagnostic biomarkers and precision microbiome-based therapies to prevent and manage a wide array of extra-intestinal conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eEthics Statement\u003c/h2\u003e\n\u003cp\u003e This study was conducted in accordance with the ethical standards of the Declaration of Helsinki. Ethical approval was obtained from the Institutional Review Board (IRB) of 21 September University for Medical and Applied Sciences, Sana’a, Yemen\u003c/p\u003e\n\u003cp\u003e(Approval No: [100001564]). Written informed consent was obtained from all participants prior to enrollment.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNot applicable, self-funded by the authors.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eHussein Mussa Muafa: Conceptualization, study design, data collection, bioinformatic analysis, manuscript drafting, and critical revision.Malika Abdu Balkam: Patient recruitment, clinical data verification, laboratory processing, statistical analysis, and manuscript editing.Both authors contributed equally to the interpretation of results, approved the final version of the manuscript, and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe thank all participants for their contribution to this study. Technical support for sequencing was provided by Government Hospitals and 21 September university for medical and applied sciences\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eRaw sequencing data are deposited in the NCBI Sequence Read Archive (SRA) under accession number [to be provided upon acceptance].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCryan JF, O\u0026apos;Riordan KJ, Cowan CSM, Sandhu KV, Bastiaanssen TFS, Boehme M, Codagnone MG, Cussotto S, Fulling C, Golubeva AV, Guzzetta KE, Jaggar M, Long-Smith CM, Lyte JM, Martin JA, Molinero-Perez A, Moloney G, Morelli E, Morillas E, O\u0026apos;Connor R, Cruz-Pereira JS, Peterson VL, Rea K, Ritz NL, Sherwin E, Spichak S, Teichman EM, van de Wouw M, Ventura-Silva AP, Wallace-Fitzsimons SE, Hyland N, Clarke G, Dinan TG. The Microbiota-Gut-Brain Axis. Physiol Rev. 2019 Oct 1;99(4):1877-2013. Doi: 10.1152/physrev.00018.2018. PMID: 31460832.\u003c/li\u003e\n\u003cli\u003eBelkaid Y, Hand TW. Role of the microbiota in immunity and inflammation. Cell. 2014 Mar 27;157(1):121-41. Doi: 10.1016/j.cell.2014.03.011. PMID: 24679531; PMCID: PMC4056765.\u003c/li\u003e\n\u003cli\u003eZhao T, Wei Y, Zhu Y, Xie Z, Hai Q, Li Z, Qin D. Gut microbiota and rheumatoid arthritis: From pathogenesis to novel therapeutic opportunities. Front Immunol. 2022 Sep 8;13:1007165. Doi: 10.3389/fimmu.2022.1007165. PMID: 36159786; PMCID: PMC9499173.\u003c/li\u003e\n\u003cli\u003eScheperjans F, Aho V, Pereira PA, Koskinen K, Paulin L, Pekkonen E, Haapaniemi E, Kaakkola S, Eerola-Rautio J, Pohja M, Kinnunen E, Murros K, Auvinen P. Gut microbiota are related to Parkinson\u0026apos;s disease and clinical phenotype. Mov Disord. 2015 Mar;30(3):350-8. Doi: 10.1002/mds.26069. Epub 2014 Dec 5. PMID: 25476529.\u003c/li\u003e\n\u003cli\u003eVogt NM, Kerby RL, Dill-McFarland KA, Harding SJ, Merluzzi AP, Johnson SC, Carlsson CM, Asthana S, Zetterberg H, Blennow K, Bendlin BB, Rey FE. Gut microbiome alterations in Alzheimer\u0026apos;s disease. Sci Rep. 2017 Oct 19;7(1):13537. Doi: 10.1038/s41598-017-13601-y. PMID: 29051531; PMCID: PMC5648830.\u003c/li\u003e\n\u003cli\u003eHorta-Baas G, Romero-Figueroa MDS, Montiel-Jarqu\u0026iacute;n AJ, Pizano-Z\u0026aacute;rate ML, Garc\u0026iacute;a-Mena J, Ram\u0026iacute;rez-Dur\u0026aacute;n N. Intestinal Dysbiosis and Rheumatoid Arthritis: A Link between Gut Microbiota and the Pathogenesis of Rheumatoid Arthritis. J Immunol Res. 2017;2017:4835189. Doi: 10.1155/2017/4835189. Epub 2017 Aug 30. PMID: 28948174; PMCID: PMC5602494.\u003c/li\u003e\n\u003cli\u003eHevia A, Milani C, L\u0026oacute;pez P, Cuervo A, Arboleya S, Duranti S, Turroni F, Gonz\u0026aacute;lez S, Su\u0026aacute;rez A, Gueimonde M, Ventura M, S\u0026aacute;nchez B, Margolles A. Intestinal dysbiosis associated with systemic lupus erythematosus. mBio. 2014 Sep 30;5(5):e01548-14. Doi: 10.1128/mBio.01548-14. PMID: 25271284; PMCID: PMC4196225.\u003c/li\u003e\n\u003cli\u003eMiyauchi E, Shimokawa C, Steimle A, Desai MS, Ohno H. The impact of the gut microbiome on extra-intestinal autoimmune diseases. Nat Rev Immunol. 2023 Jan;23(1):9-23. Doi: 10.1038/s41577-022-00727-y. Epub 2022 May 9. PMID: 35534624.\u003c/li\u003e\n\u003cli\u003eDe Vos WM, Tilg H, Van Hul M, Cani PD. Gut microbiome and health: mechanistic insights. Gut. 2022 May;71(5):1020-1032. Doi: 10.1136/gutjnl-2021-326789. Epub 2022 Feb 1. PMID: 35105664; PMCID: PMC8995832.\u003c/li\u003e\n\u003cli\u003eShivaji S. We are not alone: a case for the human microbiome in extra intestinal diseases. Gut Pathog. 2017 Mar 7;9:13. Doi: 10.1186/s13099-017-0163-3. PMID: 28286571; PMCID: PMC5339978.\u003c/li\u003e\n\u003cli\u003eJyoti, Dey, P. Mechanisms and implications of the gut microbial modulation of intestinal metabolic processes. Npj Metab Health Dis 3, 24 (2025). https://doi.org/10.1038/s44324-025-00066-1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gut microbiome, Dysbiosis, Neurological disorders, Autoimmune diseases, Gut-brain axis, Gut-immune axis, Alzheimer’s disease, Parkinson’s disease, Rheumatoid arthritis, Systemic lupus erythematosus, 16S rRNA sequencing","lastPublishedDoi":"10.21203/rs.3.rs-7607290/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7607290/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe human gut microbiome, composed of trillions of microorganisms, plays a pivotal role in maintaining health and influencing disease processes far beyond the gastrointestinal tract. Disruptions in microbial balance, known as dysbiosis, have been increasingly associated with neurological and autoimmune conditions through complex interactions involving microbial metabolites, immune modulation, and neural pathways. This study investigated gut microbiome alterations in Alzheimer\u0026rsquo;s disease, Parkinson\u0026rsquo;s disease, rheumatoid arthritis, and systemic lupus erythematosus using a cross-sectional design involving 160 participants, including 120 patients and 40 matched healthy controls. Stool samples were analyzed by 16S rRNA gene sequencing on the Illumina MiSeq platform, with bioinformatic assessment conducted using the QIIME2 pipeline. Results showed pronounced dysbiosis in all patient groups, with significant reductions in beneficial taxa such as Faecalibacterium prausnitzii and Bifidobacterium species, alongside increased pro-inflammatory taxa, particularly Proteobacteria. Distinct microbial patterns were observed between neurological and autoimmune disorders, including enrichment of Enterobacteriaceae in neurological groups and Prevotella copri in autoimmune groups. These findings suggest that gut microbial imbalances may contribute to extra-intestinal disease mechanisms and provide a rationale for microbiome-based therapeutic interventions.\u003c/p\u003e","manuscriptTitle":"The Gut Microbiome's Influence on Extra- Intestinal Diseases: A Cross-Sectional Analysis of Neurological and Autoimmune Disorders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 08:00:38","doi":"10.21203/rs.3.rs-7607290/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"30168007-2b01-48bc-8896-d8e0471bec62","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54885637,"name":"Health sciences/Diseases"},{"id":54885638,"name":"Biological sciences/Immunology"},{"id":54885639,"name":"Biological sciences/Microbiology"},{"id":54885640,"name":"Health sciences/Neurology"},{"id":54885641,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2025-09-24T04:54:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 08:00:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7607290","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7607290","identity":"rs-7607290","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}