Characterization of Fecal Microbiota in Ankylosing Spondylitis: Pathogenesis Insights and Therapeutic Opportunities - A Systematic Review
Notice bibliographique
Résumé
Objectives Recent evidence suggests that the gut microbiota may play a crucial role in the pathogenesis of ankylosing spondylitis (AS). This systematic review aims to examine the existing literature to explore changes in gut microbiota composition between AS patients and healthy controls (HC) to identify microbial signatures associated with AS. Understanding these alterations could enhance our knowledge of the mechanisms underlying AS. Methods We queried PubMed, Web of Science, Scopus, Embase, and Cochrane databases through May 2024 to identify studies comparing stool microbiota composition in AS patients and healthy controls (HC). Following PRISMA guidelines, our search yielded 1,163 studies. We included studies comparing microbiota in AS patients and HC. We excluded duplicates, animal studies, case reports, conference abstracts, non-English articles, irrelevant studies, non-full-text manuscripts, and Mendelian randomization studies, as these do not provide direct observational data on microbiota composition and could introduce methodological heterogeneity. Results After screening 184 studies, 18 manuscripts were included: 14 prospective cohort studies, 2 case-control studies, and 2 cross-sectional studies (Figure 1). These studies covered a total of 900 ankylosing spondylitis (AS) patients and 734 healthy controls (HC). The majority of the participants were male, with 73.8% in the AS group and 66.7% in the HC group, and most participants (73.4%) were of Asian descent. HLA-B27 status was reported in 13 studies, with a 92.3% positive rate among the AS patients. Notably, none of the AS or HC participants, except for 1 study, had received antibiotics in the 3 months prior to enrollment. At the phylum level, 12 studies (66.6%) reported significant changes in microbiota composition (Figure 2). Actinobacteria, Firmicutes, and Proteobacteria were increased in 8, 6, and 5 studies, respectively, while Bacteroidetes, Fusobacteria, and Verrucomicrobia were decreased in 5, 3, and 3 studies, respectively. At the genus level, 16 studies (88.8%) observed changes. Prevotella, Escherichia-Shigella, Streptococcus, and Collinsella were increased in 6, 6, 5, and 4 studies, respectively, while Bacteroides, Lachnospira, and Dialister were decreased in 9, 4, and 4 studies, respectively. Prevotella and Collinsella have been linked to inflammatory diseases in previous studies, suggesting their potential involvement in the inflammatory processes observed in AS patients. On the other hand, a reduction in Lachnospira has been associated with increased inflammation, indicating its potential protective role in inflammatory conditions. Figure 1: Alterations in Microbiota Composition at the Phylum Level in AS Patients Compared to Healthy Controls Figure 2: Alterations in Microbiota Composition at the Genus Level in AS Patients Compared to Healthy Controls Conclusion Our findings reveal significant differences in gut microbiota composition between AS patients and healthy controls, indicating a role of microbiota in AS pathogenesis. These findings highlight the potential for microbiota-targeted therapies in AS treatment.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,009 | 0,008 |
| Bibliométrie | 0,012 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».