Pharmaco-psychiatry and gut microbiome: A systematic review of effects of psychotropic drugs for bipolar disorder
Notice bibliographique
Résumé
Abstract Despite being one of the most common and debilitating mood disorders, bipolar disorder is often misdiagnosed and undertreated. Its pathogenesis is complex, with significant patient variability and inconsistent treatment effectiveness. The brain-gut-microbiota axis plays a critical role in bipolar disorder by modulating neurotransmitter secretion, gut peptides, and systemic inflammation. However, the mechanisms by which psychotropic treatments influence gut microbiota composition and their implications for clinical outcomes remain poorly understood. This systematic review evaluated the impact of psychotropic drugs on gut microbiota and their potential role in bipolar disorder treatment outcomes. A comprehensive search across Ovid MEDLINE, Embase, APA PsycINFO, Scopus, and PubMed yielded 314 articles, of which 12 met the inclusion criteria (last search: 13 August 2024). The studies included were those on adults with bipolar disorder type I or II receiving psychopharmacological treatments, those with group comparisons (e.g., medicated vs. non-medicated) investigating gut microbiome changes; no restrictions applied to psychotic features, comorbid anxiety, or prior treatment responses. Exclusions involved individual case reports, incomplete conference submissions, or early terminated studies lacking efficacy analysis. Cochrane ROBINS-I V2 tool was used to measure the risk of bias, and the GRADE approach was utilized to rate the certainty of evidence in included studies. Two authors independently extracted data into Excel spreadsheets, categorizing demographic and clinical characteristics, describing microbiome analytic methods, and summarizing findings on gut microbiome changes post-treatment. Given the high variability in methods and outcome measures across studies, all details were reported without data conversion. Data synthesis reveals that psychotropic treatments, including quetiapine and lithium, influence gut microbiota by increasing the abundance of beneficial bacteria supporting gut health and pathogenic bacteria linked to metabolic dysfunction. Notably, female patients exhibited more significant changes in microbial diversity following psychotropic treatment. Additionally, patients treated with psychotropics showed an increased prevalence of gut bacteria associated with multidrug antibiotic resistance. In bipolar patients treated with quetiapine, responders—those experiencing improved depressive symptom scores—displayed distinct gut microbiome profiles more closely resembling those of healthy individuals compared to non-responders. Responders also exhibited neural connectivity patterns similar to healthy subjects. These findings underscore the complex dual impact of psychotropic medications on gut microbiota, with potential consequences for both gut and mental health. While the enrichment of beneficial bacteria may support gut health, the rise in antibiotic-resistant and metabolically disruptive bacteria is concerning. Study limitations include methodological heterogeneity, a high risk of bias in some studies due to incomplete statistical analyses or insufficient control for confounding factors, and potential duplication of study populations arising from overlapping authorship. Further research is essential to elucidate the functional consequences of these microbial shifts and their influence on treatment efficacy. Nevertheless, this review highlights the potential of utilizing gut microbiota profiles to inform personalized treatment strategies, optimize therapeutic outcomes, and minimize side effects in bipolar disorder.
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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 | 0,005 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,007 |
| Bibliométrie | 0,008 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».