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Enregistrement W4214940545 · doi:10.2217/pgs-2022-0018

Gut Microbiota and Treatment-Resistant Schizophrenia: Many Questions, Fewer Answers

2022· editorial· en· W4214940545 sur OpenAlexaffabout
Mirko Manchia, Alessio Squassina, Federica Tozzi, Άθως Αντωνιάδης, Bernardo Carpiniello

Notice bibliographique

RevuePharmacogenomics · 2022
Typeeditorial
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueGut microbiota and health
Établissements canadiensDalhousie University
Organismes subventionnairesnon disponible
Mots-clésSchizophrenia (object-oriented programming)PsychologyGut floraMedicinePsychiatryImmunology

Résumé

récupéré en direct d'OpenAlex

PharmacogenomicsVol. 23, No. 5 EditorialGut microbiota and treatment-resistant schizophrenia: many questions, fewer answersMirko Manchia, Alessio Squassina, Federica Tozzi, Athos Antoniades & Bernardo CarpinielloMirko Manchia *Author for correspondence: E-mail Address: mirkomanchia@unica.ithttps://orcid.org/0000-0003-4175-6413Department of Medical Sciences and Public Health, Section of Psychiatry, University of Cagliari, Cagliari, 09127, ItalyUnit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, 09127, ItalyDepartment of Pharmacology, Dalhousie University, Halifax, Nova Scotia, B3H 4R2, Canada, Alessio SquassinaDepartment of Biomedical Science, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Monserrato, 09042, ItalyDepartment of Psychiatry, Dalhousie University, Halifax, Nova Scotia, B3H 2E2, Canada, Federica TozziResearch and Development, Stremble Ventures, Limassol, 3095, Cyprus, Athos AntoniadesResearch and Development, Stremble Ventures, Limassol, 3095, Cyprus & Bernardo CarpinielloDepartment of Medical Sciences and Public Health, Section of Psychiatry, University of Cagliari, Cagliari, 09127, ItalyUnit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, 09127, ItalyPublished Online:3 Mar 2022https://doi.org/10.2217/pgs-2022-0018AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit View articleKeywords: antipsychoticsdrug metabolismFASTQmicroorganismresistanceschizophreniaReferences1. Cryan JF, Dinan TG. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. 13(10), 701–712 (2012).Crossref, Medline, CAS, Google Scholar2. Mayer EA. Gut feelings: the emerging biology of gut–brain communication. Nat. Rev. Neurosci. 12(8), 453–466 (2011).Crossref, Medline, CAS, Google Scholar3. Nikolova VL, Hall MRB, Hall LJ, Cleare AJ, Stone JM, Young AH. Perturbations in gut microbiota composition in psychiatric disorders: a review and meta-analysis. JAMA Psychiatry 78(12), 1343 (2021).Crossref, Medline, Google Scholar4. Chen LL, Abbaspour A, Mkoma GF, Bulik CM, Rück C, Djurfeldt D. Gut microbiota in psychiatric disorders: a systematic review. Psychosom. Med. 83(7), 679–692 (2021).Crossref, Medline, CAS, Google Scholar5. Howes OD, Thase ME, Pillinger T. Treatment resistance in psychiatry: state of the art and new directions. Mol. Psychiatry doi:10.1038/s41380-021-01200-3 (2021) (Epub ahead of print).Crossref, Google Scholar6. Kennedy JL, Altar CA, Taylor DL, Degtiar I, Hornberger JC. The social and economic burden of treatment-resistant schizophrenia: a systematic literature review. Int. Clin. Psychopharmacol. 29(2), 63–76 (2014).Crossref, Medline, Google Scholar7. Owen MJ, Sawa A, Mortensen PB. Schizophrenia. Lancet 388(10039), 86–97 (2016).Crossref, Medline, Google Scholar8. Manchia M, Fontana A, Panebianco C et al. Involvement of gut microbiota in schizophrenia and treatment resistance to antipsychotics. Biomedicines 9(8), 875 (2021).Crossref, Medline, CAS, Google Scholar9. Zimmermann M, Zimmermann-Kogadeeva M, Wegmann R, Goodman AL. Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature 570(7762), 462–467 (2019).Crossref, Medline, CAS, Google Scholar10. Seeman MV. The gut microbiome and antipsychotic treatment response. Behav. Brain Res. 396, 112886 (2021).Crossref, Medline, Google Scholar11. Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R. Diversity, stability and resilience of the human gut microbiota. Nature 489(7415), 220–230 (2012).Crossref, Medline, CAS, Google Scholar12. Vila AV, Collij V, Sanna S et al. Impact of commonly used drugs on the composition and metabolic function of the gut microbiota. Nat. Commun. 11(1), 362 (2020).Crossref, Medline, Google Scholar13. Arias I, Sorlozano A, Villegas E et al. Infectious agents associated with schizophrenia: a meta-analysis. Schizophr. Res. 136(1–3), 128–136 (2012).Crossref, Medline, Google Scholar14. Lluch E, Miller BJ. Rates of hepatitis B and C in patients with schizophrenia: a meta-analysis. Gen. Hosp. Psychiatry 61, 41–46 (2019).Crossref, Medline, Google Scholar15. Chen A, Park TY, Li KJ, DeLisi LE. Antipsychotics and the microbiota. Curr. Opin. Psychiatry 33(3), 225–230 (2020).Crossref, Medline, Google Scholar16. Teasdale SB, Ward PB, Samaras K et al. Dietary intake of people with severe mental illness: systematic review and meta-analysis. Br. J. Psychiatry 214(5), 251–259 (2019).Crossref, Medline, Google Scholar17. Manchia M, Pisanu C, Squassina A, Carpiniello B. Challenges and future prospects of precision medicine in psychiatry. Pharmgenomics Pers. Med. 13, 127–140 (2020).Medline, CAS, Google Scholar18. Howes OD, McCutcheon R, Agid O et al. Treatment-resistant schizophrenia: treatment response and resistance in psychosis (TRRIP) working group consensus guidelines on diagnosis and terminology. Am. J. Psychiatry 174(3), 216–229 (2017).Crossref, Medline, Google Scholar19. Bergstrom A, Skov TH, Bahl MI et al. Establishment of intestinal microbiota during early life: a longitudinal, explorative study of a large cohort of Danish infants. Appl. Environ. Microbiol. 80(9), 2889–2900 (2014).Crossref, Medline, CAS, Google Scholar20. Sani G, Manchia M, Simonetti A et al. The role of gut microbiota in the high-risk construct of severe mental disorders: a mini review. Front. Psychiatry 11, 585769 (2020).Crossref, Medline, Google Scholar21. Sandstrom A, Sahiti Q, Pavlova B, Uher R. Offspring of parents with schizophrenia, bipolar disorder, and depression: a review of familial high-risk and molecular genetics studies. Psychiatr. Genet. 29(5), 160–169 (2019).Crossref, Medline, CAS, Google Scholar22. He Y, Kosciolek T, Tang J et al. Gut microbiome and magnetic resonance spectroscopy study of subjects at ultra-high risk for psychosis may support the membrane hypothesis. Eur. Psychiatry 53, 37–45 (2018).Crossref, Medline, Google Scholar23. Casals-Pascual C, González A, Vázquez-Baeza Y, Song SJ, Jiang L, Knight R. Microbial diversity in clinical microbiome studies: sample size and statistical power considerations. Gastroenterology 158(6), 1524–1528 (2020).Crossref, Medline, Google Scholar24. Piras IS, Huentelman MJ, Pinna F et al. A review and meta-analysis of gene expression profiles in suicide. Eur. Neuropsychopharmacol. 56, 39–49 (2021).Crossref, Medline, Google Scholar25. Nunes A, Trappenberg T, Alda M. The definition and measurement of heterogeneity. Transl. Psychiatry 10(1), 299 (2020).Crossref, Medline, Google ScholarFiguresReferencesRelatedDetailsCited ByCytokine Imbalance as a Biomarker of Treatment-Resistant Schizophrenia26 September 2022 | International Journal of Molecular Sciences, Vol. 23, No. 19 Vol. 23, No. 5 Follow us on social media for the latest updates Metrics Downloaded 70 times History Received 14 February 2022 Accepted 15 February 2022 Published online 3 March 2022 Published in print April 2022 Information© 2022 Future Medicine LtdKeywordsantipsychoticsdrug metabolismFASTQmicroorganismresistanceschizophreniaAuthor contributionsM Manchia wrote the first draft and performed the literature search. A Squassina, A Antoniades and F Tozzi contributed to draft preparation and critically revised the manuscript. B Carpiniello co-wrote the manuscript and critically revised it.Financial & competing interests disclosureThis work was partly funded by Fondo Integrativo per la Ricerca 2018, granted to A Squassina, and by Fondo Integrativo per la Ricerca 2020, granted to M Manchia and B Carpiniello. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.PDF download

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,488
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,009
Tête enseignante GPT0,279
Écart entre enseignants0,270 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2022
Routes d'admission2
Résumé présentoui

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