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

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

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

Bibliographic record

VenuePharmacogenomics · 2022
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsDalhousie University
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)PsychologyGut floraMedicinePsychiatryImmunology

Abstract

fetched live from 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.279
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2022
Admission routes2
Has abstractyes

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