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Multilevel Profiles of Neurobiological Profiles of Risk, and Resilience and Treatment Outcomes in Early-Stage Psychiatric Disorders: Associations With Longitudinal Functioning Trajectories-A Multi-Level Machine Learning Analysis

2025· other· W7154817030 sur OpenAlexaboutno aff
C. Vetter, F. Eichin, D. ; https://orcid.org/0000-0002-2367-9437 Popovic, C. Weyer, K. Chisholm, L. Kambeitz-Ilankovic, J. Kambeitz, L. Antonucci, S. Ruhrmann, A. Riecher-Rossler

Notice bibliographique

RevueMPG.PuRe (Max Planck Society) · 2025
Typeother
Langue
Domaine
Thématique
Établissements canadiensnon disponible
Organismes subventionnairesEconomic and Social Research CouncilNIHR Maudsley Biomedical Research CentreUniversity of West AtticaUniversitätsklinikum KölnUniversität zu KölnCentre Hospitalier Universitaire VaudoisRuhr-Universität BochumUniversity of SussexUniversità di BolognaNational Institute for Health and Care ResearchGentofte HospitalLudwig-Maximilians-Universität MünchenAalborg UniversitetHospital de Clínicas de Porto AlegreAalborg UniversitetshospitalUniversity of MelbourneMonash UniversitySwinburne University of TechnologyUniversità degli Studi di FerraraDeakin UniversityInstitut National de la Santé et de la Recherche MédicaleUniversität BaselUniversité de GenèveGeorge Washington UniversityKing's College LondonMedizinische Fakultät, Heinrich-Heine-Universität DüsseldorfUniversidade Federal do Rio Grande do SulUniversidade Federal de PelotasNational and Kapodistrian University of AthensUniversity of New South Wales
Mots-clésResilience (materials science)Psychological resilienceMultilevel modelStatistical analysisLongitudinal study
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Aims:The association between cannabis use and psychosis has emerged as a prominent societal and health service issue over the past decade.In this symposium, we provide a broad overview of the current state of the research into the prevalence and trends in cannabis related psychosis and also deal with potential mechanisms and new treatment approaches. Methods and Results:The first presentation provides an overview of the increasing prevalence of cannabis psychosis internationally and highlights that this is an issue of global concern.The second presentation explores the association between legalizing cannabis use and rates of adolescent psychosis in Canada.The third presentation presents data about the relationship between inflammatory markers and cannabis use in youth.The final presentation gives an overview of a new treatment clinic for people with psychosis and cannabis use disorder and gives the first outcome data from this innovative service.Our symposium will end with a discussant who has lived experience of a cannabis induced psychosis who will give his views of the research and thoughts on the future of this field.Conclusions: Taken together, these presentations provide an overview of trends and risk factors for cannabis psychosis alongside opportunities for intervention and support.Implications for policy and practice are discussed in partnership with an expert by experience. Paper: The Rise and Rise of Drug-Induced Psychosis Across the Globe (18085)1.05 Epidemiology, 2.07 Psychosis NOS Robin Murray, Institute of Psychiatry, Psychology and Neuroscience, Kings College London respondents aged 12-24 years at baseline with no prior psychotic disorder (N = 11,363).The primary outcome was days to first hospitalization, ED visit, or outpatient visit related to a psychotic disorder according to validated diagnostic codes.Due to non-proportional hazards, we estimated age-specific hazard ratios during adolescence (12-19 years) and young adulthood (20-33 years).Sensitivity analyses explored alternative model conditions including restricting the outcome to hospitalizations and ED visits to increase specificity.Results: Compared to no cannabis use, cannabis use was significantly associated with psychotic disorders during adolescence (aHR = 11.2;95% CI: 4.6-27.3),but not during young adulthood (aHR = 1.3; 95% CI: 0.6-2.6).When we restricted the outcome to hospitalizations and ED visits only, the strength of association increased markedly during adolescence (aHR = 26.7;95% CI: 7.7-92.8)but did not change meaningfully during young adulthood (aHR = 1.8; 95% CI: 0.6-5.4). Conclusions:This study provides new evidence of a strong but age-dependent association between cannabis use and risk of psychotic disorder, consistent with the neurodevelopmental theory that adolescence is a vulnerable time to use cannabis.The strength of association during adolescence was notably greater than previous studies, possibly reflecting the recent rise in cannabis potency. Paper: Cannabis Use in Youth is Associated With Chronic Inflammation (18088) 1.05 Epidemiology, 2.07 Psychosis NOS Emmet Power, RCSI University of Medicine and Health SciencesMarkers of inflammation and cannabis exposure are associated with increased risk of mental disorders.In the current study, we investigated associations between cannabis use and biomarkers of inflammation.Utilizing a sample of 914 participants from the Avon Longitudinal Study of Parents and Children, we investigated whether interleukin-6 (IL-6), tumour necrosis factor α (TNFα), C-reactive protein (CRP) and soluble urokinase plasminogen activator receptor (suPAR) measured at age 24 were associated with past year daily cannabis use, less frequent cannabis use and no past year cannabis use.We adjusted for a number of covariates including sociodemographic measures, body mass index, childhood trauma and tobacco smoking.We found evidence of a strong association between daily or near daily cannabis use and suPAR.We did not find any associations between less frequent cannabis use and suPAR.We did not find evidence of an association between IL-6, TNFα or CRP and cannabis use.Our finding that frequent cannabis use is strongly associated with suPAR, a biomarker of systemic chronic inflammation implicated in neurodevelopmental and neurodegenerative processes is novel.These findings may provide valuable insights into biological mechanisms by which cannabis affects the brain and impacts on risk of serious mental disorders.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,021

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0020,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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 Opus0,026
Tête enseignante GPT0,270
Écart entre enseignants0,244 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

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

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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