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Record W4401921376 · doi:10.1192/j.eurpsy.2024.166

BDNF plasma concentrations, cognitive test performances and lifetime suicide ideation in psychotic disorders: a secondary analysis

2024· article· en· W4401921376 on OpenAlexaff
Pasquale Paribello, Mirko Manchia, Ulker Isayeva, Roberto Collu, P. Federica, María Scherma, Claudia Pisanu, Alessandra Meloni, Clement C. Zai, Donatella Congiu, Alessio Squassina, Walter Fratta, Paolo Fadda, B. Carpiniello

Bibliographic record

VenueEuropean Psychiatry · 2024
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsSuicidal ideationPsychologyIdeationClinical psychologyCognitionSuicide ideationPsychiatryMedicineSuicide preventionPoison controlMedical emergency

Abstract

fetched live from OpenAlex

Introduction Psychotic disorders present a significant lifetime risk for suicide. Past estimates suggest that up to 25-50% of individuals with schizophrenia (SCZ) may attempt suicide during their lifetime. A growing body of literature indicates that the level of cognitive performances may be associated with a differing level of lifetime suicide attempts, albeit inconsistently depending on the diagnostic category and study setting. However, the vast majority of the literature in the field is composed of cross-sectional studies, limiting the overall interpretation of the available evidence. Objectives In the present study, we probed the possible association of BDNF plasma levels, cognitive functions assessed through the Brief Assessment of Cognition in Schizophrenia (BACS) and lifetime suicide ideation and/or attempts (LSI+LSA). More specifically, we tested whether such association would persist during the 2 years follow-up divided in 5 different timepoints at 6-month intervals, if present. Methods The present study represents a secondary analysis of a previously described cohort (Manchia et al. Brain Sci. 2022 Dec 4;12(12):1666). The sample comprised 105 subjects with SZC or schizoaffective disorder. We employed the 1) Wilcoxon test for non-parametric data 2) linear modelling to test the possible association of BACS-defined cognitive task performances with LSI+LSA. We also investigated if either BDNF plasma levels or four tested BDNF SNP genes would mediate this association. Results From a total of 105 subjects, data relevant to the analysis were available for 89 subjects. We observed a significant association between BACS-Letter fluency task (BACS-LF) with LSI+LSA, persisting even when adjusting for gender, duration of untreated psychosis, total Positive and Negative Syndrome Scale score, age, chlorpromazine equivalents of antipsychotic therapy and for the effect of time. The association remained significant even when adjusting with the Bonferroni-Holms method for multiple comparisons (p=0.002). No association was found either for BDNF plasma levels or the tested BDNF genes for the tested outcomes. Conclusions In our sample, higher BACS-LF performances appeared to be associated with a higher lifetime risk of LSI+LSA. This report adds to the previous literature suggesting that different cognitive performance levels may represent one of the many chronic risk factors associated with LSI+LSA, and that may ultimately complexly interact with more proximal ones. Disclosure of Interest None Declared

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.013
GPT teacher head0.292
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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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Citations0
Published2024
Admission routes1
Has abstractyes

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