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Record W4387519682 · doi:10.3389/fpsyt.2023.1234220

BDNF gene Val66Met polymorphisms as a predictor for clinical presentation in schizophrenia – recent findings

2023· review· en· W4387519682 on OpenAlexaff
Adriana Farcas, Charles C.T. Hindmarch, Felicia Iftene

Bibliographic record

VenueFrontiers in Psychiatry · 2023
Typereview
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsQueen's UniversityProvidence Health Care
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)EndophenotypeDiseasePsychologyWeb of scienceGenotypingPsychosisPsychiatryMeta-analysisBioinformaticsClinical psychologyMedicineCognitionGeneGeneticsGenotypeInternal medicineBiology

Abstract

fetched live from OpenAlex

Schizophrenia is a highly heritable, severe psychiatric disorder that involves dysfunctions in thinking, emotions, and behavior, with a profound impact on a person's ability to function normally in their daily life. Research efforts continue to focus on elucidating possible genetic underlying mechanisms of the disorder. Although the genetic loci identified to date to be significantly associated with schizophrenia risk do not represent disease-causing factors, each one of them could be seen as a possible incremental contributor. Considering the importance of finding new and more efficient pharmacological approaches to target the complex symptomatology of this disorder, in this scoping review, we are focusing on the most recent findings in studies aiming to elucidate the contribution of one of the genetic factors involved - the BDNF gene Val66Met polymorphisms. Here we performed a systematic search in Pubmed, Embase, and Web of Science databases with the search terms: (BDNF gene polymorphism) AND (schizophrenia) for articles published in the last 5 years. To be selected for this review, articles had to report on studies where genotyping for the BDNF Val66Met polymorphism was performed in participants diagnosed with schizophrenia (or schizophrenia spectrum disorders or first-episode psychosis). The search provided 35 results from Pubmed, 134 results from Embase, and 118 results from the Web of Science database. Twenty-two articles were selected to be included in this review, all reporting on studies where an implication of the BDNF Val66Met polymorphisms in the disorder's pathophysiology was sought to be elucidated. These studies looked at BDNF gene Val66Met polymorphism variants, their interactions with other genes of interest, and different facets of the illness. The Met/Met genotype was found to be associated with higher PANSS positive scores. Furthermore, Met/Met homozygous individuals appear to present with worse cognitive function and lower levels of serum BDNF. In the Val/Val genotype carriers, increased BDNF levels were found to correlate with weight gain under Risperidone treatment. However, due to heterogeneous results, the diversity in study populations and studies' small sample sizes, generalizations cannot be made. Our findings emphasize the need for further research dedicated to clarifying the role of gene polymorphisms in antipsychotic treatment to enhance specificity and efficacy in the treatment of schizophrenia.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.009
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.126
GPT teacher head0.423
Teacher spread0.296 · 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 designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations12
Published2023
Admission routes1
Has abstractyes

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