MétaCan
Menu
Back to cohort
Record W4385670146 · doi:10.1192/j.eurpsy.2023.658

Decreased plasma concentrations of kynurenine and kynurenic acid in schizophrenia patients

2023· article· en· W4385670146 on OpenAlexfundno aff
M. Markovic, Milena Stašević, Smiljana Ristič, Mirjana Stojković, T. Stojković, Marija Živković, I. Stašević Karličić, Sanja Totić Poznanović, Tatjana Nikolić, Nataša Petronijević

Bibliographic record

VenueEuropean Psychiatry · 2023
Typearticle
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsnot available
FundersCampbell Family Mental Health Research InstituteQueen's UniversityUniversity of TorontoMcMaster UniversityUniversity of Texas Southwestern Medical Center
KeywordsKynurenic acidKynurenineKynurenine pathwaySchizophrenia (object-oriented programming)Internal medicineNMDA receptorQuinolinic acidEndocrinologyPositive and Negative Syndrome ScaleTryptophanMedicineChemistryPsychosisPharmacologyPsychiatryReceptorBiochemistryAmino acid

Abstract

fetched live from OpenAlex

Introduction The kynurenine pathway of tryptophan catabolism has come into the spotlight of schizophrenia research since its catabolites exert neuroactive effects. A strong body of evidence suggests that kynurenic acid, a catabolite of kynurenine pathway, acts as the only endogenous NMDA receptor antagonist leading to the weakening of circuits in layer III of dorsolateral prefrontal cortex of schizophrenia patients. Studies exploring the levels of kynurenic acid and other metabolites of tryptophan in peripheral blood did not yield any definite conclusions. Objectives Primary objective of this study was to assess differences in concentrations of key constituents of kynurenic pathway in blood plasma – tryptophan (TRP), kynurenine (KYN) and kynurenic acid (KYNA) between schizophrenia patients (SCZ) and healthy controls (HC). Secondary objective was to explore correlations between these concentrations and clinical characteristics. Methods In our two-centre prospective case-control study we measured plasma concentrations of TRP, KYN and KYNA in 36 healthy controls (HC) and 38 schizophrenia (SCZ) patients during acute exacerbation and remission and explored the correlations with clinical parameters using PANSS scale. The patients were matched with HC by age, sex and body mass index and exclusion criteria included obesity class 2 or higher, any concomitant organic mental or neurological disorder, acute or chronic inflammatory disease, and use of immunomodulatory drugs or psychoactive substances. Results TRP concentrations were significantly higher in HC than in SCZ patients in acute phase (p<0,001) and remission (p<0,001), while SCZ patients in acute phase had significantly higher TRP levels than in remission (p<0,01). Levels of KYNA and KYN were significantly lower in SCZ patients than in HC both in acute phase and remission, all with high statistical significance (p<0,001). There was no statistically significant difference between acute phase and remission neither for KYN (p>0,05), nor for KYNA (p>0,05). There was no correlation of plasma levels of TRP, KYN and KYNA with total PANSS score, PANSS positive scale score, PANSS negative scale score and PANSS general psychopathology scores, both in acute phase and remission (p>0,05). Also, there was no correlation between plasma levels of TRP, KYN and KYNA in SCZ patients in remission with improvements measured with PANSS scale (p>0,05). Conclusions Although there are concerns about the value of measurement of metabolites of kynurenine pathway in the peripheral blood, our data suggest that significantly decreased levels of KYN and KYNA could suggest that disrupted TRP degradation in SCZ patients may be reflected in the peripheral blood as well. Further studies of peripheral levels of kynurenine pathway metabolites on larger samples should also explore effects of antipsychotic therapy, but also their correlation with other clinical parameters such as neurocognition. 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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.237
Teacher spread0.223 · 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.

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueEuropean PsychiatrySame topicTryptophan and brain disordersFrench-language works237,207