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Record W4408160809 · doi:10.3390/metabo15030176

Metabolic Syndrome and Schizophrenia: Adding a Piece to the Interplay Between the Kynurenine Pathway and Inflammation

2025· review· en· W4408160809 on OpenAlexaff
Jacopo Sapienza, Giulia Agostoni, Federica Repaci, Marco Spangaro, Stefano Comai, Marta Bosia

Bibliographic record

VenueMetabolites · 2025
Typereview
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsNeuroscienceKynurenine pathwaySchizophrenia (object-oriented programming)NeuroinflammationNeuroprotectionContext (archaeology)GlutamatergicCentral nervous systemInflammationMedicineBiologyKynurenineGlutamate receptorImmunologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

The biology of schizophrenia is highly complex and multifaceted. Numerous efforts have been made over the years to disentangle the heterogeneity of the disease, gradually leading to a more detailed understanding of its underlying pathogenic mechanisms. Two cardinal elements in the pathophysiology of schizophrenia are neuroinflammation and alterations of neurotransmission. The kynurenine (KYN) pathway (KP) is of particular importance because it is inducted by systemic low-grade inflammation in peripheral tissues, producing metabolites that are neuroactive (i.e., modulating glutamatergic and cholinergic neurotransmission), neuroprotective, or neurotoxic. Consequently, the KP is at the crossroads between two primary systems involved in the pathogenesis of schizophrenia. It bridges the central nervous system (CNS) and the periphery, as KP metabolites can cross the blood-brain barrier and modulate neuronal activity. Metabolic syndrome plays a crucial role in this context, as it frequently co-occurs with schizophrenia, contributing to a sub-inflammatory state able to activate the KP. This narrative review provides valuable insights into these complex interactions, offering a framework for developing targeted therapeutic interventions or precision psychiatry approaches of the disorder.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.025
GPT teacher head0.304
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 teacher head, not a consensus.

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

Citations8
Published2025
Admission routes1
Has abstractyes

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