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Record W4413314172 · doi:10.1093/ijnp/pyaf052.112

695. IRON DYSHOMEOSTASIS IS LINKED TO DOWNREGULATED AMYLOID PRECURSOR PROTEIN IN SCHIZOPHRENIA PREFRONTAL CORTEX: A POSTMORTEM STUDY

2025· article· en· W4413314172 on OpenAlexaff
Carlos Opazo, Amit Lotan, Sandra Luza, Deirdre A. Lane, Serafino G. Mancuso, A Pereira, Suresh Sundram, Cynthia Shannon Weickert, Chad Bousman, Ian Everall, Christos Pantelis, Ashley I. Bush

Bibliographic record

VenueThe International Journal of Neuropsychopharmacology · 2025
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPrefrontal cortexSchizophrenia (object-oriented programming)NeuroscienceAmyloid (mycology)Amyloid precursor proteinChemistryMedicinePsychologyPathologyPsychiatryAlzheimer's diseaseCognition

Abstract

fetched live from OpenAlex

Abstract Background Schizophrenia is a major neuropsychiatric disorder that is often disabling and associated with progressive brain changes, which have been linked to oxidative stress. Iron, the most abundant transition metal in the brain, is critical for key neurobehavioral pathways. However, labile iron is redox-sensitive, and its excess can provoke neuroinflammation and neurodegeneration. We have previously reported that iron is increased and iron related proteins are perturbed in schizophrenia prefrontal tissue postmortem. Aims & Objectives This study sought to evaluate whether the Amyloid Precursor Protein (APP), a protein that faciliates export of intracellular iron, is altered in prefrontal cortex (PFC) of individuals with schizophrenia. Method Specimens from the PFC of individuals with schizophrenia (n=86) and matched controls (n=85) were obtained from three independent brain tissue resources. The main biological metals (iron, copper, zinc) and relevant proteins (ferritin, amyloid precursor protein [APP] and glutathione peroxidase 4 [GPX4]) were quantified in brain supernatant fractions by ICP-MS and infrared Western blots, respectively. Results Protein levels of ferritin, which stores iron in a redox-inactive form, and of APP were decreased in schizophrenia patients (-0.63 SDs, p<0.0001, and -0.49 SDs, p=0.001, respectively), and the iron-APP relationship was grossly distorted in the patient group (t160=-3.44, p=0.0008). Copper levels were not affected by disease status and zinc was marginally higher among patients (0.28 [95%CI 0.03 to 0.54]. As a predictive tool, a labile iron index based on the interaction between tissue iron, ferritin and APP could discriminate between patients and controls (AUCROC=0.733, p<0.0001). Finally, protein levels of GPX4, which serves as the checkpoint for ferroptosis, were lower in patients (-0.326 SDs, p=0.031). Discussion & Conclusions We previously reported a conspicuous elevation of labile iron in PFC tissue of schizophrenia patients, which is most prominent in young-adulthood and coincides with the most prominent progressive brain changes reported in the disorder. Coupled with an apparent deficit in mitigating iron-dependent accumulation of toxic lipid peroxides, our findings are consistent with a mechanistic link between iron dyshomeostasis and neuroprogressive changes and introduce the therapeutic potential of targeting ferroptosis susceptibility during the early phases 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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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

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 head0.015
GPT teacher head0.338
Teacher spread0.322 · 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
Published2025
Admission routes1
Has abstractyes

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