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Record W4400345867 · doi:10.1101/2024.07.05.24309965

Probing the Biological Underpinnings of Advanced Brain Ageing in Schizophrenia

2024· preprint· en· W4400345867 on OpenAlexaff
Alexander Murray, Pedro L. Ballester, Jack Rogers, Martin Wilson, J.F.W. Deakin, Mohammad Zia Ul Haq Katshu, Peter F. Liddle, Lena Palaniyappan, Rachel Upthegrove

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteWestern UniversityHospital for Sick Children
FundersMedical Research Council
KeywordsSchizophrenia (object-oriented programming)AgeingPsychologyNeuroscienceCognitive psychologyCognitive sciencePsychiatryMedicineInternal medicine

Abstract

fetched live from OpenAlex

Background Recent evidence suggests that patients with schizophrenia may show advanced brain ageing, particularly evident after the first year of onset. However, it is unclear if accelerated ageing relents, persists or continues to increase over time. The underlying causal factors are also poorly understood. Disruptions in glutamate function, oxidative stress, and inflammation may all contribute to progressive brain changes in people with schizophrenia. We examine whether brain ageing differs between early and established stages of schizophrenia, correlates with symptom severity and varies with markers of brain function, oxidative status and inflammatory burden. Methods Two brain-age prediction models assessed 112 participants (34 recent onset psychosis, 36 established schizophrenia, 42 healthy controls). Brain age gap (BAG) was calculated by subtracting chronological age from predicted age. Shapley’s additive explanations (SHAP) identified influential structural magnetic resonance imaging (MRI) features driving brain-age prediction. Linear regression models and partial correlations, adjusting for age, explored associations between BAG and neurometabolites, inflammatory markers, medication exposure and clinical scores in the whole sample. Results The established schizophrenia group showed higher BAG (Mean = 6.21, SD = 7.30) compared to healthy individuals (Mean = -0.01, SD = 9.10), while recent-onset patients (Mean = 4.23, SD = 9.25) did not differ significantly from healthy individuals. The top 10 SHAP features diving the BAG included ventricular enlargement and total grey matter volume, which was similar in psychosis to healthy individuals. In a combined psychosis group (established + recent-onset), higher BAG correlated with more severe symptoms (PANSS total, general, and anxiodepressive subscales). BAG positively associated with Magnetic Resonance Spectroscopy measured glutathione and negatively with N-Acetyl Aspartate. Discussion Accelerated brain age in schizophrenia may be related to illness severity and poor defence against oxidative stress. The lack of differences in SHAP features between schizophrenia and healthy individuals suggests that the pattern of brain ageing is in keeping with advanced normal ageing. Findings suggest potential treatment targets to improve brain health in schizophrenia, warranting further research.

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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.305
Teacher spread0.269 · 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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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