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Record W4393321499 · doi:10.1016/j.bbi.2024.03.049

Plasma complement and coagulation proteins as prognostic factors of negative symptoms: An analysis of the NAPLS 2 and 3 studies

2024· article· en· W4393321499 on OpenAlexaff
Jonah F. Byrne, Colm Healy, Melanie Föcking, Meike Heurich, Subash Raj Susai, David Mongan, Kieran Wynne, Eleftheria Kodosaki, Scott W. Woods, Barbara A. Cornblatt, William S. Stone, Daniel H. Mathalon, Carrie E. Bearden, Kristin S. Cadenhead, Jean Addington, Elaine F. Walker, Tyrone D. Cannon, Mary Cannon, Clark Jeffries, Diana O. Perkins, David Cotter

Bibliographic record

VenueBrain Behavior and Immunity · 2024
Typearticle
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsHotchkiss Brain InstituteUniversity of Calgary
FundersNational Institute of Mental HealthEuropean Regional Development FundScience Foundation IrelandWellcome Trust
KeywordsInternal medicineSchizophrenia (object-oriented programming)Schizoaffective disorderProdromeClinical psychologyMedicinePsychosisPsychologyPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Negative symptoms impact the quality of life of individuals with psychosis and current treatment options for negative symptoms have limited effectiveness. Previous studies have demonstrated that complement and coagulation pathway protein levels are related to later psychotic experiences, psychotic disorder, and functioning. However, the prognostic relationship between complement and coagulation proteins and negative symptoms is poorly characterised. METHODS: In the North American Prodrome Longitudinal Studies 2 and 3, negative symptoms in 431 individuals at clinical high-risk for psychosis (mean age: 18.2, SD 3.6; 42.5 % female) were measured at multiple visits over 2 years using the Scale of Psychosis-Risk Symptoms. Plasma proteins were quantified at baseline using mass spectrometry. Four factors were derived to represent levels of proteins involved in the activation or regulation of the complement or coagulation systems. The relationships between standardised protein group factors and serial measurements of negative symptoms over time were modelled using generalised least squares regression. Analyses were adjusted for baseline candidate prognostic factors: negative symptoms, positive symptoms, functioning, depressive symptoms, suicidal ideation, cannabis use, tobacco use, antipsychotic use, antidepressant use, age, and sex. RESULTS: Clinical and demographic prognostic factors of follow-up negative symptoms included negative, positive, and depressive symptoms, functioning, and age. Adjusting for all candidate prognostic factors, the complement regulators group and the coagulation regulators group were identified as prognostic factors of follow-up negative symptoms (β: 0.501, 95 % CI: 0.160, 0.842; β: 0.430, 95 % CI: 0.080, 0.780 respectively. The relationship between complement regulator levels and negative symptoms was also observed in NAPLS2 alone (β: 0.501, 95 % CI: -0.037, 1.039) and NAPLS3 alone, additionally adjusting for BMI (β: 0.442, 95 % CI: 0.127, 0.757). CONCLUSION: The results indicate that plasma complement and coagulation regulator levels are prognostic factors of negative symptoms, independent of clinical and demographic prognostic factors. These results suggest complement and coagulation regulator levels could have potential utility in informing treatment decisions for negative symptoms in individuals at risk.

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 score0.429
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.062
GPT teacher head0.342
Teacher spread0.279 · 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

Citations10
Published2024
Admission routes1
Has abstractyes

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