MétaCan
Menu
Back to cohort
Record W2597709577 · doi:10.1093/schbul/sbx021.086

59.4 Networks of Blood Analytes are Collectively Informative of Risk of Conversion to Schizophrenia

2017· article· en· W2597709577 on OpenAlexaff
Clark Jeffries, Diana O. Perkins, Jean Addington, Carrie E. Bearden, Kristen S. Cadenhead, Tyrone D. Cannon, Barbara A. Cornblatt, Daniel H. Mathalon, Tom McGlashan, Larry J. Seidman, Ming T. Tsuang, Elaine F. Walker, Scott W. Woods

Bibliographic record

VenueSchizophrenia Bulletin · 2017
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychosisSchizophrenia (object-oriented programming)Internal medicinePopulationMedicinePsychologyPsychiatryBioinformaticsBiology

Abstract

fetched live from OpenAlex

Background: The presence and severity of attenuated-psychosis symptoms define a clinical high risk (CHR) population at elevated risk for psychotic disorders. The NAPLS project is a prospective study of mechanisms contributing to psychosis vulnerability in persons at CHR. Here we investigated a hypothesized role for the highly-integrated immune and redox systems in the development of psychosis. Methods: We examined expression of 143 plasma analytes from a subgroup of the NAPLS2 cohort, including 32 CHR with subsequent psychosis conversion, 40 CHR followed for 2 years without psychosis, and 35 unaffected subjects. We used a Luminex platform with analytes chosen to reflect immune, redox, hormonal, and metabolic system status, including many analytes previously associated with schizophrenia and psychosis risk. We applied correlation network analysis to discover potentially co-regulated networks associated with later development of psychosis. Results: Several robust (r > .75) and highly significant (P < .0001 after correction for multiple testing) correlation networks were found in all groups, including a network involving IL3, IL5, IL7, and IL13, and a network involving CCL5, BDNF, TSH, and PDGF. There were significantly fewer nodes in CHR-converters compared with CHR-nonconverters and unaffected subjects. In unaffected subjects, plasminogen activator inhibitor-1 (PAI-1) was highly correlated with matrix metallopeptidases (MMP) 7, 9 and 10 and CD40LG, this network was absent in CHR subjects, and in CHR-converters PAI-1 was robustly and significantly correlated with TIMP1, CCL13, and TIMP1. Conclusion: A pattern of robust and highly significant correlation networks in plasma analytes suggests shared regulatory mechanisms for the inter-correlated analytes. The lower number of correlated analytes in CHR subjects who converted to psychosis suggest a shift in regulation, as does the change in the correlation network involving PAI-1. PAI-1 is of interest given studies linking schizophrenia with reduced tissue plasminogen activator (tPA) and increases in negative regulators of tPA, including activation of both PAI-1and TIMP1 with oxidative stress. In addition, a recent study links toxoplasmosis infection and schizophrenia risk to a pathway involving PAI-1 and TIMP1. Patricio O’Donnell, Pfizer Inc.

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)0.003
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.329
Teacher spread0.305 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueSchizophrenia BulletinSame topicMental Health Research TopicsFrench-language works237,207