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Record W2135205802 · doi:10.1503/jpn.090088

Osteoprotegerin levels in patients with severe mental disorders

2010· article· en· W2135205802 on OpenAlexvenueno aff
Sigrun Hope, Ingrid Melle, Pål Aukrust, Ingrid Agartz, Steinar Lorentzen, Nils Eiel Steen, Srdjan Djurovic, Thor Ueland, Ole A. Andreassen

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBone Metabolism and Diseases
Canadian institutionsnot available
FundersNorges ForskningsrådUniversitetet i Oslo
KeywordsInternal medicineMedicineSchizophrenia (object-oriented programming)OsteoprotegerinBipolar disorderEndocrinologyType 2 diabetesConfoundingBody mass indexInflammationDiabetes mellitusPsychiatryReceptorLithium (medication)

Abstract

fetched live from OpenAlex

BACKGROUND: Severe mental disorders are associated with elevated levels of inflammatory markers. In the present study, we investigated whether osteoprotegerin (OPG), a member of the tumour necrosis factor receptor family involved in calcification and inflammation, is elevated in patients with severe mental disorders. METHODS: We measured the plasma levels of OPG in patients with severe mental disorders (n = 312; 125 with bipolar disorder and 187 with schizophrenia) and healthy volunteers (n = 239). RESULTS: The mean plasma levels of OPG were significantly higher in patients than in controls (t531 = 2.6, p = 0.01), with the same pattern in bipolar disorder and schizophrenia. The increase was significant after adjustment for possible confounding variables, including age, sex, ethnic background, alcohol consumption, liver and kidney function, diabetes, cardiovascular disease, autoimmune diseases and levels of cholesterol, glucose and C-reactive protein. LIMITATIONS: Owing to the cross-sectional design, it is difficult to determine causality. CONCLUSION: Our results indicate that elevated OPG levels are associated with severe mental disorders and suggest that mechanisms related to calcification and inflammation may play a role in disease development.

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 score0.068
Threshold uncertainty score0.194

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 studies0.0000.000
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 head0.004
GPT teacher head0.219
Teacher spread0.215 · 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

Citations23
Published2010
Admission routes1
Has abstractyes

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