Plasma complement and coagulation proteins as prognostic factors of negative symptoms: An analysis of the NAPLS 2 and 3 studies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".