Inflammatory and metabolic biomarkers of psychopathological dimensions of schizophrenia
Bibliographic record
Abstract
Introduction The concept of schizophrenia as a systemic disease includes, not only psychosis, but an increase in somatic comorbidity and cardiovascular risk [1]. Furthermore, it is known the implication of inflammation in the pathogenesis of schizophrenia [2]. Objectives To determinate potential inflammatory/metabolic biomarkers of schizophrenia's dimensions. Methods Sample: 36 outpatients with schizophrenia for less than 11 years, under stable maintenance treatment (mean age [32.25], males [63.9%]) and their 36 matched controls (age [32.53 ± 6.63]; males [72.2%]). Evaluation PANSS, Clinical Assessment Interview for Negative Symptoms(CAINS), Calgary Scale(CDS), CGI, Personal and Social Performance Scale(PSP). Biomarkers: C-reactive protein (CRP), homocysteine, glucose, insulin, HOMA-IR (insulin resistance), cholesterol, HDL, LDL, triglycerides. Results Biomarkers differences between groups are shown in Table 1. Table 2 shows the correlations found after controlling for Body Mass Index [patients(28.61 ± 5.69);controls(24.64 ± 3.80);p = 0.001] and Smoking [patients(52.8%-yes);controls(5.6%-yes);p = 0.000]. Conclusions 1. CRP, a potential inflammatory biomarker in schizophrenia, is related to depression severity. Homocysteine, representing an oxidative stress, is related to positive, negative, cognitive and depressive symptoms severity, and worse functioning. 2. Patients with schizophrenia have lower HDL–related to negative and cognitive symptoms severity and worse functioning–and insulin resistance – related to worse cognition –. Disclosure of interest The authors have not supplied their declaration of competing interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".