The Relationship of Cognitive Dysfunction with Inflammatory Markers and Carotid Intima Media Thickness in Schizophrenia
Bibliographic record
Abstract
Objectives: Schizophrenia is a devastating and chronic mental disorder that affects 1% of the population worldwide. It is also associated with cognitive dysfunction and cardiovascular risk factors. The aim of this study is to investigate the relationship between cognitive impairment and some inflammatory markers and carotid intima-media thickness (CIMT) in schizophrenia. Methods: The participants of this study were 51 schizophrenia and 57 healthy controls (HC). The Positive and Negative Syndrome Scale (PANSS) was used for severity of illness, and the Montreal Cognitive Assessment Scale (MoCA) was used for cognitive functioning. The MoCA scores, some biochemical and inflammatory markers, and CIMT were compared between schizophrenia and HC groups. Results: Of the patients with schizophrenia, 11 were women (21.6%), and 40 were men (78.4%). MoCA scores were lower, and levels of NLR, MLR, PLR, SII, CRP, ESR, and CIMT were higher in schizophrenia compared to the HC group (respectively; p < 0.001, p < 0.001, p = 0.035, p = 0.008, p = 0.002, p < 0.001, p < 0.001, p < 0.001). In the schizophrenia group, there was no correlation between MoCA and inflammatory markers. MoCA and CIMT had a significant negative and moderate correlation (p < 0.001). Conclusions: This is the first study to show the relationship between cognitive impairment and CIMT in schizophrenia. In this study, NLR, MLR, PLR, SII, CRP, and ESR markers were higher in schizophrenia compared to HC, indicating inflammation. Our finding of elevated CIMT in schizophrenia suggests that there may be an atherosclerotic process along with the inflammatory process. The finding of a positive correlation between cognitive impairment and CIMT may be promising for new therapies targeting the atherosclerotic process in the treatment of cognitive impairment.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".