The associations between insomnia symptoms and clinical features, lipid metabolism parameters, as well as inflammatory cytokines in patients with chronic schizophrenia
Bibliographic record
Abstract
BACKGROUND: Chronic schizophrenia is commonly associated with a high incidence of insomnia. Co-morbid insomnia not only exacerbates psychiatric symptoms but also significantly impairs cognitive functioning, and increases the risk of suicide. Therefore, this study sought to explore the relationships between insomnia symptoms and clinical features, lipid metabolism parameters, and inflammatory cytokines in patients with chronic schizophrenia, thereby providing a scientific basis for early identification and intervention of insomnia in this population. METHODS: From May to December 2018, 317 patients with chronic schizophrenia were included. General demographic data were collected using a self-administered questionnaire. Insomnia, psychotic symptoms, and depression were assessed using a series of validated scales. Additionally, plasma levels of lipid metabolism parameters and inflammatory cytokines, including interleukin (IL)-1β, IL-6, IL-17 A, and tumor necrosis factor-α (TNF-α), were measured. Finally, logistic regression analysis was conducted to identify the independent predictors of insomnia symptoms. RESULTS: Among patients with chronic schizophrenia, 42.3% exhibited insomnia symptoms. Univariate analyses revealed that, compared to the non-insomnia group, patients in insomnia group were older and had higher positive symptom scores, higher general psychopathology scores, higher total scores on the Calgary Depression Scale (CDSS), and elevated plasma levels of Log IL-6 (all p < 0.05). Furthermore, regression analyses identified age (OR = 1.027, 95% CI = 1.005-1.048, p = 0.013), CDSS total score (OR = 1.265, 95% CI = 1.163-1.375, p < 0.001), and Log IL-6 level (OR = 2.053, 95% CI = 1.251-3.371, p = 0.004) as independent correlates of insomnia. CONCLUSION: Patients with chronic schizophrenia are at a relatively high risk of developing insomnia. Insomnia symptoms may be associated with age, psychotic symptoms, depressive symptoms, and elevated levels of IL-6. Based on these findings, clinical staff should enhance the monitoring and assessment of sleep quality in patients with chronic schizophrenia and implement targeted early interventions for those at risk of insomnia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".