Are Anti-Inflammatory Cytokines Associated with Cognitive Impairment in Patients with Insomnia Comorbid with Depression? A Pilot Study
Bibliographic record
Abstract
Background: To distinguish insomnia comorbid with depression (ICD) from chronic insomnia disorder (CID) by exploring the relationship between serum levels of frequently overlooked anti-inflammatory cytokines and cognitive function. Methods: A total of 42 ICD patients, 63 CID patients, and 42 healthy control subjects were enrolled in the study. The Pittsburgh Sleep Quality Index and Hamilton Depression Rating Scale were used to assess sleep quality and depression severity, respectively. The Chinese-Beijing version of Montreal Cognitive Assessment scale (MoCA-C) and Nine-Box Maze Test (NBMT) were used to assess cognitive function. Serum levels of anti-inflammatory interleukins (IL-1RA, IL-4, IL-5, IL-10, IL-13, and IL-28A), transforming growth factor (TGF)-β 1, granulocyte-macrophage colony-stimulating factor, interferon-γ, and the chemokine regulated upon activation, normal T cell expressed and secreted (RANTES) were measured by enzyme-linked immunosorbent assay. Results: The ICD group had significantly more errors in the spatial reference task (H=2.55, P s =0.03) and spatial working memory task (H=5.67, P s < 0.01) of the NBMT, as well as lower levels of IL-1RA (H=− 2.85, P s =0.01), IL-4 (H=− 3.28, P s < 0.01), IL-5 (H=− 3.35, P s < 0.01), IL-10 (H=− 4.46, P s < 0.01), and IL-28A (H=− 2.75, P s =0.02) than control subjects. Compared with the CID group, the ICD group had significantly more errors in the spatial reference memory task (H=− 2.84, P s =0.01) of the NBMT, and lower levels of IL-5 (H=3.41, P s < 0.01), IL-10 (H=5.30, P s < 0.01), IL-13 (H=3.89, P s < 0.01), and GM-CSF (H=2.72, P s =0.02). A partial correlation analysis showed that the level of one or more of IL-4, IL-5, IL-10, IL-13, and TGF-β 1 was positively correlated with cognitive function (MoCA-C score and/or performance in spatial memory task) in ICD patients. Conclusion: ICD is a distinct condition that can be distinguished from CID based on immune dysfunction and specific types of cognitive dysfunction. Keywords: insomnia, depression, cytokine, cognition
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".