Correlation between Serum Levels of Sympathetic Nerve Activity Markers and Sleep Quality and Cognitive Function in Patients with Chronic Insomnia Disorder
Bibliographic record
Abstract
Objectives: To explore the changes of the serum levels of copeptin and α-amylase and the correlations with sleep quality and cognition function in the patients with chronic insomnia disorder (CID). Methods: Fifty-seven CID patients and thirty healthy controls were enrolled continuously. Pittsburgh Sleep Quality Index (PSQI), polysomnography (PSG) and Pre-Sleep Arousal Scale (PSAS) were used to assess the insomnia severity and cognitive and somatic manifestations of arousal experienced at bedtime. Montreal Cognitive Assessment scale (MoCA) and Nine-Box Maze were used to respectively assess general cognition and memories. The serum levels of copeptin and α-amylase were detected using Enzyme-Linked ImmunoSorbent Assay. Results: Compared to the controls, the CID patients had increased PSQI and PSAS scores (Z=‒7.678 and ‒7.350; Ps<0.001), decreased MoCA score (t=‒4.625, P<0.001), increased numbers of errors in the object working, spatial working and object recognition (Z=‒2.099, ‒3.935 and ‒2.266; Ps<0.05) memories, and elevated serum levels of copeptin and α-amylase (t=5.414 and 5.597, P <0.001). In the CID patients，the level of copeptin positively correlated with PSQI and PSAS scores (r=0.338 and 0.316; Ps<0.05), and PSG sleep latency, wake time and N1% (r=0.324, 0.325 and 0.278, Ps<0.05), and negatively correlated with PSG N 2% (r=‒0.279, Ps<0.05). Alpha-amylase was positively correlated with waking numbers in PSG (r=0.293, P< 0.05). Multiple linear regression analysis showed that copeptin level affected PSQI score and PSG sleep latency (P<0.05). Conclusions: The serum levels of copeptin and α-amylase elevated in the CID patients, and the serum levels of copeptin may be associated with the poor sleep quality, especially in the individuals of initial sleep difficulties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".