Total sleep time and kynurenine metabolism associated with mood symptom severity in bipolar disorder
Bibliographic record
Abstract
OBJECTIVE: Chronic, low-level inflammation is associated with symptomatic bipolar disorder (BD) and with chronic insomnia. Disrupted sleep is a feature of episodes of both mania and depression. We examined the effect of neopterin, a marker of cellular immune activation, and kynurenine (KYN), an inflammatory byproduct of the serotonin pathway, on the association between total sleep time and depression severity in BD. METHOD: Twenty-one symptomatic BD participants and 28 healthy controls (HC) were recruited and followed during usual clinical care. At baseline and after symptomatic recovery, total sleep time was objectively measured with actigraphy for 1 week and blood plasma was collected to measure the serotonin precursor tryptophan (TRP), KYN, the KYN/TRP ratio, and neopterin levels. Statistical analyses were conducted using chi-square, independent t tests and hierarchical linear multiple regression models. RESULTS: Total sleep time was correlated positively with depressive severity and negatively with manic severity. TRP was significantly reduced in BD participants compared to HC. KYN, TRP, and the KYN/TRP ratio were associated with depressive severity when total sleep time and body mass index (BMI) were included in the model. The KYN/TRP ratio trended towards a negative association with mania symptoms, controlling for BMI and total sleep time, in acutely symptomatic BD participants. Neopterin was not associated with sleep or mood severity. After usual clinical care, BD participants showed significantly decreased clinical symptoms but no significant differences in sleep phenotype or biomarkers. CONCLUSION: Inflammation, sleep, and mood are closely intertwined. Future research into the effect of inflammation on sleep in BD may lead to clinical markers of outcome.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".