Effects of antipsychotics on circadian rhythms in humans: a systematic review and meta-analysis
Bibliographic record
Abstract
Antipsychotics are widely used to treat psychiatric illness and insomnia. However, the etiology of insomnia is multifactorial, including disrupted circadian rhythms. Several studies show that antipsychotics might modulate even healthy circadian rhythms. The purpose of this systematic review is to integrate current knowledge about the effects of antipsychotics on the circadian rhythms in humans, and to conduct a meta- analysis with the available data. Nine electronic databases were searched. We followed the PRISMA guidelines and included randomized controlled trials (RCTs), non-RCTs, case-control studies, case series, and case reports. Of 7,217 articles, 70 were included. The available data was mainly from healthy individuals, or patients having schizophrenia, but the findings showed a transdiagnostic impact on circadian parameters. This was consistently seen as decreased amplitude of cortisol, melatonin, and body temperature. Particularly, a meta-analysis of 16 RCTs measuring cortisol rhythm showed that antipsychotics, especially atypical antipsychotics, decreased the cortisol area under the curve and morning cortisol level, compared to placebo. The data with melatonin or actigraphy was limited. Overall, this evidence about the circadian effect of antipsychotics showed a need for longitudinal, real-time monitoring of specific circadian markers to differentiate a change in amplitude from a shift in phasing, and for knowledge about optimal timing of administration of antipsychotics, according to individual baseline circadian parameters. Standardizing selection criteria and outcome methods could facilitate good quality intervention studies and evidence-based treatment guidelines. This is relevant considering the accumulating evidence of the high prevalence and unfavorable impact of disrupted circadian rhythms in psychiatric disorders.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".