Melatonin and melatonin‐agonists for metabolic syndrome components in patients treated with antipsychotics: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: Metabolic side effects are a limiting factor in the use of antipsychotics, which remain the cornerstone of long-term management of patients with severe mental illness. There is contrasting evidence on a possible role of melatonin and melatonin-agonists in attenuating antipsychotic-induced metabolic abnormalities. DESIGN: We conducted a systematic review (PubMed, PsycInfo, Cochrane databases, up to August 2020) and a random-effect meta-analysis of double-blind, randomized placebo-controlled trials (RCTs) involving melatonin and melatonin-agonists in the treatment of antipsychotic-induced metabolic changes. The primary outcome was the standardized mean difference (SMD) of composite metabolic outcomes built with metabolic syndrome components. Secondary outcomes were individual metabolic syndrome components, and other anthropometric, glucose metabolism, lipid profile, and psychopathology measures. RESULTS: Out of the initial 41 studies, six documented five separate RCTs randomizing 248 patients (126 to melatonin/ramelteon, 122 to placebo) affected by schizophrenia-spectrum disorders and bipolar disorder. Melatonin/ramelteon outperformed placebo on the primary outcome (SMD -0.28, 95% CI = -0.39 ÷ -0.168), as well as on all individual components of metabolic syndrome (systolic blood pressure MD -3.266, 95% CI = -6.020 ÷ -0.511; fasting glucose MD -3.766, 95% CI = -5.938 ÷ -1.593; triglycerides MD -9.800, 95% CI = -19.431 ÷ -0.169; HDL MD 2.995, 95% CI = 0.567 ÷ 5.423), except waist circumference. CONCLUSIONS: Melatonin/ramelteon augmentation may be beneficial for non-anthropometric metabolic syndrome components in patients treated with antipsychotics.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.003 | 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".