Association between antipsychotic treatment and leptin levels across multiple psychiatric populations: An updated meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: Antipsychotics (APs) are associated with metabolic syndrome, with increases in leptin proposed as an underlying mechanism of AP-induced weight gain. Currently available meta-analyses on this topic have limited their populations of interest to those diagnosed with schizophrenia. The purpose of this meta-analysis is to explore the relationship between leptin levels and AP use across multiple psychiatric diagnoses, and also in healthy controls. METHOD: Systematic electronic searches were conducted using PubMed and OVID: Medline. Longitudinal studies were included if showing leptin levels before and after AP use. We included participants with any psychiatric disorders and mentally healthy participants, if exposed to AP use. The differences in leptin levels were evaluated using Hedges' g with a random effects model. RESULTS: Forty-two studies were found (36 schizophrenia, 2 bipolar disorder, 1 anorexia nervosa, and 3 healthy controls), encompassing 66 study arms and 1,156 participants. The meta-analysis showed that regardless of diagnoses, leptin levels increase with AP use (Hedges' g = 0.811, p ≤ .001). CONCLUSION: Leptin increases induced by APs are present across all diagnoses. More comprehensive research is needed to understand the relationship between AP use and leptin levels across multiple diagnoses.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.026 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".