Antipsychotic-induced changes in blood levels of leptin in schizophrenia: a meta-analysis.
Bibliographic record
Abstract
OBJECTIVES: Weight gain is a major side effect of antipsychotics (APs), which contributes to poor treatment adherence and significant morbidity. The mechanisms involved in AP-induced weight gain are incompletely understood. Recently, it has been proposed that changes in leptin, an cadipocyte-derived hormone exerting anorexigenic effects, may be involved in AP-induced weight gain. Thus far, studies on leptin changes during AP treatment have produced inconsistent results, prompting our group to perform a meta-analysis. METHOD: A search of the literature was performed using PubMed and Embase. Studies were included only if reporting peripheral levels of leptin before and after AP treatment in schizophrenia. Effect size estimates were calculated with Hedges g and were aggregated using a random effects model as results were heterogeneous (P<0.10). Meta-regression analyses were performed using study length and changes in body mass index (BMI) as moderator variables. RESULTS: Twenty-eight studies were retrieved, including 39 comparisons. A moderate and positive effect size was observed across studies. Olanzapine, clozapine, and quetiapine produced moderate leptin elevations, whereas haloperidol and risperidone were associated with small (nonsignificant) leptin changes. Across studies, BMI changes were significantly associated with increases in leptin levels. There was no effect of sex on AP-induced changes in leptin. CONCLUSIONS: A physiological role of leptin in AP-induced weight gain is supported because the most significant leptin increases were observed with APs inducing the most weight gain and because of the observed association between leptin increases and BMI changes. The overall increase in leptin levels suggests that leptin acts as a negative feedback signal in the event of fat increase.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.054 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".