Are Leptin and Cytokines Involved in Body Weight Gain during Treatment with Antipsychotic Drugs?
Bibliographic record
Abstract
OBJECTIVE: To critically review published literature on the causal association between leptin, cytokines, and excessive body weight gain (BWG) induced by antipsychotic drugs (APs). METHODS: We completed a Medline search using the words leptin, cytokines, antipsychotic drugs, neuroleptics, psychotropic drugs, weight gain, and obesity. We also included our empirical research on this topic in the discussion. We examined the relation between leptin, cytokines (mainly tumour necrosis factor alpha [TNF-alpha] and its soluble receptors), and AP-induced BWG, using the biological sciences' current theories of causality. RESULTS: In the general field of weight regulation, there is scarce experimental evidence that leptin or TNF-alpha by themselves can induce obesity. Serum levels of leptin and TNF-alpha rather increase simultaneously as BWG occurs. This has also been reported during AP-induced BWG, with the equivocal exception of a study with clozapine. Some researchers have suggested that the absence of the expected correlation between leptin and body mass index (BMI) or serum insulin levels, and the lack of sex-related differences in leptin levels in AP-treated patients, may point to a causal relation. This contention requires more experimental support. In addition, future clinical studies must carefully control for sex and BMI. CONCLUSIONS: No conclusive evidence has been provided that leptin or TNF-alpha may induce obesity either in drug-free subjects or in AP-treated patients. In most cases, the elevated serum levels of these hormones appear to be a consequence rather than a cause of obesity. That does not mean that such an elevation is innocuous, since it may impair blood pressure and also carbohydrate and lipid metabolism regulation. Hence, all efforts should be made to prevent or attenuate BWG during treatment with APs.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".