Inflammatory Cytokines and Antipsychotic-Induced Weight Gain: Review and Clinical Implications
Bibliographic record
Abstract
Antipsychotic medications (APs), particularly second-generation APs, are associated with significant weight gain in schizophrenia patients. Recent evidence suggests that the immune system may contribute to antipsychotic-induced weight gain (AIWG) via AP-mediated alterations of cytokine levels. Antipsychotics with a high propensity for weight gain, such as clozapine and olanzapine, influence the expression of immune genes, and induce changes in serum cytokine levels to ultimately down-regulate neuroinflammation. Since inflammatory cytokines are normally involved in anorexigenic responses, reduced inflammation has been independently shown to mediate changes in feeding behaviours and other metabolic parameters, resulting in obesity. Genetic variation in pro-inflammatory cytokines is also associated with both general obesity and weight change during AP treatment, and thus, may be implicated in the pharmacogenetics of AIWG. At this time, preliminary data support a cytokine-mediated model of AIWG which may have clinical utility in developing more effective metabolic monitoring guidelines and prevention measures. However, further research is still needed to clearly elucidate the validity of this immune model. This article reviews the evidence implicating inflammatory cytokines in AIWG and its potential clinical relevance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".