Weight Gain, Schizophrenia and Antipsychotics: New Findings from Animal Model and Pharmacogenomic Studies
Bibliographic record
Abstract
Excess body weight is one of the most common physical health problems among patients with schizophrenia that increases the risk for many medical problems, including type 2 diabetes mellitus, coronary heart disease, osteoarthritis, and hypertension, and accounts in part for 20% shorter life expectancy than in general population. Among patients with severe mental illness, obesity can be attributed to an unhealthy lifestyle, personal genetic profile, as well as the effects of psychotropic medications, above all antipsychotic drugs. Novel "atypical" antipsychotic drugs represent a substantial improvement on older "typical" drugs. However, clinical experience has shown that some, but not all, of these drugs can induce substantial weight gain. Animal models of antipsychotic-related weight gain and animal transgenic models of knockout or overexpressed genes of antipsychotic receptors have been largely evaluated by scientific community for changes in obesity-related gene expression or phenotypes. Moreover, pharmacogenomic approaches have allowed to detect more than 300 possible candidate genes for antipsychotics-induced body weight gain. In this paper, we summarize current thinking on: (1) the role of polymorphisms in several candidate genes, (2) the possible roles of various neurotransmitters and neuropeptides in this adverse drug reaction, and (3) the state of development of animal models in this matter. We also outline major areas for future research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".