Can we run away from the metabolic side effects of antipsychotics?
Bibliographic record
Abstract
Abstract Antipsychotic (AP) medications are used to treat schizophrenia and a number off-label conditions. Although effective in reducing psychoses these drugs increase the risk of developing cardiometabolic disease, and are one of the reasons why individuals with schizophrenia live ∼15–20 years less than the general population. While weight gain has traditionally been thought to be the primary culprit linked to increases in rates of cardiometabolic disease, there are weight-gain independent effects of antipsychotics. The purpose of the current review was to highlight the acute metabolic complications of antipsychotics and to address the question: are exercise and targeting “exercise-activated” signaling pathways, a viable approach to offset the metabolic complications of APs? The possibility of fibroblast growth factor 21 being a common factor mediating the protective effects of exercise and certain nutritional approaches against the acute metabolic complications of antipsychotics was also discussed. The research highlighted in this narrative review provides evidence, in preclinical models, that exercise and certain exercise-activated pathways, can protect against acute perturbations in glucose metabolism. While promising, further work is needed to confirm these findings in clinical populations prescribed antipsychotics.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".