Antipsychotics, Metabolic Adverse Effects, and Cognitive Function in Schizophrenia
Bibliographic record
Abstract
Cognitive impairment is a core symptom domain of schizophrenia. The effect of antipsychotics, the cornerstone of treatment in schizophrenia, on this domain is not fully clear. There is some evidence suggesting that antipsychotics may partially improve cognitive function, and that this improvement may vary depending on the specific cognitive domain. However, this research is confounded by various factors, such as age, duration/stage of illness, medication adherence, and extrapyramidal side effects that complicate the relationship between antipsychotics and cognitive improvement. Furthermore, antipsychotics-particularly the second generation, or "atypical" antipsychotics-can induce serious metabolic side effects, such as obesity, dyslipidemia and type 2 diabetes, illnesses which themselves have been linked to impairments in cognition. Thus, the inter-relationships between cognition and metabolic side effects are complex, and this review aims to examine them in the context of schizophrenia and antipsychotic treatment. The review also speculates on potential mechanisms underlying cognitive functioning and metabolic risk in schizophrenia. We conclude that the available literature examining the inter-section of antipsychotics, cognition, and metabolic effects in schizophrenia is sparse, but suggests a relationship between metabolic comorbidity and worse cognitive function in patients with schizophrenia. Further research is required to determine if there is a causal connection between the well-recognized metabolic adverse effects of antipsychotics and cognitive deficits over the course of the illness of schizophrenia, as well as, to determine underlying mechanisms. In addition, findings from this review highlight the importance of monitoring metabolic disturbances in parallel with cognition, as well as, the importance of interventions to minimize metabolic abnormalities for both physical and cognitive health.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".