Antipsychotics and glucose metabolism: how brain and body collide
Bibliographic record
Abstract
Since the serendipitous discovery of the first antipsychotic (AP) drug in the 1950s, APs remain the cornerstone of treatment for schizophrenia. A shift over the past two decades away from first-generation, conventional APs to so-called "atypical" (or 2nd/3rd generation) APs parallels acknowledgment of serious metabolic side-effects associated in particular with these newer agents. As will be reviewed, AP drugs and type 2 diabetes are now inextricably linked, contributing to the three- to fivefold increased risk of type 2 diabetes observed in schizophrenia. However, this association is not straightforward. Biological and lifestyle-related illness factors contribute to the association between type 2 diabetes and metabolic disease independently of AP treatment. In addition, APs have a well-established weight gain propensity which could also account for elevated risk of insulin resistance and type 2 diabetes. However, compelling preclinical and clinical evidence now suggests that these drugs can rapidly and directly influence pathways of glucose metabolism independently of weight gain and even in absence of psychiatric illness. Mechanisms of these direct effects remain poorly elucidated but may involve central and peripheral antagonism of neurotransmitters implicated not only in the therapeutic effects of APs but also in glucose homeostasis, possibly via effects on the autonomic nervous system. The clinical relevance of studying "direct" effects of these drugs on glucose metabolism is underscored by the widespread use of these medications, both on and off label, for a growing number of mental illnesses, extending safety concerns well beyond schizophrenia.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".