The Metabolic Syndrome During Atypical Antipsychotic Drug Treatment: Mechanisms and Management
Bibliographic record
Abstract
The frequency of obesity, insulin resistance, type 2 diabetes mellitus and other components of metabolic syndrome appear to be significantly elevated in some psychiatric patients. This is a notable example of genetic/environment interaction. Considering the genetic contribution, evidence of insulin resistance in persons with schizophrenia was reported in the pre-pharmacological era. High insulin, glucose, and cortisol levels are observed in first episode psychosis. The frequency of type 2 diabetes mellitus is significantly increased in persons with schizophrenia and bipolar disorder and in their first-degree relatives. Finally, a link exists between schizophrenia and enzymes involved in glycolysis and between antipsychotic drug-induced weight gain and serotonin receptor polymorphism. Important environmental factors are poor dietary habits, smoking, lack of physical exercise, and drug treatment, mostly with antipsychotic drugs (APDs) and perhaps with mood stabilizers. The APDs probably induce metabolic dysfunction by producing sudden appetite increase and weight gain in predisposed subjects. However, direct drug effects on glucose and lipid metabolism independent from body weight change have been proposed. Excessive weight gain is mainly observed with clozapine, olanzapine, chlorpromazine, and thioridazine and is less consistently noted with risperidone or quetiapine. Two recently introduced APDs, ziprasidone and aripiprazole, display a neutral effect on weight and metabolism. Subjects at high risk must be identified early during APD treatment so that provide lifestyle counseling and pharmacological assistance can be provided. The immediate research agenda for the APDs is to improve the animal models of drug-induced metabolic dysfunction; to clarify mechanisms other than weight gain and appetite stimulation; and to test pharmacological agents in randomized, double-blind studies to prevent or reverse metabolic syndrome in selected patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".