Antipsychotic Metabolic Effects: Weight Gain, Diabetes Mellitus, and Lipid Abnormalities
Bibliographic record
Abstract
OBJECTIVE: To review published and nonpublished literature describing changes in weight, glucose homeostasis, and lipid milieu with antipsychotics. METHODS: A Medline search was completed using the words weight gain, diabetes mellitus, cholesterol, triglycerides, risperidone, clozapine, olanzapine, quetiapine, ziprasidone, predictors, prolactin, obesity, and conventional antipsychotics. Publications, including original articles, review articles, letters to the editor, abstracts or posters presented at professional meetings in the last 4 years, and references from published articles, were collected. Manufacturers, including Eli Lilly Canada Inc, JanssenOrtho Inc, Pfizer Canada Inc, AstraZeneca Inc, and Novartis Pharmaceuticals, were contacted to retrieve additional medical information. RESULTS: The topic of antipsychotic-induced weight gain is understudied, and there are relatively few well-controlled studies. Weight gain as a side effect has been described with both conventional and atypical antipsychotics. Moreover, some atypical antipsychotics are associated with de novo diabetes mellitus and increased serum triglyceride levels. Predictors of weight gain may be age, baseline body mass index, appetite stimulation, previous antipsychotic exposure, and antipsychotic treatment duration. CONCLUSION: Significant weight gain is reported with the existing atypical antipsychotics. The weight gain described is highly distressing to patients, may reduce treatment adherence, and may increase the relative risk for diabetes mellitus and hypertriglyceridemia. Physicians employing these agents should routinely monitor weight, fasting blood glucose, and lipid profiles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".