Bibliographic record
Abstract
OBJECTIVES: To review current evidence for the hypothesis that treatment with antipsychotic medications may be associated with increased risks for weight gain, insulin resistance, hyperglycemia, dyslipidemia, and type 2 diabetes mellitus (T2DM) and to examine the relation of adiposity to medical risk. METHODS: We identified relevant publications through a search of MEDLINE from the years 1975 to 2006, using the following primary search parameters: "diabetes or hyperglycemia or glucose or insulin or lipids" and "antipsychotic." Meeting abstracts and earlier nonindexed articles were also reviewed. We summarized key studies in this emerging literature, including case reports, observational studies, retrospective database analyses, and controlled experimental studies. RESULTS: Treatment with different antipsychotic medications is associated with variable effects on body weight, ranging from modest increases (for example, less than 2 kg) experienced with amisulpride, ziprasidone, and aripiprazole to larger increases during treatment with agents such as olanzapine and clozapine (for example, 4 to 10 kg). Substantial evidence indicates that increases in adiposity are associated with decreases in insulin sensitivity in individuals both with and without psychiatric disease. The effects of increasing adiposity, as well as other effects, may contribute to increases in plasma glucose and lipids observed during treatment with certain antipsychotics. CONCLUSION: Treatment with certain antipsychotic medications is associated with metabolic adverse events that can increase the risk for metabolic syndrome and related conditions such as prediabetes, T2DM, and cardiovascular disease.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".