Risk of Treatment-Emergent Diabetes Mellitus in Patients Receiving Antipsychotics
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes mellitus has been reported during antipsychotic treatment. OBJECTIVE: To quantify the potential risk of treatment-emergent diabetes mellitus among patients receiving antipsychotic medications. METHODS: The MEDLINE and Psychinfo databases were searched using the key words antipsychotic (including individual drug names), diabetes, risk, and incidence for all English-language articles published between 1966 and 2005. Risk calculations were performed using data obtained from pharmacoepidemiologic studies that met the following criteria: (1) cohort design, (2) determination of preexisting diabetes, (3) inclusion of antipsychotic monotherapy as an exposure variable, and (4) comparison with exposure to first-generation antipsychotics. Studies meeting these criteria were used to calculate incidence, attributable risk between agents, and number needed to harm. RESULTS: A total of 25 observational pharmacoepidemiologic studies were found comparing antipsychotics on the outcome of diabetes mellitus. Sufficient information was provided in 15 of the reports to be able to estimate attributable risk. Attributable risk for individual second-generation antipsychotics relative to first-generation antipsychotics ranged from 53 more to 46 fewer new cases of diabetes per 1000 patients. Little observable difference was noted between the individual second-generation antipsychotics versus first-generation antipsychotics on this outcome. However, few of the studies controlled for body weight, race or ethnicity, or the presence of diabetogenic medications. None adjusted for familial history of diabetes, levels of physical activity, or diet, as this information is not usually available in the databases used in pharmacoepidemiologic studies. CONCLUSIONS: Based on the published pharmacoepidemiologic reports reviewed, the avoidance of diabetes as an outcome cannot be predictably achieved with precision by choice of a second-versus a first-generation antipsychotic. Risk management for new-onset diabetes requires the assessment of established risk factors such as family history, advancing age, non-white ethnicity, diet, central obesity, and level of physical activity.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".