Metabolic syndrome and its relation to antipsychotic polypharmacy in schizophrenia, schizoaffective and bipolar disorders
Bibliographic record
Abstract
The risk of metabolic syndrome (MetS) has been attributed to antipsychotic use in psychiatric patients. To date, there is limited data on the relationship between antipsychotic polypharmacy and MetS in patients with schizophrenia, schizoaffective disorder and bipolar disorder. Therefore, we aimed to investigate the rate of MetS in patients with these disorders receiving antipsychotic monotherapy and polypharmacy. We conducted a cross-sectional study on patients seen between January 2017 and December 2020, collecting data on the class, type, route of administration and number of antipsychotics received. We used the American Association of Clinical Endocrinology criteria to diagnose MetS. We included 833 subjects of whom 573 (68.8%) received antipsychotic monotherapy and 260 (31.2%) received polypharmacy. Overall, 28.6% ( N = 238) had MetS with no statistical difference between the two groups. Diastolic blood pressure and receiving olanzapine were significant predictors for developing MetS. In conclusion, our study found no significant difference in the rate of MetS between antipsychotic monotherapy and polypharmacy. A number of variables were significant predictors for MetS. Our findings were consistent with other studies and warrant the need for careful choice of antipsychotics and regular screening and management of abnormal metabolic parameters.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".