An Assessment of the Independent Effects of Olanzapine and Risperidone Exposure on the Risk of Hyperlipidemia in Schizophrenic Patients
Bibliographic record
Abstract
BACKGROUND: The newer antipsychotic agents exhibit a superior safety profile compared with conventional antipsychotic agents in terms of extrapyramidal symptoms. Previous studies have suggested an association between olanzapine treatment and hyperlipidemia. We evaluated this association using a large health care database. METHODS: The study was derived from the England and Wales-based General Practice Research Database, composed of 3.5 million subjects followed up between June 1, 1987, and September 24, 2000. A total of 18 309 individuals diagnosed as having schizophrenia were identified. A 6:1 matched nested case-control design was used. Conditional logistic regression was used to derive adjusted odds ratios (ORs), controlling for sex, age, and other medications and disease conditions influencing lipid levels. Antipsychotic drug exposure was defined as the receipt of at least 1 prescription for an antipsychotic medication within the 3 months before the date of diagnosis of hyperlipidemia. RESULTS: There were 1268 incident cases of hyperlipidemia in the cohort, matched to 7598 control subjects. Olanzapine use was associated with nearly a 5-fold increase in the odds of developing hyperlipidemia compared with no antipsychotic exposure (OR, 4.65; 95% confidence interval [CI], 2.44-8.85) (P<.001) and more than a 3-fold increase compared with those receiving conventional agents (OR, 3.36; 95% CI, 1.77-6.39) (P<.001). Risperidone was not associated with increased odds of hyperlipidemia compared with no antipsychotic exposure (OR, 1.12; 95% CI, 0.60-2.11) (P =.72) or conventional antipsychotic exposure (OR, 0.81; 95% CI, 0.44-1.52) (P =.52). CONCLUSIONS: We observed a strong association between olanzapine exposure and hyperlipidemia in schizophrenic patients. The possible metabolic consequences of olanzapine use should be given serious consideration by treating physicians.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".