Antipsychotic Medication and Risk of Metabolic Disorders in People With Schizophrenia: A Longitudinal Study Using the UK Clinical Practice Research Datalink
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Antipsychotics are first-line drug treatments for schizophrenia. When antipsychotic monotherapy is ineffective, combining two antipsychotic drugs is common although treatment guidelines warn of possible increases in side effects. Risks of metabolic side effects with antipsychotic polypharmacy have not been fully investigated. This study examined associations between antipsychotic polypharmacy and risk of developing diabetes, hypertension, or hyperlipidemia in adults with schizophrenia, and impact of co-prescription of first- and second-generation antipsychotics. STUDY DESIGN: A population-based prospective cohort study was conducted in the United Kingdom using linked primary care, secondary care, mental health, and social deprivation datasets. Cox proportional hazards models with stabilizing weights were used to estimate risk of metabolic disorders among adults with schizophrenia, comparing patients on antipsychotic monotherapy vs polypharmacy, adjusting for demographic and clinical characteristics, and antipsychotic dose. STUDY RESULTS: Median follow-up time across the three cohorts was approximately 14 months. 6.6% developed hypertension in the cohort assembled for this outcome, with polypharmacy conferring an increased risk compared to monotherapy, (adjusted Hazard Ratio = 3.16; P = .021). Patients exposed to exclusive first-generation antipsychotic polypharmacy had greater risk of hypertension compared to those exposed to combined first- and second-generation polypharmacy (adjusted HR 0.29, P = .039). No associations between polypharmacy and risk of diabetes or hyperlipidemia were found. CONCLUSIONS: Antipsychotic polypharmacy, particularly polypharmacy solely comprised of first-generation antipsychotics, increased the risk of hypertension. Future research employing larger samples, follow-up longer than the current median of 14 months, and more complex methodologies may further elucidate the association reported in this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".