T207. METABOLIC ADVERSE EFFECTS OF OFF-LABEL USE OF SECOND-GENERATION ANTIPSYCHOTICS IN THE ADULT POPULATION: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Background Second generation antipsychotics (SGAs) are approved by the US Food and Drug Administration (FDA) for the treatment of schizophrenia, bipolar disorder and treatment refractory depression. Since the late 1990’s, prescription of antipsychotics has more than doubled and off-label use (i.e. use for indications other than those approved by the FDA) has disproportionately contributed to this increase. These drugs, when used “on-label’, have very significant metabolic adverse effects. Currently, there is no existing literature systematically investigating the metabolic adverse effects of ‘off-label’ use to guide clinical decision making practices. Methods A systematic review was conducted and MEDLINE, EMBASE, CENTRAL, PsycINFO, and CINAHL were searched for all randomized controlled trials (RCTs) that reported off-label use of SGAs in the adult population. A grey literature search was also completed on ClinicalTrials.gov and the ICTRP Search Portal. The Cochrane Risk of Bias tool was used for the assessment of potential biases. Meta-analyses of the reported metabolic outcomes were completed based on psychiatric diagnosis and subgroup analyses were conducted based on the individual prescribed SGA. Metabolic adverse events analyzed include weight, fasting plasma glucose, HbA1C, total cholesterol, HDL, LDL, and triglycerides. Sensitivity analyses were performed where studies of low quality were excluded. Results The search strategy identified 9309 references, for which 47 randomized control trials (RCTS) were included in the meta-analysis. These studies were published from 1984 to 2019 and came from North America (n=27), Europe (n=9), Asia (n=4), and other (n=7). Participants with generalized anxiety disorder (n=9 studies) treated with quetiapine gained on average 0.53kg (CI 0.22 to 0.84, p= 0.008), had increases in triglyceride levels by 0.17mmol/L (CI 0.052 to 0.29, p=0.005), and decreases in HDL levels by 0.038mmol/L (CI -0.065 to -0.011 p=0.005). Similar results were found in patients with borderline personality disorder (BPD), where olanzapine was associated with a mean weight increase of 2.67kg (CI 2.23 to 3.11, p<0.00001), and increases in triglyceride levels by 0.0029mmol/L (CI 0.0015to 0.0045, p = 0.002). In a single study in patients with trichotillomania, olanzapine was associated with a weight gain of 4.90kg (CI 3.04 to 6.76). Participants with OCD (n=8 studies) receiving quetiapine gained on average 3.40kg (CI 3.17 to 3.63). In studies examining PTSD treatment (n=7 studies), quetiapine was associated with a mean 23kg weight increase (CI 4.85 to 41.15), and olanzapine with a 6.63kg mean weight increase (CI 4.87 to 8.39, p<0.0001). The degree of weight gain associated with quetiapine could partly be attributable to the report of endpoint mean weights only between groups (rather than change data). Participants with cocaine-related substance use disorder (n= 4 studies) on risperidone gained on average 4.70kg (CI 1.78 to 7.62). Where applicable, sensitivity analyses removing low quality studies, did not affect the significance of findings. Discussion Based on the limited data available, there is a significant association between off-label antipsychotic use and weight gain. This was most commonly reported in association with quetiapine and olanzapine, which may be due to the fact they were the most widely studied agents (n =12, and 16 studies, respectively). Although other metabolic adverse events have been documented, there is not yet enough data available to draw conclusions. Our findings raise concern given the widespread use of SGAs in off-label indications, highlighting the need for raising clinical awareness, ensuring regular metabolic monitoring, and further study of these adverse effects.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.038 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".