Understanding antidepressant change patterns in the UK Biobank
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Background
Antidepressants are the most prescribed medications in psychiatry. Medical records provide a valuable opportunity to explore prescribing patterns and to uncover clinical and genetic factors that influence treatment outcomes.
Methods
Using longitudinal primary care records from the UK Biobank, we investigated antidepressant change patterns in both depression and non-depression indications. We examined outcomes including the number of antidepressant changes, and discontinuation that is likely due to intolerable side effects or inadequate response. Genetic analyses including heritability estimation, genetic correlation and polygenic score association were performed.
Results
A total of 82,633 individuals in the UK Biobank were prescribed at least one of 37 antidepressants. Of these, 28,332 had a depression diagnosis and 24,543 did not. Selective serotonin reuptake inhibitors (SSRIs), including citalopram and fluoxetine, were the most prescribed antidepressants for depression, while tricyclic antidepressants, especially amitriptyline, dominated prescriptions in non-depression indications. Individuals with depression were more likely to stay on antidepressants longer than those without depression, and to follow preferred antidepressant choices that have changed over time. For depression, discontinuation rates for SSRIs were 9% for likely intolerable side effects and 12% for likely nonresponse. Antidepressant change and discontinuation were significantly enriched for psychiatric and somatic conditions, including recurrent depression, anxiety in the depression group, and pain-related conditions in the non-depression group. Genetic analyses identified two novel variants associated with SSRI early discontinuation. Notable genetic overlap was shown between these antidepressant phenotypes and multiple psychiatric and physical traits, and polygenic scores had significant prediction for treatment outcomes.
Conclusions
These findings provide a valuable framework for characterising antidepressant change in primary care records and highlight the potential of integrating clinical and genetic data to better understand factors influencing treatment outcomes. Replication in other large-scale medical records will be essential to advance the discovery of genetic variants associated with antidepressant outcomes.
