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<p>Antidepressant side effects are prevalent, leading to significant treatment discontinuity among patients. A deeper understanding of the underlying mechanisms could help identify individuals at risk of side effects and improve treatment outcomes.We aim to investigate the role of genetic variation in <i>CYP2C19</i> and polygenic scores (PGS) for psychiatric and side effect-related phenotypes in experiencing antidepressant side effects.We pooled Estonian Biobank data from the Mental Health online Survey (<i>N</i> = 86,244), the Adverse Drug Events Questionnaire (<i>N</i> = 49,366) and from unstructured electronic health records using natural language processing (<i>N</i> = 206,066) covering 25 common side effects. The results were meta-analysed with previously published results from the Australian Genetics of Depression Study. Among 13,729 antidepressant users, 52.0% reported side effects. In a subgroup of 9,563 individuals taking antidepressants metabolised by CYP2C19, poor metabolisers had 49% higher odds of reporting a side effect (OR = 1.49, 95%CI = 1.09–2.04), while ultrarapid metabolisers had 17% lower odds (OR = 0.83, 95%CI = 0.70–0.99) compared to normal metabolisers. PGSs for schizophrenia and depression showed the most associations with overall and specific side effects. PGSs for higher body mass index (BMI), anxiety, and systolic blood pressure were associated with respective side effects among any antidepressant and selective serotonin reuptake inhibitor (SSRI) users. Meta-analysis confirmed robust evidence linking a higher BMI PGS and weight gain across nine antidepressants and moderate evidence linking PGS for headache with headache from sertraline. Our findings underscore the role of genetic factors in experiencing antidepressant side effects and have potential implications for personalised medicine approaches to improve antidepressant treatment outcomes.</p>

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Genetic influences on antidepressant side effects: a CYP2C19 gene variation and polygenic risk study in the Estonian Biobank

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月27日

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  • Hanna Maria Kariis,
  • Dage Särg,
  • Kristi Krebs,
  • Maarja Jõeloo,
  • Kadri Kõiv,
  • Kairit Sirts,
  • Georgi Hudjashov,
  • Dage Särg,
  • Maris Alver,
  • Kelli Lehto,
  • Lili Milani

摘要

Antidepressant side effects are prevalent, leading to significant treatment discontinuity among patients. A deeper understanding of the underlying mechanisms could help identify individuals at risk of side effects and improve treatment outcomes.We aim to investigate the role of genetic variation in CYP2C19 and polygenic scores (PGS) for psychiatric and side effect-related phenotypes in experiencing antidepressant side effects.We pooled Estonian Biobank data from the Mental Health online Survey (N = 86,244), the Adverse Drug Events Questionnaire (N = 49,366) and from unstructured electronic health records using natural language processing (N = 206,066) covering 25 common side effects. The results were meta-analysed with previously published results from the Australian Genetics of Depression Study. Among 13,729 antidepressant users, 52.0% reported side effects. In a subgroup of 9,563 individuals taking antidepressants metabolised by CYP2C19, poor metabolisers had 49% higher odds of reporting a side effect (OR = 1.49, 95%CI = 1.09–2.04), while ultrarapid metabolisers had 17% lower odds (OR = 0.83, 95%CI = 0.70–0.99) compared to normal metabolisers. PGSs for schizophrenia and depression showed the most associations with overall and specific side effects. PGSs for higher body mass index (BMI), anxiety, and systolic blood pressure were associated with respective side effects among any antidepressant and selective serotonin reuptake inhibitor (SSRI) users. Meta-analysis confirmed robust evidence linking a higher BMI PGS and weight gain across nine antidepressants and moderate evidence linking PGS for headache with headache from sertraline. Our findings underscore the role of genetic factors in experiencing antidepressant side effects and have potential implications for personalised medicine approaches to improve antidepressant treatment outcomes.