Machine learning reveals heterogeneous associations between environmental factors and cardiometabolic diseases across polygenic risk scores

Naito, Tatsuhiko; Inoue, Kosuke; Namba, Shinichi; Sonehara, Kyuto; Suzuki, Ken; ,; Matsuda, Koichi; Kondo, Naoki; Toda, Tatsushi; Yamauchi, Toshimasa; Kadowaki, Takashi; Okada, Yukinori

doi:10.1038/s43856-024-00596-7

Commun Med

2024

DOI: 10.1038/s43856-024-00596-7

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Machine learning reveals heterogeneous associations between environmental factors and cardiometabolic diseases across polygenic risk scores

Tatsuhiko Naito,

Kosuke Inoue,

Shinichi Namba

et al.

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Cited by 1 publication

References 51 publications

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Polygenic risk scores for precision psychiatry: a study on the effect heterogeneity of antidepressants

Goto,

Naito,

Skokauskas

et al. 2024

Preprint

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Depression continues to be a major contributor to the global burden of diseases. Antidepressants are recommended as the initial choice of treatment for moderate and severe depression among adults, but the choice of antidepressant class can be challenging, as efficacies of commonly used antidepressants are generally comparable between classes. As such, to determine which medication to prescribe to each patient, clinicians often rely on the adverse effect profile of antidepressants. One of the most discussed adverse effects is the possibility of increased suicidality with antidepressant initiation, a rare but serious adverse event. Efforts to predict this have mostly relied on clinical and sociodemographic characteristics of patients, but we do not yet have a clear picture. A promising avenue is to use patients' genetic data, but the evidence on its utility has so far been mixed, and few large scale databases have both clinical and genetic data that can evaluate its utility. Here, using genetic and clinical data on more than 7,000 patients, both children and adults, with major depressive disorder from the All of Us Research Program, we show that the genetic predisposition for psychiatric disorders may underlie the substantial heterogeneity in the risk of suicidal thoughts with antidepressant use. Specifically, using a target trial emulation framework, we show that patients with higher polygenic risk scores for psychiatric disorders, particularly for attention deficit-hyperactivity disorder, are more likely than those with lower scores to experience suicidal thoughts with the use of selective serotonin reuptake inhibitors, relative to bupropion. In the personalized medicine framework, PRSs for various psychiatric disorders may help tailor antidepressants to each patient to avoid serious adverse effects such as suicidal thoughts.

show abstract

Polygenic risk scores for precision psychiatry: a study on the effect heterogeneity of antidepressants

Goto,

Naito,

Skokauskas

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

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Machine learning reveals heterogeneous associations between environmental factors and cardiometabolic diseases across polygenic risk scores

Cited by 1 publication

References 51 publications

Polygenic risk scores for precision psychiatry: a study on the effect heterogeneity of antidepressants

Polygenic risk scores for precision psychiatry: a study on the effect heterogeneity of antidepressants

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