ArticleThe pharmacogenomics journal2023
Polygenic risk scores analyses of psychiatric and metabolic traits with antipsychotic-induced weight gain in schizophrenia: an exploratory study.
Article in The pharmacogenomics journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 10 citations in OpenAlex.
- Pharmacogenomic scores in psychiatry: systematic review of current evidence.Translational psychiatry · 2024Pooled it
- Developing a Polygenic Risk Score for Weight Gain in People Treated for Psychosis-Application in a Real-World Setting.Human psychopharmacology · 2026Article
- Pathway-Specific Polygenic Scores for Predicting Clinical Lithium Treatment Response in Patients With Bipolar Disorder.Biological psychiatry global open science · 2025Article
- Genome-wide associations with metabolic syndrome among UK Biobank participants reporting use of second-generation antipsychotics.Pharmacotherapy · 2025Article
- Clinical Utility and Implementation of Pharmacogenomics for the Personalisation of Antipsychotic Treatments.Pharmaceutics · 2024Review
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Given the polygenic nature of antipsychotic-induced weight gain (AIWG), we investigated whether polygenic risk scores (PRS) for various psychiatric and metabolic traits were associated with AIWG. We included individuals with schizophrenia (SCZ) of European ancestry from two cohorts (N = 151, age = 40.3 ± 11.8 and N = 138, age = 36.5 ± 10.8). We investigated associations of AIWG defined as binary and continuous variables with PRS calculated from genome-wide association studies of body mass index (BMI), coronary artery disease (CAD), fasting glucose, fasting insulin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), triglycerides, type 1 and 2 diabetes mellitus, and SCZ, using regression models. We observed nominal associations (uncorrected p < 0.05) between PRSs for BMI, CAD, and LDL-C, type 1 diabetes, and SCZ with AIWG. While results became non-significant after correction for multiple testing, these preliminary results suggest that PRS analyses might contribute to identifying risk factors of AIWG and might help to elucidate mechanisms at play in AIWG.
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Registered trials
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