Polygenic risk scores analyses of psychiatric and metabolic traits with antipsychotic-induced weight gain in schizophrenia: an exploratory study

Pharmacogenomic scores in psychiatry: systematic review of current evidence

In the past two decades, significant progress has been made in the development of polygenic scores (PGSs). One specific application of PGSs is the development and potential use of pharmacogenomic- scores (PGx-scores) to identify patients who can benefit from a specific medication or are likely to experience side effects. This systematic review comprehensively evaluates published PGx-score studies in psychiatry and provides insights into their potential clinical use and avenues for future development. A systematic literature search was conducted across PubMed, EMBASE, and Web of Science databases until 22 August 2023. This review included fifty-three primary studies, of which the majority (69.8%) were conducted using samples of European ancestry. We found that over 90% of PGx-scores in psychiatry have been developed based on psychiatric and medical diagnoses or trait variants, rather than pharmacogenomic variants. Among these PGx-scores, the polygenic score for schizophrenia (PGSSCZ) has been most extensively studied in relation to its impact on treatment outcomes (32 publications). Twenty (62.5%) of these studies suggest that individuals with higher PGSSCZ have negative outcomes from psychotropic treatment - poorer treatment response, higher rates of treatment resistance, more antipsychotic-induced side effects, or more psychiatric hospitalizations, while the remaining studies did not find significant associations. Although PGx-scores alone accounted for at best 5.6% of the variance in treatment outcomes (in schizophrenia treatment resistance), together with clinical variables they explained up to 13.7% (in bipolar lithium response), suggesting that clinical translation might be achieved by including PGx-scores in multivariable models. In conclusion, our literature review found that there are still very few studies developing PGx-scores using pharmacogenomic variants. Research with larger and diverse populations is required to develop clinically relevant PGx-scores, using biology-informed and multi-phenotypic polygenic scoring approaches, as well as by integrating clinical variables with these scores to facilitate their translation to psychiatric practice.

Clinical Utility and Implementation of Pharmacogenomics for the Personalisation of Antipsychotic Treatments

Decades of pharmacogenetic research have revealed genetic biomarkers of clinical response to antipsychotics. Genetic variants in antipsychotic targets, dopamine and serotonin receptors in particular, and in metabolic enzymes have been associated with the efficacy and toxicity of antipsychotic treatments. However, genetic prediction of antipsychotic response based on these biomarkers is far from accurate. Despite the clinical validity of these findings, the clinical utility remains unclear. Nevertheless, genetic information on CYP metabolic enzymes responsible for the biotransformation of most commercially available antipsychotics has proven to be effective for the personalisation of clinical dosing, resulting in a reduction of induced side effects and in an increase in efficacy. However, pharmacogenetic information is rarely used in psychiatric settings as a prescription aid. Lack of studies on cost-effectiveness, absence of clinical guidelines based on pharmacogenetic biomarkers for several commonly used antipsychotics, the cost of genetic testing and the delay in results delivery hamper the implementation of pharmacogenetic interventions in clinical settings. This narrative review will comment on the existing pharmacogenetic information, the clinical utility of pharmacogenetic findings, and their current and future implementations.

Association of clinical parameters and polygenic risk scores for body mass index, schizophrenia, and diabetes with antipsychotic-induced weight gain

Antipsychotic-induced weight gain (AIWG) is a common adverse event in schizophrenia. Genome-wide association studies (GWAS) and polygenic risk scores (PRS) for other diseases or traits are recent approaches to disentangling the genetic architecture of AIWG. 200 patients with schizophrenia treated monotherapeutically with antipsychotics were included in this study. A multiple linear regression analysis with ten-fold crossvalidation was performed to predict the percentage weight change after five weeks of treatment. Independent variables were sex, age, body mass index (BMI) at baseline, medication-associated risk, and PRSs (BMI, schizophrenia, diabetes, and metabolic syndrome). An explorative GWAS analysis was performed on the same subjects and traits. PRSs for BMI (β = 3.78; p = 0.0041), schizophrenia (β = 5.38; p = 0.021) and diabetes type 2 (β = 13.4; p = 0.046) were significantly associated with AIWG. Other significant factors were sex, baseline BMI and medication. Compared to the model without genetic factors, the addition of PRSs for BMI, schizophrenia, and diabetes type 2 increased the goodness of fit by 6.5 %. The GWAS identified the association of three variants (rs10668573, rs10249381 and rs1988834) with AIWG at a genome-wide level of p < 1 · 10-6. Using PRS for schizophrenia, BMI, and diabetes type 2 increased the explained variation of predicted weight gain, compared to a model without PRSs. For more precise results, PRSs derived from other traits (ideally AIWG) should be investigated. Potential risk variants identified in our GWAS need to be further investigated and replicated in independent samples.