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Approved Research

Investigating genome wide determinants of antipsychotic-induced metabolic syndrome (AP-Mets) using UK Biobank data

Principal Investigator: Dr Nihal El Rouby
Approved Research ID: 70270
Approval date: November 30th 2021

Lay summary

Psychiatric disorders are common, affecting tens of millions of patients each year. Antipsychotic medications are commonly prescribed for a wide range of psychiatric disease including depression, anxiety disorders, schizophrenia, obsessive-compulsive disorders, and bipolar disorders. Developing metabolic syndrome following antipsychotic medication use is a common and clinically important problem. Statistics document that one in three patients may develop metabolic syndrome after starting antipsychotic medication. This is defined by a range of metabolic disturbances including but are not limited to weight gain, changes in blood sugar and lipids, which can lead to diabetes, heart disease and cardiovascular complications. Patients with psychiatric disease are already at high risk for cardiovascular complications, and the risk is further increased by the occurrence of metabolic syndrome. Identifying the genetic and molecular basis of metabolic syndrome can help us understand the biology of this adverse event, potentially leading to developing new drug targets. Additionally, it can allow us to predict patients whose genetic make-up puts them at high risk for developing metabolic syndrome. We may then be able to intervene earlier and intensify preventive lifestyle strategies and protective drug treatments. Further, we can select drug therapy that are known to have low risk of causing metabolic changes in patients identified to be at risk of developing metabolic syndrome. In this proposed project, we plan to use clinical and rich genetic data within UK Biobank to perform genetic association studies that aim to identify genetic markers that are linked to metabolic syndrome. We plan to replicate our findings in additional Biobanks such as BioVU Biobank. Finally, we plan to build a genetic score to predict patients with high genetic risk for this adverse event. We plan to execute our research over 12 months and publish the findings, making it available to other researchers. We believe that this research can have a public health impact as it can address a common clinical problem through robust discovery and validation research using rich data.