GWAS of metabolic syndrome and its components
Approved Research ID: 64716
Approval date: November 25th 2020
Metabolic syndrome is a syndrome that is prevalent in 1 out of every 5 persons. It can be determined by checking levels of some lab values such as HDL cholesterol, triglycerids and glucose in the blood of people, as well as checking their waist size and blood pressure. When at least 3 out of 5 are out of range, a person has metabolic syndrome. This syndrome leads to increased morbidity such as diabetes type 2 and cardiovascular disease and therefore also to increased mortality. In this study, we would like to use genetic data from the UK biobank to see if we can find genetic markers that make people more susceptible to metabolic syndrome. By combining parts of the genetics that give us significant results into a 'risk-score' we can try to predict this in others. We would like to apply this risk-score to a psychiatric cohort that we have available. These are treatment-resistant schizophrenia patients using an antipsychotic medication called clozapine. This medication is used as a last resort, because it has severe side-effects. It is also associated with causing metabolic syndrome, but until now we don't know whether this is the same sort of metabolic syndrome as in the general population. Therefore, we want to investigate whether the underlying genetics of MetS overlap between the general population and patients on clozapine.
The impact would be that we have an idea about the (genetic) markers for metabolic syndrome in general and for the clozapine users. This could lead to earlier identification of people susceptible metabolic syndrome and thus early initiation of preventive measures which could reduce the morbidity and mortality of this syndrome in this vulnerable group of patients. The project duration will be 1.5 year to be able to perform the analyses and writing up the findings.
Current scope: We aim to link SNPs to metabolic syndrome (MetS) and components of it (see A3 for definition) and generate polygenic risk scores (PRS) for MetS. Currently, no GWAS has been performed on MetS, while around 20% of the general population meets the criteria for MetS which leads to increased morbidity (diabetes mellitus type 2 and cardiovascular disease).With part of the UKBB GWAS data, we would like to generate PRS and see to what extend this could predict the development of MetS in the other part of the UKBB study population. In addition, we want to apply these PRS to our schizophrenia spectrum disorder cohort of clozapine users. 53.8% of the clozapine users develop MetS (Lamberti et al., 2006). We want to investigate whether the PRS-MetS generated this way by use are applicable to the clozapine cohort to find out whether this syndrome has the same etiology in the general population as in this psychiatric cohort.
The requested phenotypic and genotypic variables remain the same but we want to propose to focus on BMI and other cardiovascular outcomes in stead of MeTS. We aim to generate polygenic risk scores (PRS) for BMI and examine the associations between psychotropic use, lifestyle, genetic risk for BMI using polygenic risk scores and BMI and we want to examine the associations between psychotropic use, lifestyle and other cardiovascular outcomes.