Discovery of Urinary Metabolite Biomarkers of Psychiatric Disorders Using Two-Sample Mendelian Randomization

Abstract

BackgroundPsychiatric disorders cause substantial patient suffering world-wide, which could be alleviated through the discovery of early diagnostic biomarkers. Urinary markers have promising practical applications; however, no robust urine biomarkers exist currently for psychiatric disorders. While the traditional biomarker discovery process is costly and time-consuming, genetic methods utilizing existing data from large-scale studies, such as Mendelian randomization, may provide an alternative, cost-effective approach to identifying such biomarkers. MethodsA two-sample Mendelian randomization analysis was conducted in R using GWAS data for seven psychiatric disorders from the Psychiatrics Genomics Consortium, as well as a meta-analysis of urinary metabolite GWAS studies and the GWAS Catalog. Mendelian randomization assumptions were assessed using the MR-Egger intercept, P-values, and genetic associations from the PhenoScanner database. OutcomesThe Mendelian randomization analysis revealed 67 analyte-disorder associations, of which 21 were exclusive to a single disorder. Most notable associations were observed between tyrosine and schizophrenia ({beta}=-0.041, SE=0.013, Q=0.027), and creatine and bipolar disorder ({beta}=-0.077, SE=0.019, Q=0.002), which validated across multiple studies, as well as that of pyridoxal ({beta}=0.10, SE=0.03, Q=0.042) and ferulic acid 4-sulfate ({beta}=0.077, SE=0.025, Q=0.037) to anorexia nervosa, and N,N-dimethylglycine to attention deficit hyperactivity disorder ({beta}=-0.39, SE=0.11, Q=0.008). InterpretationThese results indicate an association between bipolar disorder, schizophrenia, anorexia nervosa, and attention deficit hyperactivity disorder with urinary metabolite marker alterations. Most of the findings were supported by previous literature. The results provide a roadmap for future experimental and clinical validation of the identified biomarker candidates and demonstrate the utility of using genetic instruments for urinary biomarker discovery.