Integrating urinary and plasma omics to identify markers and therapeutic targets for cardiac disease

Introduction: Urinary metabolites, representing kidney regulated filtration of metabolism end products, contain cardiac disease biomarkers such as NT-proBNP. We set out to integrate plasma proteins with urinary metabolites to identify potentially druggable metabolic pathways for cardiac disease. Methods: Data was leveraged from a genome-wide association study (GWAS) on 954 urinary metabolites. Mendelian randomisation was used to identify urinary metabolites associating with atrial fibrillation (AF), heart failure (HF), dilated cardiomyopathy (DCM), or hypertrophic cardiomyopathy (HCM). By interrogating eight independent plasma protein GWAS, jointly including 92,277 participants and 1,562 unique proteins, we identified druggable plasma proteins with a directionally concordant effect on urinary metabolites and cardiac outcomes. Results: In total, 38 unique urinary metabolites associated with cardiac disease, predominantly covering breakdown products from amino acid metabolism (n=12), xenobiotic metabolism (n=5), and unclassified metabolism origins (n=16). Subsequently, we identified 32 druggable proteins expressed in cardiac tissue, which had a directionally concordant association with the identified urinary metabolites and cardiac outcomes. This included positive control findings, for example higher values of AT1B2 (targeted by digoxin) decreased the risk of HCM, which we were able to link to a novel unclassified urinary metabolite (X-15497). Additionally, we showed that increased plasma RET values, a mediator of GDF-15 signalling, reduced the risk of HF, and linked this to the novel unclassified urinary breakdown product X-23776. Conclusion: We were able to identify 32 druggable proteins affecting cardiac disease, and link these to urinary measurements of metabolite breakdown processes identifying potentially novel disease pathways. ### Competing Interest Statement AFS and CF have received funding from New Amsterdam Pharma for unrelated projects. The other authors declare that they have no conflict of interest. ### Funding Statement SD and SAEP are supported by a VIDI Fellowship (project number 09150172010050) from the Dutch Organisation for Health Research and Development (ZonMW) awarded to SAEP. AFS is supported by BHF grant PG/22/10989, the UCL BHF Research Accelerator AA/18/6/34223, MR/V033867/1, the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, and the EU Horizon scheme (AI4HF 101080430 and DataTools4Heart 101057849). This work was supported by the NWO Snellius supercomputer project (application 2023.022). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The individual GWAS data on cardiac outcomes leveraged in this study can be accessed as follows: atrial fibrillation (cases=60,620, total n=1,030,836; https://www.ebi.ac.uk/gwas/publications/30061737), heart failure (cases=47,309, total n=977,323; https://www.ebi.ac.uk/gwas/publications/31919418), dilated cardiomyopathy (cases=2,719, total n=6,980; https://www.ebi.ac.uk/gwas/publications/33677556) and hypertrophic cardiomyopathy (cases=2,993, total n=1,197,200; http://results.globalbiobankmeta.org/pheno/HCM). The GWAS data on urinary metabolite values can be accessed via https://www.ebi.ac.uk/gwas/publications/31959995 (n=1,627). The individual GWAS data on plasma protein values can be accessed as follows: deCODE (n=35,559, https://www.decode.com/summarydata/), SCALLOP (n=30,931, https://www.ebi.ac.uk/gwas/publications/33067605), Ahola-Olli et al. (n=8,293, https://www.ebi.ac.uk/gwas/publications/27989323), Framingham (n=6,861, https://www.ebi.ac.uk/gwas/publications/21909115), AGES-Reykjavik (n=5,368, https://www.ebi.ac.uk/gwas/publications/35078996), INTERVAL (n=3,301, https://www.ebi.ac.uk/gwas/publications/29875488), Gilly et al. (n=1,328, https://www.ebi.ac.uk/gwas/publications/33303764), and Yang et al. (n=636, https://www.ebi.ac.uk/gwas/publications/34239129).