Pamela Matías-García1, Rory Wilson2, Qi Guo3, Shaza Zaghlool4, James Eales5, Xiaoguang Xu6, Fadi Charchar7, John Dormer8, Haifa Maalmi9, Pascal Schlosser10, Mohamed Elhadad11, Jana Nano12, Sapna Sharma13, Annette Peters14, Alessia Fornoni15, Dennis Mook-Kanamori16, Juliane Winkelmann17, John Danesh18, Emanuele Di Angelantonio19, Willem Ouwehand20, Nicholas Watkins21, David Roberts22, Agnese Petrera23, Johannes Graumann24, Wolfgang Koenig25, Kristian Hveem26, Christian Jonasson27, Anna Köttgen28, Adam Butterworth29, Marco Prunotto30, Stefanie Hauck31, Christian Herder32, Karsten Suhre33, Christian Gieger34, Maciej Tomaszewski35, Alexander Teumer36, Melanie Waldenberger37. 1. P Matías-García, Helmoltz Zentrum München Research Unit Molecular Epidemiology, Neuherberg, Germany pamela.matias@helmholtz-muenchen.de. 2. R Wilson, Helmoltz Zentrum München Research Unit Molecular Epidemiology, Neuherberg, Germany. 3. Q Guo, Cardiovascular Epidemiology Unit, University of Cambridge Department of Public Health and Primary Care, Cambridge, United Kingdom of Great Britain and Northern Ireland. 4. S Zaghlool, Department of Physiology and Biophysics, Weill Cornell Medicine - Qatar, Doha, Qatar. 5. J Eales, The University of Manchester Division of Cardiovascular Sciences, Manchester, United Kingdom of Great Britain and Northern Ireland. 6. X Xu, The University of Manchester Division of Cardiovascular Sciences, Manchester, United Kingdom of Great Britain and Northern Ireland. 7. F Charchar, School of Health and Life Sciences, Federation University Australia, Ballarat, Australia. 8. J Dormer, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom of Great Britain and Northern Ireland. 9. H Maalmi, Institute for Clinical Diabetology, German Diabetes Center Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Dusseldorf, Germany. 10. P Schlosser, Institute of Genetic Epidemiology, University of Freiburg Faculty of Medicine, Freiburg, Germany. 11. M Elhadad, Helmoltz Zentrum München Research Unit Molecular Epidemiology, Neuherberg, Germany. 12. J Nano, Helmholtz Zentrum München Institute of Epidemiology, Neuherberg, Germany. 13. S Sharma, Helmoltz Zentrum München Research Unit Molecular Epidemiology, Neuherberg, Germany. 14. A Peters, Helmholtz Zentrum München Institute of Epidemiology, Neuherberg, Germany. 15. A Fornoni, Department of Medicine, University of Miami Katz Family Division of Nephrology and Hypertension, Miami, United States. 16. D Mook-Kanamori, Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, Netherlands. 17. J Winkelmann, Institute of Neurogenomics, Helmholtz Center Munich German Research Center for Environmental Health, Neuherberg, Germany. 18. J Danesh, Cardiovascular Epidemiology Unit, British Heart Foundation, London, United Kingdom of Great Britain and Northern Ireland. 19. E Di Angelantonio, Cardiovascular Epidemiology Unit, British Heart Foundation, London, United Kingdom of Great Britain and Northern Ireland. 20. W Ouwehand, Centre of Research Excellence, British Heart Foundation, London, United Kingdom of Great Britain and Northern Ireland. 21. N Watkins, Cambridge Biomedical Campus, NHS Blood and Transplant, Watford, United Kingdom of Great Britain and Northern Ireland. 22. D Roberts, National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, United Kingdom of Great Britain and Northern Ireland. 23. A Petrera, Research Unit Protein Science and Core Facility Proteomics, Helmholtz Center Munich German Research Center for Environmental Health, Neuherberg, Germany. 24. J Graumann, Scientific Service Group Biomolecular Mass Spectrometry, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany. 25. W Koenig, Partner Site Munich Heart Alliance, German Center for Cardiovascular Research (DZHK), Munich, Germany. 26. K Hveem, K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, , Norwegian University of Science and Technology Faculty of Medicine and Health Sciences, Trondheim, Norway. 27. C Jonasson, K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, , Norwegian University of Science and Technology Faculty of Medicine and Health Sciences, Trondheim, Norway. 28. A Köttgen, Institute of Genetic Epidemiology, University of Freiburg Faculty of Medicine, Freiburg, Germany. 29. A Butterworth, Cardiovascular Epidemiology Unit, University of Cambridge Department of Public Health and Primary Care, Cambridge, United Kingdom of Great Britain and Northern Ireland. 30. M Prunotto, School of Pharmaceutical Sciences, University of Geneva, Geneva, Switzerland. 31. S Hauck, Research Unit Protein Science and Core Facility Proteomics, Helmholtz Center Munich German Research Center for Environmental Health, Neuherberg, Germany. 32. C Herder, Institute for Clinical Diabetology, German Diabetes Center Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Dusseldorf, Germany. 33. K Suhre, Department of Physiology and Biophysics, Weill Cornell Medicine - Qatar, Doha, Qatar. 34. C Gieger, Helmoltz Zentrum München Research Unit Molecular Epidemiology, Neuherberg, Germany. 35. M Tomaszewski, The University of Manchester Division of Cardiovascular Sciences, Manchester, United Kingdom of Great Britain and Northern Ireland. 36. A Teumer, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany. 37. M Waldenberger, Helmoltz Zentrum München Research Unit Molecular Epidemiology, Neuherberg, Germany.
Abstract
BACKGROUND: Studies on the relationship between renal function and the human plasma proteome have identified several potential biomarkers. However, investigations have been conducted largely in European populations, and causality of the associations between plasma proteins and kidney function has never been addressed. METHODS: A cross-sectional study of 993 plasma proteins among 2,882 participants in four studies of European and admixed ancestries (KORA, INTERVAL, HUNT, QMDiab) identified trans-ethnic associations between eGFR/CKD and proteomic biomarkers. For the replicated associations, two-sample bidirectional Mendelian randomization (MR) was used to investigate potential causal relationships. Publicly available datasets and transcriptomic data from independent studies were used to examine the association between gene expression in kidney tissue and eGFR . RESULTS: Fifty-seven plasma proteins were associated with eGFR, including one novel protein. Twenty-three of these were additionally associated with CKD. The strongest inferred causal effect was the positive effect of eGFR on testican-2, in line with the known biological role of this protein and the expression of its protein-coding gene (SPOCK2) in renal tissue. We also observed suggestive evidence of an effect of melanoma inhibitory activity (MIA), carbonic anhydrase III, and cystatin-M on eGFR. CONCLUSIONS: In a discovery-replication setting, we identified 57 proteins trans-ethnically associated with eGFR. The revealed causal relationships are an important stepping-stone in establishing testican-2 as a clinically relevant physiological marker of kidney disease progression, and point to additional proteins warranting further investigation.
BACKGROUND: Studies on the relationship between renal function and the human plasma proteome have identified several potential biomarkers. However, investigations have been conducted largely in European populations, and causality of the associations between plasma proteins and kidney function has never been addressed. METHODS: A cross-sectional study of 993 plasma proteins among 2,882 participants in four studies of European and admixed ancestries (KORA, INTERVAL, HUNT, QMDiab) identified trans-ethnic associations between eGFR/CKD and proteomic biomarkers. For the replicated associations, two-sample bidirectional Mendelian randomization (MR) was used to investigate potential causal relationships. Publicly available datasets and transcriptomic data from independent studies were used to examine the association between gene expression in kidney tissue and eGFR . RESULTS: Fifty-seven plasma proteins were associated with eGFR, including one novel protein. Twenty-three of these were additionally associated with CKD. The strongest inferred causal effect was the positive effect of eGFR on testican-2, in line with the known biological role of this protein and the expression of its protein-coding gene (SPOCK2) in renal tissue. We also observed suggestive evidence of an effect of melanoma inhibitory activity (MIA), carbonic anhydrase III, and cystatin-M on eGFR. CONCLUSIONS: In a discovery-replication setting, we identified 57 proteins trans-ethnically associated with eGFR. The revealed causal relationships are an important stepping-stone in establishing testican-2 as a clinically relevant physiological marker of kidney disease progression, and point to additional proteins warranting further investigation.
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