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Proteomic signatures improve risk prediction for common and rare diseases.
Carrasco-Zanini, Julia; Pietzner, Maik; Davitte, Jonathan; Surendran, Praveen; Croteau-Chonka, Damien C; Robins, Chloe; Torralbo, Ana; Tomlinson, Christopher; Grünschläger, Florian; Fitzpatrick, Natalie; Ytsma, Cai; Kanno, Tokuwa; Gade, Stephan; Freitag, Daniel; Ziebell, Frederik; Haas, Simon; Denaxas, Spiros; Betts, Joanna C; Wareham, Nicholas J; Hemingway, Harry; Scott, Robert A; Langenberg, Claudia.
Afiliación
  • Carrasco-Zanini J; Human Genetics and Genomics, GSK Research and Development, Stevenage, UK. j.carrasco-zanini-sanchez@qmul.ac.uk.
  • Pietzner M; MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK. j.carrasco-zanini-sanchez@qmul.ac.uk.
  • Davitte J; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK. j.carrasco-zanini-sanchez@qmul.ac.uk.
  • Surendran P; Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany. j.carrasco-zanini-sanchez@qmul.ac.uk.
  • Croteau-Chonka DC; MRC Epidemiology Unit, School of Clinical Medicine, Institute of Metabolic Science, University of Cambridge, Cambridge, UK.
  • Robins C; Precision Healthcare University Research Institute, Queen Mary University of London, London, UK.
  • Torralbo A; Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany.
  • Tomlinson C; Human Genetics and Genomics, GSK Research and Development, Collegeville, PA, USA.
  • Grünschläger F; Human Genetics and Genomics, GSK Research and Development, Stevenage, UK.
  • Fitzpatrick N; Human Genetics and Genomics, GSK Research and Development, Cambridge, MA, USA.
  • Ytsma C; Human Genetics and Genomics, GSK Research and Development, Collegeville, PA, USA.
  • Kanno T; Institute of Health Informatics, University College London, London, UK.
  • Gade S; Institute of Health Informatics, University College London, London, UK.
  • Freitag D; National Institute for Health Research, Biomedical Research Centre, University College London Hospitals NHS Trust, London, UK.
  • Ziebell F; Heidelberg Institute for Stem Cell Technology and Experimental Medicine, Heidelberg, Germany.
  • Haas S; Division of Stem Cells and Cancer, Deutsches Krebsforschungszentrum (DKFZ) and DKFZ-ZMBH Alliance, Heidelberg, Germany.
  • Denaxas S; Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
  • Betts JC; Institute of Health Informatics, University College London, London, UK.
  • Wareham NJ; Institute of Health Informatics, University College London, London, UK.
  • Hemingway H; Human Genetics and Genomics, GSK Research and Development, Collegeville, PA, USA.
  • Scott RA; Genomic Sciences, Cellzome GmbH, GSK Research and Development, Heidelberg, Germany.
  • Langenberg C; Human Genetics and Genomics, GSK Research and Development, Stevenage, UK.
Nat Med ; 30(9): 2489-2498, 2024 Sep.
Article en En | MEDLINE | ID: mdl-39039249
ABSTRACT
For many diseases there are delays in diagnosis due to a lack of objective biomarkers for disease onset. Here, in 41,931 individuals from the United Kingdom Biobank Pharma Proteomics Project, we integrated measurements of ~3,000 plasma proteins with clinical information to derive sparse prediction models for the 10-year incidence of 218 common and rare diseases (81-6,038 cases). We then compared prediction models developed using proteomic data with models developed using either basic clinical information alone or clinical information combined with data from 37 clinical assays. The predictive performance of sparse models including as few as 5 to 20 proteins was superior to the performance of models developed using basic clinical information for 67 pathologically diverse diseases (median delta C-index = 0.07; range = 0.02-0.31). Sparse protein models further outperformed models developed using basic information combined with clinical assay data for 52 diseases, including multiple myeloma, non-Hodgkin lymphoma, motor neuron disease, pulmonary fibrosis and dilated cardiomyopathy. For multiple myeloma, single-cell RNA sequencing from bone marrow in newly diagnosed patients showed that four of the five predictor proteins were expressed specifically in plasma cells, consistent with the strong predictive power of these proteins. External replication of sparse protein models in the EPIC-Norfolk study showed good generalizability for prediction of the six diseases tested. These findings show that sparse plasma protein signatures, including both disease-specific proteins and protein predictors shared across several diseases, offer clinically useful prediction of common and rare diseases.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedades Raras / Proteómica Límite: Adult / Aged / Female / Humans / Male / Middle aged País/Región como asunto: Europa Idioma: En Revista: Nat Med Asunto de la revista: BIOLOGIA MOLECULAR / MEDICINA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Enfermedades Raras / Proteómica Límite: Adult / Aged / Female / Humans / Male / Middle aged País/Región como asunto: Europa Idioma: En Revista: Nat Med Asunto de la revista: BIOLOGIA MOLECULAR / MEDICINA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos