Proteomic signatures improve risk prediction for common and rare diseases.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
22 Jul 2024
22 Jul 2024
Historique:
received:
26
10
2023
accepted:
19
06
2024
medline:
23
7
2024
pubmed:
23
7
2024
entrez:
22
7
2024
Statut:
aheadofprint
Résumé
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.
Identifiants
pubmed: 39039249
doi: 10.1038/s41591-024-03142-z
pii: 10.1038/s41591-024-03142-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : RCUK | Medical Research Council (MRC)
ID : MC_PC_21036
Organisme : RCUK | Medical Research Council (MRC)
ID : MR/N003284/1 and MC_UU_00006/1 and MC_PC_21036
Organisme : Wellcome Trust (Wellcome)
ID : 220044/Z/19/Z
Organisme : Cancer Research UK (CRUK)
ID : C864/A14136
Informations de copyright
© 2024. The Author(s).
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