Proteomic risk scores for predicting common diseases using linear and neural network models in the UK biobank
File(s)
Published version
Author(s)
Smith, Alexander
Elliott, Paul
Mayr, Manuel
Dehghan, Abbas
Ioanna, Tzoulaki
Type
Journal Article
Abstract
Plasma proteomics provides a unique opportunity to enhance disease prediction by capturing protein expression patterns linked to diverse pathological processes. Leveraging data from 2,923 proteins measured in 53,030 UK Biobank participants, we developed proteomic risk scores for 27 common outcomes over 5- and 15-year follow-up periods using two approaches: a linear ElasticNet regression model and a deep learning neural network (NN) model. Using Cox regression, we assessed the discrimination of proteomic risk scores either in isolation or as incremental improvements over clinical risk factors. We also studied the shared and unique protein predictors across conditions. Proteomic risk scores demonstrated strong discrimination for most outcomes, with a C-index >0.80 for 12 diseases. NN models outperformed linear models for 11 outcomes, particularly for diseases such as Parkinson’s disease (C-index 0.84) and pulmonary embolism (C-index 0.83), where nonlinear relationships contributed significantly to prediction. Across all outcomes, the addition of proteomic scores to clinical models improved predictive accuracy (ΔC-index 0.03), with the greatest gains observed in 9 diseases (ΔC-index>0.1), including end-stage renal disease, pulmonary embolism, and Parkinson’s disease. Analysis of protein contributions revealed shared predictors across multiple diseases, such as growth differentiation factor 15 (GDF15), as well as unique predictors like PAEP for endometriosis. While NN models may capture complex relationships, linear models provided value through simplicity and interpretability. These findings underscore the importance of tailoring predictive approaches to specific diseases and demonstrate the pivotal potential of proteomics in advancing risk stratification and early detection.
Date Issued
2025-07-01
Date Acceptance
2025-06-06
Citation
Scientific Reports, 2025, 15
ISSN
2045-2322
Publisher
Nature Portfolio
Journal / Book Title
Scientific Reports
Volume
15
Copyright Statement
© The Author(s) 2025 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
10.1038/s41598-025-06232-1
Publication Status
Published
Article Number
20520
Date Publish Online
2025-07-01