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. 2024 Jul 10;4(7):939–948. doi: 10.1038/s43587-024-00655-7

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© The Author(s) 2024

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/.

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Extended Data Fig. 2 — Related individuals were excluded, leaving a dataset containing 51,562 individuals with 1,474 Olink protein analytes measured. Next, 3,962 individuals that had >10% missing data were excluded, followed by four proteins that had >10% missing data. The remaining missing protein measurements (1% of total measurements) were imputed through K-nearest neighbours (Knn; k=10) imputation. The final dataset was comprised of 47,600 individuals and 1,468 Olink protein analytes. Protein levels were rank-based inverse normalised and scaled to have a mean of 0 and standard deviation of 1 prior to individual Cox PH analyses. Untransformed protein levels were fed into the model pipeline for ProteinScore development and were rank-based inverse normalised and scaled to have a mean of 0 and standard deviation of 1 in train and test sets separately once these were sampled for each outcome. Created with BioRender.com.