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Predicting the probability of death using proteomics

Eiriksdottir, Thjodbjorg, Ardal, Steinthor, Jonsson, Benedikt A., Lund, Sigrun H., Ivarsdottir, Erna V., Norland, Kristjan, Ferkingstad, Egil, Stefansson, Hreinn, Jonsdottir, Ingileif, Holm, Hilma, Rafnar, Thorunn, Saemundsdottir, Jona, Norddahl, Gudmundur L., Thorgeirsson, Gudmundur, Gudbjartsson, Daniel F., Sulem, Patrick, Thorsteinsdottir, Unnur, Stefansson, Kari, Ulfarsson, Magnus O.·2021·Communications Biology
DOI 10.1038/s42003-021-02289-6PMC PMC8213855
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Abstract

Predicting all-cause mortality risk is challenging and requires extensive medical data. Recently, large-scale proteomics datasets have proven useful for predicting health-related outcomes. Here, we use measurements of levels of 4,684 plasma proteins in 22,913 Icelanders to develop all-cause mortality predictors both for short- and long-term risk. The participants were 18-101 years old with a mean follow up of 13.7 (sd. 4.7) years. During the study period, 7,061 participants died. Our proposed predictor outperformed, in survival prediction, a predictor based on conventional mortality risk factors. We could identify the 5% at highest risk in a group of 60-80 years old, where 88% died within ten years and 5% at the lowest risk where only 1% died. Furthermore, the predicted risk of death correlates with measures of frailty in an independent dataset. Our results show that the plasma proteome can be used to assess general health and estimate the risk of death.

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Metadata

Authors
Eiriksdottir, T., Ardal, S., Jonsson, B. A., Lund, S. H., Ivarsdottir, E. V., Norland, K., Ferkingstad, E., Stefansson, H., Jonsdottir, I., Holm, H., Rafnar, T., Saemundsdottir, J., Norddahl, G. L., Thorgeirsson, G., Gudbjartsson, D. F., Sulem, P., Thorsteinsdottir, U., Stefansson, K., & Ulfarsson, M. O.
Venue
Communications Biology
Publisher
Springer Science and Business Media LLC
Published
2021-06-18
Volume
4
Issue
1
ISSN
2399-3642
DOI
10.1038/s42003-021-02289-6
PMC
PMC8213855
Cited by
30
References
10
Language
en
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