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Predicting the probability of death using proteomics

Predicting the probability of death using proteomics


Title: Predicting the probability of death using proteomics
Author: Eiriksdottir, Thjodbjorg
Ardal, Steinthor
Jonsson, Benedikt A.
Lund, Sigrun H.
Ivarsdottir, Erna V.
Norland, Kristjan
Ferkingstad, Egil   orcid.org/0000-0001-8090-7988
Stefansson, Hreinn   orcid.org/0000-0002-9331-6666
Jónsdóttir, Ingileif
Holm, Hilma   orcid.org/0000-0002-9517-6636
... 9 more authors Show all authors
Date: 2021-06-18
Language: English
Scope: 758
Department: Faculty of Medicine
Clinical Laboratory Services, Diagnostics and Blood Bank
Cardio-Vascular Center
Faculty of Electrical and Computer Engineering
Series: Communications Biology; 4(1)
ISSN: 2399-3642
DOI: https://doi.org/10.1038/s42003-021-02289-6
Subject: Áhættuþættir; Dauði; Erfðafræði; Adolescent; Adult; Aged; Aged, 80 and over; Biomarkers; Blood Proteins; Female; Frailty; Humans; Iceland; Kaplan-Meier Estimate; Male; Middle Aged; Prognosis; Proteomics; Risk; Risk Assessment; Risk Factors; Young Adult; Frailty/mortality; Blood Proteins/analysis; Proteomics/methods; Biomarkers/blood; Agricultural and Biological Sciences (all); Biochemistry, Genetics and Molecular Biology (all); Medicine (miscellaneous)
URI: https://hdl.handle.net/20.500.11815/3119

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Citation:

Eiriksdottir , T , Ardal , S , Jonsson , B A , Lund , S H , Ivarsdottir , E V , Norland , K , Ferkingstad , E , Stefansson , H , Jónsdóttir , I , Holm , H , Rafnar , T , Saemundsdottir , J , Norddahl , G L , Þorgeirsson , G , Gudbjartsson , D F , Sulem , P , Thorsteinsdottir , U , Stefansson , K & Úlfarsson , M Ö 2021 , ' Predicting the probability of death using proteomics ' , Communications Biology , vol. 4 , no. 1 , 758 , pp. 758 . https://doi.org/10.1038/s42003-021-02289-6

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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Publisher Copyright: © 2021, The Author(s).

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