Blood protein assessment of leading incident diseases and mortality in UK Biobank

Biogen Bank Team; Danni Gadd; Robert F. Hillary; Zhana Kuncheva Kuncheva; Yipeng Cheng; Manju Dissanayake; Romi Admanit; Jake Gagnon; Tinchi Lin; Kyle Ferber; Heiko Runz; Christopher N Foley; Riccardo E Marioni; Benjamin B Sun

Blood protein assessment of leading incident diseases and mortality in UK Biobank

Biogen Bank Team, Danni Gadd, Robert F. Hillary, Zhana Kuncheva Kuncheva, Yipeng Cheng, Manju Dissanayake, Romi Admanit, Jake Gagnon, Tinchi Lin, Kyle Ferber, Heiko Runz, Christopher N Foley^*, Riccardo E Marioni^*, Benjamin B Sun^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality in the UK Biobank (N=47,600). We report 3,209 associations between 963 protein levels and 21 incident outcomes. Next, protein-based scores (ProteinScores) are developed using penalised Cox regression. When applied to test sets, six ProteinScores improve Area Under the Curve (AUC) estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically-relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperforms a polygenic risk score and HbA1c – a clinical marker used to monitor and diagnose type 2 diabetes. Performance of scores using metabolomic and proteomic features is also compared. These data characterise early proteomic contributions to major age-related disease, demonstrating the value of the plasma proteome for risk stratification

Original language	English
Journal	Nature Aging
Early online date	10 Jul 2024
Publication status	E-pub ahead of print - 10 Jul 2024

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Blood protein assessment of leading incident diseases and mortality in UK Biobank_finalFinal published version, 6.28 MB

Integrated multi-omics prediction of incident Alzheimer's disease and its risk factors
Marioni, R.
UK-based charities
1/09/20 → 31/08/23
Project: Research
Translational Neuroscience: Training The Next Generation Of Basic Neuroscientists To Embrace Clinical Research
Ffrench-Constant, C., Deary, I., Kind, P., Lawrie, S., Price, D., Shipston, M. & Wardlaw, J.
UK-based charities
1/09/17 → 31/08/23
Project: Research

Cite this

@article{ad51f0ce28744799b67909d3f97722ff,

title = "Blood protein assessment of leading incident diseases and mortality in UK Biobank",

abstract = "The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality in the UK Biobank (N=47,600). We report 3,209 associations between 963 protein levels and 21 incident outcomes. Next, protein-based scores (ProteinScores) are developed using penalised Cox regression. When applied to test sets, six ProteinScores improve Area Under the Curve (AUC) estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically-relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperforms a polygenic risk score and HbA1c – a clinical marker used to monitor and diagnose type 2 diabetes. Performance of scores using metabolomic and proteomic features is also compared. These data characterise early proteomic contributions to major age-related disease, demonstrating the value of the plasma proteome for risk stratification",

author = "{Biogen Bank Team} and Danni Gadd and Hillary, {Robert F.} and Kuncheva, {Zhana Kuncheva} and Tasos Mangelis and Yipeng Cheng and Manju Dissanayake and Romi Admanit and Jake Gagnon and Tinchi Lin and Kyle Ferber and Heiko Runz and Foley, {Christopher N} and Marioni, {Riccardo E} and Sun, {Benjamin B}",

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day = "10",

language = "English",

journal = "Nature Aging",

issn = "2662-8465",

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T1 - Blood protein assessment of leading incident diseases and mortality in UK Biobank

AU - Biogen Bank Team

AU - Gadd, Danni

AU - Hillary, Robert F.

AU - Kuncheva, Zhana Kuncheva

AU - Mangelis, Tasos

AU - Cheng, Yipeng

AU - Dissanayake, Manju

AU - Admanit, Romi

AU - Gagnon, Jake

AU - Lin, Tinchi

AU - Ferber, Kyle

AU - Runz, Heiko

AU - Foley, Christopher N

AU - Marioni, Riccardo E

AU - Sun, Benjamin B

PY - 2024/7/10

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N2 - The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality in the UK Biobank (N=47,600). We report 3,209 associations between 963 protein levels and 21 incident outcomes. Next, protein-based scores (ProteinScores) are developed using penalised Cox regression. When applied to test sets, six ProteinScores improve Area Under the Curve (AUC) estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically-relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperforms a polygenic risk score and HbA1c – a clinical marker used to monitor and diagnose type 2 diabetes. Performance of scores using metabolomic and proteomic features is also compared. These data characterise early proteomic contributions to major age-related disease, demonstrating the value of the plasma proteome for risk stratification

AB - The circulating proteome offers insights into the biological pathways that underlie disease. Here, we test relationships between 1,468 Olink protein levels and the incidence of 23 age-related diseases and mortality in the UK Biobank (N=47,600). We report 3,209 associations between 963 protein levels and 21 incident outcomes. Next, protein-based scores (ProteinScores) are developed using penalised Cox regression. When applied to test sets, six ProteinScores improve Area Under the Curve (AUC) estimates for the 10-year onset of incident outcomes beyond age, sex and a comprehensive set of 24 lifestyle factors, clinically-relevant biomarkers and physical measures. Furthermore, the ProteinScore for type 2 diabetes outperforms a polygenic risk score and HbA1c – a clinical marker used to monitor and diagnose type 2 diabetes. Performance of scores using metabolomic and proteomic features is also compared. These data characterise early proteomic contributions to major age-related disease, demonstrating the value of the plasma proteome for risk stratification

M3 - Article

SN - 2662-8465

JO - Nature Aging

JF - Nature Aging

ER -

Blood protein assessment of leading incident diseases and mortality in UK Biobank

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Projects

Integrated multi-omics prediction of incident Alzheimer's disease and its risk factors

Translational Neuroscience: Training The Next Generation Of Basic Neuroscientists To Embrace Clinical Research

Cite this