Methodological considerations for identifying multiple plasma proteins associated with all-cause mortality in a population-based prospective cohort

Abstract Novel methods to characterize the plasma proteome has made it possible to examine a wide range of proteins in large longitudinal cohort studies, but the complexity of the human proteome makes it difficult to identify robust protein-disease associations. Nevertheless, identification of indiv...

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Autores principales:	Isabel Drake, George Hindy, Peter Almgren, Gunnar Engström, Jan Nilsson, Olle Melander, Marju Orho-Melander
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/825622f704a643f1ae22d3d8a2adeb44
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spelling	oai:doaj.org-article:825622f704a643f1ae22d3d8a2adeb442021-12-02T17:04:36ZMethodological considerations for identifying multiple plasma proteins associated with all-cause mortality in a population-based prospective cohort10.1038/s41598-021-85991-z2045-2322https://doaj.org/article/825622f704a643f1ae22d3d8a2adeb442021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85991-zhttps://doaj.org/toc/2045-2322Abstract Novel methods to characterize the plasma proteome has made it possible to examine a wide range of proteins in large longitudinal cohort studies, but the complexity of the human proteome makes it difficult to identify robust protein-disease associations. Nevertheless, identification of individuals at high risk of early mortality is a central issue in clinical decision making and novel biomarkers may be useful to improve risk stratification. With adjustment for established risk factors, we examined the associations between 138 plasma proteins measured using two proximity extension assays and long-term risk of all-cause mortality in 3,918 participants of the population-based Malmö Diet and Cancer Study. To examine the reproducibility of protein-mortality associations we used a two-step random-split approach to simulate a discovery and replication cohort and conducted analyses using four different methods: Cox regression, stepwise Cox regression, Lasso-Cox regression, and random survival forest (RSF). In the total study population, we identified eight proteins that associated with all-cause mortality after adjustment for established risk factors and with Bonferroni correction for multiple testing. In the two-step analyses, the number of proteins selected for model inclusion in both random samples ranged from 6 to 21 depending on the method used. However, only three proteins were consistently included in both samples across all four methods (growth/differentiation factor-15 (GDF-15), N-terminal pro-B-type natriuretic peptide, and epididymal secretory protein E4). Using the total study population, the C-statistic for a model including established risk factors was 0.7222 and increased to 0.7284 with inclusion of the most predictive protein (GDF-15; P < 0.0001). All multiple protein models showed additional improvement in the C-statistic compared to the single protein model (all P < 0.0001). We identified several plasma proteins associated with increased risk of all-cause mortality independently of established risk factors. Further investigation into the putatively causal role of these proteins for longevity is needed. In addition, the examined methods for identifying multiple proteins showed tendencies for overfitting by including several putatively false positive findings. Thus, the reproducibility of findings using such approaches may be limited.Isabel DrakeGeorge HindyPeter AlmgrenGunnar EngströmJan NilssonOlle MelanderMarju Orho-MelanderNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Isabel Drake George Hindy Peter Almgren Gunnar Engström Jan Nilsson Olle Melander Marju Orho-Melander Methodological considerations for identifying multiple plasma proteins associated with all-cause mortality in a population-based prospective cohort
description	Abstract Novel methods to characterize the plasma proteome has made it possible to examine a wide range of proteins in large longitudinal cohort studies, but the complexity of the human proteome makes it difficult to identify robust protein-disease associations. Nevertheless, identification of individuals at high risk of early mortality is a central issue in clinical decision making and novel biomarkers may be useful to improve risk stratification. With adjustment for established risk factors, we examined the associations between 138 plasma proteins measured using two proximity extension assays and long-term risk of all-cause mortality in 3,918 participants of the population-based Malmö Diet and Cancer Study. To examine the reproducibility of protein-mortality associations we used a two-step random-split approach to simulate a discovery and replication cohort and conducted analyses using four different methods: Cox regression, stepwise Cox regression, Lasso-Cox regression, and random survival forest (RSF). In the total study population, we identified eight proteins that associated with all-cause mortality after adjustment for established risk factors and with Bonferroni correction for multiple testing. In the two-step analyses, the number of proteins selected for model inclusion in both random samples ranged from 6 to 21 depending on the method used. However, only three proteins were consistently included in both samples across all four methods (growth/differentiation factor-15 (GDF-15), N-terminal pro-B-type natriuretic peptide, and epididymal secretory protein E4). Using the total study population, the C-statistic for a model including established risk factors was 0.7222 and increased to 0.7284 with inclusion of the most predictive protein (GDF-15; P < 0.0001). All multiple protein models showed additional improvement in the C-statistic compared to the single protein model (all P < 0.0001). We identified several plasma proteins associated with increased risk of all-cause mortality independently of established risk factors. Further investigation into the putatively causal role of these proteins for longevity is needed. In addition, the examined methods for identifying multiple proteins showed tendencies for overfitting by including several putatively false positive findings. Thus, the reproducibility of findings using such approaches may be limited.
format	article
author	Isabel Drake George Hindy Peter Almgren Gunnar Engström Jan Nilsson Olle Melander Marju Orho-Melander
author_facet	Isabel Drake George Hindy Peter Almgren Gunnar Engström Jan Nilsson Olle Melander Marju Orho-Melander
author_sort	Isabel Drake
title	Methodological considerations for identifying multiple plasma proteins associated with all-cause mortality in a population-based prospective cohort
title_short	Methodological considerations for identifying multiple plasma proteins associated with all-cause mortality in a population-based prospective cohort
title_full	Methodological considerations for identifying multiple plasma proteins associated with all-cause mortality in a population-based prospective cohort
title_fullStr	Methodological considerations for identifying multiple plasma proteins associated with all-cause mortality in a population-based prospective cohort
title_full_unstemmed	Methodological considerations for identifying multiple plasma proteins associated with all-cause mortality in a population-based prospective cohort
title_sort	methodological considerations for identifying multiple plasma proteins associated with all-cause mortality in a population-based prospective cohort
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/825622f704a643f1ae22d3d8a2adeb44
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Methodological considerations for identifying multiple plasma proteins associated with all-cause mortality in a population-based prospective cohort

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