Your browser has javascript turned off or blocked. This will lead to some parts of our website to not work properly or at all. Turn on javascript for best performance.

The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Default user image.

Jan Nilsson

Professor

Default user image.

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

Author

  • Isabel Drake
  • George Hindy
  • Peter Almgren
  • Gunnar Engström
  • Jan Nilsson
  • Olle Melander
  • Marju Orho-Melander

Summary, in English

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.

Department/s

  • Diabetes - Cardiovascular Disease
  • EpiHealth: Epidemiology for Health
  • EXODIAB: Excellence in Diabetes Research in Sweden
  • Cardiovascular Research - Epidemiology
  • Cardiovascular Research - Immunity and Atherosclerosis
  • Cardiovascular Research - Hypertension

Publishing year

2021

Language

English

Publication/Series

Scientific Reports

Volume

11

Issue

1

Document type

Journal article

Publisher

Nature Publishing Group

Topic

  • Cardiac and Cardiovascular Systems
  • Endocrinology and Diabetes

Status

Published

Research group

  • Diabetes - Cardiovascular Disease
  • Cardiovascular Research - Epidemiology
  • Cardiovascular Research - Immunity and Atherosclerosis
  • Cardiovascular Research - Hypertension

ISBN/ISSN/Other

  • ISSN: 2045-2322