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Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes
PURPOSE OF THE REVIEW: Proteins are the central layer of information transfer from genome to phenome and represent the largest class of drug targets. We review recent advances in high-throughput technologies that provide comprehensive, scalable profiling of the plasma proteome with the potential to...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7543966/ https://www.ncbi.nlm.nih.gov/pubmed/33033935 http://dx.doi.org/10.1007/s11892-020-01340-w |
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author | Zanini, Julia Carrasco Pietzner, Maik Langenberg, Claudia |
author_facet | Zanini, Julia Carrasco Pietzner, Maik Langenberg, Claudia |
author_sort | Zanini, Julia Carrasco |
collection | PubMed |
description | PURPOSE OF THE REVIEW: Proteins are the central layer of information transfer from genome to phenome and represent the largest class of drug targets. We review recent advances in high-throughput technologies that provide comprehensive, scalable profiling of the plasma proteome with the potential to improve prediction and mechanistic understanding of type 2 diabetes (T2D). RECENT FINDINGS: Technological and analytical advancements have enabled identification of novel protein biomarkers and signatures that help to address challenges of existing approaches to predict and screen for T2D. Genetic studies have so far revealed putative causal roles for only few of the proteins that have been linked to T2D, but ongoing large-scale genetic studies of the plasma proteome will help to address this and increase our understanding of aetiological pathways and mechanisms leading to diabetes. SUMMARY: Studies of the human plasma proteome have started to elucidate its potential for T2D prediction and biomarker discovery. Future studies integrating genomic and proteomic data will provide opportunities to prioritise drug targets and identify pathways linking genetic predisposition to T2D development. |
format | Online Article Text |
id | pubmed-7543966 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-75439662020-10-09 Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes Zanini, Julia Carrasco Pietzner, Maik Langenberg, Claudia Curr Diab Rep Genetics (AP Morris, Section Editor) PURPOSE OF THE REVIEW: Proteins are the central layer of information transfer from genome to phenome and represent the largest class of drug targets. We review recent advances in high-throughput technologies that provide comprehensive, scalable profiling of the plasma proteome with the potential to improve prediction and mechanistic understanding of type 2 diabetes (T2D). RECENT FINDINGS: Technological and analytical advancements have enabled identification of novel protein biomarkers and signatures that help to address challenges of existing approaches to predict and screen for T2D. Genetic studies have so far revealed putative causal roles for only few of the proteins that have been linked to T2D, but ongoing large-scale genetic studies of the plasma proteome will help to address this and increase our understanding of aetiological pathways and mechanisms leading to diabetes. SUMMARY: Studies of the human plasma proteome have started to elucidate its potential for T2D prediction and biomarker discovery. Future studies integrating genomic and proteomic data will provide opportunities to prioritise drug targets and identify pathways linking genetic predisposition to T2D development. Springer US 2020-10-08 2020 /pmc/articles/PMC7543966/ /pubmed/33033935 http://dx.doi.org/10.1007/s11892-020-01340-w Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Genetics (AP Morris, Section Editor) Zanini, Julia Carrasco Pietzner, Maik Langenberg, Claudia Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes |
title | Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes |
title_full | Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes |
title_fullStr | Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes |
title_full_unstemmed | Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes |
title_short | Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes |
title_sort | integrating genetics and the plasma proteome to predict the risk of type 2 diabetes |
topic | Genetics (AP Morris, Section Editor) |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7543966/ https://www.ncbi.nlm.nih.gov/pubmed/33033935 http://dx.doi.org/10.1007/s11892-020-01340-w |
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