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Noninvasive proteomic biomarkers for alcohol-related liver disease
Alcohol-related liver disease (ALD) is a major cause of liver-related death worldwide, yet understanding of the three key pathological features of the disease—fibrosis, inflammation and steatosis—remains incomplete. Here, we present a paired liver–plasma proteomics approach to infer molecular pathop...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group US
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9205783/ https://www.ncbi.nlm.nih.gov/pubmed/35654907 http://dx.doi.org/10.1038/s41591-022-01850-y |
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author | Niu, Lili Thiele, Maja Geyer, Philipp E. Rasmussen, Ditlev Nytoft Webel, Henry Emanuel Santos, Alberto Gupta, Rajat Meier, Florian Strauss, Maximilian Kjaergaard, Maria Lindvig, Katrine Jacobsen, Suganya Rasmussen, Simon Hansen, Torben Krag, Aleksander Mann, Matthias |
author_facet | Niu, Lili Thiele, Maja Geyer, Philipp E. Rasmussen, Ditlev Nytoft Webel, Henry Emanuel Santos, Alberto Gupta, Rajat Meier, Florian Strauss, Maximilian Kjaergaard, Maria Lindvig, Katrine Jacobsen, Suganya Rasmussen, Simon Hansen, Torben Krag, Aleksander Mann, Matthias |
author_sort | Niu, Lili |
collection | PubMed |
description | Alcohol-related liver disease (ALD) is a major cause of liver-related death worldwide, yet understanding of the three key pathological features of the disease—fibrosis, inflammation and steatosis—remains incomplete. Here, we present a paired liver–plasma proteomics approach to infer molecular pathophysiology and to explore the diagnostic and prognostic capability of plasma proteomics in 596 individuals (137 controls and 459 individuals with ALD), 360 of whom had biopsy-based histological assessment. We analyzed all plasma samples and 79 liver biopsies using a mass spectrometry (MS)-based proteomics workflow with short gradient times and an enhanced, data-independent acquisition scheme in only 3 weeks of measurement time. In plasma and liver biopsy tissues, metabolic functions were downregulated whereas fibrosis-associated signaling and immune responses were upregulated. Machine learning models identified proteomics biomarker panels that detected significant fibrosis (receiver operating characteristic–area under the curve (ROC–AUC), 0.92, accuracy, 0.82) and mild inflammation (ROC–AUC, 0.87, accuracy, 0.79) more accurately than existing clinical assays (DeLong’s test, P < 0.05). These biomarker panels were found to be accurate in prediction of future liver-related events and all-cause mortality, with a Harrell’s C-index of 0.90 and 0.79, respectively. An independent validation cohort reproduced the diagnostic model performance, laying the foundation for routine MS-based liver disease testing. |
format | Online Article Text |
id | pubmed-9205783 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group US |
record_format | MEDLINE/PubMed |
spelling | pubmed-92057832022-06-19 Noninvasive proteomic biomarkers for alcohol-related liver disease Niu, Lili Thiele, Maja Geyer, Philipp E. Rasmussen, Ditlev Nytoft Webel, Henry Emanuel Santos, Alberto Gupta, Rajat Meier, Florian Strauss, Maximilian Kjaergaard, Maria Lindvig, Katrine Jacobsen, Suganya Rasmussen, Simon Hansen, Torben Krag, Aleksander Mann, Matthias Nat Med Article Alcohol-related liver disease (ALD) is a major cause of liver-related death worldwide, yet understanding of the three key pathological features of the disease—fibrosis, inflammation and steatosis—remains incomplete. Here, we present a paired liver–plasma proteomics approach to infer molecular pathophysiology and to explore the diagnostic and prognostic capability of plasma proteomics in 596 individuals (137 controls and 459 individuals with ALD), 360 of whom had biopsy-based histological assessment. We analyzed all plasma samples and 79 liver biopsies using a mass spectrometry (MS)-based proteomics workflow with short gradient times and an enhanced, data-independent acquisition scheme in only 3 weeks of measurement time. In plasma and liver biopsy tissues, metabolic functions were downregulated whereas fibrosis-associated signaling and immune responses were upregulated. Machine learning models identified proteomics biomarker panels that detected significant fibrosis (receiver operating characteristic–area under the curve (ROC–AUC), 0.92, accuracy, 0.82) and mild inflammation (ROC–AUC, 0.87, accuracy, 0.79) more accurately than existing clinical assays (DeLong’s test, P < 0.05). These biomarker panels were found to be accurate in prediction of future liver-related events and all-cause mortality, with a Harrell’s C-index of 0.90 and 0.79, respectively. An independent validation cohort reproduced the diagnostic model performance, laying the foundation for routine MS-based liver disease testing. Nature Publishing Group US 2022-06-02 2022 /pmc/articles/PMC9205783/ /pubmed/35654907 http://dx.doi.org/10.1038/s41591-022-01850-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Niu, Lili Thiele, Maja Geyer, Philipp E. Rasmussen, Ditlev Nytoft Webel, Henry Emanuel Santos, Alberto Gupta, Rajat Meier, Florian Strauss, Maximilian Kjaergaard, Maria Lindvig, Katrine Jacobsen, Suganya Rasmussen, Simon Hansen, Torben Krag, Aleksander Mann, Matthias Noninvasive proteomic biomarkers for alcohol-related liver disease |
title | Noninvasive proteomic biomarkers for alcohol-related liver disease |
title_full | Noninvasive proteomic biomarkers for alcohol-related liver disease |
title_fullStr | Noninvasive proteomic biomarkers for alcohol-related liver disease |
title_full_unstemmed | Noninvasive proteomic biomarkers for alcohol-related liver disease |
title_short | Noninvasive proteomic biomarkers for alcohol-related liver disease |
title_sort | noninvasive proteomic biomarkers for alcohol-related liver disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9205783/ https://www.ncbi.nlm.nih.gov/pubmed/35654907 http://dx.doi.org/10.1038/s41591-022-01850-y |
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