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  Noninvasive proteomic biomarkers for alcohol-related liver disease

Niu, L., Thiele, M., Geyer, P. E., Rasmussen, D. N., Webel, H. E., Santos, A., et al. (2022). Noninvasive proteomic biomarkers for alcohol-related liver disease. Nature Medicine, 28, 1277-1287. doi:10.1038/s41591-022-01850-y.

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 Creators:
Niu, Lili1, Author           
Thiele, Maja, Author
Geyer, P. E.1, Author           
Rasmussen, D. N., Author
Webel, H. E., Author
Santos, A., Author
Gupta, Rajat, Author
Meier, Florian1, Author           
Strauss, Maximilian T.1, Author           
Kjaergaard, M., Author
Lindvig, K., Author
Jacobsen, S., Author
Rasmussen, S., Author
Hansen, T., Author
Krag, A., Author
Mann, M.1, Author           
Affiliations:
1Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society, ou_1565159              

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Free keywords: nonalcoholic fatty liver fibrosis markers biopsy system identification complications association Biochemistry & Molecular Biology Cell Biology Research & Experimental Medicine
 Abstract: Interogation of mass-spectrometry-based proteomics of liver and plasma from a cohort of patients with alcohol-related liver disease identifies noninvasive biomarkers associated with early stages of disease progression, including significant fibrosis, inflammation and steatosis. 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.

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Language(s): eng - English
 Dates: 2022-06-02
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: Other: WOS:000805079400002
DOI: 10.1038/s41591-022-01850-y
ISSN: 1078-8956
 Degree: -

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Title: Nature Medicine
  Other : Nat. Med.
Source Genre: Journal
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Publ. Info: New York, NY : Nature Pub. Co.
Pages: - Volume / Issue: 28 Sequence Number: - Start / End Page: 1277 - 1287 Identifier: ISSN: 1078-8956
CoNE: https://pure.mpg.de/cone/journals/resource/954925606824