Proteomic Profiling of Post-COVID -19 Sequelae.

Authors

Sepehr Golriz Khatami, Rieke Baumkötter, Thomas Köck, Ayesha Syed Mamoor Alam, Julian Chalabi, Steffen Rapp, Federico Marini, Markus Schepers, Tobias Bopp, Stefan Bittner, Alexander K Schuster, Manfred E Beutel, Thomas Münzel, Karl J Lackner, Vincent Ten Cate, Philipp S Wild

Published in

Molecular & cellular proteomics : MCP. Pages 101670. Sep 28, 2026. Epub Sep 28, 2026.

Abstract

The emergence of Post-COVID sequelae (PCS) represents a global challenge. However, understanding of biological mechanisms and the definition of quantifiable risk factors remains limited. This study employed machine learning-based classification to explore the potential of proteomics in identifying associations with individual Post-COVID symptoms and their collective manifestation as PCS. The analysis utilized a panel of approximately 2900 proteins measured in 495 COVID-19 patients. The study identified 235 unique proteins associated with 15 of the 21 evaluated Post-COVID symptoms. Symptoms more closely linked by similar protein profiles tended to co-occur more frequently in patients. Six symptom clusters with characteristic molecular pathway associations were identified, with metabolic and inflammatory pathways prominently involved across several clusters. The relevance of the specific protein profiles for Post-COVID symptoms further explored by objective, quantifiable clinical tests, including cognitive and somatic assessments, providing supportive evidence for their biological relevance. Data from various modalities, including pre-existing conditions, disease risk factors and genetic susceptibility, revealed relevant relations that may contribute to PCS heterogeneity. This work illustrates the complex and multifaceted nature of Post-COVID symptoms. It emphasizes the need for systematic and more specific approaches to facilitate the identification of potential pathways for future therapeutic investigation.

PMID:
42805392
Bibliographic data and abstract were imported from PubMed on 29 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.