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COVID-19 biomarkers for severity mapped to polycystic ovary syndrome

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submitted on 2024-04-28, 11:49 and posted on 2024-05-02, 12:37 authored by Abu Saleh Md Moin, Thozhukat Sathyapalan, Stephen L. Atkin, Alexandra E. Butler

Large scale multi-omics analysis has identified significant differences in the biomarkers between COVID-19 disease and control subjects [1]. These protein panels target biological processes involved in vessel damage, platelet degranulation, the coagulation cascade and the acute phase response [1], with greater protein changes dependent on the COVID-19 severity. However, it is observed that in metabolic conditions such as polycystic ovary syndrome expressed proteins differ compared to control women [2] and PCOS patients have increased platelet aggregation and decreased plasma fibrinolytic activity, resulting in a prothrombotic propensity [3, 4], with elevated coagulation markers [5]. Therefore, any biomarkers reflecting COVID-19 disease and its severity would necessarily have to be independent of differentially-expressed proteins relating to other conditions; therefore, this proteomic analysis was undertaken in women with and without PCOS to compare with the proteomic biomarkers recently described in COVID-19 using shotgun proteomics followed by parallel reaction monitoring [1].

146 PCOS and 97 control women who presented sequentially to the Department of Endocrinology, Hull and East Yorkshire Hospitals NHS Trust were recruited to the local PCOS biobank (ISRCTN70196169) [2]. PCOS diagnosis was based on all three Rotterdam consensus diagnostic criteria. Proteins that were identified as being altered in COVID-19 disease for vessel damage (16 proteins), platelet degranulation (11 proteins), coagulation cascade (24 proteins) and acute phase response (9 proteins), shown in Table 1, were determined by Slow Off-rate Modified Aptamer (SOMA)-scan plasma protein measurement [6]. Statistics were performed using Graphpad Prism 8.0.

Correction: COVID-19 biomarkers for severity mapped to polycystic ovary syndrome: https://doi.org/10.1186/s12967-021-02782-w, published online 15 March 2021.

Other Information

Published in: Journal of Translational Medicine
License: https://creativecommons.org/licenses/by/4.0
See article on publisher's website: https://dx.doi.org/10.1186/s12967-020-02669-2

History

Language

  • English

Publisher

Springer Nature

Publication Year

  • 2020

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Biomedical Research Institute - HBKU
  • Diabetes Research Center - QBRI