Osmolality as a strong predictor of COVID-19 mortality and its possible links to other biomarkers

Cetin, S, Ulgen, A ORCID: https://orcid.org/0000-0002-0872-667X, Sivgin, H, Cetin, M and Li, W, 2025. Osmolality as a strong predictor of COVID-19 mortality and its possible links to other biomarkers. PLoS ONE, 20 (9): e0331344. ISSN 1932-6203

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Abstract

Osmolality, concentration of solute particles, was rarely used for prognosis for COVID-19. By analyzing blood samples of more than 1300 COVID-19 patients from Tokat, Turkey (including 100 surviving and 30 deceased inpatients), we found calculated osmolality to be an excellent prognostic biomarker for mortality and significantly associated with hospitalization , independent from gender and age. Although calculated osmolality is defined as a weighted sum of sodium, glucose, and urea, the three are not necessarily independent. Other blood test biomarkers, ferritin, creatine, and chloride are also correlated with osmo-lality after conditioning on age. By applying a combination of collider analysis and mediation analysis, we design a pipeline to construct a causal model among all these variables in their relationship to osmolality. We confirm that while glucose and sodium are independent contributors of osmolality, glucose and urea, urea and sodium are correlated. We also conclude that ferritin and creatine are associated with osmolality through urea, and chloride's association to osmolality is through sodium.

Item Type:	Journal article
Publication Title:	PLoS ONE
Creators:	Cetin, S., Ulgen, A., Sivgin, H., Cetin, M. and Li, W.
Publisher:	Public Library of Science
Date:	16 September 2025
Volume:	20
Number:	9
ISSN:	1932-6203
Identifiers:	Number Type 10.1371/journal.pone.0331344 DOI 2519430 Other
Rights:	© 2025 Cetin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Borcherds
Date Added:	28 Oct 2025 17:47
Last Modified:	28 Oct 2025 17:47
URI:	https://irep.ntu.ac.uk/id/eprint/54644

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