Chandelier
Senior Member (Voting Rights)
Circulating MicroRNA ratios as diagnostic biomarkers for long COVID with an independent validation cohort
This study screened circulating microRNAs as candidate biomarkers for long COVID detection by miRNA sequencing, followed by targeted qPCR quantification focusing on miRNA ratios to reduce technical variability.
Candidate microRNAs were screened by microRNA sequencing and quantified by targeted quantitative polymerase chain reaction, focusing on microRNA ratios expressed as cycle-threshold differences.
In a training cohort, we developed a logistic regression model based on two microRNA ratios with age and sex as covariates.
Internal validation was evaluated using cross validation and bootstraping.
We then test the model in an independent external validation cohort processed with the same normalization and ratio definition.
Discrimination in the training cohort is moderate (AUC: 0.760), with acceptable calibration (intercept ~0, slope ~1, Brier score 0.131).
In external validation, discrimination decreases (AUC: 0.676) and calibration reveals systematic risk overestimation (intercept −1.88, slope 0.77).
Nevertheless, both ratios are consistently lower in long COVID compared to controls.
Target enrichment indicates inflammatory signaling pathways and integration with drug perturbation profiles suggests compounds for future validation and repurposing studies.
It is associated with persistent symptoms affecting multiple organ systems, yet objective blood-based markers that could support clinical assessment remain limited.
This study evaluates whether small molecules measurable in blood, known as microRNAs, differ between individuals with long COVID and non-long COVID controls.
We used a stepwise approach: an initial screen to identify candidate microRNAs, confirmation by a method called RT-qPCR, and testing of a microRNA ratio-based prediction model in an independent validation cohort.
Several microRNA ratios show consistent differences between long COVID and controls, while the model performance was decreased in external validation, suggesting sensitivity to cohort and/or technical differences.
These findings suggest that measuring circulating microRNAs could eventually help with diagnosis, but further validation in larger, well-matched populations is required before clinical implementation.
We would like to thank TAmiRNA GmBH for their support and for providing expertise in small-RNA sequencing services.
Web | DOI | Communications Medicine | Open Access
Han, Emilie; Lukovic, Dominika; Müller-Zlabinger, Katrin; Hasimbegovic, Ena; Schefberger, Katharina; Kastner, Nina; Spannbauer, Andreas; Riesenhuber, Martin; Nitsche, Christian; Zelniker, Thomas A.; Gyöngyösi, Mariann; Hamzaraj, Kevin
Abstract
Background
Long COVID is a multisystemic condition with heterogeneous clinical presentations lacking diagnostic biomarkers with discriminatory capacity.This study screened circulating microRNAs as candidate biomarkers for long COVID detection by miRNA sequencing, followed by targeted qPCR quantification focusing on miRNA ratios to reduce technical variability.
Methods
Overall, 206 long COVID patients ( > 12 weeks after acute infection) and 71 uninfected, unvaccinated healthy controls were recruited through our prospective POSTCOV registry.Candidate microRNAs were screened by microRNA sequencing and quantified by targeted quantitative polymerase chain reaction, focusing on microRNA ratios expressed as cycle-threshold differences.
In a training cohort, we developed a logistic regression model based on two microRNA ratios with age and sex as covariates.
Internal validation was evaluated using cross validation and bootstraping.
We then test the model in an independent external validation cohort processed with the same normalization and ratio definition.
Results
Here we show both microRNA ratios (hsa-miR-484/hsa-miR-126-3p, hsa-miR-345-5p/hsa-miR-223-3p) are inversely associated with long COVID.Discrimination in the training cohort is moderate (AUC: 0.760), with acceptable calibration (intercept ~0, slope ~1, Brier score 0.131).
In external validation, discrimination decreases (AUC: 0.676) and calibration reveals systematic risk overestimation (intercept −1.88, slope 0.77).
Nevertheless, both ratios are consistently lower in long COVID compared to controls.
Conclusion
Consistent between-group ratio differences support these microRNAs as candidate biomarkers, whereas limited external model performance highlights the challenges of transportability in heterogeneous long COVID cohorts.Target enrichment indicates inflammatory signaling pathways and integration with drug perturbation profiles suggests compounds for future validation and repurposing studies.
Plain Language Summary
Long COVID is a serious condition resulting from SARS-CoV-2 infection.It is associated with persistent symptoms affecting multiple organ systems, yet objective blood-based markers that could support clinical assessment remain limited.
This study evaluates whether small molecules measurable in blood, known as microRNAs, differ between individuals with long COVID and non-long COVID controls.
We used a stepwise approach: an initial screen to identify candidate microRNAs, confirmation by a method called RT-qPCR, and testing of a microRNA ratio-based prediction model in an independent validation cohort.
Several microRNA ratios show consistent differences between long COVID and controls, while the model performance was decreased in external validation, suggesting sensitivity to cohort and/or technical differences.
These findings suggest that measuring circulating microRNAs could eventually help with diagnosis, but further validation in larger, well-matched populations is required before clinical implementation.
Acknowledgements
This research was funded by the Austrian Science Fund KLI 1064-B and Medical Scientific Fund of the Mayor of the City of Vienna 21176 to Gy.M., and by the Austrian Science Fund KLI 876-B to T.A.Z.We would like to thank TAmiRNA GmBH for their support and for providing expertise in small-RNA sequencing services.
Web | DOI | Communications Medicine | Open Access
