Circulating proteins to predict adverse COVID-19 outcomes
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Abstract
Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measured 4,701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict adverse COVID-19 outcomes in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4,701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different adverse COVID-19 outcomes were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of adverse COVID-19 outcomes. Further research is needed to understand how to incorporate protein measurement into clinical care.
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SciScore for 10.1101/2021.10.04.21264015: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study has important limitations. While the model was tested in a separate cohort, and generalized well, it should be tested in additional cohorts, especially in cohorts of diverse ancestry. The control population included individuals who were SARS-CoV-2 positive and had mild disease, in addition to individuals who were suspected to have COVID-19 but …
SciScore for 10.1101/2021.10.04.21264015: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics not detected. Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This study has important limitations. While the model was tested in a separate cohort, and generalized well, it should be tested in additional cohorts, especially in cohorts of diverse ancestry. The control population included individuals who were SARS-CoV-2 positive and had mild disease, in addition to individuals who were suspected to have COVID-19 but were SARS-CoV-2 negative. This means that the developed models provide insight into prediction of individuals who develop severe COVID-19 compared to mild COVID-19 and other acute diseases having symptoms consistent with COVID-19. Such control definitions reduce the potential for collider bias, but do not allow direct prediction of COVID-19 outcomes amongst only COVID-19 patients53. Last, the clinical translation of this study is hindered by the cost involved in measuring 4,701 circulating proteins but could be improved by developing a specific assay to the selected proteins. In summary, circulating protein levels are strongly associated with COVID-19 outcomes and able to predict the need for oxygen supplementation or death with reasonable accuracy. Measured protein levels were superior to predicting COVID-19 severity outcomes when compared to nearly all clinical risk factors tested. Further research is needed to assess whether this proteomic approach can be applied in a clinical setting to assist in triaging patients for admission to hospital.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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