COVID-19 : Researchers have identified 27 protein biomarkers that can predict severe coronavirus symptoms


Researchers at the Francis Crick Institute and Charité – Universitätsmedizin Berlin have identified 27 protein biomarkers that could be used to predict whether a patient with COVID-19 is likely to become severely ill with the disease.

People infected with SARS-CoV-2, the virus which causes COVID-19, respond differently. Some do not develop any symptoms, some need to be hospitalised and, for some, the disease is fatal.

In this study, published in Cell Systems, researchers found 27 potential biomarkers that are present in different levels in patients with COVID-19, depending on the severity of their symptoms.

The markers could help doctors to predict how ill a patient will become and provide scientists with new targets for drug development.

The researchers refined an analysis method called mass spectrometry to rapidly test for the presence and quantity of various proteins in the blood plasma. This platform was developed at the Francis Crick Institute and applied to analyse serum of 31 COVID-19 patients at the Berlin University hospital Charité.

Their results were further validated in 17 patients with COVID-19 at the same hospital and in 15 healthy people.

The researchers hope their findings will lead to the development of simple routine tests to check for the levels for one or some of these proteins in patients with COVID-19. The results of such tests could be used to support doctors in deciding what treatment to give.

Christoph Messner, one of the lead authors and postdoc in the Molecular Biology of Metabolism Laboratory at the Crick, says: “A test to help doctors predict whether a COVID-19 patient is likely to become critical or not would be invaluable.

It will help them make decisions about how to best manage the disease for each patient as well as identify those most at risk. We hope the biomarkers we’ve identified will lead to the development of these vitally needed tests.”

Three of the key proteins that the team identified were associated with interleukin IL-6, a protein which causes inflammation, a known marker for severe symptoms. The researchers suggest it may be possible to alleviate some of these symptoms by using drugs that target these associated proteins.

Markus Ralser, paper author and group leader at the Crick and Charité, says: “The robust method we’ve used in this study is a valuable and powerful tool to predict disease progression and also find potential targets for treatments.

Our approach could also be easily applied to other diseases, now and in the future, to understand more about their effects on the body.”

Vadim Demichev, another lead author and scientist in the Molecular Biology of Metabolism Laboratory at the Crick adds, “While our technology platform was not developed specifically for COVID-19, it has proven highly useful to gain novel insights into this disease. We hope it will help the development of prognostic analytical tests for a broad range of conditions in the near future”.

All protocols and software for implementing this approach are freely available.*


  • The raw data of the acquired commercial plasma and serum control samples within the GS study was submitted to the ProteomeXchange Consortium via PRIDE (Perez-Riverol et al., 2019) partner repository with the dataset identifier PXD018874.

According to the terms of consent for GenerationScotland participants, access to individual-level data (omics and phenotypes) must be reviewed by the GS Access Committee. Applications should be made to [email protected].

The DIA-NN software suite and DiaNN R package are open source and are freely available for download.

Department of Medicine III, University Hospital, LMU Munich, Munich, Germany

The pandemic Coronavirus-disease 19 (COVID-19) is characterized clinically by a highly variable course. While most patients experience only mild symptoms, a relevant proportion develop severe disease progression with increasing hypoxia up to acute respiratory distress syndrome.

About 5% of patients require intensive care treatment including mechanical ventilation. 1-3 This variability of COVID-19 and the shortage of health care resources in heavily affected regions make efficient allocation of resources towards patients at high risk for deterioration crucial.4

We aimed to identify variables that allow the prediction of patients with a high risk of respiratory failure and need of mechanical ventilation.


All patients with PCR proven symptomatic COVID-19 infection hospitalized at our institution from 29th February to 27th March 2020 (n=40) were analyzed for baseline clinical and laboratory findings. Patient data were anonymized for analysis and the study was approved by the local ethics committee (No: 20-245).

All variables with less than 50% of missing data were tested for the association with respiratory failure. Categorical variables were compared with the χ2- test, and numerical variables were tested with the Mann-Whitney-U-Test. The p-values were adjusted for multiple testing with the Bonferroni-Holm-Method. An adjusted p-value (q-value) of ≤0.05 was considered significant.


In total, 13/40 (32.5%) patients deteriorated during hospitalization and required mechanical ventilation. The time from hospital admission to intubation varied from less than one hour to 9 days (median 2 days).

All patients who required intubation were of male sex, compared to 59% of males in the group that did not require intubation (p=0.020, q=0.057). Patients requiring mechanical ventilation did not differ in age, comorbidities, radiological findings, respiratory rate or qSofa score (table).

Pulse, markers of inflammation, LDH and creatinine at admission were associated with respiratory failure (table).

Elevated interleukin-6 (IL-6) was very strongly associated with the need for mechanical ventilation (figure 1A, p=1.2·10-5, q=0.00032).

In addition to IL-6 values at first assessment, follow-up data on IL-6 were also available (median number of values per patient: 6, range 1-10). These data were used to assess the maximal IL-6 level for each patient during disease (for patients requiring ventilation, only values before intubation were used).

These values predicted respiratory failure with high accuracy (figure 1B/C, p=1.7·10-8, AUC=0.98). The statistically optimal cutoff for IL-6 was 80pg/ml, identifying all but one patient with respiratory failure correctly, and misclassifying only one patient that still does not require intubation.

The risk of respiratory failure for patients with IL-6 levels of ≥80pg/ml was 92% and thus 22 times higher compared to patients with lower IL-6 levels. After reaching an IL-6 value of 80pg/ml, the median time to mechanical ventilation was

1.5 days (range 0–4 days).


Even though IL-6 levels were significantly elevated in patients requiring ventilation, they are relatively low compared to levels observed in patients with septic shock. 5 Our data suggests that even moderately elevated IL-6 levels above 80pg/ml are sufficient to identify COVID-19 patients with a high risk of respiratory failure.

Further studies and larger sample sizes will be needed to validate our findings and possibly determine a more accurate cutoff. To date, it is unclear whether IL-6 merely represents a biomarker or a central pathogenetic element of severe COVID-19 that should be used as a parameter for therapeutic intervention.

In the current situation with overwhelmed intensive care units and overcrowded emergency rooms, correct triage of patients in need of intensive care is crucial. Our study shows that IL-6 is an effective marker that might be able to predict upcoming respiratory failure with high accuracy and help physicians correctly allocate patients at an early stage.

Acknowledgement: We thank Oliver T. Keppler, the Task Force Corona at the University Hospital, LMU Munich and all CORKUM investigators as well as all health care workers for their outstanding service.


  1. Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020. doi:10.1016/S0140- 6736(20)30566-3.
  2. Wang D, Hu B, Hu C, et al. Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan, China. JAMA. 2020. doi:10.1001/jama.2020.1585.
  3. Guan W-J, Ni Z-Y, Hu Y, et al. Clinical Characteristics of Coronavirus Disease 2019 in China. N Engl J Med. 2020. doi:10.1056/NEJMoa2002032.
  4. Emanuel EJ, Persad G, Upshur R, et al. Fair Allocation of Scarce Medical Resources in the Time of Covid-19. N Engl J Med. 2020. doi:10.1056/NEJMsb2005114.
  5. Surbatovic M, Popovic N, Vojvodic D, et al. Cytokine profile in severe Gram-positive and Gram- negative abdominal sepsis. Sci Rep. 2015;5:11355. doi:10.1038/srep11355.

Francis Crick Institute


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