A new study involving the University of Liverpool has identified new biomarkers of inflammation that both indicate the severity of COVID-19 and distinguish it from severe influenza.
Led by the UK’s International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) and supported by the UK Coronavirus Immunology Consortium (UK-CIC), the study is the largest of its kind to date.
Published in Science Immunology, the study identifies clusters of inflammatory disease markers (including two called GM-CSF and IL-6) that scale with COVID-19 severity.
IL-6 is already proven to be a target for therapies that reduce disease severity in severe COVID-19, but GM-CSF has potential as a new marker of severity that distinguishes COVID-19 from influenza, giving insights into the causes of severe disease and potentially offering a new focus for therapy.
It is important to understand why some patients with COVID-19 experience severe disease, while others recover with less medical support. A ‘cytokine storm,” where uncontrolled levels of cytokines (proteins released by immune cells) cause excessive inflammation, has been identified as a driver of COVID-19 severity.
Anti-inflammatory drugs such as corticosteroids (e.g., dexamethasone) or those that interrupt cytokine function (e.g. tocilizumab) substantially reduce mortality in COVID-19 patients. However studying the underlying inflammatory response in more detail can help researchers to identify new therapies and target healthcare resources to the most at-risk individuals.
Teams of researchers from across the UK, including Imperial College London, University of Edinburgh and University of Liverpool, combined their efforts to demonstrate that only select features of the cytokine response to COVID-19 distinguish the most severe forms of disease.
Using the ISARIC4C platform, the researchers recruited 471 hospitalized COVID-19 patients (who were stratified by disease severity) along with 39 outpatients with mild disease. They analyzed 33 disease markers in the blood plasma of these patients.
They found that many inflammatory cytokines were elevated in severe COVID-19 and that levels are generally indicative of disease severity. The investigators identified patterns within the data that are characteristic of the most severe cases of COVID-19; two cytokines in particular, IL-6 (interleukin 6) and GM-CSF (granulocyte-macrophage colony stimulating factor) playing central roles.
When compared with archived samples from severe influenza patients, GM-CSF stood out as a specific marker for severe COVID-19. This cytokine can also be detected in early COVID infection, indicating it may play a pathologic role in early disease development in some patients.
While older patients showed a greater all-round inflammatory response, age was not a specific determinant of GM-CSF levels in this study. This suggests that a disease-specific mechanism that exacerbates age-dependent inflammatory responses is likely to operate in COVID-19. There was also no difference found between responses of men and women as has previously been reported in other smaller studies.
The findings from this study could impact patient care and treatment in two ways. First, the study has rationally identified GM-CSF as a key component of the inflammatory response and as a potential therapeutic target. Further research is also needed to see if GM-CSF could be used as a marker in early disease to identify those at risk of going on to develop more severe symptoms.
Dr. Ryan Thwaites, author and Research Associate at Imperial College London, said: “Understanding the underlying mechanism of the immune response in patients who are very ill with COVID-19 is crucial to allowing us to identify potential therapeutic targets.
The immune response is incredibly complex but by assessing levels of multiple markers and how they relate to each other, we’ve been able to increase our knowledge of the immune profile of severely ill COVID-19 patients.”
Professor Calum Semple, ISARIC4C lead and Professor of Child Health and Outbreak Medicine at the University of Liverpool, said: “ISARIC’s planning for an outbreak just like this has enabled timely discoveries that are informing case management and driving therapeutic developments. Also deserving recognition are the NIHR clinical research staff at all our NHS hospitals and the university staff that support the outbreak laboratories in Glasgow and Liverpool who together collated the data and samples that enabled these discoveries.”
Despite the necessity, there is no reliable prognostic biomarker to predict disease severity and prognosis of patients with COVID-19.1 Studies on COVID-19 have built up several types of prediction models. These have been the models designed to indicate the disease risk in the general population, the diagnostic models based on medical imaging and the prognostic models. Unfortunately, these models have had some limitations that have precluded their use in clinical practice.2
Models using laboratory findings as the inputs
Researchers tried to establish the role of laboratory findings in the diagnosis of COVID-19.3 They showed that the severe cases of COVID-19 were associated with D-dimer level over 0.28 µg/L, interleukin (IL)-6 level over 24.3 pg/mL3 and lactate dehydrogenase (LDH) activity with an upper limit cut-off in the range of 240–255 U/L.4 However, the use of these laboratory parameters with the above-mentioned cut-off values was limited for the following reasons.
First, these studies were conducted on severe forms of the disease. Limited research was done on patients who were asymptomatic or had mild disease.3 5 Second, the whole spectrum of the regularly used clinical laboratory data is unavailable for non-severe patients. Thus, the published papers add justification on the diagnostic utility of separate laboratory findings, instead of working out reliable diagnostic criteria for a set of them.
Gong et al6 have generated a tool for the early prediction of severe COVID-19 pneumonia out of the following data: age, serum LDH activity, C reactive protein (CRP), the coefficient of variation of red blood cell distribution width, blood urea nitrogen, direct bilirubin, lower albumin. The resulting performance was not high (sensitivity 77.5%, specificity 78.4%).6 Supposedly, this is because the dataset used as the input consists of exceptionally the age and laboratory findings.
In another model, the inputs included basic information, symptoms and the results of laboratory tests. After the feature selection, the number of key features was set to just three laboratory results: LDH, lymphocytes and high-sensitivity CRP. The model was trained with the follow-up studies of the general, severe and critical patients.1 By feeding machine learning (ML) algorithm with the results obtained at the time of admission and in follow-up studies, the authors worked out a decision rule to predict patients at the highest risk. However, physicians are interested in the early prediction of the disease outcomes, and it is highly disputable that the model will not loose its predictive potential if applied exceptionally to the data received on admission.
We believe that a more accurate model can be built based on the simultaneous interpretation of laboratory results, clinical data and physical examination findings (eg, body mass index, body temperature, respiratory rate) at the time of presentation. The analysis using an ML algorithm could provide an accurate prediction of the disease severity.
Data used by clinicians for stratifying risks
Clinicians routinely use physical examination findings and laboratory parameters for risk stratification and hospital resources management. Commonly, each laboratory test kit has the only cut-off value to segregate the normal status from a pathology. We believe that threshold values should be re-adjusted for each disease rather than used as a common cut-off value for all pathologies.
As a standard of care, baseline blood tests and inflammatory markers are obtained on admission to the hospital. The proper approach for the risk assessment should allow physicians to forecast the patient’s future worsening out of the initial findings on admission. This is what we intend to do by applying an ML approach to the predictors routinely used in clinical practice. There are some promising data for the following set of prognostic biomarkers of COVID-19 severity.
There is evidence that IL-6 and tumour necrosis factor (TNF)-α do not indicate the level of COVID-19 progression.7 Some markers of inflammation are elevated in the serum of patients with COVID-19 compared with the healthy people, that is, the serum SARS-CoV-2 viral load (RNAaemia) is closely correlated with drastically elevated IL-6 levels in critically ill patients with COVID-19.8
However, there is no significant difference between severe and mild groups.7 In contrast to this, the indicators are reflective in the progression of the diseases caused by other coronaviruses (eg, Middle East respiratory syndrome (MERS), SARS).9 This may be explained by the huge amino acid differences in viral proteins of distinct coronaviruses. Even with different MERS-CoV strains, common cytokine signalling by TNF and IL-1α results in the differential expression of innate immune genes.10
Ferritin is a marker of iron storage. However, it is also an acute-phase reactant, the level of which elevates in processes of acute inflammation, whether infectious or non-infectious. Marked elevations have been reported in cases of COVID-19 infection.11
A common finding in most patients with COVID-19 is high D-dimer levels (>0.28 mg/L), which are associated with a worse prognosis.3 12 An exceptional interest of physicians in this biomarker comes from the fact that the vast majority of patients who died of COVID-19 fulfilled the criteria for diagnosing the disseminated intravascular coagulation. This is why the incidence of pulmonary embolism in COVID-19 is high.
In this condition, the D-dimer concentration will definitely rise up because it is a product of degradation of a blood clot formed out of fibrin protein.13 Thromboembolic complications explain the association of low levels of platelets, increased levels of D-dimer and increasing levels of prothrombin in COVID-19.14 Alternatively, the D-dimer level may go up as a direct consequence of SARS-CoV-2 itself.15
Reasonably, laboratory haemostasis may provide an essential contribution to the COVID-19 prognosis and therapeutic decisions.16 Researchers tried to forecast the severity of COVID-19 with D-dimer as a single predictor. They showed that D-dimer level >0.5 mg/L had a 58% sensitivity, 69% specificity in the forecast of the disease severity.17 In another study, D-dimer level of >2.14 mg/L predicted in-hospital mortality with a sensitivity of 88.2% and specificity of 71.3%.18 Another study highlighted that a D-dimer threshold of >2.66 mg/L detected all patients with a pulmonary embolus on the chest CT.15 So, the high levels of D-dimer are a reliable prognostic biomarker of in-hospital mortality.
In patients with COVID-19 admitted to ICU for acute respiratory failure, the level of fibrinogen is significantly higher than in healthy controls (517±148 vs 297±78 mg/dL).12 The small vessel thrombi revealed on autopsy in lungs and other organs suggest that disseminated intravascular coagulation in COVID-19 results from severe endothelial dysfunction, driven by the cytokine storm and associated hypoxaemia. As standard-dose deep vein thrombosis prophylaxis cannot prevent the consumptive coagulopathy, monitoring D-dimer and fibrinogen levels are required. This will promote the early diagnostics of hypercoagulability and its treatment with direct factor Xa inhibitors.14 19
Activated partial thromboplastin time
In a study conducted in February 2020, the levels of activated partial thromboplastin time (aPTT) as well as white blood cells (WBC), lymphocytes, aspartate aminotransferase (AST), alanine aminotransferase (ALT) and creatinine, differed negligibly between severe and mild patients.3 At the same time, other researchers showed inconsequential distinction in aPTT in survivors versus non-survivors.20 According to the results of another study published in March 2020, no significant difference in aPTT values were found in the cohort of severe cases versus the non-severe one.6
The results obtained in another study in April in Italy were the same.12 The common limitation of these early studies was a small sample size. Finally, a meta-analysis justified that the elevation of D-dimer, rather than prothrombin time and aPTT, reflects the progression of COVID-19 towards an unfavourable outcome.21
LDH and creatine kinase
Increased levels of the enzymes may reflect the level of the organ damage in a systemic disease.4 22 Reasonably, they may serve as biomarkers for COVID-19 progression.
C reactive protein
In the early stage of COVID-19, CRP levels are positively correlated with the diameter of lung lesions and severe presentation.23
Liver enzymes and total bilirubin
COVID-19 leads to elevated liver biochemistries (eg, the level of AST, ALT, gamma-glutamyl transferase, total bilirubin) in over 50% of patients on admission. AST-dominant aminotransferase elevation reflects the disease severity and true hepatic injury.24 25.
We decided to identify predictive biomarkers of COVID-19 severity and to justify their threshold values. Hypothetically, the absolute values of the biomarkers on admission to the clinics could provide physicians with an accurate prognosis on the future worsening of the patient that would require transferring the individual to the intensive care unit (ICU). Getting a reliable tool for such a prognosis will support decision making and logistical planning in clinics.
To address the objective, we designed a set of the following tasks:
- To study the linear separability of the laboratory findings values in patients with confirmed COVID-19 who were transferred to ICU versus non-severe cases of the disease, and to make the comparative analysis of the ICU department cases (both the deceased and survived cohorts) with other patients with COVID-19.
- To identify the risk factors by selecting the most valuable features for predicting the deterioration that would require transferring the patient to ICU.
- To work out the threshold criteria for the major clinical data for the early identification of the patients with a high risk of being transferred to ICU.
- To identify the accuracy of the prediction of the patient’s deterioration by the ML algorithm and by a set of the newly created threshold values of the laboratory and clinical findings.
More information: Ryan S Thwaites et al. Inflammatory profiles across the spectrum of disease reveal a distinct role for GM-CSF in severe COVID-19, Science Immunology (2021). DOI: 10.1126/sciimmunol.abg9873