Using genetics to link modifiable risk factors to risk of COVID-19
How can we answer questions of causation (“what if?” questions) when we can’t perform experiments directly? In the case of COVID-19, a typical causal question would look like this: what would happen if we prescribed people anti-inflammatory medications? Would it improve outcomes? While we can answer these questions definitively by performing a randomized trial, this approach is slow. With this outbreak, time is of the essence and there are many potential treatments to evaluate. Instead of performing the randomization ourselves, our approach is to exploit a randomization that nature has provided for us in genetics.
The intrinsically random nature of genetic transmission from parent to child provides us with a natural experiment. In one recent piece of work , we consider genetic variants that mimic the effect of angiotensin-converting enzyme inhibitors (ACE-inhibitors). ACE-inhibitors are blood pressure lowering drugs that have anecdotally been linked to risk of COVID-19 infection. We compare individuals with genetic variants that predispose them to higher or lower levels of ACE in a similar way to how in a randomized trial, we would compare individuals allocated at random to ACE-inhibitors or to a placebo. In another piece of work , we consider genetic variants that mimic inhibition of interleukin-6, suggesting that interleukin-6 inhibition has opposing effects on COVID-19 and pneumonia.
Another strand of work concerns causal questions about modifiable risk factors, such as blood pressure and smoking status. Conventional epidemiological studies assess associations between risk factors and outcomes. However, a risk factor and outcome may be correlated without the risk factor being a cause of the outcome. The chief reasons are confounding (shared causes influencing both risk factor and outcome) and reverse causation. Investigating whether genetic variants predicting a risk factor are associated with the outcome is a more reliable assessment of causation, as the genetic code cannot be influenced by confounders, and it comes before the risk factor in time, mitigating against reverse causation.
Our research tests whether genetically-predicted levels of cardiometabolic risk factors associate with sepsis and COVID-19 risk . If genetically-predicted levels of a risk factor are associated with increased risk of disease, this provides evidence the risk factor causally increases disease risk.
We observed consistent results for severe COVID-19 and sepsis: genetically-predicted BMI and smoking were associated with both COVID-19 and sepsis. The results were consistent across a range of methods. This provides a reliable line of evidence indicating that BMI and smoking both increase the risk of COVID-19.
- Why has COVID-19 mortality been lower in the second wave?
- Smoking and obesity identified to have causal link with susceptibility to severe COVID-19 and sepsis
- Are we underestimating the spread of COVID-19 based on seroprevalence surveys?
Immune recovery from COVID-19
This research aims to understand how the immune system is affected in different ways for those who have contracted COVID-19.
We use detailed clinical and immune phenotyping datasets, extracted from a large blood sample collected in patients with COVID-19 during the first peak of the pandemic, and followed up over 12 months. The unique patient follow-up of the study allows us to examine hypotheses about the impact of prolonged T and B lymphopenia upon recovery from disease, as well as inflammatory and metabolic changes over the course of the disease.
The project benefits from expertise in (1) infectious diseases and immunology, and (2) advanced statistical methodology for medical sciences. We use integrative statistical approaches aimed at taking full advantage of the rich laboratory and clinical data collected or to be collected as part of the programme. Altogether, this collaborative research has the potential to develop concrete advances in our scientific understanding of the long-term physical effects of COVID-19, and it may suggest new avenues for therapeutic intervention.
Preprint currently under review: https://www.medrxiv.org/content/10.1101/2021.01.11.20248765v1
Joint research with Professor Ken Smith’s group (Department of Medicine, Cambridge Immunology Network) and Professor Christopher Hess’ group (Department of Medicine, Cambridge Immunology Network & University of Basel, Switzerland)
Prediction of evolving in-hospital mortality risk for COVID-19 patients
This project focused on developing a risk prediction model for patients admitted to hospital with COVID-19 that updates as new clinical information (vital signs, lab tests etc) is collected during a hospital visit. While there are many COVID-19 prediction models that use only the information collected available at the point when a patient is admitted to hospital, we are not aware of reliable existing COVID-19 prediction models that are able to update their prediction as new clinical information is collected.
We used de-identified data extracted from the hospital medical records at Addenbrookes to develop our model. Addenbrookes is one of only six European hospitals to reach the highest digital maturity, meaning we have been able to use highly-granular data. We included demographic data, observations, vital signs, laboratory tests, comorbidities and information about interventions and treatments.
Using recent advances in statistical methodology for medical applications, we were able to develop a model founded on sound statistical underpinnings including: accounting properly for patients who leave hospital because they have recovered or require transfer to another hospital for more specialised treatment; and acknowledging that e.g. extra blood tests may be requested for sicker patients.
The resulting prediction model has the potential to provide physicians with an assessment of a patient’s evolving prognosis throughout the course of active hospital treatment.
Preprint currently under review: https://www.medrxiv.org/content/10.1101/2021.02.15.21251150v1
Joint research with Sarah Cowan [a], Claire Waddington [b], David Halsall [c], Victoria Keevil [d], Vince Taylor [e], Effrossyni Gkrania-Klotsas [f] and Jacobus Preller [a]
[a] Intensive Care Medicine and Acute Medicine, Addenbrooke’s Hospital, Cambridge
[b] Infectious Diseases and General Medicine, Department of Medicine, University of Cambridge
[c] Pathology, Cambridge University Hospitals
[d] Department of Medicine for the Elderly, Cambridge University Hospitals
[e] Clinical Informatics Integration Lead, Cancer Research UK, Cambridge University Hospitals
[f] Department of Infectious Diseases, Cambridge University Hospitals