Sepsis and COVID-19 patients most at risk predicted with genetic model

A new model for understanding which patients with sepsis, Covid-19 and influenza have immune dysfunction and are more likely to suffer poor outcomes has been developed by researchers at the Wellcome Sanger Institute, the University of Oxford, Queen Mary University, Imperial College and their collaborators.

The study, published 2 November 2022 in Science Translational Medicine, identified 19 genes that predict the way that the body’s immune system responds to sepsis, Covid-19 and influenza infection, and how immune response can go wrong in some individuals. The small number of genes used in the model paves the way for applying precision medicine techniques, such as prioritising individuals for particular interventions, to diseases like sepsis that have proven difficult to diagnose and treat.

Sepsis is caused by an ‘inappropriate’ immune response to infection or injury, which can spread to the whole body. For reasons unknown, in sepsis immune response becomes overactive or underactive and causes damage to healthy cells, rather than just the source of infection. It is difficult to predict who will get sepsis, who will recover and who will have poor outcomes such as post-sepsis syndrome (PSS) and death. Globally, it is estimated that there are around 49 million sepsis cases and 11 million deaths each year.

Despite hundreds of clinical trials aimed at improving sepsis outcomes, there are currently no targeted treatments. Because sepsis can arise from myriad causes it is a highly variable disease, and positive results from some drug trials have not been reproducible in others.

It is thought that a stronger understanding of sepsis at the molecular level, so that patients can be classified according to the particular characteristics of their illness, is the key to greater success in identifying those at risk and developing effective treatments.

In this new study, researchers at the Wellcome Sanger Institute and the University of Oxford set out to develop a gene expression model for understanding which patients with sepsis are more likely to have particular responses and potentially poor outcomes.

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