Insight into risk factors for Covid-19 is growing. Two studies involving Swiss scientists suggest genes and blood sugar levels play a crucial role in the course of the disease.This content was published on November 24, 2021 - 09:00
- Deutsch Nicht nur eine Frage des Alters: Warum Covid-19 manche Menschen härter trifft als andere
- Español ¿Por qué el coronavirus golpea a unos más que a otros?
- Português Por que o coronavírus afeta algumas pessoas mais do que as outras?
- 中文 不全是年龄的问题：为什么新冠肺炎对某些人群的影响大于其他人群
- Français Pourquoi le coronavirus frappe-t-il plus durement certaines personnes?
- عربي لهذا السبب يُصيب فيروس كورونا بعض الناس أكثر من غيرهم
- Pусский Стали известны новые факторы риска при заболевании Covid-19
- 日本語 コロナ重症化リスク因子、年齢だけではない 研究で明らかに
- Italiano Ecco perché il coronavirus colpisce alcune persone più duramente di altre (original)
The wide range in the severity of the Covid-19 disease has puzzled researchers and clinicians. Sufferers range from young, healthy people who find themselves in intensive care to elderly people who barely develop a sniffle, to cancer patients who develop no symptoms at all.
At the beginning of the pandemic, people over 65 years old and those with underlying medical conditions such as cancer, diabetes, heart or lung disease seemed more likely to get seriously ill from Covid-19. But the average age of patients admitted to hospital has since fallen.
Several studies have tried to explain this shift in patient demographics. Some experts are theorising that it is a result of lower vaccination rates among young adults. Others say the more infectious Delta variant may be to blame. Age and pre-existing medical conditions are, however, not the only factors influencing the outcome of a Covid-19 infection. New research, involving doctors and scientists from Switzerland, is pointing to two other important risk factors: genes and the levels of blood sugar. These findings could open up the use of machine-learning methods to identify and predict who is most at risk and requires priority treatment.
Why genes matter
When the first wave of cases hit Switzerland, the attending physician Dimitri Patriki began to care for clusters of families who were admitted to Baden Cantonal Hospital with severe Covid-19 infections but with no pre-existing medical conditions. He wondered whether genes could be at play. Patriki turned to Bettina Heidecker, an expert in autoimmune diseases at Berlin's Charité hospital, for advice.
"I've known Bettina since the time we worked together at the University Hospital in Zurich and I knew her insight into genes could be valuable," says Patriki.
Heidecker too had a hunch. She hypothesised that a group of genes called human leukocyte antigen (HLA), which is responsible for regulating the immune system, could explain why some patients in hospital developed only fairly mild symptoms, while others required breathing support.
HLA is known to be associated with other diseases, such as influenza or rheumatoid arthritis. It can also play a role in the development of myocarditis, a dangerous inflammation of the heart muscle. So Heidecker set up an international project to investigate the links between HLA and severe forms of Covid-19, drawing on patient data from hospitals around the world.
For their research, 435 patients of varying ages and sex who had tested positive for the coronavirus were assessed over the course of six months. This included 21 patients that Patriki treated. “The Baden Cantonal Hospital played a central role in our research in Switzerland”, Heidecker says about the international collaboration.
The researchers collected blood samples and analysed the genetic traits of the participants to find evidence of the influence of HLA. They distinguished between different levels of severity of the disease and characterised patients as severely ill if they required artificial ventilation.
The study, which was recently published in a journal of the renowned Lancet groupExternal link, confirmed their hypothesis: Covid-19 patients who had a particular subtype of human leukocyte antigen, called HLA-C * 04:01, were twice as likely to require ventilation than those without the gene. The research group says that this subtype either slows down the immune response, allowing the virus time to replicate quickly, or exaggerates it. An exaggerated immune response to the virus can lead to fatal inflammation.
AI shines a spotlight on blood sugar
Scientists have recently been able to add another important piece to the Covid-19 puzzle: the role of glucose, or blood sugar, in the human bodyExternal link.
The discovery comes from the Blue Brain Project group at the Swiss Federal Institute of Technology in Lausanne (EPFL), which repurposed its own brain simulation tool to analyse studies on Covid-19. The group trained the tool to detect keywords in a database of more than 240,000 academic articlesExternal link mentioning SARS-CoV-2 virus, the disease it causes, and other coronaviruses. "The tool extracted and classified more than 400,000 unique relevant keywords, a task that a human could not have performed," Henry Markram, founder and director of the Blue Brain Project, tells SWI swissinfo.ch.
The group found that the term 'glucose' was mentioned 6,326 times in the articles. For comparison, the term SARS-CoV-2 was mentioned 49,386 times. But among biochemical compounds, glucose had the broadest associations with all stages of infection, according to the analysis. In a subsequent step, the researchers analysed the most significant articles highlighting glucose as a risk factor and reconstructed the Covid-19 disease – from infection in the lungs to the development of complications and organ failures – in a graphical visualisation. This allowed them to synthesise the dense information and explore the potential role of glucose in the disease.
"Through this knowledge engineering process, we have found that high blood glucose promotes virtually every stage of SARS-CoV-2 infection," says Emmanuelle Logette, a molecular biologist at Blue Brain Project. High glucose levels compromise the initial immune response and promote viral invasion and multiplication, she says. They also facilitate acute inflammation that can lead to multi-organ dysfunction. Blood vessel activity is also disturbed, which can lead to thrombosis.
"It is therefore the combination of these multiple effects of high glucose that explains the development of severe forms of Covid-19."
High glucose and diabetes
Diabetes is a well-known risk factor for Covid-19. But it’s only one condition related to elevated levels of glucose. The analysis by the Blue Brain Project group does not address diabetes specifically, but it focuses on how the body processes glucose.
A weakness in processing glucose, even without diabetes, may compromise the primary defence of the lungs and weaken the immune system, according to the authors.End of insertion
Can machine learning help predict severe cases of Covid-19?
According to Heidecker and Patriki, the use of machine learning models like these marks a crucial step forward in Covid-19 research, not only to better understand the disease, but also to more accurately identify higher risk groups and anticipate severe disease outcomes.
"It is very difficult for us doctors to work during a pandemic. Sometimes so many patients come in that it is complicated to understand who is at risk for developing severe Covid-19 disease and who is not, especially under time pressure. It's a great help to have more elements of assessment available," says Patriki. Having a full picture of risk factors could help healthcare personnel predict how Covid-19 behaves in patients. He adds: “I hope that we will arrive at the point that we can test for genetic markers to tell someone if they have a very high risk of getting Covid-19 or being severely ill.”
Patriki is already experimenting with an algorithm, in collaboration with AWK Group, to predict the course of Covid-19 and other diseases in his patients. He hopes to have a system in place one day that can make accurate diagnoses in real time. But any working system must first have to be trained on actual patient data.
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