Gothenburg, Sweden: Oral corticosteroids (OCSs) are widely used and effective in the treatment of chronic inflammatory conditions such as arthritis, asthma, and autoimmune diseases. They work by reducing inflammation, relieving pain, and calming the immune system. However, over one in ten patients develop side effects, particularly if they use steroids over a long period. Until now, it has been difficult to identify those who will react in this way, but results from research to be presented today (Sunday) at the annual conference of the European Society of Human Genetics show that integrating genetic data into steroid prescribing can improve the prediction of risk and thus enable doctors to prescribe them more appropriately.
Dr Deniz Turkmen, a postdoctoral researcher at the University of Exeter AGE Group, Exeter, UK, and colleagues studied data from nearly 38,000 UK Biobank participants who had been prescribed steroids. They calculated how much steroid each one had taken over time; whether higher doses were linked to more side effects; examined whether genetic differences could help explain those who were at risk; and, finally, tested whether adding genetic information improved risk assessment. They found that, in patients treated with steroids, certain genetic variants increased the risk of side effects; CYP3A4 for osteoporosis and CTLA4 for stroke and cataract, among others. "We were also able to show a clear relationship between the dose of steroid and side effects," says Dr Turkmen. "This precise analysis shows the increased risk associated with long-term treatment".
Incorporating polygenic risk scores* (PRSs) for osteoporosis enabled the researchers to further improve the steroid risk assessment. This improvement went beyond routinely available factors such as age and sex, and was particularly marked in in younger individuals at the time of their first prescription. "Currently, without efficient prediction methods, clinicians try to reduce risks by using only short courses of steroids, prescribing the lowest possible dose, or switching to alternative steroid-sparing treatments such as biologics. However, biologic treatments are often more expensive and may not be easily accessible to all patients. These strategies may also be insufficient for individuals with chronic conditions who require repeated or long-term steroid treatment. The routine use of genetic information could mean that, in the future, patients at high risk could be identified and given earlier steroid-sparing treatments, or have closer monitoring for side effects," she says.
Given the widespread use of steroids, large-scale implementation of PRSs in their prescribing will present a major challenge. The most practical application is likely to be targeted to higher risk individuals, and particularly those where steroid use may be longer-term. The findings also need to be studied in other cohorts to ensure that they are applicable more widely, say the researchers. Larger and ethnically more diverse populations may also enhance predictive performance, since the pharmacogenetic effects observed in the study are consistent with other biological mechanisms that influence steroid metabolism and immune response.
"We anticipated that we would find a clear relationship between dose and adverse outcomes," says Dr Turkmen, "It was reassuring that the genetic findings involving CYP3A4 and CTLA4 aligned with their roles in steroid metabolism and immune regulation, but the improvement in prediction of osteoporosis when we incorporated polygenic risk scores data was remarkable, especially in younger patients. While single variants had a relatively limited influence on the risk of serious side effects from steroids, adding PRSs for traits such as bone mineral density improved risk prediction. We hope that, in time, greater availability of genetic data at population level will mean that it will be possible to integrate genomics into everyday healthcare and hence into prescribing decisions. That will be a major step on the road to the provision of personalised medicine for all."
Chair of the conference, Professor Alexandre Reymond, who was not involved in the research, said: "Today we are seeing more and more examples of the predictive value of compounding the risk foreseen for variants that are rare and have a large effect with those of common variants with small effects."
