Adding genetic data to steroid prescribing can help predict side effects, data suggest

by European Society of Human Genetics

edited by Sadie Harley, reviewed by Andrew Zinin

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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, more than one in 10 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 presented 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, England, 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 who was 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 cataracts, among others.

"We were also able to show a clear relationship between the dose of steroid and side effects," Turkmen says. "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 steroid risk assessment. This improvement went beyond routinely available factors such as age and sex, and was particularly marked 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," she says.

"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."

Given the widespread use of steroids, large-scale implementation of PRSs in prescribing will present a major challenge. The most practical application is likely to be targeted at higher-risk individuals, particularly those for whom steroid use may be longer term. The findings also need to be studied in other cohorts to ensure that they are applicable more widely, the researchers say.

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," Turkmen says.

"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 the prediction of osteoporosis when we incorporated polygenic risk score 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 health care and hence into prescribing decisions. That will be a major step on the road to the provision of personalized 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."

Key medical concepts

Polygenic Risk ScoresCTLA4 GeneCYP3A4OsteoporosisBone Mineral Density Test

Clinical categories

Clinical geneticsClinical pharmacology Provided by European Society of Human Genetics Who's behind this story?

Sadie Harley

BSc Life Sciences & Ecology. Microbiology lab background with pharmaceutical news experience in oil, gas, and renewable industries. Full profile →

Andrew Zinin

Master's in physics with research experience. Long-time science news enthusiast. Plays key role in Science X's editorial success. Full profile →

Citation: Adding genetic data to steroid prescribing can help predict side effects, data suggest (2026, June 13) retrieved 14 June 2026 from https://medicalxpress.com/news/2026-06-adding-genetic-steroid-side-effects.html This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.