Oral corticosteroids are commonly used to treat chronic inflammatory conditions such as arthritis, asthma, and autoimmune diseases. However, more than one in ten patients develop side effects from these medications, especially with long-term use. New research presented on June 14 at the annual conference of the European Society of Human Genetics suggests that integrating genetic data into steroid prescribing can enhance risk prediction and help doctors make more informed decisions.
Dr. Deniz Turkmen, a postdoctoral researcher at the University of Exeter AGE Group in the UK, and colleagues analyzed data from nearly 38,000 participants in the UK Biobank who had been prescribed steroids. The team calculated each participant's cumulative steroid dose over time and investigated whether higher doses were associated with increased side effects. They also examined if genetic differences could explain which patients were at greater risk and tested whether adding genetic information improved risk assessment.
The researchers found that certain genetic variants increased the likelihood of developing side effects among patients treated with steroids—specifically noting CYP3A4 for osteoporosis and CTLA4 for stroke and cataract. Incorporating polygenic risk scores for osteoporosis further improved their ability to assess steroid-related risks beyond traditional factors like age and sex. This improvement was most pronounced among younger individuals at 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," Dr. Turkmen said.
The researchers say large-scale implementation of polygenic risk scores will present challenges due to widespread steroid use but suggest targeting higher-risk individuals—especially those likely to need longer-term treatment—as a practical application. They emphasize that further studies in larger and more diverse populations are needed to ensure broader applicability since pharmacogenetic effects observed align with known biological mechanisms influencing steroid metabolism and immune response.
"We anticipated that we would find a clear relationship between dose and adverse outcomes," Dr. Turkmen said. "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... We hope that... it will be possible to integrate genomics into everyday healthcare... That will be a major step on the road to personalised medicine for all." Professor Alexandre Reymond commented, "Today we are seeing more examples of predictive value by compounding rare variants with large effect alongside common variants with small effects."
