Scientists may have found a new way to predict a patient’s risk of rare muscle disorders linked to statin treatment.
Researchers at the University of Oxford created a calculator that predicts a person’s risk of developing muscle disorders on statin treatment. A study published in The Lancet Digital Health shows how the researchers can estimate an individual’s risk of severe muscle disorders, including myopathy and rhabdomyolysis.
The calculator could help support treatment discussions between patients and doctors by providing personalized risk estimates. According to the researchers, the tool can help clinicians individualize care. They believe it will limit reliance on population averages and better guide side effects concerns patients may have.
“For the small number of people at higher risk, it gives clinicians a clearer basis for discussing monitoring, checks or alternative treatment options,” said Dr. Ting Cai, a study researcher.
New Calculator Quantifies Statin Muscle Risk
Severe muscle disorders are rare among people who take statins. However, muscle aches remain one of the most commonly reported symptoms, often raising treatment concerns.
University of Oxford researchers developed the STRATIFY-StatinMD Risk Calculator to help distinguish between the risk of common muscle symptoms and rare, serious complications. The prediction model estimates a patient’s risk of developing a severe statin-related muscle disorder. It predicts clinically significant muscle disorders that could lead to hospital admission or death.
The researchers reported that the risk of developing severe muscle complications was very low. They said understanding an individual’s risk could help support more informed discussions between clinicians and patients about statin therapy.
“Serious muscle disorders are one of the most widely discussed concerns about statins, but our findings suggest that the risk is very low for the vast majority of people who may benefit from treatment,” Cai said.
Oxford professor Constantinos Koshiaris explained the model quantifies risk at individual level without over reliance on population-level data.
“Clinical decisions are often based on estimates of potential benefit, but understanding potential harms is equally important,” Koshiaris said. “This model provides a way to quantify that risk at an individual level, helping support more balanced discussion about treatment options.”
Large British Health Databases Powers The Risk Model
Researchers developed and tested the algorithm using electronic health records from 1.7 million people in England. They then validated the model in a separate dataset of 3.9 million people to assess its accuracy.
The calculator analyzes 22 routinely collected clinical and demographic factors to generate an individualized risk score. These factors include age, sex, ethnicity, body mass index, smoking status, medication use and medical history.
The model was validated on men aged 50 years and older and women aged 60 years and older who met clinical criteria for statin therapy. Within this population, a history of a previous muscle condition was the strongest predictor of a serious statin-related muscle disorder.
Patients with a prior muscle condition had a 6.2-fold higher risk than those without one. Despite those differences in individual risk, the researchers say serious muscle disorders are uncommon. They also found that fewer than 1 in 200 patients experienced one of these complications over a decade. They estimated that about 0.04% of patients developed a serious muscle disorder within one year of starting statin therapy. The risk increased to 0.20% after five years and 0.45% after 10 years.
The researchers said the calculator offers clinicians an evidence-based way to identify patients who may benefit from closer monitoring. However, for most patients, the risk of severe muscle complications remains low.
Equitable Health Outcomes Remains A Challenge In Statin Prescribing
Researchers continue to study how ancestry, genetics and healthcare practices influence statin treatment.
Emerging research suggests that different racial populations may experience different treatment responses to statins. Individuals of East Asian ancestry may face a higher risk of statin-related muscle side effects compared with other groups.
Researchers believe this increased sensitivity may be linked to genetic differences in drug-metabolizing enzymes, affecting how the body processes treatment. As a result, some physicians may prescribe lower starting doses.
Beyond biological differences, prescribing patterns remain a health equity concern. Despite higher rates of cardiovascular disease risk factors, including high cholesterol, Black and Hispanic patients are often less likely to receive statin therapy compared with other groups.
A study published in the American College of Cardiology found that that differences in statin prescribing were not fully explained by disease severity or access to healthcare resources. It found that the care process contributed to these gaps. These include factors, such as physician bias, assumptions about long-term medication use, and patient mistrust.
The findings highlight the need for a more individualized approach to statin therapy. Health equity experts argue that more efforts are needed to ensure that patients across all backgrounds receive appropriate cardiovascular care.
These groups argue that risk assessment tools should be paired with efforts to address disparities in treatment access and care.
New Guidelines Reshape How Clinicians Assess Cardiovascular Risk
The disparate prescribing adds to the known outcome disparities highly prevalent in heart disease and cholesterol diseases.
Black Americans are less likely to have high cholesterol than white counterparts, according to the American Heart Association. However, they are 35% more likely to die from heart disease than White Americans.
“The rates of statin use in those at the highest risk for cardiovascular disease is quite low overall, but especially among Black and Hispanic adults,” said Joshua A. Jacobs, Cardiology Clinical Pharmacist at the University of Utah. “Statins are cheap and effective at the prevention of heart disease, so this is definitely a large missed opportunity.”
Last year, the clinical guidelines updated to help combat the disparity. The American Heart Association and American College of Cardiology dyslipidemia guideline introduced the PREVENT-ASCVD risk equations.
The updated model was developed using risk-adjusted data from a diverse population and incorporates measurable health factors that better capture an individual’s cardiovascular risk.
It also allow clinicians to incorporate additional factors, including the Social Deprivation Index, to further personalize risk estimates. Researchers say improving statin care will require more than better prediction models as unequal treatment outcomes remain an ongoing challenge.
