CAD polygenic risk score may guide statin initiation for younger adults

Biobank data suggest targeted polygenic risk score testing in younger adults with borderline or intermediate clinical risk may be a useful strategy for determining who will benefit most from initiating statin therapy, researchers reported.

“A polygenic risk score for CAD appears to have the greatest utility in younger individuals, where it can be used to help risk stratify those with borderline or intermediate clinical risk,” Nicholas A. Marston, MD, MPH, assistant professor of medicine, member of the TIMI Study Group and cardiologist at Brigham and Women’s Hospital and Harvard Medical School, told Healio. “As polygenic risk scores become increasingly available, the CAD polygenic risk score could be an additional tool to improve precision in the identification of who will benefit most from initiation of statin therapy.”

Assessing biobank data

Nicholas A. Marston

In a longitudinal study, Marston and colleagues analyzed data from 330,201 adults without a history of CAD who were not taking lipid-lowering therapy, using data from the UK Biobank (enrollment 2006-2010). The median age of participants was 57 years and 57% were women; researchers followed the cohort for 10 years.

Using a CAD polygenic risk score including 241 genome-wide significant single-nucleotide variations, researchers defined polygenic risk for CAD as low (bottom 20%), intermediate or high (top 20%). Researchers used pooled cohort equations to estimate 10-year atherosclerotic CVD risk and then classify participants as low (< 5%), borderline (5%-7.5%) intermediate (7.5%-20%) or high risk ( 20%) for MI and ASCVD events. Researchers performed a replication analysis using data from Biobank Japan (n = 104,439).

During the 10-year follow-up, 4,454 participants had an MI.

The findings were published in JAMA Cardiology.

Researchers found that the CAD polygenic risk score was associated with MI risk in all age groups; however, there was significantly stronger risk prediction at younger ages. For participants younger than 50 years, the HR per 1 standard deviation of polygenic risk score was 1.72 (95% CI, 1.56-1.89). The HR fell to 1.46 for participants aged 50 to 60 years (95% CI, 1.38-1.53) and to 1.42 for participants older than 60 years (95% CI, 1.37-1.48; P for interaction < .001).

Among participants younger than 50 years, researchers observed a three- to fourfold increase in associated risk for MI for those with a high polygenic risk score compared with participants of the same age range in the low polygenic risk score category. Additionally, there was a significant interaction between CAD polygenic risk score and age in the replication cohort, according to researchers.

Integration with ASCVD risk score

Among participants younger than 50 years, 88% had low ASCVD risk and 12% had borderline or intermediate ASCVD risk. The observed 10-year risk of an ASCVD events in the low, borderline and intermediate groups was 1.4%, 5.2% and 7.4%, respectively.

“When CAD polygenic risk score testing was added to the ASCVD risk score, a significant gradient of risk was identified within each ASCVD risk group,” the researchers wrote. “Most notably, the 20% of participants with borderline risk and a high-genetic risk score had an observed 10-year rate of ASCVD of 8%, thereby establishing them as intermediate risk and warranting initiation of statin therapy to reduce LDL cholesterol level in the presence of other risk-enhancing factors.”

The researchers noted that the study was limited to participants aged 40 to 74 years; therefore, conclusions cannot be drawn about adults younger than 40 years or those aged 75 years and older.

“Future work should focus on adults under the age of 40 years, as this is a group that is too young for the ASCVD risk score; however, this group may have the greatest utility from genetic testing,” Marston told Healio.

Lifetime risk prediction needed

Sadiya Sana Khan

In a related editorial, Sadiya Sana Khan, MD, MSc, FACC, FAHA, assistant professor of medicine and preventive medicine, associate program director of the cardiovascular disease fellowship and director of research in the section of heart failure at Northwestern University Feinberg School of Medicine, and Michael J. Pencina, PhD, vice dean for data science at Duke University School of Medicine and professor of biostatistics and bioinformatics at Duke University, noted that the common 10-year timeframe used in this study may be appropriate for middle-aged and older adults; however, it is “insufficient” for younger adults where lifetime risk prediction is needed to guide preventive measures.

“Even among patients with premature MI in the VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) study, the population-attributable fraction of traditional risk factors accounted for 85% of the risk,” Khan and Pencina wrote. “This suggests subclinical elevations in cardiovascular risk factors earlier in the life course may be key targets for prevention.”

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Nicholas A. Marston, MD, MPH, can be reached at [email protected]; Twitter: @marstonmd.