Genes associated with high cholesterol can be passed down through families, a process called familial hypercholesterolemia. About 1 in 200 to 1 in 250 people inherit “high-cholesterol genes,” such as mutations to the LDLR gene. Researchers from Boston created an algorithm to assess the risk someone with a mutation for elevated cholesterol has for developing heart disease by age 75. To compute these findings, the researchers paired high-risk genes for elevated cholesterol with 28 other genetic markers associated with coronary heart disease progression. Coronary heart disease is caused by a narrowing of the arteries surrounding the heart and is the most common type of heart disease. The study, partially funded by the National Heart, Lung, and Blood Institute, appears in Nature Communications.
To create the algorithm, or polygenic risk score, the researchers analyzed the genetic background of 6,420 people from the UK Biobank carrying genes for elevated cholesterol, including LDLR, APOB, and PCSK9. The researchers found genetic carriers for elevated cholesterol had a 1.3- to 12.61-fold increased risk for developing coronary heart disease by age 75, based on low- to high-level associations with the 28 genetic markers associated with coronary heart disease. These genetic expressions correlate with inflammation, cellular division, or vascular tone, and operate outside of how high-risk genes for abnormal cholesterol travel in cholesterol pathways.
The genetic carriers with lower polygenic background expression for coronary heart disease had a risk comparable to the average person for developing coronary heart disease by age 75. Conversely, genetic carriers for high cholesterol with higher polygenic associations had a higher risk for developing coronary heart disease later in life. The findings support ongoing research for tier 1 genomic conditions — those likely to benefit from early detection and intervention.
Dina Paltoo, Ph.D., M.P.H., assistant director of scientific strategy and innovation at the National Heart, Lung, and Blood Institute, notes this study supports larger efforts to help scientists “better predict, preempt, and prevent disease.”
“It’s important to have this information and understand what these polygenic risks are, combined with other omics data or lifestyle data, clinical data, or even factors such as social determinants of health and data science approaches, which can give us a bigger picture of the person’s progression of disease,” Dr. Paltoo said.