The risk of coronary artery disease (CAD) can change over a person’s lifetime, and current methods of estimating CAD risk don’t incorporate new information over time. Sarah Urbut, Pradeep Natarajan, and colleagues have developed MSGene, a model that accounts for longitudinal data, clinical covariates, and CAD polygenic risk scores to estimate transitions between 10 cardiometabolic states. The team used MSGene to analyze longitudinal data from more than 480,000 UK Biobank participants and found that it improved risk predictions compared to other risk scores. The findings, in Nature Communications, highlight the potential public health value of more accurate lifetime CAD risk estimation. #BroadInstitute #Science #ScienceNews #Research #ScientificResearch
Did this include high Lp(a), homocysteinemia, genetically high triglycerides?
Marketing Consultant @ Tremulis Stractical Marketing Consulting | MBA, Inherited CardioMetabolic Health, Women's Health
4dThe participants who gave their DNA to the UKBiobank deserve a medal of recognition for changing science for everyone globally.