Uses global myocardial transcriptomics and blood lipidomics in pediatric cardiomyopathy to identify a metabolic signature of diastolic dysfunction characterized by dysregulated lipid signaling, excess saturated lipids, and impaired oxidation. A machine learning model using gene markers classified diastolic dysfunction across subtypes. While focused on pediatric cardiomyopathy rather than semaglutide directly, this study provides fundamental insights into diastolic dysfunction pathomechanisms—relevant for understanding the metabolic basis of HFpEF that semaglutide targets.
Turinsky, Andrei L; Hanafi, Nour; Said, Abdelrahman; Kinnear, Caroline; Lesurf, Robert; López-Guillén, José Luis; Akilen, Rajadurai; Patel, Suhani; Meng, Guoliang; Wei, Wei; Robillard Frayne, Isabelle; Daneault, Caroline; Mertens, Luc; Ellis, James; Ruiz, Matthieu; Mital, Seema