Understanding Fat-Muscle Interactions for Early Cardiometabolic Risk Stratification

Cardiovascular diseases originate in childhood, with early metabolic changes predisposing individuals to long-term health risks. Excess adiposity, particularly visceral (VAT) and ectopic fat depots (liver, intramuscular, and epicardial fat), contributes to insulin resistance and systemic inflammation. Skeletal muscle plays a key role in metabolic homeostasis, regulating glucose uptake and fatty acid oxidation. Impaired muscle health – reduced mitochondrial function, elasticity, and increased intramuscular fat is linked to insulin resistance and cardiovascular dysfunction. However, the interactions between adiposity and muscle health in modulating cardiovascular risk in adolescents remains unclear.
Maternal factors such as prepregnancy obesity, gestational weight gain, and hyperglycemia impact fetal metabolic programming, influencing childhood adiposity, muscle composition, and long-term cardiometabolic risk. Early-life growth patterns, nutrition, and physical activity further modulate these relationships, impacting long-term health outcomes.
This research integrates MRI-based body composition assessments, metabolic and inflammatory biomarkers, and cardiovascular health measures to investigate these complex interactions. The findings will support early risk stratification and targeted interventions for high-risk adolescents.
The project requires a student with strong programming skills in Python and R, along with a solid foundation in statistics and data analysis. Experience with machine learning and image processing techniques is highly desirable. Basic knowledge of clinical research and human physiology would be beneficial. The ideal candidate should have a keen interest in biomedical data science and a multidisciplinary approach to solving complex health challenges. The student will collaborate with clinicians, scientists, and statisticians across multiple disciplines to investigate adiposity, muscle health, and cardiovascular risk. The research will involve implementing AI-driven deep learning models for fat quantification and applying advanced statistical techniques to assess adiposity-muscle-cardiovascular interactions. This project provides hands-on experience in state-of-the-art imaging analytics, computational modeling, and translational research, equipping the student with expertise in both clinical and data-driven approaches for cardiometabolic risk assessment.