PODiaCarD: A Digital Twin Platform for Pediatric Obesity Management

Childhood obesity has emerged as a critical public health concern, significantly increasing the risk of developing cardiovascular diseases (CVD) and type 2 diabetes (T2D) later in life. With millions of deaths attributed to these conditions globally, early intervention is essential. The PODiaCarD platform offers an innovative solution by utilizing a Digital Twin System (DTS) to monitor and predict cardiometabolic risk in children. This prototype integrates clinical, anthropometric, and lifestyle data, employing machine learning algorithms to provide personalized insights into metabolic health. The PODiaCarD platform is built on a robust three-layer architecture that includes a frontend, backend, and predictive engine, ensuring scalability and reproducibility. Trained on a dataset of 552 children aged approximately 12 years, the platform demonstrates excellent predictive performance for the Triglyceride-Glucose (TyG) index (F1 score of 0.975) and solid results for HbA1c (F1 score of 0.844). However, the predictive capacity for HOMA-IR (F1 score of 0.670) and other metabolic outcomes like blood pressure and glycemia was found to be limited, indicating a need for richer datasets to enhance accuracy. For parents and healthcare providers, the implications of PODiaCarD are significant. This platform allows for dynamic monitoring of a child's cardiometabolic risk profile, enabling early detection and personalized prevention strategies. By integrating lifestyle factors and clinical data, it empowers pediatricians to make informed decisions tailored to each child's unique health needs. Families can engage with this technology to better understand their child's metabolic health and take proactive steps towards improvement. Key biomarkers relevant to this research include the TyG index, HbA1c, and HOMA-IR. These biomarkers are crucial for assessing insulin resistance and overall metabolic health. Tools available on Metabolicum.org can help users evaluate these markers and understand their implications for health. For instance, monitoring HOMA-IR can provide insights into insulin sensitivity, while tracking HbA1c levels can help gauge long-term glucose control. In conclusion, the PODiaCarD platform represents a promising advancement in managing pediatric obesity and its associated metabolic risks. By leveraging machine learning and comprehensive data integration, it offers a forward-thinking approach to pediatric cardiometabolic care. As digital health technologies continue to evolve, platforms like PODiaCarD could play a pivotal role in shaping the future of preventive healthcare for children.