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30 December, 2024The system integrates epigenetic clinical data, allowing early detection and personalized prevention of metabolic complications, especially during puberty, a key stage to avoid future cardiovascular diseases.
A group of researchers from the Area of Pathophysiology of Obesity and Nutrition of the Biomedical Research Center in Network (Ciberobn) at the University of Granada (UGR) have managed to develop a model of Artificial Intelligence (IA) to predict the risk of metabolic alterations in children with obesity.
This work, published in the journal Artificial Intelligence Medicine, has achieved estimate the risk of metabolic complications by integrating clinical and epigenetic data, whose patterns are different in minors with metabolic alterations found in puberty compared to the prepubertal stage.
The use of this model in hospitals can help in the early detection of metabolic risks, which will allow pharmacological interventions or lifestyle adjustments to be carried out to prevent metabolic diseases, contributing to reducing comorbidities associated with obesity and lowering costs for public health, according to a statement from Ciber.
This model, which is based on data such as Body Mass Index (BMI), hormone levels (leptin and adiponectin) and new genetic markers in relevant genes, is “explainable"and "can be interpreted" by healthcare professionals.
"This combination of data allows not only for accurate risk prediction, but also for a greater understanding of how the model processes variables, enabling its application in clinical settings more effectively.", he explained Álvaro Torres, researcher at Ciberobn.
Scientists have also stated that there is a “urgent need" to implement this type of prediction programs to address obesity "early" so as to prevent the worsening of the health status of these children, since pediatric obesity "can increase dramatically"the risk of cardiometabolic disorders in later stages of life, with insulin resistance being the"cornerstone"which links adiposity with increased cardiovascular risk.
They also pointed out that puberty is a critical stage after which insulin resistance associated with obesity is more difficult to reverse, which is why they believe that timely prediction of this is necessary.
"Building effective and robust predictive systems for a complex health outcome such as insulin resistance during early life requires the adoption of longitudinal designs to obtain more causal inferences and the integration of factors of diverse nature involved in its emergence.“, they added.
The project was carried out at the Granada Health Technology Park in collaboration with researchers from the area of Obesity Pathophysiology and Nutrition of Ciber (CIBEROBN) at the University of Granada, the Biosanitary Research Institute (ibs.GRANADA), the Andalusian Interuniversity Institute for Data Science and Computational Intelligence (DaSCI) and the Health Research Institute (IIS) of Aragon.
In addition, it has received funding from the Carlos III Health Institute, the European EprObes project (Prevention of obesity throughout life through early identification of risk factors, prognosis and intervention, for its translation into Spanish) and the Health Research Fund (FIS).
Source: iSanidad