TL;DR:
- A European project called EprObes aims to prevent childhood obesity with a €10 million budget.
- The project focuses on studying obesity and overweight conditions in the early stages of life.
- Artificial intelligence (AI) plays a crucial role in data analysis and developing tools for prevention and treatment.
- Personalized recommendations will be provided through an application based on individual data.
- Researchers will investigate risk factors, biomarkers, and intervention strategies.
- Sex differences, psychological factors, and socioeconomic impacts will also be studied.
- The project brings together scientists from multiple countries and institutions.
- Prevention strategies are deemed more effective in addressing obesity-related complications.
- The initiative has implications for improving quality of life, reducing social and healthcare costs, and prioritizing mental health.
Main AI News:
Childhood obesity has become a global concern, with approximately 650 million people affected worldwide and over four million annual deaths attributed to obesity-related causes, according to the World Health Organization. Alarming statistics highlight that the problem is increasingly prevalent at younger ages. In Spain, the Gasol Foundation’s 2022 PASOS report reveals that 21.6% of children are obese, while 11.8% are overweight. In response to this pressing issue, the Center for Biomedical Research Network (CIBER) introduced a groundbreaking European project named EprObes in Madrid today, securing €10 million in funding. Over the next five years, the initiative will focus on studying obesity and overweight conditions with the aim of prevention during the early stages of life. Key areas of investigation include biomarkers, risk factors, prognosis, and early intervention strategies.
EprObes sets itself apart with its innovative utilization of artificial intelligence (AI) technology. Researchers rely on AI for comprehensive data analysis and will employ machine learning algorithms to develop tools that provide support to physicians, healthcare professionals, and patients in the prevention and treatment of overweight and obesity. Álex Bravo, a leading researcher specializing in machine learning, emphasizes the significance of AI in understanding a patient’s trajectory, stating, “to know which way he or she is going.” The team aspires to create an application capable of real-time personalized recommendations based on individual data, such as increasing physical activity levels or incorporating more fruits into one’s diet.
Manuel Tena-Sempere, the project coordinator and principal investigator of CIBEROBN (the CIBER area of Physiopathology of Obesity and Nutrition) at the University of Cordoba (Spain), underscores the importance of developing effective prevention strategies, particularly in the early stages, to mitigate the metabolic complications associated with being overweight. Tena-Sempere expresses disappointment in the limited efficacy of current treatments for the most common forms of obesity.
To provide tailored treatments for obesity and its comorbidities, the project will delve into risk and protective factors, as well as the mechanisms driving excessive weight gain during pivotal periods like pregnancy, early childhood development, and adolescence. Factors contributing to obesity include environmental impact, family conditions, maternal metabolic status, fetal growth, and epigenetics.
Tena-Sempere notes that while pregnancy-related factors can significantly impact a child’s subsequent development, detailed studies regarding the mother’s obesity before childbirth have been lacking until now. “One of the work teams seeks to specifically analyze that aspect…not only in terms of what happens, but also which molecular mechanisms cause it,” he explains.
This initiative also stands out for its investigation into sex differences to gain deeper insights into how hormones, metabolism, gender roles, social contexts, and other factors influence obesity and its associated pathologies. The physiologist from the University of Cordoba highlights that sex differences from an early age play a crucial role in biological processes that can contribute to varying predispositions to developing obesity.
The project will meticulously examine the impact of psychological and socioeconomic factors, particularly mental health and eating disorders (ED). Fernando Fernández-Aranda, the director of the CIBEROBN group, stresses that these disorders can manifest in adolescence and subsequently impact obesity or vice versa, where early obesity can lead to the development of ED in adolescence. Detecting these associated factors stands as a significant objective within this project.
To comprehensively address all facets of obesity, the project includes patient cohorts at different stages of development. Apart from mental health, individual behavior, lifestyle, and personal environment, the initiative takes into account factors such as intervention and prevention studies, focusing on diet and exercise.
EprObes unites scientists from Germany, France, Denmark, Turkey, Poland, Belgium,and Estonia in a collaborative effort. In Spain, esteemed institutions, including the University of Cordoba, the Biomedical Research Institute Foundation, the Maimonides Institute of Biomedical Research of Cordoba, the Spanish National Research Council, and the University of Valencia, alongside 18 international organizations, are actively participating.
The overarching goal of the project is to combat obesity through prevention, which is deemed a more effective approach, as stated by María Puy, the scientific director of CIBEROBN. The outcomes are expected to result in an improved quality of life for individuals grappling with obesity, while also reducing the social and healthcare costs it engenders. Puy considers the inclusion of mental health in the research framework a significant success, emphasizing the dire need for increased funding in this area.
Conclusion:
The €10 million EprObes project represents a significant step forward in combating childhood obesity. By leveraging the power of artificial intelligence, researchers aim to provide personalized interventions and recommendations for patients, healthcare professionals, and physicians. The project’s comprehensive approach, focusing on risk factors, biomarkers, and prevention strategies, has the potential to revolutionize the field of obesity prevention and treatment. Furthermore, the inclusion of mental health and socioeconomic factors highlights the project’s holistic perspective. The outcomes of EprObes have the potential to reshape the market by emphasizing the importance of early intervention, personalized care, and the integration of AI in healthcare solutions. The project’s collaboration among international institutions fosters cross-border knowledge sharing and paves the way for innovative approaches to combatting childhood obesity on a global scale.
