AI Image: do me a graphical abstract about the following topic: Integrative Multiomics and AI-based approaches

Prompt

do me a graphical abstract about the following topic: Integrative Multiomics and AI-based approaches to predict potential biomarkers and lifestyle risks for the development of childhood obesity Childhood obesity is one of the biggest public health challenges worldwide. In 2024, 35 million children under the age of 5 were overweight and over 390 million children and adolescents aged 5–19 years were overweight in 2022 (1,2). Current projections indicate that obesity are associated with several health‐related complications, including endocrine and metabolic disturbances (type 2 diabetes, dyslipidemia), cardiovascular co-morbidities (hypertension), pulmonary complications (apnea, asthma), gastrointestinal and nutritional disorders, among others (3). According to the Organisation for Economic Co-operation and Development (OECD), between 2020 and 2050, the treatment of diseases caused by overweight/obesity will cost USD 425 billion a year based on the data from 52 countries analysed, including EU28 and G20 countries (4). Obesity is a multifactorial condition, influenced by both prenatal factors (genetics, maternal diets, weight, chronic diseases) and postnatal influences (childhood dietary habits, altered sleep cycles, sedentary lifestyle). Metabolic and lipidomic profiles, adipokine signaling, gut microbiome composition, and environmental context also interact and contribute to obesity development (5,6). Multi-omics technologies are used in obesity research to capture the multi