Immune Development in Early Life (IDEaL) longitudinal cohort study protocol: Identifying biomarkers of vaccine responsiveness, respiratory infection, and asthma

Background: Early-life immune development is a critical factor in predicting the risk of childhood respiratory infections, asthma, and poor vaccine responses. Identifying immune endotypes that predispose children to these conditions could lead to the development of predictive biomarkers and early interventions, potentially improving long-term health outcomes. The IDEaL (Immune Development in Early Life)-Rome prospective pediatric cohort, based at Children's Hospital Bambino Gesu (Rome, Italy), is part of a National Institutes of Health/National Institute of Allergy and Infectious Diseases-supported longitudinal observational study. Objectives: To identify molecular biomarkers associated with increased susceptibility to respiratory infections, asthma, and poor vaccine responsiveness in early childhood. The study aims to establish predictive immune profiles that could guide interventions to redirect harmful immune trajectories. Methods: Mothers are approached during pregnancy prospectively and eligible infants are enrolled at delivery. The study includes 6 planned visits up to 5 years of age. Biosamples (blood, stool, nasal swabs, and cord blood for a subset) were collected at each visit for multiomic data (cytokines, proteomics, microbiome), alongside clinical data on vaccination, infections, and wheezing. Results: The study included 273 participants (100% enrollment completion rate). Over 2 years, clinical and multiomic data were integrated to investigate immune trajectories related to clinical outcomes. Specific data on the outcomes will be provided in future reports as longitudinal analysis continues. Conclusions: The IDEaL-Rome cohort study seeks to identify biomarkers predicting immune development trajectories. These findings could enable early interventions to redirect harmful immune trajectories in infancy and improve health outcomes, though further studies are required to validate biomarkers and refine predictive models for clinical application.