Our immune system undergoes constant changes as we age, often resulting in a diminished capacity to respond effectively to infectious diseases. However, not everyone ages the same way. Some older adults remain relatively robust to adverse health outcomes, while others are far more vulnerable. Understanding the immunological basis of these differences is key to improving prevention strategies. This becomes especially relevant now, given the rapid increase in the worldwide aging population. Due to the traditional ‘one hat fits all’ approach in healthcare settings, current vaccines and immune protection strategies do not work equally well for all older adults. Consequently, identifying the mechanisms that distinguish well-protected individuals from those at heightened risk remains both a critical and challenging objective. The identification of immune signatures associated with protective immunity and durable immune memory against pathogens could enable improved risk stratification and support the development of more tailored and effective prevention strategies.
To address this gap, this project will study the immune memory response of old-age individuals involved in AgingLines, a unique aging sub-cohort within the LifeLines cohort. We will focus on charting their memory responses to three clinically relevant respiratory viruses, namely: influenza, SARS-CoV-2 and respiratory syncytial virus (RSV). Participants will be characterized not only by chronological age but also by individual-specific frailty, a functional proxy of biological aging, enabling a more refined stratification of aging profiles. We will first characterize antibody and T-cell responses against the three viruses within the cohort. Subsequently, selected individuals exhibiting particularly strong or weak antiviral memory responses will undergo deep functional immune phenotyping, including advanced immune assays and transcriptomic profiling. These heterogenous datasets will then be integrated through Immune-MEMORIES (I-MEMORIES), a comprehensive machine learning model, to identify immune signatures associated with protection. Finally, through advanced in-vitro respiratory organoid models, we will assess if the identified immune signatures truly predict protection against viral infection.
Building on our complementary expertise, we aim to identify immune characteristics that explain why some older adults are better protected than others. By uncovering the immune signatures associated with protection, our findings will support improved risk stratification and lay the groundwork for targeted vaccination strategies, particularly for vulnerable older adults.
