Frontiers in Aging published a review on testing AI for immune aging through vaccine selection
Frontiers in Aging published a review on testing AI for immune aging through vaccine selection
On August 6, Frontiers in Aging published a review of artificial intelligence in research on immune aging, which refers to age-related changes in the immune system. The authors propose testing models in a study where model output influences the vaccination strategy and outcomes are measured by immune protection and infection incidence.
Two people of the same chronological age may respond differently to vaccination. The immune system is affected by previous infections, metabolism, thymic function, which supports the maturation of immune cells, and other biological factors. Chronological age therefore provides only a rough guide for selecting a vaccination strategy.
Machine learning models can analyze immune cell composition in the blood, inflammatory proteins, and gene activity together. In a 2021 study, researchers trained iAge, a model that estimates immune age, using blood data from 1 001 people. The model linked CXCL9, an immune signaling protein, to inflammatory aging. The researchers then tested its role in experiments using human and mouse cells.
The authors of the new review propose a multicenter study involving people aged 65 years and older. In one group, the vaccination strategy would be selected according to the standard age-based rule. In the other, it would be selected according to an immune aging measure obtained before vaccination. After 28–42 days, the groups would be compared for seroconversion, defined as the appearance or a substantial increase in protective antibodies, or for neutralizing antibody levels. T-cell responses would also be assessed. Post-vaccination infections would then be tracked throughout the season.
The proposed protocol links immune data, model output, vaccine selection, and the person’s subsequent health status. The authors propose evaluating the model by determining whether this selection process changes the immune response and the incidence of infection.