Immune Age Study in Lifelines

Whereas global life expectancy has increased with most people nowadays living into their 60s and many years beyond, these extra years are often not lived in good health 1. As people age, the risk to develop non-communicable diseases and severe infectious diseases increases 1,2. 
It has long been observed that there is person-to-person variation in the pace of aging. Whereas chronological age is an important predictor of morbidity and mortality, it cannot account for heterogeneity in the decline of physiological function and health. On the contrary, biological age is considered to reflect physiological status and functioning and to account for variation in the trajectory of human aging 3. In recent years, many studies have invested in defining biological age based on physiological, biochemical, molecular and/or immunological biomarkers. Studies initially focused on biomarkers that change with chronological age and present a measure of biological age that is predictive of all-cause mortality 4,5. More recent studies, including our latest analysis of the Rotterdam Study, have developed models of biological age that also account for differences in the physiological status and can differentiate between morbidity and mortality risk among individuals of the same chronological age 6,7. 
Aging is associated with significant changes in the immune system and characterized by two processes. This first involves a reduced functioning and capacity of the immune system to respond and clear infections, a process known as ‘immuno-senescence’. The second is a progressive inability of the immune system to control and shut down activated responses, which consequently leads to hyperreactions and can cause tissue and organ damage: a process known as ‘inflammaging’. 
Like other body systems, changes in immune functioning over lifetime are likely less dictated by chronological age than by individual trajectories that are determined by individual baselines and genetic and environmental interactions. We therefore predict that the pace of ‘immune aging’ may vary from person to person, and may be a strong and independent predictor of age-related severe infectious and non-communicable diseases, and of the ability to respond to new vaccines at older age. 
Antibodies play an important role in both immunosenescence and inflammaging 8. With increasing age, the spectrum of the B-cell repertoire and hence capacity to recognize and produce antibodies against different danger signals changes. This is characterized by a narrowing of the B-cell repertoire and clonal expansion of a limited number of specific B-cells9,10. At the same time there is an increase in auto-antibodies due to a progressive failure of self-tolerance mechanisms to deplete B-cells recognizing self-antigens 11,12. Hence, one could expect that the antibody profile of (biologically) older aged individuals is characterized by a limited diversity in antibodies against non-self antigens that is dominated by high responses against a limited set of non-self antigens, and increasing antibody responses against self-antigens. To the best of our knowledge, this has not yet been studied at a population level and would require a cohort of individuals for who biologically age has been well-defined. We propose to do for the first time in the Lifelines cohort. 
In addition, antibody functionality is regulated in part through glycan structures present on the constant region of antibodies. Hundreds of differentially glycosylated antibody variants can be present in an individual at any given time. This large individual variability in antibody glycosylation contributes to individual differences in immune functioning, and some associations with aging, auto-immunity, non-communicable, and severe infectious diseases have been found 8,13-16. Analysing IgG glycosylation profiles, Krištić et al. developed a model they called ‘Glycan Age’ that could explain 40-to-50% of variation in chronological age, whereas in the same cohort telomere length - a conservative age biomarker - accounted for only 15-to-25% of variance 17. We now propose to take these findings to the next step and identify antibody glycosylation patterns that can differentiate between individuals of different biological age. 
Several studies have identified genes that are associated with antibody glycosylation 18-21. Since antibody glycosylation is regulated by a complex interaction between genetic and environmental factors, we propose to include whole genome analysis to understand the contribution of genetics in IgG glycosylation in relation to biological aging. Whereas host genetics are known to contribute to natural variation in humoral immunity in humans, involving in particular variations in HLA gene region and the ability to seroconvert upon vaccination 22, we propose to study for the first time associations between genetic traits and the regulation of the B-cell repertoire and antibody response in relation to age through biological age. In addition, we propose to include genetic loci that are associated with general longevity and healthspan. 
Most of the existing immune aging algorithms were constructed with the basis of the association between composite biomarkers and chronological age and might be imperfect because of the practice of minimizing the deviation from chronological age through regression and not estimating this entity of interest empirically. In our previous study, we improved biological age algorithms through a population-risk-based approach in advanced-age cohorts of the Rotterdam Study [Wu et al, in submission], and showed that biological age is a predictor of all age-related diseases including dementia. We propose to, for the first time, develop a novel immune age algorithm that correlates to biological age, to capture the elevated risk of all-cause mortality/morbidities associated with accelerated immune aging.