| Literature DB >> 35455356 |
Tamas Fulop1, Anis Larbi2, Graham Pawelec3,4, Alan A Cohen5, Guillaume Provost6, Abedelouahed Khalil1, Guy Lacombe1, Serafim Rodrigues6,7, Mathieu Desroches8,9, Katsuiku Hirokawa10, Claudio Franceschi11,12, Jacek M Witkowski13.
Abstract
Organismal ageing is associated with many physiological changes, including differences in the immune system of most animals. These differences are often considered to be a key cause of age-associated diseases as well as decreased vaccine responses in humans. The most often cited vaccine failure is seasonal influenza, but, while it is usually the case that the efficiency of this vaccine is lower in older than younger adults, this is not always true, and the reasons for the differential responses are manifold. Undoubtedly, changes in the innate and adaptive immune response with ageing are associated with failure to respond to the influenza vaccine, but the cause is unclear. Moreover, recent advances in vaccine formulations and adjuvants, as well as in our understanding of immune changes with ageing, have contributed to the development of vaccines, such as those against herpes zoster and SARS-CoV-2, that can protect against serious disease in older adults just as well as in younger people. In the present article, we discuss the reasons why it is a myth that vaccines inevitably protect less well in older individuals, and that vaccines represent one of the most powerful means to protect the health and ensure the quality of life of older adults.Entities:
Keywords: COVID-19 vaccine; adaptive complex systems; herpes–zoster vaccine; immunobiography; immunosenescence; inflammaging; influenza vaccine; mathematical model; pneumococcal vaccine; tipping point; trained immunity; vaccination
Year: 2022 PMID: 35455356 PMCID: PMC9030923 DOI: 10.3390/vaccines10040607
Source DB: PubMed Journal: Vaccines (Basel) ISSN: 2076-393X
Past and present vaccines for older subjects considering their clinical efficiency.
| Vaccines | Younger Individuals | Older Individuals |
|---|---|---|
| Influenza | ||
| Standard dose | +/− | - |
| High dose | + | ++ |
| Herpes Zoster | ||
| Zostavax | NIL | + |
| Shingrix | NIL | ++ |
| SARS-CoV-2 (after 3rd dose) | + | + |
| Pneumococcus | ||
| Polysaccharide | +/− | - |
| Conjugated | + | + |
| Yellow fever | + | + |
| Hepatitis B virus | + | + |
| Japanese encephalitis virus | + | + |
-: almost not efficient in older individuals; +/−: efficient vaccine, but not for everybody; +: efficient in most individuals (young or elderly); ++: very efficient in almost all older subjects.
Figure 1Mathematical modelling of the immune history and critical transitions towards differential ageing. (Panel A): Time-varying energy landscape (green) induced by the interaction between immunobiographical variables with different timescales, which is given by the differential equations; the functions f and g describe the evolution law of the immunobiographical variables, as well as their interactions. Finally, ε is a small parameter, a mathematical representation to capture the idea that time is contracted or dilated. The immune history evolves on this landscape (see the black trajectory segment). (Panel B): The immune system as an adaptive complex multiscale system, where each layer (scale) is a network or simplicial complex of interacting components. Each layer can be summarised by an order parameter Ii. (Panel C): A specific model example of a 2-dimensional multiscale immune system; the function h can, for instance, be a quadratic polynomial, and ε and α are parameters. The different immune history is shown in the phase plane, where different perturbations lead to different immune history (trajectories) outcome that reach “immune exhaustion” with different time delays (i.e., τ1, τ2, and τ3). Note that different immune histories can be associated with different individuals or with the same individual receiving different perturbations. (Panel D1,D2): The corresponding trajectories of I1 and I2 in chronological time. (Panel E): A zoom of the lower part of figure C. The competition of timescales between I1 and I2 creates a funnel structure and a tipping point. Trajectories first contract onto the funnel and, initially, their biological age is not affected; however, past the tipping point T, different biological ages are induced (i.e., τ1, τ2, and τ3) which is dependent on small perturbations. (Panel F): Chronological time is linear, while biological time is nonlinear, with many components inducing either slow or fast timescales depending on the individuals and the various perturbations that they will suffer across life, indicating differential adaptations of the immune system during ageing underlying the differential vaccine response.
Figure 2When we age from young adults to old adults, we experience immunosenescence and inflammaging, which impact our response to vaccinations, making it suboptimal (red track). However, if studies on the mechanisms of ageing (esp. immune system ageing) give us the targets (described in the text), we may intervene, on one hand, in the processes of inflammaging and immunosenescence, and, on the other, by modifying the vaccine to better suit old subjects (green track).