Chronic antigenic stress, immunosenescence and human survivorship over the 3 last centuries: heuristic value of a mathematical model

https://doi.org/10.1016/S0047-6374(03)00022-8Get rights and content

Abstract

In previous investigations we pursued the hypothesis that lifelong, chronic antigenic load (CAL) is the major driving force of immunosenescence, which impacts on human lifespan by reducing the number of virgin antigen-non experienced (ANE) T cells, and filling the immunological space with expanded clones of memory and effector, antigen-experienced (AE) T cells. A model has been proposed to relate CAL with the conversion rate from ANE to AE CD8+ T cells. In addition, in order to account for individual variations of immunosenescence and lifespan, a noise term has been introduced to describe the individual fluctuations of CAL. This model was able to follow with a reasonable approximation the age-related decrease of ANE CD8+ T cells, as well as human survival curves. In this paper we extend this approach to historical survival curves, starting from 1750 until present days, and show that the quality of the fit of historical demographic data improves as we approach the recent, quantitative and qualitative decrease of CAL. Indeed, the almost linearity in the increase of lifespan and in the decrease of the noise fluctuation amplitude within this historical period suggests that the improvement of life conditions has steadily lowered the intensity of CAL and restricted the variability which results from the interaction between the individuals and their immunological environment. On the whole, this approach allows to appreciate when and how immunosenescence has started to impact on survivorship, and predicts its increasing, crucial role in explaining human mortality in hygienized, economically developed societies.

Introduction

Recent data suggest that age-associated immune remodelling, i.e. immunosenescence, may have a clinical impact, being responsible, at least in part, of some major age-related pathologies (Franceschi et al., 1995). However, until now, no reliable biomarker of immunosenescence is available. Taking into account that a major characteristic of immunosenescence is the accumulation of memory plus effector antigen-experienced (AE) T cells (Fagnoni et al., 1996, Wack et al., 1998), accompanied by a decrease of virgin antigen-non experienced (ANE) T cells (Fagnoni et al., 2000), it is reasonable to hypothesize that such parameters can be assumed bona fide as candidate biomarkers of immunosenescence (Franceschi et al., 1999, Franceschi et al., 2000a, Pawelec et al., 2002a). Indeed, the so called ‘Immunological Risk Phenotype’, capable of predicting impinging morbidity and mortality in the elderly, includes the number of CD4+ and CD8+ AE T cells, as well as other parameters related to chronic inflammation (Franceschi et al., 2000c, Bonafè et al., 2001, Pawelec et al., 2002b). A unifying approach suggests that both the accumulation and the expansion of AE T cells, and particularly of CD8+ T cells, and the increase of inflammatory markers present in old people, is a consequence of the largely inescapable, lifelong chronic antigenic load (CAL) (Franceschi et al., 2000b, Franceschi et al., 2000c).

This scenario has been conceptualized and modelled, and the results suggest that the number and the concentration of ANE CD95− CD8+ T cells (Fagnoni et al., 2000) can be assumed as a biomarker capable of predicting mortality in humans (Luciani et al., 2001a, Luciani et al., 2001b). In this model we pursued the hypothesis that CAL can be represented by an average component which quantifies the conversion rate from ANE CD95− to AE CD95+ CD8+ T cells and determines the average lifespan. In addition, in order to account for individual variations of immunosenescence and lifespan, we introduced an additional noise term for the fluctuations of CAL. This parameter was able to describe with a reasonable approximation not only the age-related decrease of AE CD8+ T cells but also the shape of human survival curves.

In the present paper we extend this model to human survival curves starting from 1750 until present days in Swedish cohorts, and show that the fitting of these demographic data improves as we approach recent, improved conditions of life, where CAL underwent a qualitative and quantitative reduction, suggesting that immunosenescence is becoming more and more important as a possible cause of mortality.

Section snippets

Stochastic model for virgin CD8+ T lymphocytes

In order to describe the CD8+ T lymphocyte dynamics on a long time scale, we consider the relation between the ANE CD95− and AE CD95+ CD8+ T cell pools. Indeed, the model we have recently proposed (Luciani et al., 2001a, Luciani et al., 2001b, Mariani et al., 2002), takes into account the conversion from ANE CD95− (V) to AE CD95+ (M) CD8+ T cells occurring with a constant rate α due to primary antigenic stimulation. The secondary antigenic stimulation is responsible for a further expansion of

Survival probabilities

We assume the ANE CD95− CD8+ T cell concentration as a biomarker of mortality, which implies that an organism dies when this cell pool is exhausted (Fagnoni et al., 2000) and we identify the concentration of this T cell subset with a sort of vitality function, often proposed to relate individual heterogeneity to demographic survival probability (Yashin et al., 2000, Piantanelli et al., 2001, Rossolini and Piantanelli, 2001). As a first consequence of this assumption, the average death age T of

Bestfitting of historical survival data

In order to infer the historical changes of parameters α, β and ε of our model, we have compared the survival probability function S(t), given by Eq. (7), with the historical records of Swedish cohorts born in the period (1750–1910) (taken at regular time intervals of 20 years). These data were chosen owing to their availability and remarkable accuracy. The bestfitting between the function S(t) and the different sets of data, associates to every cohort specific values of the parameters α, β and

Conclusions

The immunological stochastic model we have used describes the time evolution for ANE CD8+ T cell concentration during the human lifespan and, considering this cell concentration as a biomarker of mortality, gives a mathematical expression of the survival curve for adult age t>T/2 in good agreement with the demographic data.

The CAL explains the spread of ANE CD8+ T cell concentration in a given population, and the flattening of the survival probability curves at very advanced ages. Within this

Acknowledgements

This work was supported by grants from M.I.U.R. Project COFIN 2001 ‘A multidisciplinary approach to the regulation of the immune system’ to C.F.

References (19)

There are more references available in the full text version of this article.

Cited by (11)

  • One parameter family of master equations for logistic growth and BCM theory

    2015, Communications in Nonlinear Science and Numerical Simulation
  • PON1 is a longevity gene: Results of a meta-analysis

    2009, Ageing Research Reviews
    Citation Excerpt :

    We have published several papers with both positive and negative data, and we have identified several genes with a key role in ageing (Capri et al., 2006, 2008; De Benedictis and Franceschi, 1998, 2006; Franceschi et al., 2007). Genetic variants that modulate insulin/insulin-like growth factor-1 (IGF-1) signalling (Franceschi et al., 2005), inflammation (Franceschi et al., 2000; Salvioli et al., 2006), and stress resistance (Franceschi et al., 2005; Mariani et al., 2003; Ottaviani and Franceschi, 1998), among other factors, differ significantly between very long-lived humans (such as centenarians) and shorter lived humans. These same genes were then suggested to be pivotal in age associated diseases (cardiovascular diseases, Alzheimer's, cancer and diabetes).

  • Immunoproteasomes and immunosenescence

    2003, Ageing Research Reviews
  • Peripheral selection rather than thymic involution explains sudden contraction in naive CD4 T-cell diversity with age

    2012, Proceedings of the National Academy of Sciences of the United States of America
  • Forgive to live: Forgiveness, health, and longevity

    2012, Journal of Behavioral Medicine
View all citing articles on Scopus
View full text