The DOI matches its Crossref record; 55 of the 56 references it cites were found in a registry or an index.
Biomarkers of immunosenescence within an evolutionary perspective: the challenge of heterogeneity and the role of antigenic load
Abstract
Under an evolutionary perspective, antigens can be considered nothing else than chronic stressors that constituted the major selective pressure for immune system emergence and evolution. In this review, recent data are discussed under the hypothesis that human immunosenescence is the consequence of the continuous attrition caused by chronic antigenic overload/stress. The advantage of this theoretical approach is that a unifying hypothesis is proposed, which tries to fill in the current gap between the conceptualizations concerning the mechanisms which counteract aging and favor longevity in invertebrates and vertebrates. The hypothesis is that the immune system is, at a higher level of biological organization and complexity, the counterpart of the anti-stress response network identified in invertebrates as the major determinant of survival. We argue that some of the most important characteristics of immunosenescence, i.e. the accumulation and the clonal expansion of memory and effector T cells, the reduction/exhaustion of naive T cells, and the shrinkage of T cell repertoire, are compatible with this assumption. Thus, immunosenescence can be envisaged as a global reduction of the "immunological space." Concomitantly, immunosenescence results in the progressive generation of cellular mosaicism which is the consequence of the heterogeneous replicative histories and telomere shortening of T and B cell subsets, as well as hemopoietic stem cells. Most of the parameters affected by immunosenescence appear to be under genetic control, and future research on biomarkers should address this point. On the whole, immunosenescence can be taken as a proof that the beneficial effects of the immune system, devoted to the neutralization of dangerous/harmful agents early in life and in adulthood, turn to be detrimental late in life, in a period largely not foreseen by evolution. This perspective fits with basic assumptions of evolutionary theories of aging, such as antagonistic pleiotropy.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Changes in thymic function with age and during the treatment of HIV infection
Douek, D. C., McFarland, R. D., Keiser, P. H., Gage, E. A., Massey, J. M., Haynes, B. F., Polis, M. A., Haase, A. T., Feinberg, M. B., Sullivan#, J. L., et al. (1998) · Nature · doi:10.1038/25374 · Cited by 1,893
Associations of elevated interleukin-6 and C-reactive protein levels with mortality in the elderly
Harris, T. B., Ferrucci, L., Tracy, R. P., Corti, M. C., Wacholder, S., Ettinger, W. H., Heimovitz, H., Cohen, H. J., & Wallace, R. (1999) · The American Journal of Medicine · doi:10.1016/s0002-9343(99)00066-2 · Cited by 1,488
Longevity, Stress Response, and Cancer in Aging Telomerase-Deficient Mice
Rudolph, K. L., Chang, S., Lee, H.-W., Blasco, M., Gottlieb, G. J., Greider, C., & DePinho, R. A. (1999) · Cell · doi:10.1016/s0092-8674(00)80580-2 · Cited by 1,349
Essential role of mouse telomerase in highly proliferative organs
Lee, H.-W., Blasco, M. A., Gottlieb, G. J., Horner, J. W., Greider, C. W., & DePinho, R. A. (1998) · Nature · doi:10.1038/33345 · Cited by 1,270
Evidence for a mitotic clock in human hematopoietic stem cells: loss of telomeric DNA with age.
Vaziri, H., Dragowska, W., Allsopp, R. C., Thomas, T. E., Harley, C. B., & Lansdorp, P. M. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.21.9857 · Cited by 1,132
Serum IL‐6 Level and the Development of Disability in Older Persons
Ferrucci, L., Harris, T. B., Guralnik, J. M., Tracy, R. P., Corti, M., Cohen, H. J., Penninx, B., Pahor, M., Wallace, R., & Havlik, R. J. (1999) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.1999.tb01583.x · Cited by 809
Genetic determination of telomere size in humans: a twin study of three age groups.
Slagboom, P. E., Droog, S., & Boomsma, D. I. (1994) · PubMed Central · Cited by 734
Rufer, N., Brümmendorf, T. H., Kolvraa, S., Bischoff, C., Christensen, K., Wadsworth, L., Schulzer, M., & Lansdorp, P. M. (1999) · The Journal of Experimental Medicine · doi:10.1084/jem.190.2.157 · Cited by 709
Increased cytokine production in mononuclear cells of healthy elderly people
Fagiolo, U., Cossarizza, A., Scala, E., Fanales‐Belasio, E., Ortolani, C., Cozzi, E., Monti, D., Franceschi, C., & Paganelli, R. (1993) · European Journal of Immunology · doi:10.1002/eji.1830230950 · Cited by 665
Self-citationTersmette, M. (1989) · The Lancet · doi:10.1016/s0140-6736(89)92628-7 · Cited by 576
Research works printed but not linked: 2
- Franceschi, 1978, T and B lymphocyte subpopulations in Down’s syndrome. Study on not-institutionalized subjects, J Ment Defic Res, 22, 179
- Slagboom, 1994, Genetic determination of telomere size in humans, Am J Hum Genet, 55, 876
Cited by
Sorted by citations
Inflamm‐aging: An Evolutionary Perspective on Immunosenescence
FRANCESCHI, C., BONAFÈ, M., VALENSIN, S., OLIVIERI, F., DE LUCA, M., OTTAVIANI, E., & DE BENEDICTIS, G. (2000) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2000.tb06651.x · Cited by 5,358
Self-citationInflammation and immune dysfunction in Parkinson disease
Tansey, M. G., Wallings, R. L., Houser, M. C., Herrick, M. K., Keating, C. E., & Joers, V. (2022) · Nature Reviews Immunology · doi:10.1038/s41577-022-00684-6 · Cited by 1,382
Immune activation and inflammation in HIV‐1 infection: causes and consequences
Appay, V., & Sauce, D. (2007) · The Journal of Pathology · doi:10.1002/path.2276 · Cited by 780
“Paradoxically, it represents the main determinant of the most common age-related diseases and a major determinant of the ageing rate [126].”
BackgroundFranceschi, C., Bonafè, M., & Valensin, S. (2000) · Vaccine · doi:10.1016/s0264-410x(99)00513-7 · Cited by 495
Self-citationInflamm‐ageing and lifelong antigenic load as major determinants of ageing rate and longevity
Martinis, M. D., Franceschi, C., Monti, D., & Ginaldi, L. (2005) · FEBS Letters · doi:10.1016/j.febslet.2005.02.055 · Cited by 495
“An exhaustion of naive T cells occurs in centenarians [32].”
BackgroundSelf-citation
Metadata
- Authors
- Franceschi, C., Valensin, S., Fagnoni, F., Barbi, C., & Bonafè, M.
- Affiliations
- Istituto Nazionale di Riposo e Cura per Anziani (IT)
- University of Bologna (IT)
- University of Parma (IT)
- Corresponding author
- Franceschi, Claudio
- Venue
- Experimental Gerontology
- Publisher
- Elsevier BV
- Published
- 1999-12
- Volume
- 34
- Issue
- 8
- Pages
- 911-921
- ISSN
- 0531-5565 (ISSN-L)
- OpenAlex
- W1991422276
- Cited by
- 154
- References
- 56
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 6 · 2023: 6 · 2022: 7 · 2021: 5 · 2020: 8 · 2019: 5 · 2018: 6 · 2017: 2 · 2016: 2 · 2015: 8 · 2014: 3 · 2013: 3 · 2012: 6
- FWCI
- 0.60
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Experimental Gerontology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Telomeres, Telomerase, and Senescence
- Keywords
- immunosenescence, biomarkers, chronic stress, immune system evolution, T cell exhaustion, T cell receptor repertoire, cellular mosaicism, telomere shortening, hematopoietic stem cells, antagonistic pleiotropy, evolutionary theory of aging, longevity
- MeSH terms
- Aging, Antigens, Biological Evolution, Humans, Immune System, Genetic Variation, Biomarkers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review