Cited by 364T-cell and B-cell ImmunologyGenetics, Aging, and Longevity in Model OrganismsArtificial Immune Systems Applications

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Thymic involution and rising disease incidence with age

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Palmer, Sam, Albergante, Luca, Blackburn, Clare C., Newman, T. J.·2018·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.1714478115PMC PMC5828591

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Abstract

Significance: Understanding the risk factors of carcinogenesis is a major goal of biomedical research. Historically, the focus has been on the role of somatic mutations, and the reason for cancer typically occurring late in life is predominantly attributed to a gradual accumulation of such mutations. We challenge that view and propose that the decline of the immune system is the primary reason why cancer is an age-related disease. The immunological model featured here captures risk profiles for many cancer types and infectious diseases, suggesting that therapies reversing T cell exhaustion or restoring T cell production will be promising avenues of treatment.

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Fig. 1. Declining T cell production leads to increasing disease incidence. Our model assumes that immunogenic cells arise with the same probability at any age, and, after a period of being targeted, the population may overwhelm the immune system by crossing an immune escape threshold. This threshold is assumed to be proportional to T cell production, which decreases with age. This provides a prediction for the possible forms of disease incidence curves.CC BY-NC-ND · PubMed Central

Fig. 2. Infectious disease incidence. Log-linear plots of incidence (per 100,000 person-years) by age group for all ABC bacterial infections, West Nile virus (WNV) disease, and Influenza A, ordered from best fit to worst. Bacterial and viral diseases are shaded yellow and green, respectively. The two-parameter IM-II is in red, while the one-parameter IM-I is in orange. Incidence often decreases initially from birth due to an underdeveloped immune system in infants; therefore, models are fitted only to data points for ages greater than 18 y. Error bars show 99% CIs for all diseases.CC BY-NC-ND · PubMed Central

Fig. 3. Cancer incidence. Log-linear plots of incidence (per 100,000 person-years). Data taken from SEER (11). (A and B) Some cancer types rise exponentially fitting IM-I (A), while some cancer types rise like power laws, although can still be fit by IM-II (B). Fitting curves for IM-II and PLM are shown in red and green, respectively. (C) The top 20 best-fitting incidence curves as measured by Akaike Information Criterion (AIC) for IM-II. (D and E) Universal scaling functions for all cancers with defined pivot ages (84 out of 101 cancer types) plotted in gray with the top 20 incidence curves highlighted. Data shown for both genders (D) and gender-separated data (E), with dotted lines showing the model predictions for IM-I and IM-II. The gender-separated curves are fitted with higher independently determined values for α in males than females, reflecting the gender bias in T cell production (Methods). A purely exponential incidence curve would correspond to a pivot age of negative infinity, and therefore, for the purposes of plotting, we set a minimum pivot age of −50 y. Models are fitted only for ages greater than 18 y. Error bars show 95% CIs for all diseases.CC BY-NC-ND · PubMed Central

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Additional file 9 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
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Additional file 12 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
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Additional file 5 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
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Additional file 15 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
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Additional file 2 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
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Additional file 4 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
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Additional file 3 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
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Additional file 8 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
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Additional file 7 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
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Additional file 1 of Age-associated changes in the circulating human antibody repertoire are upregulated in autoimmunity
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Authors: Palmer, S., Albergante, L., Blackburn, C. C., & Newman, T. J.
Affiliations: University of Dundee (GB)
Inserm (FR)
MRC Centre for Regenerative Medicine (GB)
Corresponding author: Palmer, Sam
Newman, T. J.
ORCID: Palmer, Sam
Albergante, Luca
Blackburn, Clare C.
Newman, T. J.
Venue: Proceedings of the National Academy of Sciences
Publisher: National Academy of Sciences
Published: 2018-02-05
Volume: 115
Issue: 8
Pages: 1883-1888
ISSN: 0027-8424 (ISSN-L)
1091-6490
DOI: 10.1073/pnas.1714478115
PMC: PMC5828591
OpenAlex: W2788857602
Cited by: 364
References: 45
Citations by year: 2026: 51 · 2025: 53 · 2024: 45 · 2023: 43 · 2022: 37 · 2021: 36 · 2020: 56 · 2019: 26 · 2018: 17
FWCI: 15.04
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
Discovery Research Portal (University of Dundee) (submittedVersion, cc-by-nc-nd)
Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by-nc-nd)
Funders: SULSA (U54 CA143682)
EC | Seventh Framework Programme (602587)
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
Physical Sciences › Computer Science › Artificial Intelligence › Artificial Immune Systems Applications
Keywords: thymic involution, immunosenescence, cancer incidence, infectious disease incidence, power law model, mutation accumulation, aging
MeSH terms: Aging, Female, Humans, Male, Models, Biological, Mutation, Neoplasms, Thymus Gland, Genetic Predisposition to Disease

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Thymic involution and rising disease incidence with age

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