Palmer · Proceedings of the National Academy of Sciences of the United States of America 2018 · Mathematical modeling study · n=?

Thymic involution and rising disease incidence with age.

Cited 385 times in the scientific literature.

Level 5 - mechanism / opinion, no new human data

Mechanism-based mathematical modeling using aggregate epidemiological data without new human clinical data

PubMed 29432166 · doi:10.1073/pnas.1714478115 · record verified 2026-08-30

What was done

Researchers developed a minimal mathematical model linking age-related disease incidence to thymic involution and declining T cell production, which declines exponentially in humans with an estimated half-life of approximately 16 years. Combining immunological and epidemiological datasets, they evaluated the model's ability to describe incidence data for infectious diseases and multiple cancer types compared to traditional somatic mutation power-law models with an equal number of fitting parameters.

What was found

The immune decline model outperformed the power-law mutation model in fitting cancer incidence across a wide spectrum of malignancies and provided fits for infectious disease incidence. Aside from the reported ~16-year half-life of T cell production, the abstract provides no specific numerical values, fit metrics, or error estimates.

Why it matters

The findings offer a mathematical framework suggesting that age-related decline in T cell production driven by thymic involution is a major factor in rising cancer and infectious disease incidence, challenging the assumption that cancer incidence curves reflect only somatic mutation accumulation.

Limits

The abstract reports no specific numerical fit statistics, sample sizes, or dataset sources. The study relies on mathematical modeling of aggregate population data and mechanistic reasoning rather than direct experimental or clinical measurement in individual patients.

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