Comparing the age and sex trajectories of SARS-CoV-2 morbidity with other respiratory pathogens points to potential immune mechanisms

Metcalf, C. Jessica E.; Paireau, Juliette; O’Driscoll, Megan; Pivette, Mathilde; Hubert, B.; Pontais, Isabelle; Cummings, Derek A. T.; Cauchemez, Simon; Salje, Henrik

doi:10.1101/2021.01.07.21249381

Cited by 4 publications

(1 citation statement)

References 39 publications

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“…risk is proportional to e 0.044t , where t is age, or equivalently increasing by about 4.5% per year [4]. These diseases are caused by a range of pathogens, from bacterial (methicillin-resistant Staphylococcus aureus (MRSA), S. pneumoniae) to viral (West Nile virus, MERS-CoV [25]) and even include some cancers (chronic myeloid leukaemia, heart and brain cancers). Since thymus volume and T-cell production both decrease with age exponentially, halving every 16 years [5], disease risk is therefore inversely proportional to T-cell production for these diseases.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 hospitalization rates rise exponentially with age, inversely proportional to thymic T-cell production

Palmer

Cunniffe

Donnelly

2021

J. R. Soc. Interface.

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Here, we report that COVID-19 hospitalization rates follow an exponential relationship with age, doubling for every 16 years of age or equivalently increasing by 4.5% per year of life ( R 2 = 0.98). This mirrors the well-studied exponential decline of both thymus volume and T-cell production, which halve every 16 years. COVID-19 can therefore be added to the list of other diseases with this property, including those caused by methicillin-resistant Staphylococcus aureus , MERS-CoV, West Nile virus, Streptococcus pneumoniae and certain cancers, such as chronic myeloid leukaemia and brain cancers. In addition, the incidence of severe disease and mortality due to COVID-19 are both higher in men, consistent with the degree to which thymic involution (and the decrease in T-cell production with age) is more severe in men compared to women. Since these properties are shared with some non-contagious diseases, we hypothesized that the age dependence does not come from social-mixing patterns, i.e. that the probability of hospitalization given infection rises exponentially, doubling every 16 years. A Bayesian analysis of daily hospitalizations, incorporating contact matrices, found that this relationship holds for every age group except for the under 20s. While older adults have fewer contacts than young adults, our analysis suggests that there is an approximate cancellation between the effects of fewer contacts for the elderly and higher infectiousness due to a higher probability of developing severe disease. Our model fitting suggests under 20s have 49–75% additional immune protection beyond that predicted by strong thymus function alone, consistent with increased juvenile cross-immunity from other viruses. We found no evidence for differences between age groups in susceptibility to infection or infectiousness to others (given disease state), i.e. the only important factor in the age dependence of hospitalization rates is the probability of hospitalization given infection. These findings suggest the existence of a T-cell exhaustion threshold, proportional to thymic output and that clonal expansion of peripheral T-cells does not affect disease risk. The strikingly simple inverse relationship between risk and thymic T-cell output adds to the evidence that thymic involution is an important factor in the decline of the immune system with age and may also be an important clue in understanding disease progression, not just for COVID-19 but other diseases as well.

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Section: Introductionmentioning

confidence: 99%

COVID-19 hospitalization rates rise exponentially with age, inversely proportional to thymic T-cell production

Palmer

Cunniffe

Donnelly

2021

J. R. Soc. Interface.

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COVID-19 hospitalisation rates rise exponentially with age, inversely proportional to thymic T-cell production

Palmer

Cunniffe

Donnelly

2020

Preprint

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Here we report that COVID-19 hospitalisation rates follow an exponential relationship with age, increasing by 4.5% per year of life (95% CI: 4.2-5.2%). This mirrors the exponential decline of thymus volume and T-cell production (decreasing by 4.5% per year). COVID-19 can therefore be added to the list of other diseases with this property, including those caused by MRSA, West Nile virus, Streptococcus Pneumonia and certain cancers, such as chronic myeloid leukemia and brain cancers. In addition, incidence of severe disease and mortality due to COVID-19 are both higher in men, consistent with the degree to which thymic involution (and the decrease in T-cell production with age) is more severe in men compared to women. For under 20s, COVID-19 incidence is remarkably low. A Bayesian analysis of daily hospitalisations, accounting for contact-based and environmental transmission, indicates that non-adults are the only age group to deviate significantly from the exponential relationship. Our model fitting suggests under 20s have 53-77% additional immune protection beyond that predicted by strong thymus function alone. We found no evidence for differences between age groups in susceptibility to overall infection, or, relative infectiousness to others. The simple inverse relationship between risk and thymus size we report here suggests that therapies based on T-cell mechanisms may be a promising target.

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Application of Machine Learning Prediction of Individual SARS-CoV-2 Vaccination and Infection Status to the French Serosurveillance Survey From March 2020 to 2022: Cross-Sectional Study

Bougeard¹,

Huneau‐Salaün²,

Attia³

et al. 2023

JMIR Public Health Surveill

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Background The seroprevalence of SARS-CoV-2 infection in the French population was estimated with a representative, repeated cross-sectional survey based on residual sera from routine blood testing. These data contained no information on infection or vaccination status, thus limiting the ability to detail changes observed in the immunity level of the population over time. Objective Our aim is to predict the infected or vaccinated status of individuals in the French serosurveillance survey based only on the results of serological assays. Reference data on longitudinal serological profiles of seronegative, infected, and vaccinated individuals from another French cohort were used to build the predictive model. Methods A model of individual vaccination or infection status with respect to SARS-CoV-2 obtained from a machine learning procedure was proposed based on 3 complementary serological assays. This model was applied to the French nationwide serosurveillance survey from March 2020 to March 2022 to estimate the proportions of the population that were negative, infected, vaccinated, or infected and vaccinated. Results From February 2021 to March 2022, the estimated percentage of infected and unvaccinated individuals in France increased from 7.5% to 16.8%. During this period, the estimated percentage increased from 3.6% to 45.2% for vaccinated and uninfected individuals and from 2.1% to 29.1% for vaccinated and infected individuals. The decrease in the seronegative population can be largely attributed to vaccination. Conclusions Combining results from the serosurveillance survey with more complete data from another longitudinal cohort completes the information retrieved from serosurveillance while keeping its protocol simple and easy to implement.

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Comparing the age and sex trajectories of SARS-CoV-2 morbidity with other respiratory pathogens points to potential immune mechanisms

Cited by 4 publications

References 39 publications

COVID-19 hospitalization rates rise exponentially with age, inversely proportional to thymic T-cell production

COVID-19 hospitalization rates rise exponentially with age, inversely proportional to thymic T-cell production

COVID-19 hospitalisation rates rise exponentially with age, inversely proportional to thymic T-cell production

Application of Machine Learning Prediction of Individual SARS-CoV-2 Vaccination and Infection Status to the French Serosurveillance Survey From March 2020 to 2022: Cross-Sectional Study

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