How competition governs whether moderate or aggressive treatment minimizes antibiotic resistance
- Imperial College London, United Kingdom
- Yale University, United States
Abstract
Understanding how our use of antimicrobial drugs shapes future levels of drug resistance is crucial. Recently there has been debate over whether an aggressive (i.e. high dose) or more moderate (i.e. lower dose) treatment of individuals will most limit the emergence and spread of resistant bacteria. Here we demonstrate how one can understand and resolve these apparently contradictory conclusions. We show that a key determinant of which treatment strategy will perform best at the individual level is the extent of effective competition between resistant and sensitive pathogens within a host. We extend our analysis to the community level, exploring the spectrum between strict inter-strain competition and strain independence. From this perspective as well, we find that the magnitude of effective competition between resistant and sensitive strains determines whether an aggressive approach or moderate approach minimizes the burden of resistance in the population.
Article and author information
Author details
Reviewing Editor
- Michael Doebeli, University of British Columbia, Canada
Version history
- Received: August 3, 2015
- Accepted: September 18, 2015
- Accepted Manuscript published: September 22, 2015 (version 1)
- Accepted Manuscript updated: September 25, 2015 (version 2)
- Version of Record published: November 11, 2015 (version 3)
Copyright
© 2015, Colijn & Cohen
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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How competition governs whether moderate or aggressive treatment minimizes antibiotic resistance
eLife 4:e10559.
https://doi.org/10.7554/eLife.10559
Further reading
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A mathematical model has been used to explore different approaches to minimizing antibiotic resistance.
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- Cancer Biology
- Epidemiology and Global Health
Background:
Age is the most important risk factor for cancer, but aging rates are heterogeneous across individuals. We explored a new measure of aging-Phenotypic Age (PhenoAge)-in the risk prediction of site-specific and overall cancer.
Methods:
Using Cox regression models, we examined the association of Phenotypic Age Acceleration (PhenoAgeAccel) with cancer incidence by genetic risk group among 374,463 participants from the UK Biobank. We generated PhenoAge using chronological age and nine biomarkers, PhenoAgeAccel after subtracting the effect of chronological age by regression residual, and an incidence-weighted overall cancer polygenic risk score (CPRS) based on 20 cancer site-specific polygenic risk scores (PRSs).
Results:
Compared with biologically younger participants, those older had a significantly higher risk of overall cancer, with hazard ratios (HRs) of 1.22 (95% confidence interval, 1.18–1.27) in men, and 1.26 (1.22–1.31) in women, respectively. A joint effect of genetic risk and PhenoAgeAccel was observed on overall cancer risk, with HRs of 2.29 (2.10–2.51) for men and 1.94 (1.78–2.11) for women with high genetic risk and older PhenoAge compared with those with low genetic risk and younger PhenoAge. PhenoAgeAccel was negatively associated with the number of healthy lifestyle factors (Beta = –1.01 in men, p<0.001; Beta = –0.98 in women, p<0.001).
Conclusions:
Within and across genetic risk groups, older PhenoAge was consistently related to an increased risk of incident cancer with adjustment for chronological age and the aging process could be retarded by adherence to a healthy lifestyle.
Funding:
This work was supported by the National Natural Science Foundation of China (82230110, 82125033, 82388102 to GJ; 82273714 to MZ); and the Excellent Youth Foundation of Jiangsu Province (BK20220100 to MZ).
