Knowing the composition of the lung microbiome in COPD may one day make it possible to predict the nature of future exacerbations and better target treatment, new findings from the AERIS study suggest.
The results published in Thorax confirm that subtypes of COPD have distinct bacterial compositions and show for the first time the stability of the lung microbiome in COPD and the non-random nature of exacerbations experienced by an individual over time.
The researchers surveyed 584 sputum samples from 101 patients in the AERIS study (Acute Exacerbation and Respiratory Infections in COPD) to characterise longitudinal changes in the lung microbiome in patients with COPD at both stable and exacerbation time points over one year.
They found that the lung microbiome shows significantly less variation within an individual than between individuals, indicating some degree of temporal stability of an individual’s lung microbiome.
That said, they also observed notable dysbiosis events within individuals – there was higher microbiome variability in exacerbations than stable time points and individuals with a higher frequency of exacerbations were more likely to experience significant changes in lung microbiome patterns.
Speaking to the limbic, researcher Tom Wilkinson, Professor of Respiratory Medicine at the University of Southampton said: “This adds to the evidence base that there is an abnormal microbiome in the context of COPD that is associated with the risk of exacerbation and the frequency of exacerbation.”
For him the key novel point of the study is that it looked longitudinally at changes in the lung microbiome.
“We haven’t done a cross sectional study; we’ve looked with repeated measurements in the patient cohort, and what that demonstrates is that the nature of bacterial or eosinophilic exacerbations is a longitudinal phenotype,” he explained.
“So patients who have an abnormal microbiome tend to have repeated patterns of that issue, and eosinophilic patients have less of an abnormal microbiome and maintain that phenotype, whereas viral exacerbations tend to be more random and occur in an unpredictable fashion.”
According to the researchers, the ability to model exacerbation phenotypes as stochastic processes has important implications for the diagnosis and treatment of acute exacerbations of COPD if the phenotypes of future clinical events can be accurately predicted.
“We’ve tended to treat each exacerbation as if it were a random, unpredictable one, but we might be able to preordain a treatment pathway, which could add extra value to the patient by understanding what’s going on in advance,” Professor Wilkinson said.
While early days, he thinks this study is probably the first solid building block in the idea of a predictive medicine approach to either preventing or treating COPD exacerbations in a stratified way based on a measurement in advance of an event that is going to happen in the future.
“The underlying story is that the microbiome is a very important marker and indeed player in understanding who is going to exacerbate and why,” he said.
“It may be that in a few years time we have very different approaches to treating different forms of inflammation in COPD, understanding the patient often has that pattern of inflammation for many years.”
Professor Wilkinson points out that as this is an observational study there is caution associated with inferring potential clinical benefit. However, he believes it provides a very strong theoretical framework to design intervention studies.
“We’d like to now design an intervention study in which patients are characterised at an initial exacerbation event and then have a treatment pathway that is peculiar to their pattern of inflammation or infection.
“And to see if by taking that approach you could not only improve their outcome but also reduce the burden of treatment, for example excessive use of antibiotics and oral steroids, which carry potential risks for patients and the broader community when we think about antibiotic resistance,” he said.