Factors associated with COVID-19 mortality include age, obesity, the need for mechanical ventilation following respiratory failure, and certain comorbidities.1,2 Recent research has also suggested that features of the lung microbiome – such as the presence of gut bacteria in the lung – could predict worse clinical outcomes.3 While there has been extensive research on this disease and its progression throughout the spread of this pandemic, there remains no validated lower respiratory tract biomarkers that predict clinical outcomes.1
In an effort to identify these markers, Sulaiman et al. examined if bacterial respiratory infections were related to deteriorated COVID-19 outcomes.1 Their study involved an observational cohort of 589 patients, all of whom were on mechanical ventilation. Of these, SARS-CoV-2 load was quantified in 142 patients who underwent bronchoscopy. Their lower respiratory tract microbiome was also investigated using metagenomics and metatranscriptomics to identify any patterns in disease progression and/or mortality.
Notable findings from their investigations were as follows1:
Previous research found that the enrichment of oral commensals in the lower respiratory tract predisposed to pro-inflammatory state in several diseases including lung cancer.4 As such, the role of oral commensals such as M. salivarium in COVID-19 and other respiratory diseases presents an area for further research, with some current data suggesting that its mechanism of action could be related to its ability to impair key immune cells.5
Overall, Sulaiman et al.’s finding that high SARS-CoV-2 load impairs effective immune response suggests that treatments targeting viral replication or inducing anti-COVID 19 immune responses would be a good approach for hospitalised patients with COVID-19 who are mechanically ventilated. The discovery that the lung microbiome and bacterial load are associated with mortality is useful for the design of new methods to predict COVID-19 outcomes in mechanically ventilated patients, and thus better inform treatment decisions on a case-by-case basis.