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. 2022 Jun;291(6):801-812.
doi: 10.1111/joim.13458. Epub 2022 Mar 17.

Respiratory dysfunction three months after severe COVID-19 is associated with gut microbiota alterations

Affiliations

Respiratory dysfunction three months after severe COVID-19 is associated with gut microbiota alterations

Beate Vestad et al. J Intern Med. 2022 Jun.

Abstract

Background: Although coronavirus disease 2019 (COVID-19) is primarily a respiratory infection, mounting evidence suggests that the gastrointestinal tract is involved in the disease, with gut barrier dysfunction and gut microbiota alterations being related to disease severity. Whether these alterations persist and are related to long-term respiratory dysfunction remains unknown.

Methods: Plasma was collected during hospital admission and after 3 months from the NOR-Solidarity trial (n = 181) and analyzed for markers of gut barrier dysfunction and inflammation. At the 3-month follow-up, pulmonary function was assessed by measuring the diffusing capacity of the lungs for carbon monoxide (DLCO ). Rectal swabs for gut microbiota analyses were collected (n = 97) and analyzed by sequencing the 16S rRNA gene.

Results: Gut microbiota diversity was reduced in COVID-19 patients with respiratory dysfunction, defined as DLCO below the lower limit of normal 3 months after hospitalization. These patients also had an altered global gut microbiota composition, with reduced relative abundance of 20 bacterial taxa and increased abundance of five taxa, including Veillonella, potentially linked to fibrosis. During hospitalization, increased plasma levels of lipopolysaccharide-binding protein (LBP) were strongly associated with respiratory failure, defined as pO2 /fiO2 (P/F ratio) <26.6 kPa. LBP levels remained elevated during and after hospitalization and were associated with low-grade inflammation and respiratory dysfunction after 3 months.

Conclusion: Respiratory dysfunction after COVID-19 is associated with altered gut microbiota and persistently elevated LBP levels. Our results should be regarded as hypothesis generating, pointing to a potential gut-lung axis that should be further investigated in relation to long-term pulmonary dysfunction and long COVID.

Keywords: SARS-CoV-2; microbiome; pulmonary function.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flow chart of patients included in the present add‐on study based on the original NOR‐Solidarity study protocol.
Fig. 2
Fig. 2
Gut microbiota diversity in patients with or without respiratory dysfunction at the 3‐month follow‐up. Beta diversity by principal coordinate analysis showing Bray–Curtis distances separating patients with or without respiratory dysfunction (diffusing capacity of the lungs for carbon monoxide < lower limit of normal, n = 83) (a), with overlapping microbiota composition in relation to antibiotic use (b). Alpha diversity measured with observed ASVs (c) and Faith`s PD (d) in patients with or without respiratory dysfunction. Abbreviations: ASVs, amplicon sequence variants; PD, phylogenetic diversity.
Fig. 3
Fig. 3
Gut microbial composition in patients with respiratory dysfunction at the 3‐month follow‐up (diffusing capacity of the lungs for carbon monoxide < lower limit of normal, n = 83). (a) LDA score of taxa abundance differences using LefSe analysis. (b) Taxonomic cladogram highlighting differentially abundant taxa (p < 0.05) by LEfSe. (c) Volcano plot from ALDEx2 analysis showing effect size representing the median of “difference in clr (centered log‐ratio) values between groups divided by largest difference in clr values within group" on log2‐scale by p‐value of differentially abundant genera. Abbreviations: LDA, linear discriminant analysis; LEfSe, linear discriminant analysis effect size.
Fig. 4
Fig. 4
Temporal profile of soluble lipopolysaccharide‐binding protein (LBP) levels (n = 144) in relation to acute respiratory failure (n = 44) during hospitalization (pO2/fiO2 [P/F] ratio < 26.6 kPa) (a). The p‐value reflects the overall effect of respiratory failure (RF) from the repeated measures regression analysis. Blue areas reflect levels in age‐ and sex‐matched healthy controls (n = 24). (b) Spearman correlations between LBP and P/F ratio at different time points (baseline orange; days 3–5, yellow green; days 7–10, brown) during hospitalization. *p < 0.05, **p < 0.005. Observations per time point: Baseline (BL), n = 144; 3–5 days, n = 134; 7–10 days, n = 84.

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