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. 2023 Sep 13:14:1260112.
doi: 10.3389/fimmu.2023.1260112. eCollection 2023.

Changes of gut microbiota under different nutritional methods in elderly patients with severe COVID-19 and their relationship with prognosis

Jiawei Zhang  1   2   3   4 , Jiaxin Deng  1   2   3   4 , Juan Li  1   2   3   4 , Yuping Su  1   2   3   4 , Jiancong Hu  1   2   3   4 , Dezheng Lin  1   2   3   4 , Mingli Su  1   2   3   4 , Yongcheng Chen  1   2   3   4 , Sen Liao  1   2   3   4 , Xuhao Bai  5 , Miwei Lv  5 , Tian Xu  5 , Qinghua Zhong  1   2   3   4 , Xuefeng Guo  1   2   3   4
Affiliations

Changes of gut microbiota under different nutritional methods in elderly patients with severe COVID-19 and their relationship with prognosis

Jiawei Zhang et al. Front Immunol. .

Abstract

Background: The clinical progression of individuals afflicted with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection exhibits significant heterogeneity, particularly affecting the elderly population to a greater extent. Consequently, the association between nutrition and microbiota has garnered considerable interest. Hence, the objective of this study was to gather clinical data pertaining to the influence of diverse nutritional support interventions on the prognosis of geriatric patients with COVID-19, while additionally examining the fecal microbiota of these individuals to assess the repercussions of microecological alterations on their prognostic outcomes.

Results: A total of 71 elderly patients diagnosed with severe COVID-19 were included in this study. These patients were subsequently divided into two groups, namely the enteral nutrition (EN) group and the parenteral nutrition (PN) group, based on the type of nutritional support therapy they received after admission. The occurrence of complications was observed in 10.4% of patients in the EN group, whereas it was significantly higher at 69.6% in the PN group (P<0.001). Furthermore, the 60-day mortality rate was 2.1% (1/48) in the EN group, while it was notably higher at 30.4% (7/23) in the PN group (P=0.001). To identify the independent predictors of 60-day mortality, stepwise logistic regression analysis was employed. Among different bacterial groups, Enterococcus_faecium (18.19%) and Pseudomonas_aeruginosa (1.91%) had higher average relative abundance in the PN group (P<0.05). However, the relative abundance of Ruminococcus was higher in the EN group. Further Spearman correlation analysis showed that Enterococcus_faecium was positively correlated with poor clinical prognosis, while Ruminococcus was negatively correlated with poor clinical prognosis.

Conclusions: This study shows that the changes in the composition of intestinal flora in elderly COVID-19 patients receiving different nutritional support strategies may be related to different clinical outcomes. The abundance of Enterococcus_faecium in elderly COVID-19 patients receiving PN is significantly increased and is closely related to poor clinical outcomes. It highlights the potential of microbiome-centric interventions to mitigate and manage COVID-19 in older adults with different nutritional support options.

Keywords: COVID-19; enteral nutrition; gut microbiota; old age; parenteral nutrition.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The flowchart of patient allocation.
Figure 2
Figure 2
Changes of gut microbiota in the EN group and PN group. (A) Alpha diversity of gut microbiota genes between the EN group and PN group, (B) Alpha diversity of gut microbiota species between the EN group and PN group, (C) The β diversity of gut microbiota in the EN group and PN group was illustrated using PCoA diagrams, (D) The β diversity of gut microbiota in the EN group and PN group was illustrated using NMDS plots.
Figure 3
Figure 3
Analysis of similarities of gut microbiota between the EN group and PN group.
Figure 4
Figure 4
Relative abundance of gut microbiota on phylum in the EN group and PN group.
Figure 5
Figure 5
Comparison of the relative abundance of the gut microbiota on the phylum between the EN group and PN group.
Figure 6
Figure 6
Relative abundance of gut microbiota species in the EN group and PN group.
Figure 7
Figure 7
LEfSe analysis was used to compare the relative abundance of gut microbiota species between the EN group and PN group.
Figure 8
Figure 8
P values of dysbiosis index between patients in the EN group and PN group.
Figure 9
Figure 9
Cox regression analysis for dysbiosis index with covariates that show significant differences between the EN group and PN group.
Figure 10
Figure 10
The estimated Spearman’s r values, as shown in the heat map, were obtained by correlation analysis between the phase abundance of differential gut microbiota species between the two groups and the values of clinical prognostic parameters. Red and dark blue indicate positive and negative correlations between the two color, respectively.
Figure 11
Figure 11
Metagenomic function prediction based on KEGG pathway analysis. Extended error bar plots of 30 different KEGG pathways through free filtering in patients in the EN group and PN group. Error bars showed significant differences in metabolic pathways between patients in the EN group and those in the PN group (P< 0.05, Kruskal Wallis test). The difference in the relative abundance of KEGG pathways was negative in the EN group and positive in the PN group.
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The authors declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by Supported by National Key Clinical Discipline.
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