Recent alcohol intake impacts microbiota in adult burn patients

Abstract

Alcohol use is associated with an increased incidence of negative health outcomes in burn patients due to biological mechanisms that include a dysregulated inflammatory response and increased intestinal permeability. This study used phosphatidylethanol (PEth) in blood, a direct biomarker of recent alcohol use, to investigate associations between a recent history of alcohol use and the fecal microbiota, short chain fatty acids, and inflammatory markers in the first week after a burn injury for nineteen participants. Burn patients were grouped according to PEth levels of low or high and differences in the overall fecal microbial community were observed between these cohorts. Two genera that contributed to the differences and had higher relative abundance in the low PEth burn patient group were Akkermansia, a mucin degrading bacteria that improves intestinal barrier function, and Bacteroides, a potentially anti-inflammatory bacteria. There was no statistically significant difference between levels of short chain fatty acids or intestinal permeability across the two groups. To our knowledge, this study represents the first report to evaluate the effects of burn injury and recent alcohol use on early post burn microbiota dysbiosis, inflammatory response, and levels of short chain fatty acids. Future studies in this field are warranted to better understand the factors associated with negative health outcomes and develop interventional trials.

Keywords: Alcohol; Burn Injury; Inflammation; Microbiota; Short Chain Fatty Acids.

Figure 1.

(A) Differences in the relative abundances of the top six most abundant phyla between PEth groups, (B) Firmicutes to Bacteroides ratio between PEth groups. Box plots of % relative abundance are shown in each group, (C) Six most abundant phyla by PEth group and participant. For (A) and (B) median is solid line, box is interquartile between of 25^th - 75^th percentile, whiskers are 1.5 times interquartile. Based on differential abundant testing, none of these phyla were significantly different based on PEth groups.

Figure 2.

Differences in the relative abundances of the top nine most abundant genera between Peth groups. Box plots of % relative abundance are shown in each group (median is solid line, box is interquartile between of 25^th - 75^th percentile, whiskers are 1.5 times interquartile). Based on differential abundant testing, none of these genera were significantly different based on PEth groups.

Figure 3.

Differences in PEth groups for (A) Observed Features, (B) Shannon Diversity Index, (C) Pielous’s Evenness, (D) Weighted Unifrac, (E) Unweighted UniFrac. (Statistical differences in A-C from generalized linear model, ellipse in D-E created through stat_ellipse command with level of 0.8).

Figure 4.

Differences in PEth groups for (A) IL-1ra, (B) IL-1B (C) IL-6, (D) IL-8, (E) IL-18, (F) Eotaxin (CCL11), (G) SDF-1a, (H) MCP-1, (I) M-CSF, (J) TNF-A. (Statistical differences from generalized linear model)

Figure 5.

Differences in PEth groups for (A) acetate, (B) priopionate, and (C) butyrate (statistical differences between PEth groups reported from generalized linear model), and (D) Correlation between SCFAs and most abundant and/or significantly associated genera (effect size and significance from MaAslin2, *** = p< 0.001, ** =p< 0.01, * p<0.05).

Figure 6.

Correlation matrix between metadata (blue label), short chain fatty acids (purple label), cytokines (black label), and intestinal permeability (brown label) for (A) low PEth group and (B) high PEth group. Correlations and p-values calculated based on Pearson Correlation Coefficient (*** = p< 0.001, ** =p< 0.01, * p<0.05).