Characteristics of Gut Microbial Profiles of Offshore Workers and Its Associations With Diet

Abstract

The composition of gut microbiota is not a static state in humans but fluctuates in response to changes in environments, diet, and lifestyle factors. Here, we explored differences in gut microbiota between populations worked offshore and onshore and further studied microbiota-associated variables in offshore workers (OFWs). We investigated the gut microbiota of 168 healthy subjects (offshore: 145 and onshore: 23) using 16S rRNA sequencing. Our results indicated that the marine environment caused significant changes in intestinal microbial structure, which was mainly reflected in the increase in bacterial diversity, changes in composition, and the emergence of more specific bacteria in OFWs. In addition, characteristics of gut microbiota in OFWs were further explored, and the genus Holdemanella was considered a potential contributor to the stable state of health. Besides, some dietary factors, namely, duck, mutton, dairy products, and algae vegetables were identified as the gut microbial covariates in the OFWs cohort and were positively correlated with the genus Holdemanella. This suggests the positive intervention of diet on Holdemanella. Our data highlight, for the first time to our knowledge, that the marine geographical environment plays an important role in shaping the gut mycobiome composition. And diet could be considered as the targeted intervention that alters the composition of the microbiome to improve host health.

Keywords: Holdemanella; diet; gut microbiota; marine environment; offshore workers.

Figure 1

The diversity and relative abundances of the microbial community of ONWs and OFWs. Alpha-diversity sanalysis with Shannon (A), Chao (B), and ACE (C) index separately. (D) PLS-DA on OTU level. Relative abundance of the microbial community at phylum (E) and genus levels (F). ONWs, onshore workers; OFWs, offshore workers.

Figure 2

The microbiota profile differs between ONWs and OFWs. (A) Histogram of the linear discriminant analysis (LDA) scores for differentially abundant taxonomic features between ONWs and OFWs. (B) Cladogram of the microbial taxa associated with ONWs and OFWs. Significance obtained by LDA score >2. (C) The Venn diagram compares and contrasts the number of taxa at genus levels of ONWs and OFWs. (D) The top 10 genera are unique to OFWs. ONWs, onshore workers; OFWs, offshore workers.

Figure 3

Venn diagram of distinct genera identified by LEfSe analysis among each of the population groups in the OFWs cohort. (A) Distinct genera in the Full health group. (B) Distinct genera in the other groups (Overweight obesity, High stress, Sleepiness, Backache, and Acne).

Figure 4

Identification of the gut microbial covariates in the OFWs cohort. Horizontal bars showing the amount of inferred variance (adjusted R²) explained by each identified covariate as determined by PERMANOVA with Bray–Curtis dissimilarities at the genus (left) and KO (right) levels. The categories are color-coded and ranked by the highest explained variation in the respective category. Only statistically significant covariates with an adjusted p < 0.05 using the Benjamini–Hochberg (BH) method are shown. **BH-adjusted p < 0.01; *BH-adjusted p < 0.05.

Figure 5

Associations between microbial genera and multiple factors. Shown is a heatmap of the microbial genera (x-axis) that were found to be significantly associated with different factors (y-axis) using generalized linear models adjusted for confounding factors. The significant heatmap cells are represented by the direction of association (indicated by color, e.g., red is positively associated). Each factor is colored by the category to which it belongs, and each genus is colored by the phylum to which it belongs.

Figure 6

Associations between microbial pathways and multiple factors. Shown is a heatmap of the microbial pathways (x-axis) that were found to be significantly associated with different factors (y-axis) using generalized linear models adjusted for confounding factors. The significant heatmap cells are represented by the direction of association (indicated by color, e.g., red is positively associated). Each factor is colored by the category to which it belongs, and each microbial pathways is colored by the functional class to which it belongs.