Intestinal microbiology shapes population health impacts of diet and lifestyle risk exposures in Torres Strait Islander communities

Abstract

Poor diet and lifestyle exposures are implicated in substantial global increases in non-communicable disease burden in low-income, remote, and Indigenous communities. This observational study investigated the contribution of the fecal microbiome to influence host physiology in two Indigenous communities in the Torres Strait Islands: Mer, a remote island where a traditional diet predominates, and Waiben a more accessible island with greater access to takeaway food and alcohol. Counterintuitively, disease markers were more pronounced in Mer residents. However, island-specific differences in disease risk were explained, in part, by microbiome traits. The absence of Alistipes onderdonkii, for example, significantly (p=0.014) moderated island-specific patterns of systolic blood pressure in multivariate-adjusted models. We also report mediatory relationships between traits of the fecal metagenome, disease markers, and risk exposures. Understanding how intestinal microbiome traits influence response to disease risk exposures is critical for the development of strategies that mitigate the growing burden of cardiometabolic disease in these communities.

Trial registration: ClinicalTrials.gov NCT02909218 NCT03789448.

Keywords: cardiometabolic disease; chronic inflammatory disease; epidemiology; global health; gut microbiome; human; indigenous health; infectious disease; metagenomics; microbiology; risk exposures.

Conflict of interest statement

FM, LL, FT, ST, LH, JC, ST, SW, RM, KI, GR No competing interests declared

© 2020, Mobegi et al.

Figures

Figure 1.. Island-specific differences in the species composition of the microbiome.

(a) Non-metric Multidimensional Scaling of the Bray Curtis similarity resemblance matrix, with ellipses at 95% confidence interval. n = 100 (b) Fold differences in LDA mean proportions of differentially abundant species in Waiben and Mer. Bars were sorted based on the sequential ranking of significance and separated by the two populations. Only taxa significant at a logarithmic LDA score ≥3 and a factorial Kruskal-Wallis test Alpha (α)≤0.05 are shown. n = 100 (c) A phylogenetic tree based on the NCBI taxonomy of differentially abundant species identified using LEfSe. All species designated ‘unclassified’ were not used in generating the tree. Branches/edges are colored according to phylum rank classification and terminal nodes/species labels are colored based on study site overrepresentation. n = 100.

Figure 1—figure supplement 1.. Taxonomic profile of the 100 Torres Strait Islander participants included in the study.

The size of the nodes represents the logarithmically scaled relative abundances of taxa. Only clades consisting of at least five markers, with nodes constituting at least 1% relative abundance, are highlighted. Annotations begin from the phylum level.

Figure 2.. Functional pathways of the gut microbiota that significantly differed in abundance between Mer and Waiben communities.

(a) Pathway abundances at the whole community level were statistically compared between the groups, and the 95% confidence interval of the effect-size for each pathway was determined using a non-parametric bootstrap method. (b) The heatmap represents community pathway abundances stratified into significant contributions from bacterial species to between-group differences. The color scale depicts the magnitude of the difference of stratified pathway abundances between the groups. All statistical comparisons were performed using the Mann-Whitney test and corrected for multiple testing using the false discovery rate method. Statistical significance for all comparisons was determined at p<0.05. Heatmap boxes with an ‘-’ indicated non-significant stratified pathway abundance differences between the groups.

Figure 2—figure supplement 1.. Heatmap representation of stratified contribution to whole-community pathway abundances.

Bacterial species that significantly contributed to the stratified pathway abundances between the groups are depicted (Mann-Whitney test with false discovery rate correction for multiple testing, p

Figure 3.. Structural equation model describing the pattern of interrelationships between exposures, microbiome, and host inflammation.

A modified path diagram of the final SEM supporting the mediatory effect of the gut microbiota on inflammation. The heatmaps represent direct associations of diet, behavior, and demographics with the gut microbiota and inflammatory biomarkers. Only significant paths of the effect of the gut microbiota on inflammation are shown. Standardized β coefficients are reported. Species Factors 1–6 denote latent variables of the community gut microbiota modeled as exploratory factor analysis (EFA) regression scores of the species relative abundance; Biomarker Factors 1–3 denote latent variables representing community inflammatory biomarkers as grouped using EFA. *p 0.1–0.05, **p 0.05–0.001, ***p

Figure 3—figure supplement 1.. Theoretical models for assessing the structure of associations between the various human exposures, the gut microbiota, and the host’s inflammatory profile.

(a) Final framework (framework 1): The gut microbiome mediates, in part, the relationship between exposure risk factors and host inflammation. (b) Framework 2: The gut microbiome and exposure risk factors both, independently, impact host inflammation. (c) Framework 3: Exposure risk factors are associated with gut microbiome and inflammatory profile, but there is no relationship between microbiome and inflammation. (d) Framework 4: Exposure risk factors are associated with the inflammatory profile, and the inflammatory profile predicts the gut microbiome composition.

Figure 4.. Lachnospiraceae bacterium 8_1_57FAA mediates the unadjusted relation of sugar-sweetened beverage intake with the serum concentration of Interleukin 15 and mean arterial pressure.

(a) Lachnospiraceae bacterium 8_1_57FAA mediates the unadjusted relation of sugar-sweetened beverage intake with the serum concentration of Interleukin 15. The total effect (95% confidence interval) for the model was 0.054 (0.023, 0.090), P-value: <0.001. n = 100 (b) Lachnospiraceae bacterium 8_1_57FAA mediates the unadjusted relation of sugar-sweetened beverage intake with mean arterial pressure. The total effect (95% confidence interval) for the model was −0.006 (–1.530, 1.420), P-value: 0.950. n = 100 a ACME (average causal mediation effects); b ADE (average direct effects).

Figure 4—figure supplement 1.. Role of Lachnospiraceae bacterium 8 1 57FAA in mediating the relation of sugar-sweetened beverage intake with the serum concentration of Interleukin 15.

(a) Age-adjusted model. The total effect (95% confidence interval) for the model was 0.049 (0.015, 0.086) P-value: 0.004. (b) Age- and island-adjusted model. The total effect (95% confidence interval) for the model was 0.046 (0.008, 0.085) P-value: 0.016. (c) Multivariate-adjusted model. Includes adjustment for age, island, body mass index, gender, cigarette use, and intakes of fruits, vegetables, takeaways, seafood, and alcohol. The total effect (95% confidence interval) for the model was 0.045 (–0.0003, 0.088) P-value: 0.084.

Figure 4—figure supplement 2.. Role of Lachnospiraceae bacterium 8 1 57FAA in mediating the relation of sugar-sweetened beverage intake with the mean arterial pressure.

(a) Age-adjusted model. The total effect (95% confidence interval) for the model was 0.842 (−0.822, 2.520) P-value: 0.300. (b) Age-and island-adjusted model. The total effect (95% confidence interval) for the model was 0.948 (−0.694, 2.670) P-value: 0.246. (c) Multivariate-adjusted model. Includes adjustment for age, island, body mass index, gender, cigarette use, and intakes of fruits, vegetables, takeaways, seafood, and alcohol. The total effect (95% confidence interval) for the model was 0.295 (−1.730, 2.170) P-value: 0.808.

Figure 4—figure supplement 3.. 16S rRNA-based phylogeny of Lachnospiraceae.

Lachnospiraceae family-restricted blast search (blast parameters e-value = 1 e-10, Max targets = 250) using Lachnospiraceae bacterium 8_1_57FAA 16 rRNA as a query against the NCBI non-redundant genes database was performed. The resulting top 250 best blast hits were used to construct a Grishin distance tree using the fast-minimum evolution model in NCBI BLAST. Terminal node labels are Taxonomic Name (Sequence ID).

Figure 5.. Systolic blood pressure in the context of an interaction between Alistipes onderdonkii and island systolic blood pressure.

(a) Unadjusted model, (b) age-adjusted model, and (c) multivariate-adjusted model which includes the adjustment for age, body mass index, gender, smoking status, and intakes of fruits, vegetables, takeaways, sugar-sweetened beverages, seafood, and alcohol. Only significant P values (p<0.05) are shown n = 100.