Sociobiome - Individual and neighborhood socioeconomic status influence the gut microbiome in a multi-ethnic population in the US

Abstract

Lower socioeconomic status (SES) is related to increased incidence and mortality due to chronic diseases in adults. Association between SES variables and gut microbiome variation has been observed in adults at the population level, suggesting that biological mechanisms may underlie the SES associations; however, there is a need for larger studies that consider individual- and neighborhood-level measures of SES in racially diverse populations. In 825 participants from a multi-ethnic cohort, we investigated how SES shapes the gut microbiome. We determined the relationship of a range of individual- and neighborhood-level SES indicators with the gut microbiome. Individual education level and occupation were self-reported by questionnaire. Geocoding was applied to link participants' addresses with neighborhood census tract socioeconomic indicators, including average income and social deprivation in the census tract. Gut microbiome was measured using 16SV4 region rRNA gene sequencing of stool samples. We compared α-diversity, β-diversity, and taxonomic and functional pathway abundance by SES. Lower SES was significantly associated with greater α-diversity and compositional differences among groups, as measured by β-diversity. Several taxa related to low SES were identified, especially an increasing abundance of Prevotella copri and Catenibacterium sp000437715, and decreasing abundance of Dysosmobacter welbionis in terms of their high log-fold change differences. In addition, nativity and race/ethnicity have emerged as ecosocial factors that also influence the gut microbiota. Together, these results showed that lower SES was strongly associated with compositional and taxonomic measures of the gut microbiome, and may contribute to shaping the gut microbiota.

Fig. 1. Alpha diversity by socioeconomic characteristics.

a Faith’s phylogenetic diversity of 16S rRNA gut microbiome samples. Measures were compared using a null hypothesis of no difference between groups (Regression, p < 0.05). The regression model was adjusted for age, sex, smoking status, exercise, dietary acculturation index, and body mass index. The boxplot displays the median (center line inside the box), interquartile range (IQR, bounds of the box), minimums and maximums within 1.5 times the IQR (whiskers), and outliers (points beyond the whiskers). b Visual comparison of SDI score and Faith’s PD in NYC by zip code. Significant positive spatial autocorrelation was observed for SDI score and Faith’s PD (Moran’s I = 0.120, Moran’s I = 0.024, both p < 0.005). PD Phylogenetic diversity, HS grad High School graduate, OSEI Occupational Socioeconomic Index, SDI Social Deprivation Index.

Fig. 2. Beta diversity by socioeconomic characteristics.

a–d Principal Coordinate Analysis (PCoA) plot and boxplot of the JSD distance. Statistical significance between socioeconomic indicators was determined using permutational multivariate analysis of variance (PERMANOVA) after adjusting for age, sex, smoking status, exercise, dietary acculturation index, and body mass index. The significance of differences among the groups was tested using pairwise-PERMANOVA. e Multivariate PERMANOVA model. The bars depict the amount of variance (R²) explained by each socioeconomic variable in JSD distance. Size effect and statistical significance were calculated by PERMANOVA including sociodemographic variables in one model. Stars denote the level of significance (Bonferroni post-hoc-tests; •p value < 0.1; *p value < 0.05; **p value < 0.01; ***p value < 0.001). HS grad: High School graduate; OSEI: Occupational Socioeconomic Index, SDI: Social Deprivation Index.

Fig. 3. Differential abundance by socioeconomic characteristics.

Forest plot showing the log-fold changes (x-axis) by species (y-axis) derived from the ANCOM-BC model, with 95% confidence interval error bars. ANCOM-BC was conducted by each socioeconomic indicator after adjusting for age, sex, smoking status, exercise, dietary acculturation index, and body mass index. Each dot is colored by the significance level. Log-fold change values greater than 0 indicate the fold change increase in the low SES (deprived) groups, while log-fold change values less than zero indicate the fold change decrease in the low SES groups.

Fig. 4. Deprivation of socioeconomic status and functional pathway.

Functional pathways were predicted from 16S rRNA gene-based microbial compositions using the PICRUSt2 algorithm to make inferences from the MetaCyc pathway database. a Volcano plot showing the standardized log-fold changes (x-axis) by the negative log-transformed p-value (y-axis) derived from the ANCOM-BC model. ANCOM-BC was conducted by each socioeconomic indicator after adjusting for age, sex, smoking status, exercise, dietary acculturation index, and body mass index. b Only functional pathways relating to low socioeconomic status are included in the heatmap. Stars denote the significance of the ANCOM-BC (* FDR < 0.05). OSEI: Occupational Socioeconomic Index, SDI: Social Deprivation Index.

Fig. 5. Microbiome profiles by nativity and race/ethnicity.

a Nativity α-diversity and β-diversity b Race/ethnicity α-diversity and β-diversity c Upset plot showing the number of differentially abundant bacterial species identified via ANCOM-BC in individual comparisons of nativity and race/ethnicity, and shared species among various combinations of nativity and race/ethnicity. The set size on the left indicates the number of differential species in each comparison, while the connected dots indicate the common differential species across intersecting nativity and race/ethnicity comparisons. ANCOM-BC was conducted by nativity and race/ethnicity after adjusting for age, sex, smoking status, exercise, dietary acculturation index, and body mass index. NH: Non-Hispanic.