Dietary and lifestyle associations with microbiome diversity

One-hundred-four patients underwent screening colonoscopy with a mean age of 60 years (range 41–78 years, SD ± 8.7). Adenomatous polyps were identified in 46% of participants and were most commonly located in the ascending colon (58%). The vast majority of adenomas were tubular adenomas (87% vs 2% tubulovillous and 11% sessile). Cohorts with and without adenomas were similar in dietary practices and patient characteristics with the exception of smoking (25% controls, 48% adenoma formers, p = 0.015) and regular activity (79% controls, 58% adenoma formers, p = 0.026) [8]. To identify associations between patient characteristics and microbiome diversity we coupled redundancy analysis with stepwise model selection. Overall, oral (F(12,71) = 1.71; p = 0.001; R2 = 0.09), fecal (F(21,67) = 1.47; p = 0.001; R2 = 0.1), and mucosal (F(12,77) = 1.45; p = 0.001; R2 = 0.06) microbial communities were moderately associated with host dietary habits and health. Oral microbial community diversity varied significantly with Bristol score, patient BMI, calcium supplementation, red meat consumption, and consumption of fermented foods (Table 1). There was little overlap between the factors that significantly associated with oral microbiome diversity and those that associated with fecal and mucosal diversity. However, fecal, and mucosal microbial communities were both substantially impacted by age, fruit consumption, diabetes, calcium supplementation and hormone replacement therapy. Fecal diversity also associated with alcoholic beverage and grain consumption, but while observed in mucosal compartments as well, these associations did not reach significance (Table 1). Consistent with our previous work [8], only mucosal microbiome diversity linked with adenoma burden (Table 1).

To evaluate associations between patient characteristics and health behaviors and microbiome diversity we fit each patient and health factor that our model selection procedure identified as contributing to microbiome diversity to a constrained ordination of microbiome diversity (Table 2). This allowed us to identify linear association between microbiome diversity and patient characteristics and how these associations were related. Oral microbiome diversity correlated with BMI (R2 = 0.27, p = 1.0 × 10− 3), red meat consumption (R2 = 0.1019, p = 0.02) and calcium supplementation (R2 = 0.08, p = 2.0 × 10− 3; Fig. 2A). Instead, fruit and grain consumption (R2 = 0.22, p = 1.0 × 10− 3) dominated the correlation with fecal microbial diversity (Fig. 2B). In addition, regular activity (R2 = 0.15, p = 1.0 × 10− 3), diabetes (R2 = 0.21, p = 1.0 × 10− 3), Bristol stool scale (R2 = 0.29, p = 1.0 × 10− 3), age (R2 = 0.01, p = 1.0 × 10− 3), vitamin D consumption (R2 = 0.04, p = 3.1 × 10− 2) and hormonal therapy (R2 = 0.04, p = 1.7 × 10− 2) were also associated with fecal microbiome diversity.

Table 2 Environmental fitting identifies linear associations between microbial diversity and host lifestyleFig. 2figure 2

Patient lifestyle is associated with microbiome diversity. Constrained ordinations of A oral, B fecal, C mucosal diversity overlaid with vectors (arrows) representing the direction and magnitude of the correlation of between parameter and the primary axes of microbiome diversity variation. Factors shown include body mass index (BMI); fermented food (Ferment), red meat (RedMeat), fruit, grain, and alcoholic beverage (EtOHN) consumption; and adenoma number (Adenoma). D A boxplot of adenoma number per patient in patients with adenomas in the lower right (LR) and upper left (UL) quadrants of the mucosal microbial diversity ordination. An * denotes p < 0.05

There were some similarities between the mucosal microbiome diversity from that of the fecal microbiome. Fruit consumption was again associated with microbiome diversity (R2 = 0.20, p = 1.0 × 10− 3) and there was a trend in grain consumption though not significant (R2 = 0.30, p = 0.26; Fig. 2C). Fermented foods and alcohol consumption were also significantly associated with mucosal microbiome diversity (R2 = 0.11, p = 4.0 × 10− 3, R2 = 0.09, p = 0.17). Other patient factors such as age (R2 = 0.26, p = 1.0 × 10− 3), diabetes (R2 = 0.29, p = 1.0 × 10− 3), and hormonal therapy (R2 = 0.51, p = 0.12) were also associated. The greatest association between diversity and the mucosal microbiome was adenoma presence (R2 = 0.32, p = 1.0 × 10− 3). In fecal communities, the vectors of association between alcoholic beverage and grain consumption and microbiome diversity suggest that these lifestyle factors may have opposing effects on the microbiome. Similarly, in mucosal samples, total adenoma burden and consumption of fermented foods and fruit exhibit distinct opposing effect on microbiome diversity, suggesting that individuals with high fruit and fermented food intake tend to manifest patterns of microbiome diversity that associate with lower adenoma burden. Supporting this observation, patients in the ordination quadrant associated with higher fruit consumption (lower right) had significantly fewer adenomas when compared in the quadrant opposite (upper left; Fig. 2D).

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