Defining Dysbiosis for a Cluster of Chronic Diseases

Abstract

The prevalence of many chronic diseases has increased over the last decades. It has been postulated that dysbiosis driven by environmental factors such as antibiotic use is shifting the microbiome in ways that increase inflammation and the onset of chronic disease. Dysbiosis can be defined through the loss or gain of bacteria that either promote health or disease, respectively. Here we use multiple independent datasets to determine the nature of dysbiosis for a cluster of chronic diseases that includes urinary stone disease (USD), obesity, diabetes, cardiovascular disease, and kidney disease, which often exist as co-morbidities. For all disease states, individuals exhibited a statistically significant association with antibiotics in the last year compared to healthy counterparts. There was also a statistically significant association between antibiotic use and gut microbiota composition. Furthermore, each disease state was associated with a loss of microbial diversity in the gut. Three genera, Bacteroides, Prevotella, and Ruminococcus, were the most common dysbiotic taxa in terms of being enriched or depleted in disease populations and was driven in part by the diversity of operational taxonomic units (OTUs) within these genera. Results of the cross-sectional analysis suggest that antibiotic-driven loss of microbial diversity may increase the risk for chronic disease. However, longitudinal studies are needed to confirm the causative effect of diversity loss for chronic disease risk.

Figure 1

The effect of antibiotics on chronic disease and the microbiota. (A) Antibiotic use within the last year for individuals with or without chronic disease. For diabetes, cardiovascular disease, kidney disease, obesity, and their healthy counterparts, antibiotic history was derived from the subset of AGP samples randomly selected for this study (N = 300 for each group except kidney disease which had 100 samples; Table 1). Only one study on the microbiome of USD patients included metadata associated with antibiotic use (N = 43 healthy individuals and 24 individuals with USD; Table 1). Proportions of antibiotic use were compared between chronic disease states and healthy populations with a relative risk ratio followed by a post-hoc Fisher’s exact test, which was Holm’s corrected for multiple comparisons. *p < 0.05; **p < 0.01; ***p < 0.001 compared to the healthy population. (B) PCoA plot based on a weighted UniFrac analysis the microbiome composition from the AGP data. Community composition based on antibiotic use was compared by PERMANOVA with 999 permutations. p = 0.006.

Figure 2

An example of differential abundance analysis for each of the disease states. Each dot represents an OTU. Grey dots are OTUs that do not exhibit significant differential abundance, while red dots are differentially abundant OTUs. (A) Stochastic metadata; (B) USD; (C) Cardiovascular disease; (D) Diabetes; (E) Obesity; (F) Kidney disease.

Figure 3

Average fold difference in the number of OTUs enriched in either the healthy group/stochastic group 1 or in the disease group/stochastic group 2. Positive values reflect greater enrichment in healthy group/stochastic group 1, whereas negative values reflect greater enrichment in disease group/stochastic group 2. Significance was determined with a one-sample t-test against an expected value of 1. *p < 0.05; **p < 0.01; ***p < 0.001.

Figure 4

Heatmaps showing the most common dysbiotic genera for each disease. Genera were counted for each independent population comparison had at least one dysbiotic OTU associated with it. The proportion of populations each genera showed up in is plotted. (A) Genera depleted in the disease populations (potential probiotic bacteria lost); (B) Genera enriched in disease populations (potential pathogenic bacteria). Hierarchal cluster analysis shows clustering of disease states with the approximately unbiased alpha levels (AU) and bootstrap probability (BP) provided for each cluster (AU/BP). AU values > 95 are considered significant and are bolded.

Figure 5

Total genus diversity vs. dysbiotic OTUs per genus. Correlations were calculated with a Spearman’s rank order correlation (r). (A) Cardiovascular disease; (B) Obesity; (C) Diabetes; (D) Kidney disease; (E) USD.