Gut microbiota varies by opioid use, circulating leptin and oxytocin in African American men with diabetes and high burden of chronic disease

Abstract

Objective: The gut microbiota is known to be related to type 2 diabetes (T2D), psychiatric conditions, and opioid use. In this study, we tested the hypothesis that variability in gut microbiota in T2D is associated with psycho-metabolic health.

Methods: A cross-sectional study was conducted among African American men (AAM) (n = 99) that were outpatients at a Chicago VA Medical Center. The main outcome measures included fecal microbiota ecology (by 16S rRNA gene sequencing), psychiatric disorders including opioid use, and circulating leptin and oxytocin as representative hormone biomarkers for obesity and psychological pro-social behavior.

Results: The study subjects had prevalent overweight/obesity (78%), T2D (50%) and co-morbid psychiatric (65%) and opioid use (45%) disorders. In the analysis of microbiota, the data showed interactions of opioids, T2D and metformin with Bifidobacterium and Prevotella genera. The differential analysis of Bifidobacterium stratified by opioids, T2D and metformin, showed significant interactions among these factors indicating that the effect of one factor was changed by the other (FDR-adjusted p [q] < 0.01). In addition, the pair-wise comparison showed that participants with T2D not taking metformin had a significant 6.74 log2 fold increase in Bifidobacterium in opioid users as compared to non-users (q = 2.2 x 10-8). Since metformin was not included in this pair-wise comparison, the significant 'q' suggested association of opioid use with Bifidobacterium abundance. The differences in Bifidobacterium abundance could possibly be explained by opioids acting as organic cation transporter 1 (OCT1) inhibitors. Analysis stratified by lower and higher leptin and oxytocin (divided by the 50th percentile) in the subgroup without T2D showed lower Dialister in High-Leptin vs. Low-Leptin (p = 0.03). Contrary, the opposite was shown for oxytocin, higher Dialister in High-Oxytocin vs. Low-Oxytocin (p = 0.04).

Conclusions: The study demonstrated for the first time that Bifidobacterium and Prevotella abundance was affected by interactions of T2D, metformin and opioid use. Also, in subjects without T2D Dialister abundance varied according to circulating leptin and oxytocin.

Fig 1. Shannon index of alpha diversity.

Pairwise Mann-Whitney test was used to compare alpha diversity estimates, MF- vs. MF+ (p = 0.05). MF- group includes DM- plus DM+/MF- (n = 69), MF+ group includes DM+/MF+ (n = 30). Abbreviations: DM = type 2 diabetes mellitus, MF = Metformin.

Fig 2. Non-metric multidimensional scaling (NMDS) ordination plot.

The NMDS is based on Bray-Curtis dissimilarities between OTU-level microbiota communities in three groups: 1 = DM-, 2 = DM+/MF-, 3 = DM+/MF+, ND = not defined. Pairwise ANOSIM comparison showed a trend for DM+/MF- vs. DM+/MF+ (p = 0.067). Abbreviations: DM = type 2 diabetes mellitus, MF = Metformin.

Fig 3. The interactive influence of T2D on Bifidobacterium genus in men using or not using opioids.

Data are pair-wise comparisons for the relative sequence abundance of Bifidobacterium. The differential subgroup analysis was done using edgeR, the false discovery rate (FDR) adjusted p values (q values) were calculated using the Benjamini-Hochberg FDR correction. Abbreviations: 0/1 = factor absent/present. CPM = count per million, DM = type 2 diabetes, Substance = opioids. (A) 2.3 log2 fold decrease in subjects with vs. without T2D when both groups are not using opioids (q = 0.03). (B) No difference between without vs. with T2D when both groups are using opioids.

Fig 4. The interactive influence of opioids on Bifidobacterium genus in men with and without T2D.

Data and analysis are the same as in Fig 3. (A) No difference between without vs. with opioids when both groups are without T2D. (B) 3.2 log2 fold increase in those with vs. without opioids when both groups have T2D (q = 2.5x10^-4).

Fig 5. The interactive influence of metformin on Bifidobacterium genus in men with T2D and using or not using opioids.

Data are pair-wise comparisons for the relative sequence abundance of Bifidobacterium. The differential subgroup analysis was done using edgeR, the false discovery rate (FDR) adjusted p values (q values) were calculated using the Benjamini-Hochberg FDR correction. Abbreviations: 0/1 = factor absent/present. CPM = count per million, MF = metformin, Substance = opioids. (A) 6.74 log2 fold increase in Bifidobacterium in opioid users vs. non-users when both groups are not taking metformin (q = 2.2 x 10⁻⁸). (B) No difference between opioid users vs. non-users when both groups are taking metformin.

Fig 6. The interactive influence of opioids on Bifidobacterium genus in men with T2D and taking or not taking metformin.

Data and analysis are the same as in Fig 5. (A) 3.17 log2 fold increase in Bifidobacterium in men taking vs. not taking metformin when both groups are not opioid users (q = 0.03). (B) 3.67 log2 fold decrease in Bifidobacterium in men taking vs. not taking metformin when both groups are opioid users (q = 0.01).

Fig 7. Taxa abundance based on circulating leptin and oxytocin.

Data are relative counts for taxa abundance of genus Dialister and order Lactobacillales (Lacto) in subjects without diabetes. The subjects were divided based on low (Low) or high (High) level of Leptin (Lep) and oxytocin (OT). Pairwise Mann-Whitney test was used to compare the groups. There was lower abundance of Dialister in High-Leptin vs. Low-Leptin (p = 0.03), but higher abundance of Dialister in High-Oxytocin vs. Low-Oxytocin (p = 0.04). The opposite trends were observed for the order Lactobacillales, a higher abundance in High-Leptin vs. Low-Leptin (p = 0.06) and vice versa for oxytocin (p = 0.05).