Evidence for a distinct gut microbiome in kidney stone formers compared to non-stone formers.

The trillions of microbes that colonize our adult intestine are referred to as the gut microbiome (GMB). Functionally it behaves as a metabolic organ that communicates with, and complements, our own human metabolic apparatus. While the relationship between the GMB and kidney stone disease (KSD) has not been investigated, dysbiosis of the GMB has been associated with diabetes, obesity and cardiovascular disease. In this pilot study we sought to identify unique changes in the GMB of kidney stone patients compared to patients without KSD. With an IRB-approved protocol we enrolled 29 patients into our pilot study. 23 patients were kidney stone formers and six were non-stone forming controls. Specimens were collected after a 6h fast and were flash frozen in dry ice and then stored at -80 °C. Microbiome: determination of bacterial abundance was by analysis of 16 s rRNA marker gene sequences using next generation sequencing. Sequencing of the GMB identified 178 bacterial genera. The five most abundant enterotypes within each group made up to greater than 50 % of the bacterial abundance identified. Bacteroides was 3.4 times more abundant in the KSD group as compared to control (34.9 vs 10.2 %; p = 0.001). Prevotella was 2.8 times more abundant in the control group as compared to the KSD group (34.7 vs 12.3 %; p = 0.005). In a multivariate analysis including age, gender, BMI, and DM, kidney stone disease remained an increased risk for high prevalence for Bacteroides (OR = 3.26, p = 0.033), whereas there was an inverse association with Prevotella (OR = 0.37, p = 0.043). There were no statistically significant differences in bacterial abundance levels for Bacteroides or Prevotella when comparing patients with and without DM, obesity (BMI >30), HTN or HLD. 11 kidney stone patients completed 24 h urine analysis at the time of this writing. Looking at the bacterial genuses with at least 4 % abundance in the kidney stone group, Eubacterium was inversely correlated with oxalate levels (r = -0.60, p < 0.06) and Escherichia trended to an inverse correlation with citrate (r = -0.56, p < 0.08). We also compared bacterial abundance between uric acid (UA) stone formers (n = 5) and non UA stone formers (n = 18) and found no significant difference between them. We identified two genus of bacteria in the GMB that had significant association with KSD. Interestingly, components of the 24-h urine appear to be correlated to bacterial abundance. These preliminary studies for the first time associate differences in the GMB with kidney stone formation. Further studies are warranted to evaluate the potential causative role of preexisting dysbiosis in kidney stone disease.

The Effect of Disease Severity on 24-Hour Urine Parameters in Kidney Stone Patients With Type 2 Diabetes.

OBJECTIVE: To characterize the changes in urine composition associated with increasing severity of diabetes, we analyzed urine composition relative to glycated hemoglobin (HbA1c) and treatment strategy in a largely minority population. METHODS: Patients treated for kidney stones between 2001 and 2013 at a single tertiary institution and had 24-hour urine collections were included in the study. Patients with type 1 diabetes or taking either thiazide diuretics or alkalinizing agents were excluded. Analysis was performed in IBM SPSS Statistics version 20 using multivariate regression, and Kruskal-Wallis testing was used. RESULTS: Nine hundred fifty-five patients were included in this study-268 (28%) with type 2 diabetes mellitus, of whom 53 (19.8%) used insulin. Patients with diabetes had lower urine pH, calcium, and phosphate when compared with the control group, but no significant differences were found between the diabetes groups. Multivariate analysis found that HbA1c had a positive correlation with citrate (P = .008), creatinine (P = .037), urine volume (P = .044), and a trend toward a positive association with urinary calcium calcium (P = .064). Insulin use did not have a significant relationship with urinary parameters but trended toward an inverse relationship with calcium (P = .051). pH was not a significant predictor of any urine constituent. CONCLUSION: In an ethnically diverse inner city patient population, patients with diabetes mellitus type 2 who use insulin have no significant differences in urine parameters when compared with those on oral hypoglycemics. Worsening glucose control as measured by HbA1c levels predicts increased urine citrate and volume.