Kidney stone disease (KSD) imparts significant morbidity and financial burdens on the public health. Preventative measures for development and treatment of KSD are hampered because no biomarker has been identified that can assess the risk of developing, severity and recurrence of KSD. Although it is widely accepted that pathological changes in metabolism play a major role in its development no metabolic biomarker(s) associated with risk of developing KSD have been identified. Urinary metabolites are reflective of pathologic changes in metabolism, and analysis of the urinary metabolome has been used as a diagnostic tool in the risk stratification of patients with other types of kidney disease. Our own preliminary data demonstrates that patients with kidney stones have a unique urinary metabolomic profile compared to a group of non-kidney stone formers. Although diet, because of its influence on metabolism, is generally believed to be an important factor in development of KSD there is little evidence regarding successful treatment of kidney stone patients through dietary modification. This may in part be because of the influence of the gut microbiome (GMB) affecting the metabolic implications of diet. In other physiologic systems a strong association has been reported between the GMB and diabetes, obesity and cardiovascular disease. Kidney stone formation is a similarly complex process involving a cascade of metabolic events that may also be influenced by the GMB, but has not previously been studied with regard to kidney stones. Our preliminary results indicate significant differences in the GMB between control and kidney stone patients. These reports, combined with our preliminary data, lead us to the following hypothesis to be tested in this proposal, namely that: Patients with KSD will have metabolic changes that are reflected in the metabolome of urine. Changes in the GMB will also be associated with KSD and may be a factor in the metabolic changes in KSD patients. These studies would be the first step in defining a metabolic/GMB biomarker profile unique to KSD. To test this hypothesis we will use metabolomic profiling to compare the metabolome of urine from KSD patients to matched controls in order to establish a metabolite profile unique to kidney stone patients. In a second aim we will use microbiome sequencing to determine if a unique GMB associates with kidney stone patients. Furthermore, we will explore the relationship between urinary metabolite levels and GMB to better understand the association between metabolism, the GMB and kidney stone formation.
Preventative measures for development and treatment of kidney stone disease (KSD) are hampered because no biomarker has been identified that can assess the risk of developing, severity and recurrence of KSD. Our goal is to identify KSD biomarkers by utilizing two cutting edge techniques, metabolomics and microbiome sequencing, to compare the urinary metabolome and the gut microbiome between KSD patients and matched controls. These studies could, for the first time, define a panel of biomarkers unique to KSD and form the basis of a clinical assay that predicts the risk of developing KSD.
