Cross-Sectional Study of Metabolomic Profiles and the Association with Kidney Stone Disease in the Nurses’ Health Studies I and II

Background: Kidney stone disease is a painful and expensive health condition with a high recurrence rate and substantial morbidity; however, the mechanisms underlying the disease remain incompletely understood. Metabolomics is one novel approach that might provide important insights into the etiology of stone disease. Methods: In a subset of participants from the Nurses' Health Study I and II cohorts (NHSI and NHSII), subjects were divided into stone and non-stone former groups. Data from existing mass-spectrometry based plasma metabolomic profiling that had been performed in multiple case-control studies of other diseases were used. Multivariable logistic regression models were employed to identify metabolites which were associated with kidney stone history after adjusting for multiple comparisons using false detection rate correction. Results: We included 230 prevalent kidney stone cases among 5380 NHSI participants and 114 cases among 2283 NHSII participants. 277 metabolites were measured and passed the 10% missing threshold. In NHSI, one metabolite was significantly inversely associated with kidney stones (p=0.01) and passed the false-detection rate correction for multiple testing. The identified metabolite was cinnamoylglycine (HMDB0011621), which is a metabolite in the carboxylic acids and derivatives class. There were no significant metabolites in NHSII. When the cohorts were combined, HMDB0011621 was significantly inversely associated with stone history (p<0.01). The odds ratio per standard deviation increase in the metabolite for the combined cohorts was 0.87 (0.81, 0.95). Conclusions: We identified one plasma metabolite associated with a history of kidney stones. The metabolite has been recently identified as one of the potential biomarkers of proximal tubule function, colonization of antibiotic resistant gut microbiome, and diabetes, which are also known to correlate with kidney stone disease. Larger studies are needed to identify other potential metabolites that may be involved in kidney stone formation. Funding: NIDDK Support, Private Foundation Support Table of resultsCI, confidence interval