The Plasma Metabolome and Risk of Incident Kidney Stones

Background Information on metabolomic profiles in kidney stone formers is limited. To examine independent associations between plasma metabolomic profiles and the risk of incident, symptomatic kidney stones in adults, we conducted prospective nested case-control studies in two large cohorts. Methods We performed plasma metabolomics on 1758 participants, including 879 stone formers (346 from the Health Professionals Follow-Up Study [HPFS] cohort, 533 from the Nurses' Health Study [NHS] II cohort) and 879 non-stone formers (346 from HPFS, 533 from NHS II) matched for age, race, time of blood collection, fasting status, and (for NHS II) menopausal status and luteal day of menstrual cycle for premenopausal participants. Conditional logistic regression models were used to estimate the odds ratio (OR) of kidney stones adjusted for body mass index; hypertension; diabetes; thiazide use; and intake of potassium, animal protein, oxalate, dietary and supplemental calcium, caffeine, and alcohol. A plasma metabolite-based score was developed in each cohort in a conditional logistic regression model with a lasso penalty. The scores derived in the HPFS ("kidney stones metabolite score [KMS]_HPFS") and the NHS II ("KMS_NHS") were tested for their association with kidney stone risk in the other cohort. Results A variety of individual metabolites were associated with incident kidney stone formation at prespecified levels of metabolome-wide statistical significance. We identified three metabolites associated with kidney stones in both HPFS and NHS II cohorts: beta-cryptoxanthin, sphingomyelin (d18:2/24:1, d18:1/24:2), and sphingomyelin (d18:2/24:2). The standardized KMS_HPFS yielded an OR of 1.23 (95% confidence interval, 1.05 to 1.44) for stones in the NHS II cohort. The standardized KMS_NHS was in the expected direction but did not reach statistical significance in HPFS (OR, 1.16; 95% confidence interval, 0.97 to 1.39). Conclusions The findings of specific metabolites associated with kidney stone status in two cohorts and a plasma metabolomic signature offer a novel approach to characterize stone formers.