The following is a summary of “Plasma Metabolome and Risk of Incident Kidney Stones,” published in the June 2024 issue of Nephrology by Ferraro et al.

Metabolomic profiles in people prone to kidney stones have not been well studied. 

Researchers conducted a prospective study exploring how specific plasma metabolomic profiles independently relate to the risk of developing symptomatic kidney stones in adults through two extensive cohort studies. 

They analyzed plasma metabolomics in 1,758 participants, including 879 with kidney stones (346 from Health Professionals Follow-up [HPFS] and 533 from Nurses’ Health Study [NHS] II cohorts) and 879 without stones (346 from HPFS, 533 from NHS II) matched for age, race, fasting status, and other factors. A logistic regression model was used to estimate the OR of kidney stones adjusted for BMI, hypertension, diabetes, and dietary factors. A metabolite-based score for kidney stone risk was developed in each cohort in a logistic regression model with a lasso penalty. In the other cohort, scores from HPFS (‘KMS_HPFS’) and NHS II (‘KMS_NHS’) were tested for their association with kidney stone risk. 

The results showed that several individual metabolites were linked to the development of kidney stones with significant statistical confidence across both HPFS and NHS II cohorts. Beta-cryptoxanthin and specific sphingomyelins (d18:2/24:1, d18:1/24:2, d18:2/24:2) showed consistent associations. The standardized KMS_HPFS predicted a 23% higher risk in NHS II 1.23 (95% CI 1.05-1.44), while KMS_NHS trended similarly but without significant findings in HPFS, (OR 1.16, 95% CI 0.97-1.39).

Investigators concluded that identifying specific metabolites linked to kidney stone presence across two cohorts and a plasma metabolomic signature provides a new method to profile individuals prone to kidney stones.

Source: journals.lww.com/jasn/abstract/9900/the_plasma_metabolome_and_risk_of_incident_kidney.351.aspx