Nuclear Magnetic Resonance-Based Metabolomics and Risk of CKD

Rationale & Objective: Chronic kidney disease (CKD) leads to lipid and metabolic abnormalities, but a comprehensive investigation of lipids, lipoprotein particles, and circulating metabolites associated with the risk of CKD has been lacking. We examined the associations of nuclear magnetic resonance (NM R)-based metabolomics data with CKD risk in the UK Biobank study. Study Design: Observational cohort study. Setting & Participants: A total of 91,532 participants in the UK Biobank Study without CKD and not receiving lipid-lowering therapy. Exposure: Levels of metabolites including lipid concentration and composition within 14 lipoprotein subclasses, as well as other metabolic biomarkers were quantified via NMR spectroscopy. Outcome: Incident CKD identified using ICD codes in any primary care data, hospital admission records, or death register records. Analytical Approach: Cox proportional hazards regression models were used to estimate hazard ratios and 95% confidence intervals. Results: We identified 2,269 CKD cases over a median follow-up period of 13.1 years via linkage with the electronic health records. After adjusting for covariates and correcting for multiple testing, 90 of 142 biomarkers were significantly associated with incident CKD. In general, higher concentrations of very-low density lipoprotein (VLDL) particles were associated with a higher risk of CKD whereas higher concentrations of high-density lipoprotein (HDL) particles were associated with a lower risk of CKD. Higher concentrations of cholesterol, phospholipids, and total lipids within VLDL were associated with a higher risk of CKD, whereas within HDL they were associated with a lower risk of CKD. Further, higher triglyceride levels within all lipoprotein subclasses, including all HDL particles, were associated with greater risk of CKD. We also identified that several amino acids, fatty acids, and inflammatory biomarkers were associated with risk of CKD. Limitations: Potential underreporting of CKD cases because of case identification via electronic health records. Conclusions: Our findings highlight multiple known and novel pathways linking circulating metabolites to the risk of CKD.