Plasma metabolomics improves prognostic prediction of chronic kidney disease: evidence from a large-scale population-based prospective cohort study.
TL;DR
Multiple plasma metabolic biomarkers associated with all-cause and cardiovascular mortality in patients with CKD were identified and incorporating these biomarkers into conventional risk models significantly enhanced prediction accuracy.
Abstract
Background
Chronic kidney disease (CKD) is a major global health concern associated with increased mortality and cardiovascular events. Traditional clinical models fail to capture the complex metabolic disturbances in CKD progression. This study aimed to assess the value of circulating Nuclear Magnetic Resonance (NMR) metabolic biomarkers in predicting all-cause and cardiovascular mortality in CKD patients.
Methods
Using NMR-based metabolomic data from the UK Biobank, we analyzed baseline plasma samples from 16,306 participants. The multivariable-adjusted Cox proportional hazards models were applied to evaluate associations between NMR metabolic biomarkers and mortality in CKD. The full cohort was randomly assigned to a training set and test set to develop and validate the CKD prognostic risk prediction model using the least absolute shrinkage and selection operator (LASSO) regression and Cox proportional hazards regression analyses. Predictive performance was assessed using Harrell's C-index, with incremental value measured by continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI).
Results
Of the 143 circulating metabolic biomarkers analyzed, 122 and 90 were significantly associated with all-cause and cardiovascular mortality, respectively, in patients with CKD (all FDR adjusted P value < 0.05). Very-low-density lipoprotein (VLDL) and low-density lipoprotein (LDL) particles, small high-density lipoprotein (HDL) sub-classes, polyunsaturated fatty acids, and branched-chain amino acids were negatively associated with mortality risk. Conversely, larger HDL particles, specific triglycerides, and the inflammatory marker glycoprotein acetylation (GlycA) were positively associated with mortality risk. Harrell's C-index of the conventional prediction model was 0.729 (95% CI: 0.707, 0.750) for all-cause mortality; after adding the circulating NMR metabolic biomarkers, the C-index increased to 0.750 (95% CI: 0.728, 0.771). For cardiovascular mortality, the C-index improved from 0.761 (95% CI: 0.724, 0.798) to 0.770 (95% CI: 0.733, 0.807). The continuous NRI and IDI were 0.097 (95% CI: 0.032, 0.158) and 0.043 (95% CI: 0.034, 0.051) for all-cause mortality, and 0.125 (95% CI: 0.007, 0.234) and 0.011 (95% CI: 0.005, 0.018) for cardiovascular mortality, respectively.
Conclusion
This study identified multiple plasma metabolic biomarkers associated with all-cause and cardiovascular mortality in patients with CKD. Incorporating these biomarkers into conventional risk models significantly enhanced prediction accuracy.