Metabolomic analysis of plasma improves the prediction from hyperuricemia to gout incidence
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Objectives
To investigate the metabolome perturbation in the progression of hyperuricemia (HUA) into gout, and evaluate the predictive power of metabolomics.
Methods
Circulating metabolomics data from 24225 individuals were measured using nuclear magnetic resonance (NMR) technology, and we used the Cox models to assess the hazard ratios of metabolites in HUA-to-gout progression. Key metabolites were selected through 10-fold cross-validated elastic net regression, with 10-year prediction models developed using multivariate Cox regression. The predictive performance was differentiated by comparing the area under the receiver operating characteristic curves (AUCs). We used Net Reclassification Improvement (NRI) to estimate the improvement in reclassification ability with the addition of metabolites to the conventional prediction model.
Results
Of the 24225 HUA patients, the median follow-up period was 13.6 years, during which 1584 participants developed gout. 18 metabolites showed significant associations; the most positive association was with glycoprotein acetyl (HR 1.10; 95% CI: 1.04, 1.16) and the most negative association was with IDL particle concentration (HR 0.91; 95% CI: 0.87, 0.96). The predictive ability (AUC: 0.80 vs 0.78) and reclassification ability (NRI = 2.83%, P < 0.001) of the new combined model were significantly improved with the addition of selected metabolites (n = 44), allowing the identification of high-risk groups.
Conclusions
Our analyses identified various metabolomic profiles significantly associated with the development of HUA into an incident event of gout, and implied that metabolomics can enhance predictive accuracy for clinical progression from HUA to gout.
