Targeted metabolomics identified novel metabolites, predominantly phosphatidylcholines and docosahexaenoic acid-containing lipids, predictive of incident chronic kidney disease in middle-to-elderly-aged Chinese adults.

BACKGROUND

Evidence is limited regarding the association of circulating metabolites with decline of kidney function, letting alone their value in prediction of development of chronic kidney disease (CKD).

METHODS

This study included 3802 participants aged 64.1 ± 7.4 years from the Dongfeng-Tongji cohort, among whom 3327 were CKD-free at baseline (estimated glomerular filtration rate [eGFR] > 60 ml/min per 1.73 m). We measured baseline levels of 211 metabolites with liquid chromatography coupled with mass spectrometry, including 25 amino acids, 12 acyl-carnitines, 161 lipids, and 13 other metabolites.

RESULTS

The mean (SD) absolute annual change in eGFR was -0.14 ± 4.11 ml/min per 1.73 m per year, and a total of 472 participants who were free of CKD at baseline developed incident CKD during follow-up of 4.6 ± 0.2 years (14.2 %). We identified a total of 22 metabolites associated with annual eGFR change and survived Bonferroni correction for multiple testing, including seven metabolites associated with eGFR increase (six being docosahexaenoic acid [DHA]-containing lipids) and 15 associated with eGFR decline (nine being phosphatidylcholines [PCs]). Among them, eight metabolites obtained non-zero coefficients in least absolute shrinkage and selection operator (LASSO) regression on incident CKD, indicating predictive potential, including one amino acid (arginine), one acyl-carnitine (C2), one lysophosphatidylcholine (LPC 22:6), two PCs (32:1 and 34:3), one triacylglycerol (TAG 56:8 [22:6]) and two other metabolites (inosine, niacinamide), and the composite score of these eight metabolites showed an odds ratio (OR) of 8.79 (95 % confidence interval [CI]: 7.49, 10.32; P < 0.001) per SD increase in association with incident CKD. The addition of the metabolite score increased the c-statistic of the reference model of traditional risk factors (including baseline eGFR) by 0.065 (95 % CI: 0.046 to 0.084; P = 3.39 × 10) to 0.765 (0.742 to 0.788) in 1000 repetitions of 10-fold cross-validation, while the application of two advanced machine learning algorithms, random forest (RF), and extreme gradient boosting (XGBoost) models produced similar c-statistics, to 0.753 (0.729 to 0.777) and 0.778 (0.733 to 0.824) with increases of 0.074 (0.055 to 0.093; P = 4.11 × 10) and 0.073 (0.032 to 0.114; P = 4.00 × 10), respectively.

CONCLUSIONS

In this study, we identified 22 metabolites associated with longitudinal eGFR change, nine of which were PCs and six were DHA-containing lipids. We screened out a panel of eight metabolites which improved prediction for the development of CKD by 9 % beyond traditional risk factors including baseline eGFR. Our findings highlighted involvement of lipid metabolism in kidney function impairment, and provided novel predictors for CKD risk.