Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology

Abstract

Background: Untargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD based on diagnosis: FSGS, obstructive uropathy (OU), aplasia/dysplasia/hypoplasia (A/D/H), and reflux nephropathy (RN).

Methods: Untargeted metabolomic quantification (GC-MS/LC-MS, Metabolon) was performed on plasma from 702 Chronic Kidney Disease in Children study participants (n: FSGS=63, OU=122, A/D/H=109, and RN=86). Lasso regression was used for feature selection, adjusting for clinical covariates. Four methods were then applied to stratify significance: logistic regression, support vector machine, random forest, and extreme gradient boosting. ML training was performed on 80% total cohort subsets and validated on 20% holdout subsets. Important features were selected based on being significant in at least two of the four modeling approaches. We additionally performed pathway enrichment analysis to identify metabolic subpathways associated with CKD cause.

Results: ML models were evaluated on holdout subsets with receiver-operator and precision-recall area-under-the-curve, F1 score, and Matthews correlation coefficient. ML models outperformed no-skill prediction. Metabolomic profiles were identified based on cause. FSGS was associated with the sphingomyelin-ceramide axis. FSGS was also associated with individual plasmalogen metabolites and the subpathway. OU was associated with gut microbiome-derived histidine metabolites.

Conclusion: ML models identified metabolomic signatures based on CKD cause. Using ML techniques in conjunction with traditional biostatistics, we demonstrated that sphingomyelin-ceramide and plasmalogen dysmetabolism are associated with FSGS and that gut microbiome-derived histidine metabolites are associated with OU.

Keywords: chronic kidney disease; machine learning; machine learning collection; metabolomics; pediatric nephrology.

Graphical abstract

Figure 1.

The analytic flow plan for identifying metabolites associated with CKD etiology. The flow of the analytic approach used to identify individual metabolites associated with each CKD cause. Lasso generated panels of metabolites for subsequent analysis while accounting for metabolite and clinical covariate multicollinearity. Second-pass ML classifiers additionally accounted for dimensionality and multicollinearity among the Lasso-selected metabolites.

Figure 2.

Volcano plots demonstrate ML stratification of important metabolite signals. These plots visualize the stratification of Lasso-selected metabolites for FSGS and OU. Implication by Benjamini–Hochberg is the least restrictive. Implication by ML modeling is the most restrictive. The signals detected by ML would meet both Benjamini–Hochberg and Bonferroni thresholds.

Figure 3.

Lasso feature selection improved ML model performance. We performed SVM for FSGS with no feature selection (842 metabolites) and with a forward feature selection (122 metabolites on the basis of LR P<0.05). All three iterations demonstrated better performance than no-skill selection in 20% holdout validation subsets. The SVM with Lasso feature selection outperformed both no-selection and forward-selection models. There were not significant differences in metabolite subpathway signals detected. 95% CI, 95% confidence interval.