The study was done to identify dysregulated metabolic pathways in ALS versus control participants through untargeted metabolomics.
Untargeted metabolomics was performed on plasma from 125 ALS participants and 71 healthy controls. Individual differential metabolites were assessed by Wilcoxon rank-sum tests, adjusted logistic regression and PLS-DA, while group lasso explored sub-pathway-level differences. Metabolomics pathway enrichment analysis was performed on metabolites. Machine learning classification algorithms were applied and metabolites were evaluated for classifying case status.
Both groups were comparable on the basis of sex, age and BMI. Significant metabolites selected were 303 by Wilcoxon, 300 by logistic regression, 295 by PLS-DA and 259 by group lasso, corresponding to 11, 13, 12 and 22 enriched sub-pathways, respectively. ‘Benzoate metabolism’, ‘ceramides’, ‘creatine metabolism’, ‘fatty acid metabolism (acyl carnitine, polyunsaturated)’ and ‘hexosyl ceramide’ sub-pathways were enriched by all methods, and ‘sphingomyelins’ by all but Wilcoxon, indicating these pathways significantly associated with ALS. Finally, machine learning prediction of ALS cases using group lasso-selected metabolites achieved the best performance by regularised logistic regression with elastic net regularisation, with an area under the curve of 0.98 and specificity of 83%.
This study concluded that the ALS led to significant metabolic pathway alterations, which had correlations to known ALS pathomechanisms in the basic and clinical literature, and may represent important targets for future ALS therapeutics.