Table 2.
Top 9 common metabolites derived using unbiased feature selection methods.
| Preliminary Annotation | RVU in TBI Controls | RVU in Subacute mTBI Cases |
|---|---|---|
| * Monoacylglycerol (MG) C16:0_N | Low | High |
| Taurine_N | Low | High |
| Sphingosine 1 Phosphate_P (S1P_P) | Low | High |
| * Glutamic Acid_N | Low | High |
| Glucosylceramide (GlcCer) d18:1/26:0_N | High | Low |
| * Creatinine_N | High | Low |
| GlcCer d18:0/26:0_N | High | Low |
| Phosphatidylcholine (PC) ae C41:1_N | High | Low |
| PC ae C44:5_N | Low | High |
Common metabolites were derived from the top 15 of each feature selection methodology, including linear support vector machine (LinSVM), partial least squares discriminant analysis (PLS-DA), and random forest (RandFor) unbiased algorithms. Comparisons of relative metabolite RVU abundances in TBI controls and cases are presented for each metabolite. * Denotes a top-15 metabolite via the LinSVM, PLS-DA, RandFor, and LASSO feature selection methods. RVU = relative value unit. LASSO = least absolute shrinkage and selection operator. The six metabolites in bold combined to provide a convergent logistic regression model. The ae designations for the two PCs indicate that acyl- and alkyl- side chains were represented. Final metabolite identifications will require additional tandem mass spectrometry (MS/MS) analyses. Metabolites confirmed via MS/MS are considered fully validated, to a high degree of confidence.