Unsaturated fatty acid profiles and prognostic significance in epilepsy patients: A comprehensive analysis using UPLC-MS/MS and SVM algorithm.

Abstract

Unsaturated fatty acids (UFAs) play a crucial physiological role in human body. However, the concentration-related changes and prognostic significance of UFAs in epilepsyremain unclear. An optimized ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) approach was developed to measure six key UFAs: oleic acid (OA), linoleic acid (LA), arachidonic acid (AA), α-linolenic acid (ALA), docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA). Subsequently, the levels of these six UFAs were determined in 40 healthy individuals and 49 epilepsy patients. The diagnostic value of UFAs and clinical examination indicators were assessed using statistical analysis and the support vector machine (SVM) algorithm. The results showed that the UPLC-MS/MS method successfully quantified the levels of OA, LA, AA, ALA, EPA, and DHA in both the healthy individuals and epilepsy patients. Compared with the healthy group, the levels of ALA, AA, and DHA were significantly elevated in the epilepsy group (P < 0.05). Pearson correlation analysis revealed a strong positive correlation among the UFAs in the epilepsy group. The orthogonal partial least squares-discriminant analysis (OPLS-DA) model showed that DHA and EPA were more important than cholesterol in distinguishing between two groups, although the separation was not complete. The SVM model achieved better separation, with an area under the curve (AUC) of 0.95 when including the six UFAs. The EPA/DHA ratio was identified as a key feature, with a significant contribution to the model's performance. Removing the six UFAs from the model reduced the AUC to 0.91, highlighting the predictive value of UFAs for epilepsy. In conclusion, ALA, AA, and DHA, are altered in epilepsy patients. The EPA/DHA ratio was found to be a key predictive indicator for epilepsy. The use of UFAs in conjunction with clinical examination data improved the predictive power of the SVM model, suggesting that UFAs have potential as biomarkers for epilepsy.