Investigation of Potential Biomarker and Lipid - Related Mechanism in Neurodegenerative Disorders

Parkinson’s disease (PD) is a multifactorial neurodegenerative disorder with no reliable biomarkers for early diagnosis. This thesis explores the role of lipids as molecular indicators and mediators of PD through three complementary studies. In the first, plasma lipidomics combined with in silico prediction and PRM validation identified 43 oxidized lipids (epilipids), 17 of which were robustly detected across cohorts. Two of them, PC(16:0_16:1) and PC(16:0_18:1), showed excellent diagnostic performance at early stages (AUC > 0.85). Machine learning models trained on these features reached up to 91.7% accuracy, supporting their biomarker potential. The second study examined how Deep Brain Stimulation (DBS) and transcranial Direct Current Stimulation (tDCS) modulate lipid profiles. DBS influenced lipids involved in inflammation and membrane structure, while tDCS primarily altered oxidative stress-related lipids in neuronal cells. The third study focused on PD patients with TMEM175 mutations, revealing distinct lipid signatures in both plasma and fibroblasts, suggesting lysosomal and vesicular dysfunction. Together, these findings demonstrate the power of lipidomics and epilipidomics in uncovering disease-specific molecular alterations. This work highlight how the combination of advanced mass spectrometry, bioinformatic prediction, and experimental validation can help identify new lipid biomarkers and improve our understanding of the different forms of Parkinson’s disease.